CN109656270B - Edge cloud-based control system and method for unmanned aerial vehicle formation cooperative flight - Google Patents
Edge cloud-based control system and method for unmanned aerial vehicle formation cooperative flight Download PDFInfo
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Abstract
The embodiment of the invention relates to a system and a method for controlling unmanned aerial vehicle formation cooperative flight based on edge cloud. Wherein, this system includes: the communication satellite network transport layer is used for: acquiring a flight request sent by an unmanned aerial vehicle formation, and transmitting the flight request to an unmanned aerial vehicle cooperative edge gateway access layer, wherein the flight request is a radar detection and flight path planning request; the unmanned aerial vehicle cooperates with the edge gateway access stratum to be used for: transmitting the flight request to an unmanned aerial vehicle cooperative edge data center layer; the unmanned aerial vehicle collaborative edge data center layer is used for: and calling first flight information which is stored by the unmanned aerial vehicle and corresponds to the first flight request, and transmitting the first flight information to the unmanned aerial vehicle cooperative edge gateway access layer. When avoiding handling unmanned aerial vehicle data through centralized data processing mode among the prior art, inefficiency, technical drawback that the resource consumes greatly has realized high-efficient and accurate technological effect of handling data, and has realized the technological effect of unmanned aerial vehicle formation safe and reliable collaborative flight.
Description
Technical Field
The embodiment of the invention relates to the technical field of unmanned aerial vehicles, in particular to a system and a method for controlling formation and cooperative flight of unmanned aerial vehicles based on edge cloud.
Background
With the rapid development of the internet of things, the related technology of the unmanned aerial vehicle is widely applied.
In the prior art, unmanned aerial vehicle data is processed in a centralized data processing mode.
However, in the process of implementing the present invention, the inventor finds that processing the data of the unmanned aerial vehicle by a centralized data processing method at least includes: long data processing time and low precision.
Disclosure of Invention
In order to solve the technical problem, the embodiment of the invention provides a system and a method for controlling formation and cooperative flight of unmanned aerial vehicles based on edge cloud.
According to an aspect of the embodiments of the present invention, an embodiment of the present invention provides a control system for formation and cooperative flight of unmanned aerial vehicles based on edge cloud, the system including: a communication satellite network transport layer, an unmanned aerial vehicle cooperative edge gateway access layer, and an unmanned aerial vehicle cooperative edge data center layer, wherein,
the communication satellite network transport layer is to: acquiring a flight request sent by an unmanned aerial vehicle formation, and transmitting the flight request to an unmanned aerial vehicle cooperative edge gateway access layer, wherein the flight request is a radar avoidance and track planning request;
the unmanned aerial vehicle collaborative edge gateway access layer is used for: transmitting the flight request to the unmanned aerial vehicle collaborative edge data center layer;
the unmanned aerial vehicle collaborative edge data center layer is used for: calling first flight information which is stored by the unmanned aerial vehicle and corresponds to a first flight request, and transmitting the first flight information to the unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request comprises the first flight request;
the unmanned aerial vehicle collaborative edge gateway access layer is further configured to: transmitting the first flight information to the communication satellite network transport layer;
the communications satellite network transport layer is further configured to: transmitting the first flight information to the formation of drones.
The embodiment provides that: by the technical scheme that the unmanned aerial vehicle cooperates with the edge gateway access layer to carry out interaction respectively in a communication satellite network transmission layer and an unmanned aerial vehicle cooperation edge data center layer, the technical defects that in the prior art, when unmanned aerial vehicle data are processed through a centralized data processing mode, the efficiency is low, the resource consumption is large are overcome, the technical effect of processing the data efficiently and accurately is realized, and the technical effect of safe and reliable cooperative flight of unmanned aerial vehicle formation is realized.
Further, the system further comprises: the drones cooperate with a central analysis layer, wherein,
the unmanned aerial vehicle collaborative edge gateway access layer is further configured to: transmitting the second flight request to the unmanned aerial vehicle coordination center analysis layer, wherein the flight request further comprises a second flight request;
the unmanned aerial vehicle collaborative center analysis layer is used for: calling second flight information corresponding to the second flight request stored by the unmanned aerial vehicle, and transmitting the second flight information to the unmanned aerial vehicle cooperative edge gateway access layer;
the unmanned aerial vehicle collaborative edge gateway access layer is further configured to: transmitting the second flight information to the communication satellite network transport layer;
the communications satellite network transport layer is further configured to: transmitting the second flight information to the formation of drones.
The embodiment provides that: the unmanned aerial vehicle cooperates edge gateway access layer still carries out the technical scheme that communicates with unmanned aerial vehicle cooperation center analysis layer, has realized being handled the second flight request by unmanned aerial vehicle cooperation center analysis layer to with the second flight information feedback that corresponds with the second flight request to unmanned aerial vehicle cooperation edge gateway access layer, thereby realized handling respectively of data, accelerated the treatment effeciency, reduced the processing load.
Further, the unmanned aerial vehicle collaborative center analysis layer is further configured to:
performing iterative analysis processing on the second flight request according to a preset first iterative analysis rule to obtain a plurality of sub-request information, wherein the second flight request comprises a plurality of sub-requests, and one sub-request corresponds to one sub-request information;
summarizing the sub-request information to obtain request information;
and performing iterative analysis processing on the request information according to a preset second iterative analysis rule to obtain the second flight information.
The embodiment provides that: according to the technical scheme, the second flight request is subjected to iterative analysis processing according to the first iterative analysis rule, and the request information is subjected to iterative analysis processing according to the second iterative analysis rule, so that the effect of efficiently and accurately obtaining the second flight information is achieved.
