CN111695845A - Method and device for determining congestion area, electronic equipment and readable medium - Google Patents
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Abstract
The disclosure provides a method and a device for determining a congestion area, electronic equipment and a readable medium, and belongs to the field of warehouse logistics. The method comprises the following steps: acquiring position information of a plurality of moving targets in the measuring area to obtain a plurality of position information; constructing a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas; within a sequence length of time, respectively calculating evaluation functions of the leaf node areas in the map snapshot according to a slice period; and determining a congestion area in the measurement area according to the value of the evaluation function. According to the method, an efficient and fast data structure is established for the position information of the moving target in the two-dimensional space based on the quad-tree, so that the congestion area in the measurement area can be rapidly determined by calculating the evaluation function of each leaf node area, the effective real-time detection and management of vehicle group distribution are realized, and the transportation efficiency of vehicles is improved.
Description
Technical Field
The present disclosure relates generally to the field of warehouse logistics, and in particular, to a method, an apparatus, an electronic device, and a readable medium for determining a congestion area.
Background
In the existing logistics industry, intelligent transportation of goods is generally achieved through an unmanned Guided Vehicle (AGV for short, also called AGV trolley). Because the AGV trolley is provided with the automatic guiding device such as an electromagnetic device or an optical device, the traveling route and the behavior of the AGV trolley can be controlled by a computer, and therefore the AGV can travel along the specified guiding path through the remote control of the computer.
However, in the prior art, the congestion area of the AGV in the unmanned storehouse cannot be effectively detected, the design of the AGV scheduling strategy is relatively passive, the congestion area of the AGV in the unmanned storehouse cannot be effectively detected, and then an effective obstacle avoidance strategy cannot be timely adopted.
Therefore, there is still a need for improvement in the prior art solutions.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a method, an apparatus, an electronic device and a readable medium for determining a congestion area, which solve the above technical problems.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a method of determining a congestion area, including:
acquiring position information of a plurality of moving targets in the measuring area to obtain a plurality of position information;
constructing a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas;
within a sequence length of time, respectively calculating evaluation functions of the leaf node areas in the map snapshot according to a slice period;
and determining a congestion area in the measurement area according to the value of the evaluation function.
In one embodiment of the present disclosure, the constructing a map snapshot of the measurement area for the plurality of location information based on the quadtree includes:
quartering the two-dimensional map of the measurement area to obtain four primary leaf node areas;
marking the four primary leaf node areas by using primary leaf nodes respectively;
establishing a corresponding relation between each primary leaf node area and the number of moving objects distributed in the area according to the position information;
and constructing the map snapshot according to the corresponding relation.
In an embodiment of the present disclosure, before the constructing the map snapshot of the measurement area for the plurality of location information based on the quadtree, the method further includes:
setting a threshold value for the number of the moving objects in any of the leaf node areas.
In one embodiment of the present disclosure, the method further comprises:
and when the number of the moving targets in the first-level leaf node area is greater than the threshold value, performing quartering splitting on the first-level leaf node area based on the quadtree until the number of the moving targets in the N-level leaf node area after splitting does not exceed the threshold value, wherein N is greater than or equal to 2.
In an embodiment of the present disclosure, the calculating, in a sequence of lengths of time, evaluation functions of the leaf node areas in the map snapshot according to a slice period includes:
counting the depth of each primary leaf node area of a quadtree in the map snapshot, wherein the maximum value of the depth of the quadtree is N;
carrying out congestion marking on the first-level leaf node area according to the comparison result of the depth and a preset value to obtain marking data, wherein the preset value is greater than or equal to 2;
performing Run Length Encoding (RLE) compression on the marked data to obtain RLE data;
calculating a merit function for the plurality of leaf node regions from the RLE data and the map snapshot.
In an embodiment of the present disclosure, the performing congestion marking on the first-level leaf node area according to the comparison result between the depth and a preset value includes:
when the depth of the first-level leaf node area is greater than or equal to a preset value, marking the first-level leaf node area as congestion, and representing the congestion by using marking data 1;
and when the depth of the first-level leaf node area is smaller than the preset value, marking the leaf node area as not jammed and representing the leaf node area by marking data 0.
