CN116151039A - Distributed collaborative scheduling method and system based on random access task - Google Patents
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Abstract
The invention relates to a distributed collaborative scheduling method and system based on random access tasks. The method comprises the following steps: aiming at the characteristics of a certain random access task, establishing an expression model of the random access task by utilizing a resource transfer theory, wherein the expression model comprises an insertion mode and a path transfer mode of the random access task; according to the insertion mode and the path transfer mode, adopting a knowledge graph technology to construct an association relationship among a plurality of functional windows to form a graph network of a certain area; evaluating the transfer capacity of the multi-beam resources according to the occupation condition of the multi-beam resources at the current moment in the graph network to form a multi-beam virtual resource pool of the area; and respectively carrying out distributed cooperative scheduling on a plurality of areas by utilizing a central scheduling subsystem according to the multi-beam resource occupation condition and transfer capacity of the multi-beam virtual resource pool of each area. The invention not only improves the response speed of the random access task, but also improves the utilization efficiency of multi-beam resources.
Description
Technical Field
The disclosure relates to the technical field of aerospace measurement and control network scheduling, in particular to a distributed collaborative scheduling method and system based on random access tasks.
Background
Compared with the traditional communication satellite, the Low Earth Orbit (LEO) system can provide larger communication capacity and lower communication delay. Therefore, in recent years, the development of low-orbit satellite systems has been getting hot worldwide. Many foreign companies have proposed low-orbit constellation plans that project thousands of satellites to construct a global-covering low-orbit constellation. Low-orbit constellation plans in China, such as the "wild goose plan", "rainbow Yun Xing plan" and the like, are also under steady promotion.
The large-scale, miniaturized and intelligent satellite deployment brings great challenges to the existing aerospace measurement and control network. Firstly, the quantity of satellites deployed by LEO plan is huge, and the current mode of point-to-point-based satellite-to-ground link and full radian measurement and control is difficult to meet the requirement of mass measurement and control; secondly, the satellite is decentered by improving the intelligent level, so that the satellite can independently initiate resource requests, namely the capacity of random access, and the current space measurement and control scheduling mode which is allocated according to needs and driven by a plan cannot adapt to new changes; in addition, according to the situation of China, in a future period, the measurement and control mode of centralized pre-allocation and concurrent access along with meeting is a new state of aerospace measurement and control of China, but at present, a mature and available scheduling framework is not yet available. Accordingly, there is a need to provide a solution to improve one or more of the problems of the related art described above.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
A first aspect of an embodiment of the present disclosure provides a distributed collaborative scheduling method based on an on-demand access task, including the following steps:
aiming at the characteristics of a certain random access task, establishing an expression model of the random access task by utilizing a resource transfer theory; the resource transfer theory comprises a plurality of functional windows in different types, and the expression model comprises an insertion mode and a path transfer mode of the random access task;
according to the insertion mode and the path transfer mode, a knowledge graph technology is adopted to construct association relations among a plurality of functional windows, and a graph network of a certain area is formed;
evaluating the transfer capacity of the multi-beam resources according to the occupation condition of the multi-beam resources at the current moment in the graph network to form a multi-beam virtual resource pool of the area;
iteratively performing all the steps to form the multi-beam virtual resource pools of a plurality of areas respectively;
According to the occupation condition of the multi-beam resources of the multi-beam virtual resource pool of each region and the transfer capacity of the multi-beam resources, a central scheduling subsystem is utilized to respectively perform distributed cooperative scheduling on a plurality of regions;
each area at least comprises a satellite and a multi-beam device, and the multi-beam device can generate the multi-beam virtual resource pool.
In an exemplary embodiment of the present disclosure, the plurality of function windows includes: the plurality of function windows includes: the method comprises the following access task window, a scheduling task window, a conflict window, an alternative window, an idle resource window and a parallel window; wherein a plurality of the scheduling task windows and a plurality of the idle resource windows are arranged in a crossing manner.
In an exemplary embodiment of the present disclosure, the insertion pattern includes a 1:1 insertion type, a 1:2 insertion type, and a 1:3 insertion type; wherein,
the 1:1 insertion type includes: the random access task is contained in 1 scheduling task window and the idle resource window adjacent to one side of the scheduling task window;
the 1:2 insertion type includes: the random access task is contained in 2 scheduling task windows and a plurality of idle resource windows associated with the 2 scheduling task windows;
The 1:3 insertion type includes: the random access task is contained in 3 scheduling task windows and a plurality of idle resource windows associated with the 3 scheduling task windows;
according to the resource transfer theory, there are 5 cases among the random access task window, the idle resource window and the scheduling task window in each type of the insertion mode:
first case: the random access task window is completely overlapped with the scheduling task window, and the idle resource window is kept unchanged after the resource is transferred;
second case: the random access task window fully occupies the scheduling task window, occupies part of the idle resource window, and reduces the length of the idle resource window after resource transfer;
third case: the random access task window is partially overlapped with the scheduling task window, and fully occupies the idle resource window on the left side of the scheduling task window, and after resource transfer, the number and the length of the idle resource window are changed;
fourth case: the random access task window is partially overlapped with the scheduling task window, and completely occupies the idle resource window on the right side of the scheduling task window, and after resource transfer, the number and the length of the idle resource window are changed;
Fifth case: and the random access task window fully occupies the scheduling task window and all the idle resource windows, and the idle resource windows do not exist after the resources are transferred.
In an exemplary embodiment of the present disclosure, the path transfer mode includes a 1:1 transfer type, a 1:2 transfer type, and a 1:3 transfer type; wherein,
each transfer type comprises a single-path transfer type and a multi-path transfer type;
the number of transfer layers of the multi-path transfer type is equal to the number of the scheduling task windows.
In an exemplary embodiment of the disclosure, the step of constructing an association relationship between a plurality of functional windows according to the insertion mode and the path transfer mode by using a knowledge graph technology, and forming a graph network of a certain area includes:
defining the set of scheduling task windows as:
Defining the set of idle resource windows as
；
wherein ,
a number representing the multi-beam device; />
Representing the relative circle number; />
Representing a start time of a current function window; / >
Representing the end time of the current function window; />
Representing a start time of a preamble function window; />
Representing the end time of the preamble function window; />
Representing a start time of a subsequent function window; />
Representing the ending time of the subsequent function window; then (I)>
Representing any of the scheduled task windows +.>
The number of the multi-beam device, the relative circle number, the start time of the current function window, the end time of the current function window, the start time of the preceding function window, the end time of the preceding function window, the start time of the following function windowThe end time of the subsequent function window; />
Representing any of the scheduled task windows +.>
Corresponding->
The number of the multi-beam device with the conflict window, the relative circle number, the starting time of the current function window and the ending time of the current function window; />
Representing any of the scheduled task windows +.>
Corresponding->
The number of the multi-beam device of each alternative window, the relative circle number, the starting time of the current function window and the ending time of the current function window; />
Indicate->
Numbering multi-beam equipment of each idle resource window, starting time of the current function window and ending time of the current function window;
The construction of the graph network includes the following 7 sub-steps:
a first substep: for any of the scheduled task windows
From a certain said conflict window->
At the beginning of the process,
when->
And->
，/>
，/>
When true, then the conflict window +.>
Included in the dispatch task window->
And the window formed by the adjacent idle resource windows belongs to a 1:1 insertion type; then->
and />
There is transfer relation between them, and a directed association relation is established->
，
Turning to a seventh substep, otherwise, continuing the second substep;
wherein ,
representing any of the scheduled task windows +.>
Corresponding->
Relative circle number of each collision window;
representing any of the scheduled task windows +.>
Is the relative circle number of (2); />
Representing any of the scheduled task windows +.>
Corresponding->
Numbering of multi-beam devices for each collision window; />
Indicate->
Numbering of the multi-beam devices of the individual scheduling task windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The start time of the current function window of the collision window; />
Indicate->
Start time of the preamble function window of the individual scheduled task window; />
Representing any of the scheduled task windows +.>
Corresponding->
The end time of the current function window of the collision window; / >
Indicate->
The end time of the subsequent function window of the individual scheduled task window; the contact represents an inclusion relationship; friend represents a friendship;
a second substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the previous scheduled task window->