Further, the unmanned aerial vehicle collaborative center analysis layer is specifically configured to:
analyzing the previous sub-request according to a preset multi-dimensional space classification and aggregation splitting clustering reinforcement learning strategy to obtain previous request information corresponding to the previous sub-request;
determining a subsequent sub-request according to a preset iteration parameter;
analyzing the subsequent sub-request according to the multidimensional space classification and the clustering split reinforcement learning strategy to obtain subsequent request information corresponding to the subsequent sub-request;
wherein the sub-request includes the preceding sub-request and the following sub-request, and the sub-request information includes the preceding request information and the following request information.
The embodiment provides that: according to the technical scheme of analyzing the prior sub-request (or the subsequent sub-request) according to the multidimensional space classification and the clustering split clustering reinforcement learning strategy, the prior sub-request (or the subsequent sub-request) is accurately analyzed, and the technical effect of obtaining the corresponding prior request information (or the subsequent request information) is achieved.
Further, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
judging whether the previous request information meets a preset deep analysis evaluation condition or not to obtain a judgment result;
when the judgment result is negative, determining the subsequent request information according to the iteration parameter;
analyzing the subsequent request information according to the multidimensional space classification and the clustering reinforcement learning strategy to obtain subsequent flight information corresponding to the subsequent request information;
wherein the second flight information includes the post-flight information.
The embodiment provides that: the technical scheme of determining the post-request information according to the judgment result and the iteration parameters and analyzing the post-request information according to the multi-dimensional space classification and clustering and clustering reinforcement learning strategy realizes the technical effect of accurately obtaining the post-flight information.
Further, when the current request information is kth request information, the unmanned aerial vehicle cooperation center analysis layer is further specifically configured to:
judging whether the kth request information meets a preset deep analysis evaluation condition according to the formula 1, wherein the formula 1 is as follows:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
a joint evaluation function corresponding to the kth request message,
for the expected accumulated value difference corresponding to the kth request message,
a desired cumulative value difference corresponding to the k-1 th request information,
planning the flight path corresponding to the kth request informationThe distance of the vehicle to the vehicle,
the number of times of radar detection corresponding to the kth request message,
for the queuing time delay corresponding to the kth request message,
planning a route for the flight path corresponding to the k-1 request information,
the number of times of radar detection corresponding to the k-1 th request message,
and i, j and t are values of a direction vector of a pre-stored depth analysis model respectively for the formation time delay corresponding to the kth-1 request information.
Further, when the post-request information is the (k + 1) th request information and the post-flight information is the (k + 1) th flight information, the unmanned aerial vehicle cooperation center analysis layer is further specifically configured to:
determining the k +1 flight information according to equation 2
Formula 2:
wherein the content of the first and second substances,
as the information on the k-th flight,
for the preset cluster adjustment factor corresponding to the (k + 1) th request message,
the information is a preset reinforcement learning factor corresponding to the (k + 1) th request information.
Further, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
determining the above according to equation 3
Formula 3:
wherein L isminGPlanning the route for the historical shortest route,
planning the mean value of the routes from the first request information to the path corresponding to the kth request information, CminGFor the smallest number of radar detections in history,
is the mean value, Y, of the detected radar times corresponding to the first to k-th request informationminGFor the historical minimum queuing delay,
and the average value of the formation delay corresponding to the first request message to the kth request message is obtained.
Further, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
determining the above according to equation 4
Formula 4:
wherein L isminkPlanning a route for the shortest route corresponding to the kth request information, CminkThe minimum detected radar frequency, Y, corresponding to the kth request informationminkAnd the minimum queuing delay corresponding to the kth request message is obtained.
According to another aspect of the embodiments of the present invention, there is provided a method for controlling formation coordinated flight of unmanned aerial vehicles based on edge cloud, the method being based on the system of any one of the above claims 1 to 9, the method including:
the method comprises the steps that a communication satellite network transmission layer obtains a flight request sent by an unmanned aerial vehicle formation, and transmits the flight request to an unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request is a radar detection and flight path planning request;
the unmanned aerial vehicle cooperation edge gateway access layer transmits the flight request to an unmanned aerial vehicle cooperation edge data center layer;
the unmanned aerial vehicle collaborative edge data center layer calls first flight information corresponding to a first flight request stored by the unmanned aerial vehicle collaborative edge data center layer and transmits the first flight information to the unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request comprises the first flight request;
the unmanned aerial vehicle cooperates with the edge gateway access layer to transmit the first flight information to the communication satellite network transmission layer;
the communication satellite network transmission layer transmits the first flight information to the formation of unmanned aerial vehicles.
Further, the method further comprises:
the unmanned aerial vehicle cooperation edge gateway access layer transmits the second flight request to an unmanned aerial vehicle cooperation center analysis layer, wherein the flight request further comprises the second flight request;
the unmanned aerial vehicle cooperation center analysis layer calls second flight information corresponding to the second flight request and stored by the unmanned aerial vehicle cooperation center analysis layer, and transmits the second flight information to the unmanned aerial vehicle cooperation edge gateway access layer;
the unmanned aerial vehicle cooperates with the edge gateway access layer to transmit the second flight information to the communication satellite network transmission layer;
and the communication satellite network transmission layer transmits the second flight information to the unmanned aerial vehicle formation.