In one embodiment of the present disclosure, the determining a congestion area within the measurement area according to the value of the evaluation function includes:
and comparing the values of the evaluation functions of all levels of leaf node areas in the measurement area with reference values respectively, wherein if the value of the evaluation parameter of one leaf node area exceeds the reference value, the leaf node area is a congestion area.
According to still another aspect of the present disclosure, there is provided an apparatus for determining a congestion area, including:
the acquisition position module is configured to acquire position information of a plurality of moving targets in the measurement area to obtain a plurality of position information;
a snapshot constructing module configured to construct a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas;
an evaluation function module configured to calculate evaluation functions of the plurality of leaf node areas in the map snapshot according to a slice period within a sequence of length time;
a congestion determination module configured to determine a congestion area within the measurement area according to the value of the evaluation function.
According to yet another aspect of the present disclosure, there is provided an electronic device comprising a processor; a memory storing instructions for the processor to control the method steps as described above.
According to another aspect of the present disclosure, there is provided a computer-readable medium having stored thereon computer-executable instructions that, when executed by a processor, implement the method steps as described above.
According to the method, the device, the electronic equipment and the computer readable medium for determining the congestion area, on one hand, an efficient and fast data structure is established for the position information of the moving target in the two-dimensional space based on the quad-tree, so that the congestion area in the measurement area can be rapidly determined by calculating the evaluation function of each leaf node area, the effective real-time detection and management of the vehicle group distribution are realized, and the transportation efficiency of the vehicle is improved. On the other hand, data support can be provided for a follow-up obstacle avoidance strategy according to the determined congestion area, so that vehicle scheduling can be reasonably planned, a transportation route is optimized, and warehouse management is facilitated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 shows a flowchart of a method for determining a congestion area provided in an embodiment of the present disclosure.
Fig. 2 shows a flowchart in step S120 in fig. 1 according to an embodiment of the disclosure.
Fig. 3 shows a schematic diagram of a measurement region Q equally divided into four parts NW, NE, SW and SE in an embodiment of the present disclosure.
FIG. 4 shows a schematic diagram of a quadtree according to an embodiment of the present disclosure.
Fig. 5 shows a region segmentation map after the first-level leaf node region is further split in an embodiment of the present disclosure.
FIG. 6 shows a quadtree diagram after multi-level splitting in an embodiment of the disclosure.
Fig. 7 shows a flowchart of step S130 in fig. 1 according to an embodiment of the disclosure.
Fig. 8 is a schematic diagram illustrating an apparatus for determining a congestion area according to another embodiment of the present disclosure.
Fig. 9 shows a schematic diagram of the construct snapshot module 820 in another embodiment of the present disclosure.
Fig. 10 shows a schematic diagram of the evaluation function module 830 in another embodiment of the present disclosure.
Fig. 11 shows a schematic structural diagram of an electronic device suitable for implementing an embodiment of the present application according to still another embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The advantages of the AGV dispatching method directly influence the overall transportation efficiency of the unmanned storehouse, and if the transportation efficiency needs to be improved, the real-time road conditions of the AGV in the transportation path need to be effectively controlled, so that the vehicle dispatching is reasonably planned, and the transportation path is optimized. However, in the prior art, the congestion condition of the AGV in the unmanned warehouse cannot be effectively detected, and an effective avoidance strategy cannot be designed, so that the overall transportation efficiency of the unmanned warehouse is inevitably affected.
FIG. 1 is a flow chart illustrating a method for determining a congestion area for AGV carts in an unmanned bay according to an embodiment of the present disclosure, the method comprising the steps of:
as shown in fig. 1, in step S110, position information of a plurality of moving objects in the measurement area is obtained, and a plurality of position information is obtained.
As shown in fig. 1, in step S120, a map snapshot of the measurement area is constructed for the plurality of location information based on the quadtree, wherein the map snapshot includes a plurality of leaf node areas.
As shown in fig. 1, in step S130, evaluation functions of the leaf node areas in the map snapshot are calculated in a sequence of time lengths according to a slice period.
As shown in fig. 1, in step S140, a congestion area within the measurement area is determined according to the value of the evaluation function.