The window formed by the idle resource window and the adjacent idle resource window belongs to a 1:2 insertion type; then->
And->
and />
Composition continuous schedulingThe tasks have transfer relation, and a storage is created>
and />
A first parallel window p1 of information of (2) establishing a directed association relationship
，/>
，/>
，
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep, and vice versa, go to the fourth substep; wherein (1)>
Indicate->
A number of the multi-beam device of a previous scheduled task window of the scheduled task windows; />
Indicate->
The start time of the preamble function window of the previous scheduling task window of the scheduling task windows;
a third substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the latter scheduling task window->
The window formed by the idle resource window and the adjacent idle resource window belongs to a 1:3 insertion type; then->
And->
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created >
and />
The second parallel window p2 of the information of (2) establishes a directed association relation +.>
，
，/>
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep, and vice versa, go to the fourth substep; wherein (1)>
Indicate->
Numbering of the multi-beam device of the next scheduled task window of the scheduled task windows; />
Indicate->
The end time of the subsequent function window of the subsequent scheduling task window of the plurality of scheduling task windows;
a fourth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the first two said scheduled task windows +.>
、/>
Within the window consisting of the adjacent free resource window +.>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
A third parallel window p3 of the information of (2) establishes a directed association relationship
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Turning to the seventh substep, and conversely, turning to the first substep; wherein (1)>
Indicate->
Numbering of the multi-beam devices of the first two scheduling task windows of the plurality of scheduling task windows; />
Indicate->
Starting time of the front function window of the first two scheduling task windows of the plurality of scheduling task windows;
fifth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window- >
And the latter two said scheduled task windows +.>
、/>
Within the window formed by the adjacent idle resource window, and (2)>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
A fourth association window p4 of the information of (2) establishing a directed association relationship
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Turning to the seventh substep, and conversely, turning to the first substep; wherein (1)>
Indicate->
Numbering of the multi-beam device of the last two scheduling task windows of the plurality of scheduling task windows; />
Indicate->
The end time of the subsequent function window of the last two scheduling task windows of the plurality of scheduling task windows;
sixth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And front and back two said scheduled task windows->
、
Within the window formed by the adjacent idle resource window, and (2)>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
The fifth parallel window p5 of the information of (2) establishes a directed association relation +.>
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Turning to the seventh substep, and vice versa turning to the first substep;
seventh substep: for any successfully scheduled task window
From a certain said alternative window +.>
Start, crosstalk>
When->
And->
，/>
When true, the alternative window +. >
Is included in the free resource window->
In (I)>
and />
There is transfer relation between them, and a directed association relation is established->
And
, wherein ,/>
Representing any of the scheduled task windows +.>
Corresponding->
The relative circle numbers of the candidate windows; />
Representing any of the scheduled task windows +.>
Corresponding->
Numbering of the multi-beam devices for the individual candidate windows; />
Indicate->
Numbering of multi-beam devices for the individual idle resource windows; />
Representing any of the scheduled task windows
Corresponding->
The start time of the current function window of the candidate windows; />
Indicate->
The start time of the current function window of the idle resource windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The end time of the current function window of the candidate windows; />
Indicate->
The end time of the current function window of the idle resource windows; belong represents membership.
In an exemplary embodiment of the present disclosure, the step of evaluating the transferability of the multi-beam resources according to the occupancy of the multi-beam resources at the current time in the graph network, and forming a multi-beam virtual resource pool of the area,
the occupation condition of the multi-beam resources comprises idle resources, transferable resources and non-transferable resources; wherein,
The idle resources refer to the unoccupied multi-beam resources;
the transferable resource refers to the multi-beam resource with the transfer layer number smaller than 6;
the non-transferable resource refers to the multi-beam resource with a transfer layer number of 6 or more or the multi-beam resource with higher priority that cannot be transferred.
In an exemplary embodiment of the disclosure, the step of evaluating the transferability of the multi-beam resources according to the occupation situation of the multi-beam resources at the current moment in the graph network, and forming the multi-beam virtual resource pool of the area includes:
defining a set of devices for a region as
, wherein ,/>
Represents a multi-beam device, Y represents other devices, the multi-beam set of the multi-beam device is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of occupancy of the multi-beam resource is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the optimal transfer paths of the occupation situation of the multi-beam resources is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of non-transferable resources provided by the central scheduling subsystem is
The method comprises the steps of carrying out a first treatment on the surface of the The set of the idle resource windows is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the scheduling task window in a certain area is +.>
；
The construction of the multi-beam virtual resource pool comprises the following 7 sub-steps:
a first substep: from the multi-beam set 
Beam +.>
Initially, when the beam ∈ ->
For idle beam, then ∈>
，/>
The method comprises the steps of carrying out a first treatment on the surface of the When the beam->
When in use, then->
，/>
The method comprises the steps of carrying out a first treatment on the surface of the When the beam->
When selecting to occupy the beam +.>
Starting from any one of the idle resource windows j i ，/>
Extracting a graph network G associated between the starting point and the ending point as the ending point;
a second substep: setting the weight of each edge of the graph network G as 1, starting from the starting point, searching a scheduling task window t with a transfer relation with the starting point, and adding the scheduling task window t into a search sequence;
a third substep: searching the dispatch task window with transfer relation with the dispatch task window t serving as a new starting point until an end point appears for the first time, and obtaining the shortest path from the starting point to the end point in the graph network G
The method comprises the steps of carrying out a first treatment on the surface of the When the set of optimal transfer paths +.>
Does not comprise said shortest path +.>
Define the set of transfer paths as +.>
The shortest path +.>
Add to the set +.>
In (a) and (b);
a fourth substep: continuing to judge the shortest transfer path between the starting point and other idle resource windows and continuing to add the shortest transfer path to the set
After that, the first area is further divided into a first area set according to the area distribution difference of the path nodes >
And second inter-region set->
；
Fifth substep: when the region is assembled
Is>
When the number of transfer layers is less than 6, +.>
And according to the transfer layer number pair +.>
Performing assignment; when the first region is set +.>
Is>
When the transfer layer number is more than or equal to 6, further judging;
sixth substep: when the second inter-region is assembled
Is>
When the number of transfer layers is less than 6, +.>
And according to the transfer layer number pair +.>
Performing assignment; when said second inter-region set +.>
Is>
When the transfer layer number is more than or equal to 6, the +.>
，/>
；
Seventh substep: repeating all the steps until the current time is the set
After the transfer capacity analysis of all beams in the system is completed, the evaluation of the next moment is continued.
In an exemplary embodiment of the disclosure, the step of using a central scheduling subsystem to perform distributed collaborative scheduling on a plurality of areas according to an occupation situation of the multi-beam resources and transfer capability of the multi-beam resources in the multi-beam virtual resource pool of each area includes:
the occupation condition set of the multi-beam resources is that
The method comprises the steps of carrying out a first treatment on the surface of the The set of occupancy types of the multi-beam resource is +. >
, wherein ,/>
Representing a set of idle multi-beam resources; />
Representing a set of transferable multi-beam resources within an area; />
Representing a set of inter-region transferable multi-beam resources; />
Representing a set of non-transferable multi-beam resources;
defining the multi-beam device
Is +.>
，/>
Representing the number of free multi-beam resources; />
Representing the number of transferable multi-beam resources within the region; />
Representing the number of multi-beam resources that are transferable between said regions; />
Indicating the amount of non-transferable multi-beam resources, then,
when (when)
When the idle multi-beam resources exist, the multi-beam resource transfer probability is that
The calculation formula of (1) comprises:
when (when)
When (and->
In the absence of the free multi-beam resource, the multi-beam resource transfer probability +.>
The calculation formula of (1) comprises:
when (when)
，/>
When the idle multi-beam resource and the transferable multi-beam resource are not present, the multi-beam resource transfer probability +.>
The calculation formula of (1) comprises:
when (when)
，/>
When all multi-beam resources are not transferable, the multi-beam resources are transferredProbability->
The value of (2) is 0, (-)>
Representing a multibeam transfer layer->
A set of inter-transferable multi-beam resources.
A second aspect of an embodiment of the present disclosure provides a distributed collaborative scheduling system based on-demand access tasks, the system comprising:
the central scheduling subsystem is used for carrying out distributed collaborative scheduling on all random access tasks;
a plurality of regions, each region being regulated by the central scheduling subsystem; wherein each of the regions comprises at least one satellite and one multi-beam device capable of generating a multi-beam virtual resource pool.