Further, the method further comprises:
the unmanned aerial vehicle cooperation center analysis layer performs iterative analysis processing on the second flight request according to a preset first iterative analysis rule to obtain a plurality of sub-request information, wherein the second flight request comprises a plurality of sub-requests, and one sub-request corresponds to one sub-request information;
the unmanned aerial vehicle cooperation center analysis layer collects the sub-request information to obtain request information;
and the unmanned aerial vehicle cooperation center analysis layer performs iterative analysis processing on the request information according to a preset second iterative analysis rule to obtain the second flight information.
Further, the unmanned aerial vehicle cooperation center analysis layer performs iterative analysis processing on the second flight request according to a preset first iterative analysis rule to obtain a plurality of sub-request information, and the method specifically includes:
analyzing the previous sub-request according to a preset multi-dimensional space classification and aggregation splitting clustering reinforcement learning strategy to obtain previous request information corresponding to the previous sub-request;
determining a subsequent sub-request according to a preset iteration parameter;
analyzing the subsequent sub-request according to the multidimensional space classification and the clustering split reinforcement learning strategy to obtain subsequent request information corresponding to the subsequent sub-request;
wherein the sub-request includes the preceding sub-request and the following sub-request, and the sub-request information includes the preceding request information and the following request information.
Further, the unmanned aerial vehicle cooperation center analysis layer performs iterative analysis processing on the request information according to a preset second iterative analysis rule to obtain the second flight information, and the method specifically includes:
judging whether the previous request information meets a preset deep analysis evaluation condition or not to obtain a judgment result;
when the judgment result is negative, determining the subsequent request information according to the iteration parameter;
analyzing the subsequent request information according to the multidimensional space classification and the clustering reinforcement learning strategy to obtain subsequent flight information corresponding to the subsequent request information;
wherein the second flight information includes the post-flight information.
Further, when the current request information is the kth request information, the determining whether the previous request information meets a preset deep analysis evaluation condition specifically includes:
judging whether the kth request information meets a preset deep analysis evaluation condition according to the formula 1, wherein the formula 1 is as follows:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
a joint evaluation function corresponding to the kth request message,
for the expected accumulated value difference corresponding to the kth request message,
a desired cumulative value difference corresponding to the k-1 th request information,
planning a route for the flight path corresponding to the kth request information,
the number of times of radar detection corresponding to the kth request message,
for the queuing time delay corresponding to the kth request message,
planning a route for the flight path corresponding to the k-1 request information,
the number of times of radar detection corresponding to the k-1 th request message,
and i, j and t are values of a direction vector of a pre-stored depth analysis model respectively for the formation time delay corresponding to the kth-1 request information.
Further, when the following request information is the (k + 1) th request information and the following flight information is the (k + 1) th flight information, analyzing the following request information according to the multidimensional space classification and agglomerative splitting clustering reinforcement learning strategy to obtain the following flight information corresponding to the following request information, specifically comprising:
determining the k +1 flight information according to equation 2
Formula 2:
wherein the content of the first and second substances,
as the information on the k-th flight,
for the preset cluster adjustment factor corresponding to the (k + 1) th request message,
the information is a preset reinforcement learning factor corresponding to the (k + 1) th request information.
Preferably, the determination is made according to equation 3
Formula 3:
wherein L isminGPlanning the route for the historical shortest route,
planning the mean value of the routes from the first request information to the path corresponding to the kth request information, CminGFor the smallest number of radar detections in history,
is the mean value, Y, of the detected radar times corresponding to the first to k-th request informationminGFor the historical minimum queuing delay,
and the average value of the formation delay corresponding to the first request message to the kth request message is obtained.
Preferably, the determination is made according to equation 4
Formula 4:
wherein L isminkPlanning a route for the shortest route corresponding to the kth request information, CminkThe minimum detected radar frequency, Y, corresponding to the kth request informationminkAnd the minimum queuing delay corresponding to the kth request message is obtained.
Drawings
Fig. 1 is a schematic structural diagram of a control system for formation cooperative flight of unmanned aerial vehicles based on edge clouds according to an embodiment of the present invention;
FIG. 2 is a functional architecture diagram of a synergistic analysis processor according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a control method for unmanned aerial vehicle formation cooperative flight based on edge cloud according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a process of determining second flight information by the unmanned aerial vehicle collaboration center analysis layer according to a second flight request according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a depth analysis model according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a depth analysis according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The embodiment of the invention provides a system and a method for controlling formation and cooperative flight of unmanned aerial vehicles based on edge cloud.
According to one aspect of the embodiment of the invention, the embodiment of the invention provides a control system for unmanned aerial vehicle formation cooperative flight based on edge cloud.
The first embodiment:
referring to fig. 1, fig. 1 is a schematic structural diagram of a control system for formation and cooperative flight of unmanned aerial vehicles based on edge clouds according to an embodiment of the present invention.
As shown in fig. 1, the system includes: a communication satellite network transport layer, an unmanned aerial vehicle cooperative edge gateway access layer, and an unmanned aerial vehicle cooperative edge data center layer, wherein,
the communication satellite network transport layer is used for: the method comprises the steps of obtaining a flight request sent by an unmanned aerial vehicle formation, and transmitting the flight request to an unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request is a radar detection and flight path planning request.
As can be seen in fig. 1, the formation of drones in the radar tracking layer sends a flight request to the communication satellite network transport layer. Wherein the radar tracking layer includes: unmanned aerial vehicle tracking equipment such as ground vehicle-mounted phase array radar, ground fixed radar stations, water surface battleship radar, air early warning radar and the like so as to realize the tracking and positioning of unmanned aerial vehicle formation.