On one hand, the method for determining the congestion area provided by the embodiment of the disclosure establishes an efficient and fast data structure for the position information of the moving target in the two-dimensional space based on the quad-tree, so that the congestion area in the measurement area can be rapidly determined by calculating the evaluation function of each leaf node area, the effective real-time detection and management of the vehicle group distribution are realized, and the transportation efficiency of the vehicle is improved. On the other hand, data support can be provided for a follow-up obstacle avoidance strategy according to the determined congestion area, so that vehicle scheduling can be reasonably planned, a transportation route is optimized, and warehouse management is facilitated.
It should be noted that the application scenario of the method for determining the congestion area provided by the present disclosure may be an unmanned warehouse, an unmanned restaurant, an unmanned supermarket, etc., and the detected moving target is an AGV cart, a transfer robot, a server robot, a tally robot, or similar robot or device that can automatically move according to an instruction; it may also be a manned warehouse, but where the moving object is equipment that works and moves under human control, such as a forklift or the like. In conclusion, the method is mainly used for determining the congestion condition of the moving target in a certain area.
The method for determining a congestion area provided by the present disclosure is described in detail below with reference to the flowchart shown in fig. 1, specifically as follows:
in step S110, position information of a plurality of moving objects in the measurement area is obtained, and a plurality of position information is obtained.
In an embodiment of the present disclosure, the position information of the moving object obtained in this step may be determined according to a specific application scenario, for example, if the moving object itself has a communication function (such as 4G, 5G, wifi, bluetooth, RFID, etc.), the moving object may directly report its position information to the server; if the moving target does not have the communication function, the position information of the moving target can be reported manually by an operator of the moving target.
In one embodiment of the present disclosure, the moving target may be further located based on the two-dimensional code in this step. The positioning based on the two-dimensional code is to position the moving target by reading the label value of the two-dimensional code, and can obtain the coordinate value above the two-dimensional code. For example, taking an AGV trolley as an example, by pasting a two-dimensional code strip on a rail, a track and position information are stored in the two-dimensional code strip, the AGV trolley automatically identifies the two-dimensional code strip through a two-dimensional code scanning module installed on the AGV trolley in the moving process, the position of a code point of the AGV trolley can be acquired, then the AGV trolley reports the current position in real time, and the reported current position is the position of the code point where the AGV trolley is located currently, that is, the position information of the AGV trolley is obtained. The AGV comprises a AGV, a monitoring module, a control module and a control module, wherein the current position reported by the AGV in real time is reported by proxy, the proxy refers to a system for controlling the AGV and is provided with a module for monitoring hardware information of the AGV and controlling a movement track of the AGV.
In step S120, a map snapshot of the measurement area is constructed for the plurality of location information based on the quadtree, wherein the map snapshot includes a plurality of leaf node areas.
In one embodiment of the present disclosure, a quadtree is used in this step to manage a vehicle snapshot map of a two-dimensional space. The basic idea of the quadtree indexing is to recursively divide a geographic space into tree structures of different levels, equally divide the space of a known range into four equal subspaces, and recursion is carried out in such a way until the levels of the trees reach a certain depth or meet certain requirements, and then the splitting is stopped. The quadtree has a simple structure and has high spatial data insertion and query efficiency when the spatial data objects are uniformly distributed, so that the quadtree is one of the spatial indexes commonly used in a Geographic Information System (GIS). Typically, the geospatial objects of the quadtree are all stored on leaf nodes, with intermediate nodes and root nodes not storing geospatial objects.
Fig. 2 shows a flowchart in step S120 in fig. 1 according to an embodiment of the present disclosure, which specifically includes the following steps:
as shown in fig. 2, in step S210, the two-dimensional map of the measurement area is quartered to obtain four primary leaf node areas.
Fig. 3 shows a schematic diagram of a measurement region Q equally divided into four parts NW, NE, SW and SE in an embodiment of the present disclosure. For the measurement region shown in FIG. 3, the type of the marked interval is leaf node, the minimum point of the measurement region Q is s, and the coordinate is(s)x，sy) The maximum point is e, and the coordinates are (e)x，ey)。
As shown in fig. 2, in step S220, the four primary leaf node regions are respectively marked with primary leaf nodes.
Fig. 4 is a schematic diagram of a quadtree according to an embodiment of the present disclosure, where four primary leaf nodes of the quadtree are NW, NE, SW, and SE, and the corresponding four partitions are primary leaf node areas. By combining fig. 3 and 4, the starting point of the quadtree region is s(s)x，sy) The end point is e (e)x，ey) And the range of the quadtree is represented by the following constraint conditions:
s＝q,q∈Q st.min(qx+qy)
e＝q,q∈Q st.max(qx+qy)
the center coordinate of the measurement region Q is (x)O，yO) The following are:
xO＝(ex-s _ x)/2, i.e. the x-axis intercept
yO＝(ey-ex) And/2, the y-axis intercept.