In one exemplary embodiment of the present disclosure, the multi-beam device of each zone is capable of bi-directional information interaction and scheduling with other zones and the central scheduling subsystem.
The technical scheme provided by the disclosure can comprise the following beneficial effects:
the embodiment of the disclosure provides a distributed collaborative scheduling method based on an on-demand access task, which introduces multiple beams into a virtual resource pool based on a resource transfer theory by establishing an insertion mode and a path transfer mode of the on-demand access task, and regulates and controls the multiple beams through a central scheduling subsystem, so that the response speed of the on-demand access task is improved, and the utilization efficiency of multiple beam resources is also improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a schematic diagram of steps of a distributed collaborative scheduling method based on-demand access tasks in an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a flow diagram of a distributed collaborative scheduling method based on-demand access tasks in an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of the principles of resource transfer in an exemplary embodiment of the present disclosure;
FIG. 4 illustrates a schematic diagram of insert types in an insert mode of an on-demand access task in an exemplary embodiment of the present disclosure;
FIG. 5 illustrates a schematic diagram of an insertion pattern of an on-demand access task in an exemplary embodiment of the present disclosure;
FIG. 6 illustrates a schematic diagram of a path transfer mode for an on-demand access task in an exemplary embodiment of the present disclosure;
fig. 7 is a schematic diagram of evaluating transfer capability of multi-beam resources to form a multi-beam virtual resource pool in an exemplary embodiment of the present disclosure;
FIG. 8 illustrates a schematic diagram of a framework of a distributed collaborative scheduling system based on-demand access tasks in an exemplary embodiment of the present disclosure;
FIG. 9 is a schematic diagram of a general process of handling an on-demand access task and a resource transfer coordination mechanism in an exemplary embodiment of the present disclosure;
FIG. 10 is a schematic diagram showing the results of graph network generation in a simulation experiment of an exemplary embodiment of the present disclosure;
fig. 11 shows a schematic diagram of the analysis results of the multi-beam a transfer capability in the simulation experiment of the exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many 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 the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, 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 a repetitive description thereof will be omitted. 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 software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
A first aspect of the present exemplary embodiment provides a distributed collaborative scheduling method based on an on-demand access task, as shown in fig. 1 and fig. 2, which may include the following steps:
step S101: aiming at the characteristics of a certain random access task, establishing an expression model of the random access task by utilizing a resource transfer theory; the resource transfer theory comprises a plurality of functional windows of different types; the expression model comprises an insertion mode and a path transfer mode of the random access task;
step S102: according to the insertion mode and the path transfer mode, adopting a knowledge graph technology to construct association relations among a plurality of functional windows to form a graph network of a certain area;
step S103: evaluating the transfer capacity of the multi-beam resources according to the occupation condition of the multi-beam resources at the current moment in the graph network to form a multi-beam virtual resource pool of the area;
step S104: iteratively performing all the steps to respectively form multi-beam virtual resource pools of a plurality of areas;
step S105: and carrying out distributed cooperative scheduling on a plurality of areas by utilizing a central scheduling subsystem according to the occupation condition of the multi-beam resources of the multi-beam virtual resource pool of each area and the transfer capacity of the multi-beam resources.
Here, each region contains at least one satellite and one multi-beam device that is capable of generating a multi-beam virtual resource pool.
The embodiment of the disclosure provides a distributed collaborative scheduling method based on an on-demand access task, which introduces multiple beams into a virtual resource pool based on a resource transfer theory by establishing an insertion mode and a path transfer mode of the on-demand access task, and regulates and controls the multiple beams through a central scheduling subsystem, so that the response speed of the on-demand access task is improved, and the utilization efficiency of multiple beam resources is also improved.
Next, each step of the above-described method in the present exemplary embodiment will be described in more detail.
In step S101, before specifying the random access problem, the basic concept of resource transfer needs to be clarified. The resource transfer specifically means that after a periodic scheduling plan is prepared, when a satellite initiates a new measurement and control requirement, no measurement and control resource is directly available at present, and a scheduling task occupying a resource window is transferred to a standby window, so that the measurement and control requirement of the new task is met. Specifically, the plurality of function windows may include: random access task window, scheduling task window, conflict window, alternative window, idle resource window, parallel window, etc. As shown in fig. 3, the scheduling task window refers to selecting a certain resource window meeting measurement and control requirements for the random access task in the period scheduling. In practice, there are multiple resource windows meeting measurement and control requirements, and the unselected windows are divided into two types: one is a conflict window, which means that the window is occupied by other tasks and cannot be directly used; another is an alternative window, which means that the window is not occupied by other tasks and can be used directly. The scheduling task window, the conflict window, and the alternative window may be collectively referred to as a feasible solution window. The free resource window refers to an unoccupied resource window. The alternative window is part of the free resource window. As shown in fig. 3, no transfer is required when there is a free resource window that meets new task requirements and is directly available; in contrast, indirectly available idle resource windows need to be found by scheduling multiple resource transfers between task windows.
In this embodiment, for the same ground device, the visible windows of different satellites have different degrees of offset due to the track plane difference, and even for the same satellite, due to the influence of the perturbation force, the track planes of different circles have the same difference, so the probability that the satellite initiated random access task window and the scheduling task window completely coincide is very low.
As shown in fig. 4, the insertion mode of the random access task may include a 1:1 insertion type, a 1:2 insertion type and a 1:3 insertion type, where a plurality of the scheduling task windows and a plurality of the idle resource windows are arranged in a crossing manner; as can be seen from FIG. 4, the random access task r-1 can directly find the idle resource window of the beam n, the window length of the random access task r-2 is equivalent to that of the scheduling task, but the offset occurs, only partial coincidence exists, and the task r-1 and the task r-2 are both of a 1:1 insertion type; task r-3 belongs to the 1:2 insertion type and task r-4 belongs to the 1:3 insertion type.
Referring to fig. 4, it is shown:
the 1:1 insertion type includes: the random access task is contained in 1 scheduling task window and an idle resource window adjacent to one side of the scheduling task window;
the 1:2 insertion types include: the random access task is contained in 2 scheduling task windows and a plurality of idle resource windows associated with the 2 scheduling task windows;
The 1:3 insertion types include: the random access task is contained within 3 scheduled task windows and a plurality of idle resource windows associated with the 3 scheduled task windows.
Of course, 1:4 insertion types or more complex types may also be included herein.
In combination with these three insertion types, the number, length and position of resource windows are inevitably affected in resource transfer. Resulting in 5 cases between the random access task window, the idle resource window and the scheduling task window under each different insertion type.
As shown in fig. 5, these 5 cases include:
fig. 5 (a-1, b-1, c-1) belongs to the first case, i.e. the random access task window is completely coincident with the scheduling task window, and the idle resource window remains unchanged after the resource transfer.
Fig. 5 (a-2, b-2, c-2) belongs to the second case, i.e. the on-demand task window completely contains the scheduling task window and occupies part of the idle resource window, and the length of the idle resource window decreases after the resource transfer.
Fig. 5 (a-3, b-3, c-3) belongs to the third case, that is, the random access task window is partially overlapped with the scheduling task window, and the idle resource window on the left side of the scheduling task window is fully occupied, and after the resource is transferred, the number and the length of the idle resource windows are changed.
Fig. 5 (a-4, b-4, c-4) is a fourth case, that is, the random access task window overlaps with the scheduling task window partially, and fully occupies the idle resource window on the right side of the scheduling task window, and after the resource is transferred, the number and the length of the idle resource windows are changed.
Fig. 5 (a-5, b-5, c-5) belongs to the fifth case, i.e. the on-demand task window completely contains the scheduled task window and all the idle resource windows, and after resource transfer, there is no idle resource window.
Here, a represents five cases of 1:1 insertion type; b represents five cases of 1:2 insertion type; c represents five cases of 1:3 insertion type. The 1:2 insertion type and the 1:3 insertion type are similar to the five cases of the 1:1 insertion type.
Likewise, as shown in fig. 6, the path transfer mode of the on-demand access task also includes 3 types, i.e., a 1:1 transfer type, a 1:2 transfer type, and a 1:3 transfer type.
The 1:1 transfer type is exemplified herein, and the 1:2 transfer type and the 1:3 transfer type are similar to the 1:1 transfer type.