Wherein the communication satellite network transport layer comprises at least one communication satellite. That is, in particular, the drones are queued to send flight requests to communication satellites in the communication satellite network transport layer.
Preferably, the formation of drones sends a flight request to the communication satellite through a preset encrypted channel. To ensure safety and reliability in the flight request transmission process.
The unmanned aerial vehicle cooperates with the edge gateway access stratum to be used for: transmitting the flight request to the unmanned aerial vehicle collaborative edge data center layer.
As can be seen in connection with fig. 1, the drone collaborative edge gateway access layer includes at least one collaborative edge gateway. That is, in particular, a communication satellite in the communication satellite network transport layer transmits a flight request to a cooperating edge gateway in the drone cooperating edge gateway access layer.
Preferably, the communication satellite transmits the flight request to the cooperating edge gateway through a pre-set encrypted channel.
The unmanned aerial vehicle collaborative edge data center layer is used for: calling first flight information which is stored by the unmanned aerial vehicle and corresponds to the first flight request, and transmitting the first flight information to an unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request comprises the first flight request.
As can be seen in fig. 1, the unmanned aerial vehicle collaborative edge data center layer includes at least one collaborative edge server, and the first flight request is processed by the collaborative edge server. Specifically, the method comprises the following steps:
after acquiring the first flight request, the cooperative edge server retrieves first flight information corresponding to the first flight request from a plurality of kinds of flight information stored in the cooperative edge server (namely, the cooperative edge server). And transmitting the first flight information to the collaborative border gateway.
The unmanned aerial vehicle cooperates with the edge data center layer to be stored as flight information of a partial area. That is, the edge drone formation is to the corresponding flight information. So as to avoid the technical defects of low efficiency and the like when the treatment is processed in a centralized way.
In a possible implementation scheme, after acquiring the flight request, the cooperative edge server acquires the edge flight request corresponding to the flight request, that is, the first flight request, from the flight request, and then searches the memory in the cooperative edge server (that is, the system edge server) for the first flight information corresponding to the first flight request. Specifically, the first flight information may be obtained by means of keyword search, or may be obtained by means of setting a table of correspondence between requests and information.
Of course, in another possible implementation scheme, after acquiring the flight request, the cooperative edge gateway filters the flight request first, so as to determine the first flight request in the flight request. Similarly, the cooperative edge gateway may also separate the first flight request from the flight request by setting a key or requesting a table corresponding to the information. And transmitting the first flight request to the cooperating edge server.
The unmanned aerial vehicle cooperates with the edge gateway access stratum to be used for: and transmitting the first flight information to a communication satellite network transmission layer.
The communication satellite network transport layer is further configured to: and transmitting the first flight information to the formation of the unmanned aerial vehicles.
Second embodiment:
the present embodiment is based on the first embodiment. In this embodiment, as can be seen from fig. 1, the system further includes: the drones cooperate with a central analysis layer, wherein,
the unmanned aerial vehicle cooperates with the edge gateway access stratum to be used for: and transmitting the second flight request to the unmanned aerial vehicle coordination center analysis layer, wherein the flight request further comprises the second flight request.
The unmanned aerial vehicle collaborative center analysis layer is used for: and calling second flight information which is stored by the unmanned aerial vehicle and corresponds to the second flight request, and transmitting the second flight information to the unmanned aerial vehicle cooperative edge gateway access layer.
As can be seen in fig. 1, the unmanned coordinated central analysis layer includes at least one coordinated analysis processor and at least one coordinated analysis database. When the number of the collaborative analysis processors is multiple, namely, the unmanned collaborative center analysis layer comprises a plurality of collaborative analysis processors and a collaborative analysis database cluster.
In one possible implementation, the second flight request is separated from the flight request by the cooperating edge gateway, as described in the first embodiment.
In another possible implementation, the collaborative edge gateway transmits the flight request to the collaborative analysis processor, and the collaborative analysis processor separates the flight request from its corresponding second flight request. Similarly, the co-analysis processor may also separate the second flight request from the flight request by way of a keyword or a table of requests and information.
In one possible implementation, the second flight request is analyzed by the co-analysis processor, and then second flight information corresponding to the second flight request is retrieved from the co-analysis database cluster. Similarly, the cooperative analysis processor may also retrieve the second flight information from the cooperative analysis database cluster by means of a keyword or a request and information correspondence table.
Fig. 2 is a functional architecture diagram of a co-analysis processor according to an embodiment of the present invention. As can be seen from fig. 2, when the cooperative analysis processor obtains the radar avoidance detection and flight path planning request 1, the radar avoidance detection and flight path planning request 2, and the … … radar avoidance detection and flight path planning request m, the m radar avoidance detection and flight path planning requests are analyzed and processed respectively to obtain n analysis results of the radar avoidance detection and flight path planning requests. Wherein, each radar detection avoiding and flight path planning request is independent and not interfered with each other.
The unmanned aerial vehicle cooperates with the edge gateway access stratum to be used for: and transmitting the second flight information to a communication satellite network transmission layer.
The communication satellite network transport layer is further configured to: and transmitting the second flight information to the formation of the unmanned aerial vehicles.
The third embodiment:
the present embodiment is based on the second embodiment. In this embodiment, the unmanned aerial vehicle collaborative center analysis layer is further configured to:
performing iterative analysis processing on a second flight request according to a preset first iterative analysis rule to obtain a plurality of sub-request information, wherein the second flight request comprises a plurality of sub-requests, and one sub-request corresponds to one sub-request information;
summarizing the sub-request information to obtain request information;
and performing iterative analysis processing on the request information according to a preset second iterative analysis rule to obtain second flight information.