SW＝([sx,sy],[sx+xO,sy+yO]) A minimum point and a maximum point for representing a primary leaf node region SW;
SE＝([sx+xO,sy],[ex,sy+yO]) The minimum point and the maximum point are used for representing the primary leaf node area SE;
NW＝([sx,sy+yO],[sx+xO,ey]) A minimum point and a maximum point for representing the primary leaf node area NW;
NE＝([sx+xO,sy+yO],[ex,ey]) The minimum point and the maximum point used to represent the primary leaf node area NE.
As shown in fig. 2, in step S230, a corresponding relationship between each of the primary leaf node areas and the number of moving objects distributed in the area is established according to the plurality of position information.
In an embodiment of the present disclosure, taking an AGV car as an example, in this step, a relationship between a primary leaf node area and the AGV car, that is, how many AGV cars are in each primary leaf node area, may be established according to the position information of the AGV car.
As shown in fig. 2, in step S240, the map snapshot is constructed according to the corresponding relationship.
Based on the steps S210 to S240 shown in fig. 2, a quadtree is used to establish an efficient and fast data structure, i.e., a map snapshot, for a two-dimensional vehicle group.
In an embodiment of the present disclosure, before the step S120 of constructing the map snapshot of the measurement area for the plurality of location information based on the quadtree, the method further includes:
and setting a threshold value for the number of the moving targets in any leaf node area in the plurality of leaf node areas, wherein the size of the threshold value can be set and adjusted according to the requirements of an actual use scene. The moving object is still exemplified herein as an AGV.
In one embodiment of the present disclosure, the method further comprises:
and when the number of the moving targets in the first-level leaf node area is greater than the threshold value, performing quartering splitting on the first-level leaf node area based on the quadtree until the number of the moving targets in the N-level leaf node area after splitting does not exceed the threshold value, wherein N is greater than or equal to 2.
Based on the above, the splitting depth of the quadtree can be controlled by setting a threshold value, that is, if the number of AGV carts in a certain level of leaf node area is greater than the threshold value, the level of leaf node needs to be split continuously, that is, four secondary leaf nodes are split according to the structure of the quadtree, the corresponding area is a secondary leaf node area, and meanwhile, the depth of the quadtree is + 1.
Still taking the area shown in fig. 3 as an example, fig. 5 shows an area segmentation graph after a first-level leaf node area is continuously split in an embodiment of the present disclosure, and correspondingly, fig. 6 shows a quadtree schematic diagram after multi-level splitting in an embodiment of the present disclosure, as shown in fig. 5 and 6, nodes 2 and 3 are first-level leaf nodes, nodes a, D and 4 are second-level leaf nodes, and nodes C and D are third-level leaf nodes.
In step S130, the evaluation functions of the leaf node areas in the map snapshot are calculated according to the slice period in a sequence of length time.
In one embodiment of the present disclosure, by slicing the map snapshot in this step, the congestion condition of the leaf node area in a period of time can be detected.
Fig. 7 shows a flowchart of step S130 in fig. 1 according to an embodiment of the present disclosure, which specifically includes the following steps:
as shown in fig. 7, in step S710, the depth of each of the first-level leaf node regions of the quadtree in the map snapshot is counted, wherein the maximum value of the depth of the quadtree is N.
Taking the example shown in fig. 6 above, the maximum depth of the quadtree is 3, while the depths for leaf node a and leaf node D are both 2, and the depths for leaf node B and leaf node C are both 3.
As shown in fig. 7, in step S720, a congestion marking is performed on the first-level leaf node area according to a comparison result between the depth and a preset value, so as to obtain marking data, where the preset value is greater than or equal to 2.
In one embodiment of the present disclosure, the main basis in this step is to mark:
when the depth of the first-level leaf node area is greater than or equal to a preset value, marking the first-level leaf node area as congestion, and representing the congestion by using marking data 1; and when the depth of the first-level leaf node area is smaller than the preset value, marking the leaf node area as not jammed and representing the leaf node area by marking data 0.