In the 1:1 transfer type, yield paths
Comprises only 1 random access task, 1 scheduling task window, 1 alternative window and 1 idle resource window, belonging to single path Transfer mode. After the scheduling task transfers the resource to the on-demand access task, an alternative window is found in the feasible solution, the demand of the on-demand access task is met through layer 1 transfer, the transfer path is shortest, and the transfer cost is minimum.
Route of giving way
Comprises 1 random access task, 2 scheduling task windows, 1 conflict window, 1 alternative window and 1 idle resource window. Transfer route->
Comprises 1 random access task, 7 scheduling task windows, 3 conflict windows, 4 alternative windows and 4 idle resource windows.
The 1:2 transfer type and the 1:3 transfer type belong to a multipath transfer type, and the multipath transfer type occurs because a one-to-many situation occurs between a collision window and a scheduling task window in which transfer relationships exist. The number of transfer layers of the multi-path transfer type is consistent with the number of the scheduling task windows, and the transfer cost is high.
In step S102, defining a random access task window, a scheduling task window, a conflict window, an overlapping window, an alternative window and a free resource window in the graph network as nodes; information such as equipment corresponding to the windows, relative circle numbers, start time, end time and the like is stored in the nodes, and correlations among the windows are used as edges. According to the insertion mode and the path transfer mode, a knowledge graph technology is adopted, the association relation among all the function windows is judged through equipment and window attributes, and the inclusion relation of the dispatch task node pointing to a certain conflict window node is established. The conflict window node points to a relationship of a certain scheduling task node, an overlapping window node or an alternative node. The candidate window nodes point to the membership of a certain idle resource window node. And iteratively processing each scheduling task node until the scheduling task node is finished to form a graph network.
The following gives the sub-steps of the graph network construction, which are defined first as follows, before the construction sub-steps:
defining a set of scheduled task windows as:
The constructed graph network includes the following seven sub-steps:
a first substep: for any of the scheduled task windows
From a certain said conflict window->
At the beginning of the process,
when->
And->
，/>
，/>
When true, then the conflict window +.>
Included in the dispatch task window->
And the window formed by the adjacent idle resource windows belongs to a 1:1 insertion type; then->
and />
There is transfer relation between them, and a directed association relation is established->
，/>
Turning to the seventh substep, which follows, and vice versa, the second substep follows.
wherein ,
representing any of the scheduled task windows +.>
Corresponding->
Relative circle number of each collision window;
representing any of the scheduled task windows +.>
Is the relative circle number of (2); />
Representing any of the scheduled task windows +.>
Corresponding->
Numbering of multi-beam devices for each collision window; / >
Indicate->
Numbering of the multi-beam devices of the individual scheduling task windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The start time of the current function window of the collision window; />
Indicate->
Start time of the preamble function window of the individual scheduled task window; />
Representing any of the scheduled task windows +.>
Corresponding->
The end time of the current function window of the collision window; />
Indicate->
The end time of the subsequent function window of the individual scheduled task window; the contact represents an inclusion relationship; friend represents a friendship.
A second substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Included in the dispatch task windowMouth->
And the previous scheduled task window->
The window formed by the idle resource window and the adjacent idle resource window belongs to a 1:2 insertion type; then->
And->
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
and />
A first parallel window p1 of information of (2) establishing a directed association relationship
，/>
，/>
，
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep to follow-up, and vice versa, go to the fourth substep to follow-up; wherein (1)>
Indicate->
A number of the multi-beam device of a previous scheduled task window of the scheduled task windows; />
Indicate->
The start time of the preamble function window of the previous scheduled task window of the scheduled task windows.
A third substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the latter scheduling task window->
The window formed by the idle resource window and the adjacent idle resource window belongs to a 1:3 insertion type; then->
And->
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
and />
A second parallel window p2 of the information of (2) establishes a directed association relationship
，/>
，/>
，
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep to follow-up, and vice versa, go to the fourth substep to follow-up; wherein (1)>
Indicate->
Numbering of the multi-beam device of the next scheduled task window of the scheduled task windows; />
Indicate->
The end time of the subsequent function window of the subsequent scheduled task window of the scheduled task windows.
A fourth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the first two said scheduled task windows +.>
、/>
Within the window consisting of the adjacent free resource window +.>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
A third parallel window p3 of the information of (2) establishes a directed association relationship
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep, and vice versa, go to the first substep; wherein (1) >
Indicate->
Numbering of the multi-beam devices of the first two scheduling task windows of the plurality of scheduling task windows; />
Indicate->
The start times of the preamble function windows of the first two scheduled task windows of the respective scheduled task windows.
Fifth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the latter two said scheduled task windows +.>
、/>
Within the window formed by the adjacent idle resource window, and (2)>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
A fourth association window p4 of the information of (2) establishing a directed association relationship
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep, and vice versa, go to the first substep; wherein (1)>
Indicate->
Numbering of the multi-beam device of the last two scheduling task windows of the plurality of scheduling task windows; />
Indicate->
The end time of the subsequent function window of the last two scheduled task windows of the respective scheduled task windows.
Sixth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And front and back two said scheduled task windows->
、
Within the window formed by the adjacent idle resource window, and (2)>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created >
、/>
and />
The fifth parallel window p5 of the information of (2) establishes a directed association relation +.>
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep and vice versa to the first substep.
Seventh substep: for any successfully scheduled task window
From a certain said alternative window +.>
Start, crosstalk>
When->
And->
，/>
When true, the alternative window +.>
Is included in the free resource window->
In (I)>
and />
There is transfer relation between them, and a directed association relation is established->
and />
, wherein ,/>
Representing any of the scheduled task windows +.>
Corresponding->
The relative circle numbers of the candidate windows; />
Representing any of the scheduled task windows +.>
Corresponding->
Numbering of the multi-beam devices for the individual candidate windows; />
Indicate->
Numbering of multi-beam devices for the individual idle resource windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The start time of the current function window of the candidate windows; />
Indicate->
The start time of the current function window of the idle resource windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The end time of the current function window of the candidate windows; />
Indicate->
The end time of the current function window of the idle resource windows; belong represents membership.
Through the steps, the construction of the graph network is completed.
In step S103, as shown in fig. 7, in the step of evaluating the transfer capability of the multi-beam resources and forming the multi-beam virtual resource pool in the area, the occupation situations of the multi-beam resources are classified into two types: one is a free resource (numbered 0 in fig. 7), which refers to an unoccupied multi-beam resource; another type is occupied resources, which can be divided into transferable resources and non-transferable resources, wherein non-transferable resources refer to multi-beam resources (numbered: -1 and 6-10 in fig. 7) that have a transfer layer number of 6 or more or that cannot be transferred with higher priority; the transferable resource refers to a multi-beam resource (numbered 1-5 in fig. 7) with a transfer layer number of less than 6. The transferable resources are further subdivided into intra-regional and inter-regional transferable resources. The transferable resources in the area mean that transfer paths are only distributed in the area with multi-beam as a center and the geographic distribution is more concentrated; inter-regional transferable resources refer to a transfer path distributed across multiple regions. Each transfer type is classified into 5 types according to the number of transfer layers.
The essence of resource cost transfer is to search the shortest transfer path between the scheduling task window occupying the resources and the idle resource window in the knowledge graph constructed by the scheduling plan result; while the uniqueness of the nodes in the different transfer paths should be guaranteed. A breadth first (Breadth First Search, BFS) algorithm is used to find the shortest path between the scheduled task node occupying the beam resource and the free resource window in the graph network as the beam transfer capability value.
Specifically, the construction of the multi-beam virtual resource pool also includes 7 sub-steps, which are defined as follows before the construction of the 7 sub-steps:
defining a set of devices for a region as
, wherein ,/>
Represents a multi-beam device, Y represents other devices, the multi-beam set of the multi-beam device is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of occupancy of the multi-beam resource is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the optimal transfer paths of the occupation situation of the multi-beam resources is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of non-transferable resources provided by the central scheduling subsystem is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the idle resource windows is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the scheduling task windows in a certain area is
。
The 7 sub-steps of constructing the multi-beam virtual resource pool are:
a first substep: from the multi-beam set
Beam +.>
Initially, when the beam ∈ ->
For idle beam, then ∈>
，/>
The method comprises the steps of carrying out a first treatment on the surface of the When the beam->
When in use, then->
，/>
The method comprises the steps of carrying out a first treatment on the surface of the When the beam->
When selecting to occupy the beam +.>
Starting from any one of the idle resource windows j i ，/>
As an end point, a graph network G is extracted that is associated between the start point and the end point.