The fourth embodiment:
the present embodiment is based on the third embodiment. In this embodiment, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
analyzing the previous sub-request according to a preset multi-dimensional space classification and aggregation splitting clustering reinforcement learning strategy to obtain previous request information corresponding to the previous sub-request;
determining a subsequent sub-request according to a preset iteration parameter;
analyzing the subsequent sub-requests according to a multi-dimensional space classification and aggregation splitting clustering reinforcement learning strategy to obtain subsequent request information corresponding to the subsequent sub-requests;
the sub-request comprises a front sub-request and a back sub-request, and the sub-request information comprises front request information and back request information.
Fifth embodiment:
the present embodiment is based on the fourth embodiment. In this embodiment, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
judging whether the previous request information meets a preset deep analysis evaluation condition or not to obtain a judgment result;
if not, determining subsequent request information according to the iteration parameters;
analyzing the subsequent request information according to a multi-dimensional space classification and aggregation splitting clustering reinforcement learning strategy to obtain subsequent flight information corresponding to the subsequent request information;
wherein the second flight information includes post-flight information.
Sixth embodiment:
the present embodiment is based on the fifth embodiment. In this embodiment, when the current request information is the kth request information, the unmanned aerial vehicle collaboration center analysis layer is further specifically configured to:
judging whether the kth request information meets a preset deep analysis evaluation condition according to the formula 1, wherein the formula 1 is as follows:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
for the joint evaluation function corresponding to the kth request message,
for the expected cumulative value difference corresponding to the kth request message,
a desired cumulative value difference corresponding to the k-1 th request information,
planning the route for the route corresponding to the kth request information,
the number of times of radar detection corresponding to the kth request message,
for the queuing delay corresponding to the kth request message,
planning a route for the flight path corresponding to the k-1 th request information,
the number of times of radar detection corresponding to the k-1 th request message,
and i, j and t are values of the pre-stored direction vector of the depth analysis model for the formation time delay corresponding to the kth-1 request information.
Seventh embodiment:
the present embodiment is based on the sixth embodiment. In this embodiment, when the following request information is the (k + 1) th request information and the following flight information is the (k + 1) th flight information, the unmanned aerial vehicle cooperation center analysis layer is further specifically configured to:
determining the k +1 flight information according to equation 2
Formula 2:
wherein the content of the first and second substances,
as the information on the k-th flight,
for the preset cluster adjustment factor corresponding to the (k + 1) th request message,
is a preset reinforcement learning factor corresponding to the (k + 1) th request information.
Eighth embodiment:
the present embodiment is based on the seventh embodiment. In this embodiment, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
determination according to equation 3
Formula 3:
wherein L isminGPlanning the route for the historical shortest route,
planning the mean value of the routes from the first request information to the kth request informationminGFor the smallest number of radar detections in history,
is the mean value of the detected times of radar corresponding to the first to the k-th request information, YminGFor the historical minimum queuing delay,
is the first requestAnd solving the average value of the formation delay from the information to the kth request information.
Ninth embodiment:
this embodiment is based on the eighth embodiment. In this embodiment, the unmanned aerial vehicle collaborative center analysis layer is further specifically configured to:
determination according to equation 4
Formula 4:
wherein L isminkPlanning the route for the shortest route corresponding to the kth request information, CminkMinimum detected radar frequency, Y, corresponding to the kth request informationminkAnd the minimum queuing delay corresponding to the kth request message is obtained.
According to another aspect of the embodiments of the present invention, an embodiment of the present invention provides a method for controlling formation and cooperative flight of unmanned aerial vehicles based on an edge cloud, where the method is based on the system described in any one of the first to ninth embodiments.
Referring to fig. 3, fig. 3 is a schematic flowchart of a method for controlling formation of cooperative flight of unmanned aerial vehicles based on edge cloud according to an embodiment of the present invention.
As shown in fig. 3, the method includes:
s100: the method comprises the steps that a communication satellite network transmission layer obtains a flight request sent by an unmanned aerial vehicle formation, and transmits the flight request to an unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request is a radar detection and flight path planning request;
s200: the unmanned aerial vehicle cooperation edge gateway access layer transmits a flight request to an unmanned aerial vehicle cooperation edge data center layer;
s300: the unmanned aerial vehicle collaborative edge data center layer calls first flight information corresponding to the first flight request and stored by the unmanned aerial vehicle collaborative edge data center layer, and transmits the first flight information to an unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request comprises the first flight request;
s400: the unmanned aerial vehicle cooperates with the edge gateway access layer to transmit the first flight information to a communication satellite network transmission layer;
s500: the communication satellite network transmission layer transmits the first flight information to the unmanned aerial vehicle formation.
As can be seen in fig. 3, in one possible implementation scheme, the method further includes:
s600: the unmanned aerial vehicle cooperation edge gateway access layer transmits a second flight request to an unmanned aerial vehicle cooperation center analysis layer, wherein the flight request further comprises the second flight request;
s700: the unmanned aerial vehicle cooperation center analysis layer calls second flight information corresponding to the second flight request and stored by the unmanned aerial vehicle cooperation center analysis layer, and transmits the second flight information to the unmanned aerial vehicle cooperation edge gateway access layer;
s800: the unmanned aerial vehicle cooperates with the edge gateway access layer to transmit the second flight information to a communication satellite network transmission layer;
s900: and the communication satellite network transmission layer transmits the second flight information to the unmanned aerial vehicle formation.