Continuing with the example shown in fig. 6, assuming that the preset value is 3, in this step, the congestion evaluation function is calculated only for the areas corresponding to the nodes with the depth greater than or equal to 3 (i.e., the leaf nodes B and C) as congestion areas, and the areas corresponding to the nodes with the depth less than 3 (i.e., the leaf nodes a and D) are considered to be congested, but the congestion situation is still good, so the calculation of the congestion evaluation function is not performed.
In one embodiment of the present disclosure, since the position of a moving object (such as an AGV cart) changes from moment to moment, map snapshots need to be obtained according to a slice period within a time sequence length, and then congestion marking is performed according to leaf node areas in historical map snapshots. In this step, a timed slicing task is set, the slicing period is n minutes, if n is 1 minute, the sequence length is m, and m is 10, i.e. 10 minutes.
Table 1 shows the congestion conditions of leaf node areas of a certain area within the time series length from t1 to t 10:
| region(s) | t1 | t2 | t3 | t4 | t5 | t6 | t7 | t8 | t9 | t10 |
| q1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| q2 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
| q3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
TABLE 1
It can be seen that, based on table 1, the congestion condition of a certain area at different time also varies from time to time, wherein the slice period and the time series length can be selected according to practical experience.
As shown in fig. 7, in step S730, run-length encoding RLE compression is performed on the mark data to obtain RLE data.
In an embodiment of the present disclosure, the data format shown in table 1 above is converted into an RLE structure, that is, the states of adjacent time slices are the same and are superimposed, and table 2 shows the congestion condition of a leaf node area RLE compressed in a certain area within the time series length from t1 to t 10:
| region(s) | t1 | t2 | t3 | t4 | t5 | t6 | t7 | t8 | t9 | t10 |
| q1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| q2 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| q3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 |
TABLE 2
Based on table 1, taking the region q2 as an example, the times t3 to t5 are all congested, that is, marked as 1, so after overlapping the states of adjacent time slices with the same state, the time t3 is marked as 3, the times t4 and t5 are marked as 0, and RLE compression in other regions is similar, and will not be described again here.
As shown in fig. 7, in step S740, the evaluation functions of the plurality of leaf node areas are calculated from the RLE data and the map snapshot.
In one embodiment of the present disclosure, taking the above tables 1 and 2 as an example, the formula (1) of the evaluation function in one embodiment of the present disclosure is
Where T is a time slice set and v is the value of RLE compression, i.e., the accumulation of consecutive time slices of the congestion area.
The evaluation function of each leaf node area is calculated according to the formula (1), and based on the formula (1), the more continuous the slice (i.e., the continuity of the slice in time), the more congested the area. The closer the time, the more congested the area.
In step S140, a congestion area within the measurement area is determined based on the value of the evaluation function.
In one embodiment of the present disclosure, the step includes:
and comparing the values of the evaluation functions of all levels of leaf node areas in the measurement area with reference values respectively, wherein if the value of the evaluation parameter of one leaf node area exceeds the reference value, the leaf node area is a congestion area.
For example, the evaluation function values of the leaf node areas may be sorted in a descending order, the value of the evaluation function is the calculation cost, and since the entry parameters in the formula (1) include conditions such as a congestion area and time continuity, the higher the calculated value is, it is indicated that the congestion area is congested continuously for a period of time, the congestion situation is poor, and the planning needs to be changed in time to alleviate the congestion.
To sum up, according to the method for determining a congestion area provided by the embodiment of the disclosure, on one hand, an efficient and fast data structure is established for the position information of a moving target in a two-dimensional space based on a quadtree, so that the congestion area in a measurement area can be quickly determined by calculating the evaluation function of each leaf node area, the effective real-time detection and management of vehicle group distribution are realized, and the transportation efficiency of vehicles is improved. On the other hand, data support can be provided for a follow-up obstacle avoidance strategy according to the determined congestion area, so that vehicle scheduling can be reasonably planned, a transportation route is optimized, and warehouse management is facilitated.
Fig. 8 is a schematic diagram of an apparatus for determining a congestion area according to another embodiment of the present disclosure, and as shown in fig. 8, the apparatus 800 for determining a congestion area includes: a get location module 810, a construct snapshot module 820, an evaluation function module 830, and a determine congestion module 840.