A second substep: and setting the weight of each side of the graph network G as 1, searching a dispatching task window t with a transfer relation from the starting point, and adding the dispatching task window t into a search sequence.
A third substep: searching the dispatch task window with transfer relation with the dispatch task window t serving as a new starting point until an end point appears for the first time, and obtaining the shortest path from the starting point to the end point in the graph network G
The method comprises the steps of carrying out a first treatment on the surface of the When the set of optimal transfer paths +.>
Does not comprise said shortest path +.>
Define the set of transfer paths as +.>
The shortest path +.>
Add to the set +.>
Is a kind of medium.
A fourth substep: continuing to judge the shortest transfer path between the starting point and other idle resource windows and continuing to add the shortest transfer path to the set
In the process, the difference is then distributed according to the area of the path nodeFurther divided into a first intra-area set +.>
And second inter-region set->
。
Fifth substep: when the region is assembled
Is>
When the number of transfer layers is less than 6, +.>
And according to the transfer layer number pair +.>
Performing assignment; when the first region is set +.>
Is>
When the transfer layer number of (2) is 6 or more, further judgment is made.
Sixth substep: when the second inter-region is assembled
Is>
When the number of transfer layers is less than 6, +.>
And according to the transfer layer number pair +. >
Performing assignment; when said second inter-region set +.>
Is>
When the transfer layer number is more than or equal to 6, the +.>
，/>
。
Seventh substep: repeating all the steps until the current time is the set
After the transfer capacity analysis of all beams in the system is completed, the evaluation of the next moment is continued.
And then, step S104 is carried out, all the steps are carried out iteratively, and the multi-beam virtual resource pools of a plurality of areas are respectively formed.
In step S105, as shown in fig. 8, during this step, a probability model is constructed according to the resource occupation and transfer capability in the virtual resource pool, and the probability model is dynamically updated along with the response and resource allocation changes of the random access task according to the principle of "idle first, then regional second and then outside the domain", so as to form a scheduling decision model, and then a distributed collaboration mechanism is designed for the multi-regional distributed characteristics, so that the general process of the random access task is explicitly responded, and the core function of the dual-layer scheduling framework is formed with the scheduling decision model.
The following detailed description is made:
multibeam device in a certain area
The set of occupancy of multi-beam resources is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of multi-beam resource occupancy types is +. >
, wherein ,/>
Representing a set of free beam resources; />
Representing a set of transferable multi-beam resources within an area; />
Representing a set of inter-region transferable multi-beam resources; />
Representing a set of non-transferable multi-beam resources;
defining the multi-beam device
Is +.>
，/>
Representing the number of free beam resources; />
Representing the number of transferable multi-beam resources within the region; />
Representing the number of multi-beam resources that are transferable between said regions; />
Indicating the amount of non-transferable multi-beam resources. The method can obtain the following steps:
when (when)
When the idle multi-beam resources exist, the multi-beam resource transfer probability is +.>
The calculation formula of (1) comprises:
at this time, the multi-beam resource transfer probability set
In the method, only the transfer probability of the idle multi-beam resources is larger than 0, and the transfer probabilities of other multi-beam resources are all 0, so that the condition that the idle multi-beam resources can only be selected preferentially according to the access task is ensured.
When (when)
When (and->
If the idle multi-beam resource does not exist, the multi-beam resource transfer probability is +.>
The calculation formula of (1) comprises:
at this time, the multi-beam resource transfer probability set
Only the beam with larger transfer probability of the transferable multi-beam resource in the area is larger than 0, and the larger the transfer layer number is, the smaller the transfer probability is; the ratio of the assignment probabilities between different assignment layer number beams is equal to the inverse of the assignment layer number. The transfer probability of other multi-beam resources is 0, so that when no idle multi-beam resources exist, only the multi-beam resources which can be transferred in the area can be preferentially selected along with the access task.
When (when)
，/>
When the free multi-beam resources and the transferable multi-beam resources are not existed, the multi-beam resources transfer probability is +.>
The calculation formula of (1) comprises:
at this time, the multi-beam resource transfer probability set
In which only the probability of transferring multi-beam resources between areas is greater than 0, the calculation mode of the transfer probability is basically identical with that of the areas, but the number of multi-beam transfer layers is expressed by +.>
And 5 is subtracted so that the number of transfer layers of the multi-beam resource which can be transferred between the areas is corrected to be a true value. The transfer probability of other multi-beam resources is 0, so that when no idle multi-beam resources exist and multi-beam resources can be transferred in the areas, only multi-beam resources which can be transferred between the areas can be preferentially selected according to the access task.
When (when)
，/>
When all multi-beam resources are not transferable. At this time, all multi-beam resource transfer probability +.>
The values of (2) are all 0, indicating that most resources are not capable of responding to requests for an on-demand access task.
Here, the multi-beam resource transfer probability dynamically changes along with the response of the following access task and the change of the resource allocation, and when the original beam distribution situation changes after responding to one following access task, the transfer probability set needs to be updated again according to a new state, and the beam states are synchronized in real time, so that the local randomness and the global optimality of the resource transfer cost are ensured.
It should be noted that, as shown in fig. 9, the following description needs to be given to the general procedure of processing the random access task and the internal logic of the resource transfer coordination mechanism:
(1) Data preprocessing: the central scheduling subsystem integrates the demands of different task centers to generate a periodic scheduling plan. At this time, since no new dynamic scheduling plan is fused before the start time of the new scheduling period, the periodic scheduling plan at this time can be regarded as the resource scheduling plan in the initial state. The method adopts a knowledge graph technology to generate a graph network from a periodic scheduling plan, and performs regional slicing on the graph network according to the geographic distribution characteristics of equipment to complete the networking of scheduling results.
(2) Data preparation: after the networking of the dispatching result is completed, extracting a multi-beam plan in the area, classifying the multi-beam resources according to the occupation condition of the multi-beam resources, and calculating the transfer capacity of each multi-beam one by one to form a multi-beam virtual resource pool.
(3) Idle beam task response: when the regional dispatching system receives the random access request, firstly judging based on the beam resource occupation condition of the regional multi-beam virtual resource pool, if idle multi-beams exist, calculating the multi-beam resource transfer probability in the current state through a dynamic probability model, autonomously selecting multi-beam resources to respond to the random access request, and feeding back the newly-added dispatching plan state information to the dynamic plan module; if no idle multi-beam exists, the next judgment is carried out.
(4) In-region task response: when the multi-beam is transferred in the area, updating the multi-beam resource transfer probability in the current state through a dynamic probability model, automatically selecting multi-beam resource response and meeting an access request, adjusting an in-area scheduling plan according to the in-area transfer mode, and feeding back newly-added and changed scheduling plan state information to a dynamic planning module; if the multi-beam is not transferred in the area, the next judgment is carried out.
(5) Inter-zone task response: when the inter-area transfer multi-beam exists, the multi-beam resource transfer probability in the current state is updated through a dynamic probability model, an inter-area transfer scheme for independently selecting multi-beam resources is sent out to a central dispatching subsystem through a regional dispatching system, if the central dispatching subsystem judges that the transfer scheme does not conflict with resources in an associated area, a random access request is responded, and newly added and changed dispatching plan state information is fed back to a dynamic plan module; otherwise, updating the multi-beam resource transfer probability, and continuing the judgment; if the conflict exists in the residual resources, the random access request cannot be responded.
In addition, since there may be a 1:2 or 1:3 scenario with the insert mode and path transfer mode of access tasks, when such tasks are encountered, processing is done according to the following principles: when the multi-beam types are the same, processing according to the same mode; when the multi-beam types are different, processing is performed in a relatively complex mode, for example, when the multi-beam types include in-region multi-beam and out-of-region multi-beam, processing is performed in an out-of-region mode, and similarly, in the transfer path, processing is performed in an out-of-region mode when there is an out-of-region node.