With reference to fig. 3 and fig. 4 (fig. 4 is a schematic flowchart of a process of determining second flight information according to a second flight request by a coordinated central analysis layer of an unmanned aerial vehicle according to an embodiment of the present invention), in a possible implementation scheme, before S700, the method further includes:
s1: and the unmanned aerial vehicle cooperation center analysis layer performs iterative analysis processing on the second flight request according to a preset first iterative analysis rule to obtain a plurality of sub-request information, wherein the second flight request comprises a plurality of sub-requests, and one sub-request corresponds to one sub-request information.
Wherein, S1 specifically includes:
s11: and analyzing the previous sub-request according to a preset multi-dimensional space classification and clustering split clustering reinforcement learning strategy to obtain previous request information corresponding to the previous sub-request.
S12: and determining a subsequent sub-request according to a preset iteration parameter, wherein the sub-request comprises a previous sub-request and a subsequent sub-request, and the sub-request information comprises previous request information and subsequent request information.
S13: and analyzing the subsequent sub-requests according to the multidimensional space classification and the clustering split reinforcement learning strategy to obtain subsequent request information corresponding to the subsequent sub-requests.
Such as: the iteration parameters include: the maximum number of iterations, the number of iterations increases. Specifically, the method comprises the following steps:
the initial number of iterations is 0, i.e. the first word request is analyzed. And then adding 1 to the iteration times to obtain the first iteration, and analyzing the secondary sub-requests.
S2: and the unmanned aerial vehicle cooperation center analysis layer collects the sub-request information to obtain the request information.
S3: and the unmanned aerial vehicle cooperation center analysis layer performs iterative analysis processing on the request information according to a preset second iterative analysis rule to obtain second flight information.
Wherein, S3 specifically includes:
s31: and judging whether the previous request information meets a preset deep analysis evaluation condition or not to obtain a judgment result.
Specifically, when the current request information is the kth request information, S31 specifically includes: judging whether the kth request information meets a preset deep analysis evaluation condition according to the formula 1, wherein the formula 1 is as follows:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
for the joint evaluation function corresponding to the kth request message,
for the expected cumulative value difference corresponding to the kth request message,
a desired cumulative value difference corresponding to the k-1 th request information,
planning the route for the route corresponding to the kth request information,
the number of times of radar detection corresponding to the kth request message,
for the queuing delay corresponding to the kth request message,
planning a route for the flight path corresponding to the k-1 th request information,
the number of times of radar detection corresponding to the k-1 th request message,
for the formation delay corresponding to the kth-1 request message, i, j, and t are values in a direction vector of a pre-stored depth analysis model (see fig. 5, and fig. 5 is a schematic structural diagram of a depth analysis model according to an embodiment of the present invention).
The depth analysis principle is explained in detail with reference to fig. 6. Fig. 6 is a schematic diagram of depth analysis according to an embodiment of the present invention. The idea of multidimensional space classification and agglomerative splitting clustering reinforcement learning analysis in each iteration is that in multidimensional space, a plurality of depth analysis schemes migrate to the direction determined by the optimal optimization scheme according to a multidimensional space classification and agglomerative splitting clustering reinforcement learning strategy mode, namely the position of the solid line sphere in fig. 6.The right part of fig. 6 is the multidimensional space classification and clustering separation reinforcement learning principle: the unmanned aerial vehicle main body obtains the accumulated reward of the collaborative formation flight reality through the flight state sensing feedback, and carries out the flight action reinforcement learning according to the accumulated reward. And after the clustering is separated, t initial formation core point unmanned planes are selected, wherein t is a specified parameter, namely the expected number of formations. In each iteration loop, each unmanned aerial vehicle is pointed to and flies to the nearest core point unmanned aerial vehicle, and the unmanned aerial vehicles are clustered and separated to each core point unmanned aerial vehicle to form t subsets. And then, updating the core point unmanned aerial vehicle of each subset according to the optimal likelihood estimation value of each subset and the optimal likelihood estimation value of the unmanned aerial vehicle of the subset. Wherein the optimal likelihood estimation value
The pointing and updating operations are repeated until the core point drone no longer changes significantly. Separating and clustering: according to
(eta is a separation threshold of a certain connected graph, separation is carried out after the threshold is exceeded, edge is the number of edges in the certain connected graph, and point is the number of unmanned aerial vehicle points in the certain connected graph). Unmanned aerial vehicles (unique identification information) are brought into a formation coordinator one by one, after each unmanned aerial vehicle in the formation coordinator is subjected to clustering and clustering separation training and analysis, the learning analysis idea is enhanced by combining multi-dimensional space classification and clustering separation and clustering, and results are obtained based on deep analysis of theoretical advantages of multi-dimensional space, classification, clustering, probability theory, biology, operational research, intelligent optimization, machine learning and the like.
S32: and when the judgment result is negative, determining the subsequent request information according to the iteration parameter.
Such as: the iteration parameters include: the maximum number of iterations, the number of iterations increases. Specifically, the method comprises the following steps:
the initial iteration number is 0, namely, the first request information is analyzed. And then adding 1 to the iteration times to obtain the first iteration, and analyzing the next request information.
In one possible implementation scheme, the current iteration number is compared with the maximum iteration number, and when the current iteration number is greater than the maximum iteration number, the process is ended. When the current iteration number is less than or equal to the maximum iteration number, the process proceeds to S31.