The obtaining position module 810 is configured to obtain position information of a plurality of moving objects in the measurement area, resulting in a plurality of position information; the construct snapshot module 820 is configured to construct a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas; the evaluation function module 830 is configured to calculate evaluation functions of the leaf node areas in the map snapshot according to a slice period in a sequence of length time; the determine congestion module 840 is configured to determine a congestion area within the measurement area based on the value of the evaluation function.
In an embodiment of the present disclosure, the position information of the moving object obtained in the position obtaining module 810 may be determined according to a specific application scenario, for example, if the moving object itself has a communication function (such as 4G, 5G, wifi, bluetooth, RFID, etc.), the moving object may directly report the position information thereof to the server; if the moving target does not have the communication function, the position information of the moving target can be reported manually by an operator of the moving target.
For an AGV trolley, a moving target can be positioned based on the two-dimensional code. The positioning based on the two-dimensional code is to position the moving target by reading the label value of the two-dimensional code, and can obtain the coordinate value above the two-dimensional code. For example, taking an AGV trolley as an example, by pasting a two-dimensional code strip on a rail, a track and position information are stored in the two-dimensional code strip, the AGV trolley automatically identifies the two-dimensional code strip through a two-dimensional code scanning module installed on the AGV trolley in the moving process, the position of a code point of the AGV trolley can be acquired, then the AGV trolley reports the current position in real time, and the reported current position is the position of the code point where the AGV trolley is located currently, that is, the position information of the AGV trolley is obtained.
Fig. 9 shows a schematic diagram of a snapshot constructing module 820 according to another embodiment of the present disclosure, and a snapshot map of a vehicle in a two-dimensional space is managed by using a quadtree in the snapshot constructing module 820. As shown in FIG. 9, the construct snapshot module 820 includes: a equipartition sub-module 821, a labeling sub-module 822, a mapping sub-module 823, and a mapping sub-module 824.
The halving sub-module 821 is configured to quarterly divide the two-dimensional map of the measurement area to obtain four primary leaf node areas, fig. 3 illustrates a schematic diagram of equally dividing the measurement area Q into four parts, that is, NW, NE, SW, and SE, in an embodiment of the present disclosure, for the measurement area illustrated in fig. 3, the type of the mark interval is a leaf node, the minimum point of the measurement area Q is s, and the coordinate is(s)x，sy) The maximum point is e, and the coordinates are (e)x，ey)。
The labeling submodule 822 is configured to label the four primary leaf node regions with primary leaf nodes, respectively. Fig. 4 is a schematic diagram of a quadtree according to an embodiment of the present disclosure, where four primary leaf nodes of the quadtree are NW, NE, SW, and SE, and the corresponding four partitions are primary leaf node areas. By combining fig. 3 and 4, the starting point of the quadtree region is s(s)x，sy) The end point is e (e)x，ey) And the range of the quadtree is represented by the following constraint conditions:
s＝q,q∈Q st.min(qx+qy)
e＝q,q∈Q st.max(qx+qy)
the center coordinate of the measurement region Q is (x)O，yO) The following are:
xO＝(ex-s _ x)/2, i.e. the x-axis intercept
yO＝(ey-ex) And/2, the y-axis intercept.
SW＝([sx,sy],[sx+xO,sy+yO]) A minimum point and a maximum point for representing a primary leaf node region SW;
SE＝([sx+xO,sy],[ex,sy+yO]) The minimum point and the maximum point are used for representing the primary leaf node area SE;
NW＝([sx,sy+yO],[sx+xO,ey]) A minimum point and a maximum point for representing the primary leaf node area NW;
NE＝([sx+xO,sy+yO],[ex,ey]) The minimum point and the maximum point used to represent the primary leaf node area NE.
The mapping sub-module 823 is configured to establish a correspondence relationship between each of the primary leaf node areas and the number of moving objects distributed in the area thereof according to the plurality of location information.
In an embodiment of the present disclosure, taking an AGV car as an example, in this step, a relationship between a primary leaf node area and the AGV car, that is, how many AGV cars are in each primary leaf node area, may be established according to the position information of the AGV car.
The map sub-module 824 is configured to construct the map snapshot according to the correspondence.