A second aspect of an embodiment of the present disclosure provides a distributed collaborative scheduling system based on-demand access tasks, the system comprising:
the central scheduling subsystem is used for carrying out distributed collaborative scheduling on all random access tasks;
a plurality of regions, each region being regulated by the central scheduling subsystem; each region contains at least one satellite and one multi-beam device, each capable of generating a multi-beam virtual resource pool.
Each region performs bidirectional information interaction and scheduling with other regions and a central scheduling subsystem through the multi-beam equipment of the region.
In order to verify the effect of the distributed collaborative scheduling method based on the random access task, the following simulation experiment is performed:
First, the configuration of the scheduling plan of the simulation experiment is set as shown in table 1 below:
TABLE 1 basic case of Dispatch plan configuration
First, parameters in table 1 and scheduling result scenario configuration parameters are input. And defining different types of windows as nodes, defining the relationship among the windows as edges, and constructing the association relationship among the scheduling tasks, the alternative windows and the idle resources by adopting a knowledge graph technology to form a graph network as shown in fig. 10. The graph network constructed in this way has the characteristics of multidimensional expression, dynamic updating and efficient indexing.
And then, evaluating the transfer capacity of the multi-beam resources according to the occupation condition of the multi-beam resources at the current moment in the graph network to form a multi-beam virtual resource pool.
Taking area a in table 1 as an example, the beam transfer capability of the multi-beam device of area a is shown in table 2 below:
table 2 beam transfer capability of the multi-beam device of region a
As can be seen from table 2, region a includes 1181 scheduled tasks for a total of 16 beams, each beam containing a relatively uniform number of tasks. The transfer capability of each beam along with the access task has a certain trend change characteristic, the transfer capability of the beam is continuously reduced along with the increase of the number of the beam, and the visual appearance is that the number of the non-transferable (-1) tasks is continuously increased. This is because the number of nodes available for transfer is limited because the uniqueness of the nodes included in the different beam transfer paths is ensured in the construction of the virtual resource pool. In this embodiment, the calculation of the beam transfer capability is performed in the order of increasing beam numbers, which necessarily results in fewer transfer nodes available for subsequent beams, an increasing number of non-transferable tasks, and a decrease in transfer capability response, and no matter what processing order is actually used, there is no effect on the overall transfer capability of the multiple beams in the area a. As shown in table 2, tasks up to 52.67% ((1181-559)/1181×100%) of multiple beams in region a are assigned. In addition, the number of transfer tasks within each beam area is substantially the same as the number of transfer tasks between areas, since the priority of devices within an area is not emphasized in the transfer capability calculation, and therefore the transfer capability within an area and between areas is balanced.
Referring to FIG. 11, the allocation of the scheduling task on 16 beams in the time period 2021/06/25 00:00:00-2021/06/08 00:00:00 is shown in the area A, and the progress bars with different colors represent different transfer capacities of the beams. The non-transferable black progress bar task (-1) increases with increasing beam number. In different time periods, the transfer state of the same beam is different, the detail display is carried out by intercepting part of data, the situation that different beams are mutually staggered in the task in the same time period is found, and interception is carried out
The data of the time period show on its right side more intuitively that the multibeam in region A is +.>
Multi-beam transfer capability results over a period of time. The numbers in the figures are beam numbers, where beams 155 and 158 are free beams, 150, 152, 154, 156, 157, 159 and 160 are non-transferable beams, 145, 147 and 148 are intra-zone transferable beams, and 146, 149, 151 and 153 are inter-zone transferable beams. In summary, the multibeam in region A is +.>
In the time period, 9 beams can execute random access tasks in total, and a multi-beam virtual resource pool is generated.
And inputting a group of random access tasks into the distributed collaborative scheduling framework, and testing the response capability of the group of random access tasks. As shown in table 3 below:
TABLE 3 random Access tasks entered into a distributed co-scheduling framework
The number of tasks with task numbers A-1 in Table 3 is 10, respectively.
Also shown in table 4 below:
TABLE 4 Beam resource Allocation results for task A-1
Table 4 shows the beam resource allocation results of 10 tasks in task A-1, and the tasks with sequence numbers 1-9 successfully perform resource allocation, but the transfer path of task 10 is judged to conflict with other area resources, the beam resource transfer probability is updated, and beams 0-151 are reselected to respond to the request of the random access task.
As can be seen from simulation experiments, the method provided by the present disclosure can be effectively configured when the sudden random access task request is responded, and the real-time response capability of regional dispatching and the central coordination dynamic sensing and quick decision capability taking multiple beams as cores are highlighted.
It should be noted that although several units of the system for action execution are mentioned in the detailed description above, this partitioning is not mandatory. Indeed, the features and functions of two or more of the units described above may be embodied in one unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied. Some or all of the units may be selected according to actual needs to achieve the objectives of the disclosed solution. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. The distributed collaborative scheduling method based on the random access task is characterized by comprising the following steps:
aiming at the characteristics of a certain random access task, establishing an expression model of the random access task by utilizing a resource transfer theory; the resource transfer theory comprises a plurality of functional windows in different types, and the expression model comprises an insertion mode and a path transfer mode of the random access task;
according to the insertion mode and the path transfer mode, a knowledge graph technology is adopted to construct association relations among a plurality of functional windows, and a graph network of a certain area is formed;
Evaluating the transfer capacity of the multi-beam resources according to the occupation condition of the multi-beam resources at the current moment in the graph network to form a multi-beam virtual resource pool of the area;
iteratively performing all the steps to form the multi-beam virtual resource pools of a plurality of areas respectively;
according to the occupation condition of the multi-beam resources of the multi-beam virtual resource pool of each region and the transfer capacity of the multi-beam resources, a central scheduling subsystem is utilized to respectively perform distributed cooperative scheduling on a plurality of regions;
each area at least comprises a satellite and a multi-beam device, and the multi-beam device can generate the multi-beam virtual resource pool.
2. The distributed collaborative scheduling method based on random access tasks according to claim 1, wherein a plurality of the function windows include: the method comprises the following access task window, a scheduling task window, a conflict window, an alternative window, an idle resource window and a parallel window; wherein a plurality of the scheduling task windows and a plurality of the idle resource windows are arranged in a crossing manner.
3. The distributed collaborative scheduling method based on-demand access tasks according to claim 2, wherein the insertion pattern includes a 1:1 insertion type, a 1:2 insertion type, and a 1:3 insertion type; wherein,
The 1:1 insertion type includes: the random access task is contained in 1 scheduling task window and the idle resource window adjacent to one side of the scheduling task window;
the 1:2 insertion type includes: the random access task is contained in 2 scheduling task windows and a plurality of idle resource windows associated with the 2 scheduling task windows;
the 1:3 insertion type includes: the random access task is contained in 3 scheduling task windows and a plurality of idle resource windows associated with the 3 scheduling task windows;
according to the resource transfer theory, there are 5 cases among the random access task window, the idle resource window and the scheduling task window in each type of the insertion mode:
first case: the random access task window is completely overlapped with the scheduling task window, and the idle resource window is kept unchanged after the resource is transferred;
second case: the random access task window fully occupies the scheduling task window, occupies part of the idle resource window, and reduces the length of the idle resource window after resource transfer;
Third case: the random access task window is partially overlapped with the scheduling task window, and fully occupies the idle resource window on the left side of the scheduling task window, and after resource transfer, the number and the length of the idle resource window are changed;
fourth case: the random access task window is partially overlapped with the scheduling task window, and completely occupies the idle resource window on the right side of the scheduling task window, and after resource transfer, the number and the length of the idle resource window are changed;
fifth case: and the random access task window fully occupies the scheduling task window and all the idle resource windows, and the idle resource windows do not exist after the resources are transferred.
4. The distributed collaborative scheduling method based on-demand access tasks according to claim 3, wherein the path transfer mode includes a 1:1 transfer type, a 1:2 transfer type, and a 1:3 transfer type; wherein,
each transfer type comprises a single-path transfer type and a multi-path transfer type;
the number of transfer layers of the multi-path transfer type is equal to the number of the scheduling task windows.