S33: and analyzing the subsequent request information according to a multi-dimensional space classification and clustering split reinforcement learning strategy to obtain subsequent flight information corresponding to the subsequent request information, wherein the second flight information comprises the subsequent flight information.
Wherein, when the post-request information is the (k + 1) th request information and the post-flight information is the (k + 1) th flight information, then S33 specifically includes: determining the k +1 flight information according to equation 2
Formula 2:
wherein the content of the first and second substances,
as the information on the k-th flight,
for the preset cluster adjustment factor corresponding to the (k + 1) th request message,
is a preset reinforcement learning factor corresponding to the (k + 1) th request information.
Wherein the determination is made according to equation 3
Formula 3:
wherein L isminGPlanning the route for the historical shortest route,
planning the mean value of the routes from the first request information to the kth request informationminGFor the smallest number of radar detections in history,
is the mean value of the detected times of radar corresponding to the first to the k-th request information, YminGFor the historical minimum queuing delay,
the average value of the queuing delay corresponding to the first request message to the kth request message is obtained.
Wherein the determination is made according to equation 4
Formula 4:
wherein L isminkPlanning the route for the shortest route corresponding to the kth request information, CminkMinimum detected radar frequency, Y, corresponding to the kth request informationminkAnd the minimum queuing delay corresponding to the kth request message is obtained.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should also be understood that, in the embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. An edge cloud-based control system for formation and coordinated flight of unmanned aerial vehicles, the system comprising: a communication satellite network transport layer, an unmanned aerial vehicle cooperative edge gateway access layer, and an unmanned aerial vehicle cooperative edge data center layer, wherein,
the communication satellite network transport layer is to: acquiring a flight request sent by an unmanned aerial vehicle formation, and transmitting the flight request to an unmanned aerial vehicle cooperative edge gateway access layer, wherein the flight request is a radar avoidance and track planning request;
the unmanned aerial vehicle collaborative edge gateway access layer is used for: transmitting the flight request to the unmanned aerial vehicle collaborative edge data center layer;
the unmanned aerial vehicle collaborative edge data center layer is used for: calling first flight information which is stored by the unmanned aerial vehicle and corresponds to a first flight request, and transmitting the first flight information to the unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request comprises the first flight request;
the unmanned aerial vehicle collaborative edge gateway access layer is further configured to: transmitting the first flight information to the communication satellite network transport layer;
the communications satellite network transport layer is further configured to: transmitting the first flight information to the formation of drones; wherein the system further comprises: the drones cooperate with a central analysis layer, wherein,
the unmanned aerial vehicle collaborative edge gateway access layer is further configured to: transmitting the second flight request to the unmanned aerial vehicle coordination center analysis layer, wherein the flight request further comprises a second flight request;
the unmanned aerial vehicle collaborative center analysis layer is used for: calling second flight information corresponding to the second flight request stored by the unmanned aerial vehicle, and transmitting the second flight information to the unmanned aerial vehicle cooperative edge gateway access layer;
the unmanned aerial vehicle collaborative edge gateway access layer is further configured to: transmitting the second flight information to the communication satellite network transport layer;
the communications satellite network transport layer is further configured to: transmitting the second flight information to the formation of drones.
2. The control system of edge cloud based formation of drones for collaborative flight according to claim 1, wherein the drone collaboration center analysis layer is further configured to:
performing iterative analysis processing on the second flight request according to a preset first iterative analysis rule to obtain a plurality of sub-request information, wherein the second flight request comprises a plurality of sub-requests, and one sub-request corresponds to one sub-request information;
summarizing the sub-request information to obtain request information;
and performing iterative analysis processing on the request information according to a preset second iterative analysis rule to obtain the second flight information.
3. The edge cloud-based unmanned aerial vehicle formation cooperative flight control system of claim 2, wherein the unmanned aerial vehicle cooperative center analysis layer is specifically configured to:
analyzing the previous sub-request according to a preset multi-dimensional space classification and aggregation splitting clustering reinforcement learning strategy to obtain previous request information corresponding to the previous sub-request;
determining a subsequent sub-request according to a preset iteration parameter;
analyzing the subsequent sub-request according to the multidimensional space classification and the clustering split reinforcement learning strategy to obtain subsequent request information corresponding to the subsequent sub-request;
wherein the sub-request includes the preceding sub-request and the following sub-request, and the sub-request information includes the preceding request information and the following request information.
4. The edge cloud-based unmanned aerial vehicle formation cooperative flight control system of claim 3, wherein the unmanned aerial vehicle cooperative center analysis layer is further specifically configured to:
judging whether the previous request information meets a preset deep analysis evaluation condition or not to obtain a judgment result;
when the judgment result is negative, determining the subsequent request information according to the iteration parameter;
analyzing the subsequent request information according to the multidimensional space classification and the clustering reinforcement learning strategy to obtain subsequent flight information corresponding to the subsequent request information;
wherein the second flight information includes the post-flight information.
5. The edge cloud-based unmanned aerial vehicle formation cooperative flight control system according to claim 4, wherein when the current request information is a kth request information, the unmanned aerial vehicle cooperative center analysis layer is further specifically configured to:
judging whether the kth request information meets a preset deep analysis evaluation condition according to the formula 1, wherein the formula 1 is as follows:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
a joint evaluation function corresponding to the kth request message,
for the expected accumulated value difference corresponding to the kth request message,
a desired cumulative value difference corresponding to the k-1 th request information,
planning a route for the flight path corresponding to the kth request information,
the number of times of radar detection corresponding to the kth request message,
for the queuing time delay corresponding to the kth request message,
is the firstThe k-1 requests the flight path planning corresponding to the information,
the number of times of radar detection corresponding to the k-1 th request message,
and i, j and t are values of a direction vector of a pre-stored depth analysis model respectively for the formation time delay corresponding to the kth-1 request information.