Before the map is constructed, a threshold value needs to be set for the number of AGV vehicles in any leaf node area in the multiple leaf node areas, wherein the threshold value can be set and adjusted according to the requirements of an actual use scene. And when the number of the moving targets in the first-level leaf node area is greater than the threshold value, performing quartering splitting on the first-level leaf node area based on the quadtree until the number of the moving targets in the N-level leaf node area after splitting does not exceed the threshold value, wherein N is greater than or equal to 2.
The splitting depth of the quad tree can be controlled by setting a threshold value, namely if the number of AGV trolleys in a certain level of leaf node area is larger than the threshold value, the splitting of the level of leaf node is required to be continued, namely four secondary leaf nodes are split according to the structure of the quad tree, the corresponding area is a secondary leaf node area, and meanwhile, the depth of the quad tree is + 1.
Still taking the area shown in fig. 3 as an example, fig. 5 shows an area segmentation graph after the division is continuously performed on the first-level leaf node area, and correspondingly, fig. 6 shows a quadtree schematic diagram after the multi-level division is performed, as shown in fig. 5 and fig. 6, nodes 2 and 3 are first-level leaf nodes, nodes a, D and 4 are second-level leaf nodes, and nodes C and D are third-level leaf nodes.
Fig. 10 shows a schematic diagram of the evaluation function module 830 in another embodiment of the present disclosure, and as shown in fig. 10, the evaluation function module 830 includes: a statistics submodule 831, a congestion marking submodule 832, a compression submodule 833 and a calculation submodule 834.
The statistics submodule 831 is configured to perform statistics on the depth of each of the first-level leaf node regions of the quadtrees in the map snapshot, wherein the maximum value of the depth of the quadtree is N; the congestion marking submodule 832 is configured to mark congestion of the first-level leaf node area according to the comparison result of the depth and a preset value, and marking data are obtained, wherein the preset value is greater than or equal to 2; the compression sub-module 833 is configured to perform Run Length Encoding (RLE) compression on the mark data to obtain RLE data; the calculation submodule 834 is configured to calculate a merit function for the plurality of leaf node areas from the RLE data and the map snapshot.
In an embodiment of the present disclosure, the congestion determining module 840 compares the evaluation function values of each level of leaf node areas in the measurement area with reference values, and if the evaluation parameter value of one leaf node area exceeds the reference value, the leaf node area is a congestion area.
The functions of each module in the apparatus are described in the above method embodiments, and are not described again here.
To sum up, according to the device for determining a congestion area provided by the embodiment of the disclosure, on one hand, a high-efficiency and fast data structure is established for the position information of a moving target in a two-dimensional space based on a quadtree, so that the congestion area in a measurement area can be quickly determined by calculating the evaluation function of each leaf node area, the effective real-time detection and management of vehicle group distribution are realized, and the transportation efficiency of vehicles is improved. On the other hand, data support can be provided for a follow-up obstacle avoidance strategy according to the determined congestion area, so that vehicle scheduling can be reasonably planned, a transportation route is optimized, and warehouse management is facilitated.
In another aspect, the present disclosure also provides an electronic device, including a processor and a memory, where the memory stores operating instructions for the processor to control the following method:
acquiring position information of a plurality of moving targets in the measuring area to obtain a plurality of position information; constructing a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas; within a sequence length of time, respectively calculating evaluation functions of the leaf node areas in the map snapshot according to a slice period; and determining a congestion area in the measurement area according to the value of the evaluation function.
Referring now to FIG. 11, shown is a block diagram of a computer system 1100 suitable for use in implementing the electronic device of an embodiment of the present application. The electronic device shown in fig. 11 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 11, the computer system 1100 includes a Central Processing Unit (CPU)1101, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)1102 or a program loaded from a storage section 1107 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data necessary for the operation of the system 1100 are also stored. The CPU 1101, ROM 1102, and RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.
The following components are connected to the I/O interface 1105: an input portion 1106 including a keyboard, mouse, and the like; an output portion 1107 including a signal output unit such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1108 including a hard disk and the like; and a communication section 1109 including a network interface card such as a LAN card, a modem, or the like. The communication section 1109 performs communication processing via a network such as the internet. A driver 1110 is also connected to the I/O interface 1105 as necessary. A removable medium 1111 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1110 as necessary, so that a computer program read out therefrom is mounted into the storage section 1108 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 1109 and/or installed from the removable medium 1111. The above-described functions defined in the system of the present application are executed when the computer program is executed by a Central Processing Unit (CPU) 1101.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable medium or any combination of the two. A computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a transmitting unit, an obtaining unit, a determining unit, and a first processing unit. The names of these units do not in some cases constitute a limitation to the unit itself, and for example, the sending unit may also be described as a "unit sending a picture acquisition request to a connected server".