5. The distributed collaborative scheduling method based on random access tasks according to claim 2, wherein the step of constructing association relations among a plurality of functional windows by using a knowledge graph technology according to the insertion mode and the path transfer mode to form a graph network of a certain area comprises:
defining the set of scheduling task windows as:
Defining the set of idle resource windows as
；
wherein ,
a number representing the multi-beam device; />
Representing the relative circle number; />
Representing a start time of a current function window; />
Representing the end time of the current function window; />
Representing a start time of a preamble function window; />
Representing the end time of the preamble function window; />
Representing a start time of a subsequent function window; />
Representing the ending time of the subsequent function window; then (I)>
Representing any of the scheduled task windows +.>
The number of the multi-beam device, the relative circle number, the starting time of the current function window, the ending time of the current function window, the starting time of the preceding function window, the ending time of the preceding function window, the starting time of the subsequent function window and the ending time of the subsequent function window; / >
Representing any of the scheduled task windows +.>
Corresponding->
The number of the multi-beam device with the conflict window, the relative circle number, the starting time of the current function window and the ending time of the current function window; />
Representing any of the scheduled task windows
Corresponding->
The number of the multi-beam device of each alternative window, the relative circle number, the starting time of the current function window and the ending time of the current function window; />
Indicate->
Numbering of multi-beam devices for individual idle resource windows, start of current function windowTime and end time of the current function window;
the construction of the graph network includes the following 7 sub-steps:
a first substep: for any of the scheduled task windows
From a certain said conflict window->
Start, crosstalk>
When->
And->
，/>
，/>
When true, then the conflict window +.>
Included in the dispatch task window->
And the window formed by the adjacent idle resource windows belongs to a 1:1 insertion type; then->
and />
There is transfer relation between them, and a directed association relation is established->
，/>
Turning to a seventh substep, otherwise, continuing the second substep;
wherein ,
representing any of the scheduled task windows +.>
Corresponding->
Relative circle number of each collision window; / >
Representing any of the scheduled task windows +.>
Is the relative circle number of (2); />
Representing any of the scheduled task windows +.>
Corresponding->
Numbering of multi-beam devices for each collision window; />
Indicate->
Numbering of the multi-beam devices of the individual scheduling task windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The start time of the current function window of the collision window; />
Indicate->
Start time of the preamble function window of the individual scheduled task window; />
Representing any of the scheduled task windows +.>
Corresponding->
The end time of the current function window of the collision window; />
Indicate->
The end time of the subsequent function window of the individual scheduled task window; the contact represents an inclusion relationship; friend represents a friendship;
a second substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the previous scheduled task window->
The window formed by the idle resource window and the adjacent idle resource window belongs to a 1:2 insertion type; then->
And->
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
and />
A first parallel window p1 of the information of (a) establishing a directed association relation +.>
，
，/>
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep, and vice versa, go to the fourth substep; wherein (1) >
Indicate->
A number of the multi-beam device of a previous scheduled task window of the scheduled task windows; />
Indicate->
The start time of the preamble function window of the previous scheduling task window of the scheduling task windows;
a third substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the latter scheduling task window->
With adjacent said idleThe window formed by the resource windows belongs to a 1:3 insertion type; then->
And->
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
and />
The second parallel window p2 of the information of (2) establishes a directed association relation +.>
，
，/>
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Go to the seventh substep, and vice versa, go to the fourth substep; wherein (1)>
Indicate->
Numbering of the multi-beam device of the next scheduled task window of the scheduled task windows; />
Indicate->
The end time of the subsequent function window of the subsequent scheduling task window of the plurality of scheduling task windows;
a fourth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the first two said scheduled task windows +.>
、/>
Within the window consisting of the adjacent free resource window +.>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created >
、/>
and />
A third parallel window p3 of the information of (2) establishes a directed association relationship
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Turning to the seventh substep, and conversely, turning to the first substep; wherein (1)>
Indicate->
Numbering of the multi-beam devices of the first two scheduling task windows of the plurality of scheduling task windows; />
Indicate->
Starting time of the front function window of the first two scheduling task windows of the plurality of scheduling task windows;
fifth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And the latter two said scheduled task windows +.>
、/>
Within the window formed by the adjacent idle resource window, and (2)>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
A fourth association window p4 of the information of (2) establishing a directed association relation +.>
，
，/>
，/>
，
The method comprises the steps of carrying out a first treatment on the surface of the Turning to the seventh substep, and conversely, turning to the first substep; wherein (1)>
Represent the first
Numbering of the multi-beam device of the last two scheduling task windows of the plurality of scheduling task windows; />
Indicate->
The end time of the subsequent function window of the last two scheduling task windows of the plurality of scheduling task windows;
sixth substep: when (when)
And->
，/>
，/>
When true, the conflict window +.>
Is included in the dispatch task window->
And front and back two said scheduled task windows- >
、
Within the window formed by the adjacent idle resource window, and (2)>
And->
、/>
and />
Transfer relation exists among the composition continuous scheduling tasks, and memory is created>
、/>
and />
The fifth parallel window p5 of the information of (2) establishes a directed association relation +.>
，/>
，/>
，
，/>
The method comprises the steps of carrying out a first treatment on the surface of the Turning to the seventh substep, and vice versa turning to the first substep;
seventh substep: for any successfully scheduled task window
From a certain said alternative window +.>
Start, crosstalk>
When->
And->
，/>
When true, the alternative window +.>
Is included in the free resource window->
In (I)>
and />
There is transfer relation between them, and a directed association relation is established->
and />
, wherein ,/>
Representing any of the scheduled task windows +.>
Corresponding->
The relative circle numbers of the candidate windows; />
Representing any of the scheduled task windows +.>
Corresponding->
Numbering of the multi-beam devices for the individual candidate windows; />
Indicate->
Numbering of multi-beam devices for the individual idle resource windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The start time of the current function window of the candidate windows; />
Indicate->
The start time of the current function window of the idle resource windows; />
Representing any of the scheduled task windows +.>
Corresponding->
The end time of the current function window of the candidate windows; / >
Indicate->
The end time of the current function window of the idle resource windows; belong represents membership.
6. The distributed cooperative scheduling method based on the random access task according to claim 2, wherein in the step of evaluating the transferability of the multi-beam resources according to the occupation situation of the multi-beam resources at the current moment in the graph network and forming the multi-beam virtual resource pool of the area,
the occupation condition of the multi-beam resources comprises idle resources, transferable resources and non-transferable resources; wherein,
the idle resources refer to the unoccupied multi-beam resources;
the transferable resource refers to the multi-beam resource with the transfer layer number smaller than 6;
the non-transferable resource refers to the multi-beam resource with a transfer layer number of 6 or more or the multi-beam resource with higher priority that cannot be transferred.
7. The distributed cooperative scheduling method based on the random access task according to claim 6, wherein the step of evaluating the transferability of the multi-beam resources according to the occupation situation of the multi-beam resources at the current moment in the graph network, and forming the multi-beam virtual resource pool of the area includes:
Defining a set of devices for a region as
, wherein ,/>
Represents a multi-beam device, Y represents other devices, the multi-beam set of the multi-beam device is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of occupancy of the multi-beam resource is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the optimal transfer paths of the occupation condition of the multi-beam resources is that
The method comprises the steps of carrying out a first treatment on the surface of the By a means ofThe set of non-transferable resources provided by the central scheduling subsystem is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the idle resource windows is +.>
The method comprises the steps of carrying out a first treatment on the surface of the The set of the scheduling task windows in a certain area is
；
The construction of the multi-beam virtual resource pool comprises the following 7 sub-steps:
a first substep: from the multi-beam set
Beam +.>
Initially, when the beam ∈ ->
In the case of idle beam, then
，/>
The method comprises the steps of carrying out a first treatment on the surface of the When the beam->
When in use, then->
，/>
The method comprises the steps of carrying out a first treatment on the surface of the When the beam->
When selecting to occupy the beam +.>
Starting from any one of the idle resource windows j i ，/>
Extracting a graph network G associated between the starting point and the ending point as the ending point;
a second substep: setting the weight of each edge of the graph network G as 1, starting from the starting point, searching a scheduling task window t with a transfer relation with the starting point, and adding the scheduling task window t into a search sequence;
a third substep: searching the dispatch task window with transfer relation with the dispatch task window t serving as a new starting point until an end point appears for the first time, and obtaining the shortest path from the starting point to the end point in the graph network G 
The method comprises the steps of carrying out a first treatment on the surface of the When the set of optimal transfer paths +.>
Does not comprise said shortest path +.>
Define the set of transfer paths as +.>
The shortest path +.>
Add to the set +.>
In (a) and (b);
a fourth substep: continuing to judge the shortest rotation between the starting point and other idle resource windowsLet the path and continue to add it to the collection
After that, the first area is further divided into a first area set according to the area distribution difference of the path nodes>
And second inter-region set->
；
Fifth substep: when the region is assembled
Is>
When the number of transfer layers is less than 6, +.>
And according to the transfer layer number pair +.>
Performing assignment; when the first region is set +.>
Is the shortest path of (a)
When the transfer layer number is more than or equal to 6, further judging; />
Sixth substep: when the second inter-region is assembled
Is>
Has small transfer layer numberAt 6, get +.>
And according to the transfer layer number pair +.>
Performing assignment; when said second inter-region set +.>
Is>
When the transfer layer number is more than or equal to 6, the +.>
，/>
；
Seventh substep: repeating all the steps until the current time is the set
After the transfer capacity analysis of all beams in the system is completed, the evaluation of the next moment is continued.