6. The edge cloud-based control system for formation and cooperative flight of unmanned aerial vehicles according to claim 5, wherein when the post-request information is a (k + 1) -th request information and the post-flight information is a (k + 1) -th flight information, the unmanned aerial vehicle cooperative center analysis layer is further specifically configured to:
determining the k +1 flight information according to equation 2
Formula 2:
wherein the content of the first and second substances,
as the information on the k-th flight,
for the preset cluster adjustment factor corresponding to the (k + 1) th request message,
the information is a preset reinforcement learning factor corresponding to the (k + 1) th request information.
7. The edge cloud-based unmanned aerial vehicle formation cooperative flight control system of claim 6, wherein the unmanned aerial vehicle cooperative center analysis layer is further specifically configured to:
determining the above according to equation 3
Formula 3:
wherein L isminGPlanning the route for the historical shortest route,
planning the mean value of the routes from the first request information to the path corresponding to the kth request information, CminGFor the smallest number of radar detections in history,
is the mean value, Y, of the detected radar times corresponding to the first to k-th request informationminGFor the historical minimum queuing delay,
and the average value of the formation delay corresponding to the first request message to the kth request message is obtained.
8. The system of claim 7, wherein the unmanned aerial vehicle collaborative central analysis layer is further specifically configured to:
determining the above according to equation 4
Formula 4:
wherein L isminkPlanning a route for the shortest route corresponding to the kth request information, CminkThe minimum detected radar frequency, Y, corresponding to the kth request informationminkAnd the minimum queuing delay corresponding to the kth request message is obtained.
9. A method for controlling formation coordinated flight of unmanned aerial vehicles based on edge cloud, wherein the method is based on the system of any one of the preceding claims 1-8, and the method comprises:
the method comprises the steps that a communication satellite network transmission layer obtains a flight request sent by an unmanned aerial vehicle formation, and transmits the flight request to an unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request is a radar detection and flight path planning request;
the unmanned aerial vehicle cooperation edge gateway access layer transmits the flight request to an unmanned aerial vehicle cooperation edge data center layer;
the unmanned aerial vehicle collaborative edge data center layer calls first flight information corresponding to a first flight request stored by the unmanned aerial vehicle collaborative edge data center layer and transmits the first flight information to the unmanned aerial vehicle collaborative edge gateway access layer, wherein the flight request comprises the first flight request;
the unmanned aerial vehicle cooperates with the edge gateway access layer to transmit the first flight information to the communication satellite network transmission layer;
the communication satellite network transmission layer transmits the first flight information to the formation of unmanned aerial vehicles.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106969778A (en) * | 2017-02-28 | 2017-07-21 | 南京航空航天大学 | A kind of multiple no-manned plane cooperates with the paths planning method of dispenser |
| CN107181323A (en) * | 2017-07-18 | 2017-09-19 | 国网江西省电力公司南昌供电分公司 | A kind of unmanned plane inspection system and method |
| WO2018130274A1 (en) * | 2017-01-11 | 2018-07-19 | Nokia Solutions And Networks Oy | Security architecture for machine type communications |
| CN108449383A (en) * | 2018-02-11 | 2018-08-24 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Distributed thin cloud computing system mobile in real time |
| CN108833577A (en) * | 2018-06-29 | 2018-11-16 | 中国联合网络通信集团有限公司 | Data Communication Optimization system and method based on edge calculations |
| CN109067490A (en) * | 2018-09-29 | 2018-12-21 | 郑州航空工业管理学院 | Cellular Networks join lower multiple no-manned plane and cooperate with mobile edge calculations method for distributing system resource |
| CN109219943A (en) * | 2016-07-01 | 2019-01-15 | 英特尔公司 | The automatic configuration of Machine To Machine system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9923705B2 (en) * | 2014-10-06 | 2018-03-20 | Parallel Wireless, Inc. | Full-duplex mesh networks |
-
2019
- 2019-01-21 CN CN201910055299.3A patent/CN109656270B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109219943A (en) * | 2016-07-01 | 2019-01-15 | 英特尔公司 | The automatic configuration of Machine To Machine system |
| WO2018130274A1 (en) * | 2017-01-11 | 2018-07-19 | Nokia Solutions And Networks Oy | Security architecture for machine type communications |
| CN106969778A (en) * | 2017-02-28 | 2017-07-21 | 南京航空航天大学 | A kind of multiple no-manned plane cooperates with the paths planning method of dispenser |
| CN107181323A (en) * | 2017-07-18 | 2017-09-19 | 国网江西省电力公司南昌供电分公司 | A kind of unmanned plane inspection system and method |
| CN108449383A (en) * | 2018-02-11 | 2018-08-24 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Distributed thin cloud computing system mobile in real time |
| CN108833577A (en) * | 2018-06-29 | 2018-11-16 | 中国联合网络通信集团有限公司 | Data Communication Optimization system and method based on edge calculations |
| CN109067490A (en) * | 2018-09-29 | 2018-12-21 | 郑州航空工业管理学院 | Cellular Networks join lower multiple no-manned plane and cooperate with mobile edge calculations method for distributing system resource |
Non-Patent Citations (1)
| Title |
|---|
| 面向云服务CPS架构下的无人机通地站智能总装车间设计;李杰林等;《测控技术》;20180918;第80-88页 * |
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