In another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include the method steps of:
acquiring position information of a plurality of moving targets in the measuring area to obtain a plurality of position information; constructing a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas; within a sequence length of time, respectively calculating evaluation functions of the leaf node areas in the map snapshot according to a slice period; and determining a congestion area in the measurement area according to the value of the evaluation function.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A method of determining congestion zones, comprising:
acquiring position information of a plurality of moving targets in the measuring area to obtain a plurality of position information;
constructing a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas;
within a sequence length of time, respectively calculating evaluation functions of the leaf node areas in the map snapshot according to a slice period;
and determining a congestion area in the measurement area according to the value of the evaluation function.
2. The method of determining congestion areas as claimed in claim 1, wherein said constructing a map snapshot of said measurement area for said plurality of location information based on a quadtree comprises:
quartering the two-dimensional map of the measurement area to obtain four primary leaf node areas;
marking the four primary leaf node areas by using primary leaf nodes respectively;
establishing a corresponding relation between each primary leaf node area and the number of moving objects distributed in the area according to the position information;
and constructing the map snapshot according to the corresponding relation.
3. The method for determining congestion areas as claimed in claim 2, wherein before said constructing a map snapshot of said measurement area for said plurality of location information based on a quadtree, further comprising:
setting a threshold value for the number of the moving objects in any of the leaf node areas.
4. The method of determining congestion areas as claimed in claim 3, further comprising:
and when the number of the moving targets in the first-level leaf node area is greater than the threshold value, performing quartering splitting on the first-level leaf node area based on the quadtree until the number of the moving targets in the N-level leaf node area after splitting does not exceed the threshold value, wherein N is greater than or equal to 2.
5. The method for determining the congestion area according to claim 4, wherein the calculating the evaluation functions of the leaf node areas in the map snapshot according to the slice period in a sequence of lengths of time respectively comprises:
counting the depth of each primary leaf node area of a quadtree in the map snapshot, wherein the maximum value of the depth of the quadtree is N;
carrying out congestion marking on the first-level leaf node area according to the comparison result of the depth and a preset value to obtain marking data, wherein the preset value is greater than or equal to 2;
performing Run Length Encoding (RLE) compression on the marked data to obtain RLE data;
calculating a merit function for the plurality of leaf node regions from the RLE data and the map snapshot.
6. The method for determining the congestion area according to claim 5, wherein the performing congestion marking on the primary leaf node area according to the comparison result of the depth and the preset value comprises:
when the depth of the first-level leaf node area is greater than or equal to a preset value, marking the first-level leaf node area as congestion, and representing the congestion by using marking data 1;
and when the depth of the first-level leaf node area is smaller than the preset value, marking the leaf node area as not jammed and representing the leaf node area by marking data 0.
7. The method for determining congestion areas according to claim 6, wherein determining congestion areas within the measurement area according to the value of the merit function comprises:
and comparing the values of the evaluation functions of all levels of leaf node areas in the measurement area with reference values respectively, wherein if the value of the evaluation parameter of one leaf node area exceeds the reference value, the leaf node area is a congestion area.
8. An apparatus for determining congestion zones, comprising:
the acquisition position module is configured to acquire position information of a plurality of moving targets in the measurement area to obtain a plurality of position information;
a snapshot constructing module configured to construct a map snapshot of the measurement area for the plurality of location information based on a quadtree, wherein the map snapshot includes a plurality of leaf node areas;
an evaluation function module configured to calculate evaluation functions of the plurality of leaf node areas in the map snapshot according to a slice period within a sequence of length time;
a congestion determination module configured to determine a congestion area within the measurement area according to the value of the evaluation function.
9. An electronic device, comprising:
a processor;
memory storing instructions for the processor to control the method steps according to any one of claims 1-7.
10. A computer-readable medium having stored thereon computer-executable instructions, which when executed by a processor, perform the method steps of any one of claims 1-7.
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