8. The distributed collaborative scheduling method according to claim 7, wherein the step of using a central scheduling subsystem to perform distributed collaborative scheduling on a plurality of the areas according to the occupation situation of the multi-beam resources and the transfer capability of the multi-beam resources in the multi-beam virtual resource pool of each of the areas includes:
the occupation condition set of the multi-beam resources is that
The method comprises the steps of carrying out a first treatment on the surface of the The set of occupancy types of the multi-beam resource is +.>
, wherein ,/>
Representing a set of idle multi-beam resources; />
Representing a set of transferable multi-beam resources within an area; />
Representing a set of inter-region transferable multi-beam resources; />
Representing a set of non-transferable multi-beam resources;
defining the multi-beam device
Is +.>
，
Representing the number of free multi-beam resources; />
Representing the number of transferable multi-beam resources within the region; />
Representing the number of multi-beam resources that are transferable between said regions; />
Indicating the amount of non-transferable multi-beam resources, then,
when (when)
When the idle multi-beam resource exists, the multi-beam resource transfer probability is +.>
The calculation formula of (1) comprises:
When (when)
When (and->
In the absence of the free multi-beam resource, the multi-beam resource transfer probability +.>
The calculation formula of (1) comprises:
when (when)
，/>
When the idle multi-beam resource and the transferable multi-beam resource are not present, the multi-beam resource transfer probability +.>
The calculation formula of (1) comprises: />
When (when)
，/>
In this case, all of the multi-beam resources are not transferable, and the multi-beam resources are not transferable with probability +.>
The value of (2) is 0, (-)>
Representing a multibeam transfer layer->
A set of inter-transferable multi-beam resources.
9. A distributed collaborative scheduling system based on random access tasks, comprising:
the central scheduling subsystem is used for carrying out distributed collaborative scheduling on all random access tasks;
a plurality of regions, each of said regions being regulated by said central scheduling subsystem; wherein each of the regions comprises at least one satellite and one multi-beam device capable of generating a multi-beam virtual resource pool.
10. The on-demand task based distributed co-scheduling system of claim 9, wherein the multi-beam device of each zone is capable of bi-directional information interaction and scheduling with other zones and the central scheduling subsystem.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104467945A (en) * | 2013-09-16 | 2015-03-25 | 中国人民解放军总参谋部第六十一研究所 | Virtual bus-based distributed asterism network resource management method |
| EP3182614A1 (en) * | 2015-12-18 | 2017-06-21 | Thales | Method for satellite communication with beam-hopping flexible capacity distribution and fractional re-use pattern |
| CN107070534A (en) * | 2017-01-26 | 2017-08-18 | 清华大学 | The dynamic preemptive type method for scheduling task and system of a kind of repeater satellite load balancing |
| CN108335012A (en) * | 2017-12-26 | 2018-07-27 | 佛山科学技术学院 | A kind of intelligence remote sensing satellite stratification distributed freedom cotasking planning system |
| CN109358345A (en) * | 2018-10-12 | 2019-02-19 | 合肥工业大学 | Dummy constellation cooperation observation method based on Agent |
| CN110380764A (en) * | 2019-07-27 | 2019-10-25 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Multi-beam subarea-scanning promotes full airspace telemetry communication with the method for meeting access performance |
| CN112418719A (en) * | 2020-12-08 | 2021-02-26 | 军事科学院系统工程研究院网络信息研究所 | Satellite resource dynamic scheduling method based on solution set construction and pheromone deposition |
| CN113852406A (en) * | 2021-08-24 | 2021-12-28 | 合肥工业大学 | Multi-beam relay satellite task scheduling method and device |
| CN113852405A (en) * | 2021-08-24 | 2021-12-28 | 合肥工业大学 | Method and device for constructing multi-beam relay satellite task scheduling model |
| CN114915537A (en) * | 2022-05-11 | 2022-08-16 | 军事科学院系统工程研究院网络信息研究所 | Satellite communication frequency-orbit resource distributed cooperative monitoring system and method |
| US20220353742A1 (en) * | 2021-04-25 | 2022-11-03 | Xidian University | Hierarchical network operation and resource control system and method for mega satellite constellations |
| CN115664501A (en) * | 2022-10-24 | 2023-01-31 | 中国空间技术研究院 | Adaptive weight scheduling method and system based on space-based distributed satellite cluster |
-
2023
- 2023-04-19 CN CN202310417554.0A patent/CN116151039B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104467945A (en) * | 2013-09-16 | 2015-03-25 | 中国人民解放军总参谋部第六十一研究所 | Virtual bus-based distributed asterism network resource management method |
| EP3182614A1 (en) * | 2015-12-18 | 2017-06-21 | Thales | Method for satellite communication with beam-hopping flexible capacity distribution and fractional re-use pattern |
| CN107070534A (en) * | 2017-01-26 | 2017-08-18 | 清华大学 | The dynamic preemptive type method for scheduling task and system of a kind of repeater satellite load balancing |
| CN108335012A (en) * | 2017-12-26 | 2018-07-27 | 佛山科学技术学院 | A kind of intelligence remote sensing satellite stratification distributed freedom cotasking planning system |
| CN109358345A (en) * | 2018-10-12 | 2019-02-19 | 合肥工业大学 | Dummy constellation cooperation observation method based on Agent |
| CN110380764A (en) * | 2019-07-27 | 2019-10-25 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Multi-beam subarea-scanning promotes full airspace telemetry communication with the method for meeting access performance |
| CN112418719A (en) * | 2020-12-08 | 2021-02-26 | 军事科学院系统工程研究院网络信息研究所 | Satellite resource dynamic scheduling method based on solution set construction and pheromone deposition |
| US20220353742A1 (en) * | 2021-04-25 | 2022-11-03 | Xidian University | Hierarchical network operation and resource control system and method for mega satellite constellations |
| CN113852406A (en) * | 2021-08-24 | 2021-12-28 | 合肥工业大学 | Multi-beam relay satellite task scheduling method and device |
| CN113852405A (en) * | 2021-08-24 | 2021-12-28 | 合肥工业大学 | Method and device for constructing multi-beam relay satellite task scheduling model |
| CN114915537A (en) * | 2022-05-11 | 2022-08-16 | 军事科学院系统工程研究院网络信息研究所 | Satellite communication frequency-orbit resource distributed cooperative monitoring system and method |
| CN115664501A (en) * | 2022-10-24 | 2023-01-31 | 中国空间技术研究院 | Adaptive weight scheduling method and system based on space-based distributed satellite cluster |
Non-Patent Citations (5)
| Title |
|---|
| BO REN; JIANPING LIU: "Satellite Requirement Preference Driven TT&C Resources Scheduling Algorithm for Time Sensitive Missions", 《IEEE 3RD INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION AND COMMUNICATION TECHNOLOGY》 * |
| 冯明月;汤绍勋;何俊;李国辉;易先清;: "双层探测卫星网络半分布式资源调度方法", 小型微型计算机系统, no. 01 * |
| 刘建平，张天昱 ，李伟，李智远: "面向全球覆盖、随遇接入的测运控服务构想", 《第十一届中国卫星导航年会》 * |
| 常晓宇，张伟嘉，李旭东，张晓男，王港，贾钢: "低轨互联网星座随遇接入遥感卫星和在轨智能处理研究", 《电信科学》 * |
| 王睿;韩笑冬;王超;龙军;: "天基信息网络资源调度与协同管理", 通信学报, no. 1 * |
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