CN112578811A

CN112578811A - Unmanned aerial vehicle cluster performance method and device

Info

Publication number: CN112578811A
Application number: CN202011390589.2A
Authority: CN
Inventors: 何文灿; 钟豫粤; 陈楚雄; 黄智勇; 吴文韵; 郑仲岳; 何春霞; 何棱; 赵汝桂; 王维维
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2020-12-02
Filing date: 2020-12-02
Publication date: 2021-03-30
Anticipated expiration: 2040-12-02
Also published as: CN112578811B

Abstract

The application provides a method and a device for unmanned aerial vehicle cluster performance, wherein the method comprises the following steps: acquiring flight data of the unmanned aerial vehicle cluster through testing, wherein the flight data comprises three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster and flight flow data of the unmanned aerial vehicle cluster; determining a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster; determining key indexes of the unmanned aerial vehicle cluster performance according to flight data of the unmanned aerial vehicle cluster; optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance; and performing unmanned aerial vehicle cluster performance based on the optimized configuration. The unmanned aerial vehicle cluster performance method and the unmanned aerial vehicle cluster performance device can improve the success rate of large-scale unmanned aerial vehicle access under low delay, thereby ensuring smooth performance of large-scale unmanned aerial vehicle cluster performance.

Description

Unmanned aerial vehicle cluster performance method and device

Technical Field

The application relates to the field of unmanned aerial vehicles, in particular to a method and a device for unmanned aerial vehicle cluster performance.

Background

In recent years, with the gradual maturity of unmanned aerial vehicle technology, the application of unmanned aerial vehicle cluster is more and more extensive. Nowadays, in some large-scale activities, often need a plurality of unmanned aerial vehicles to form an unmanned aerial vehicle cluster and carry out the performance, a plurality of unmanned aerial vehicles in the performance receive the unified regulation and control of a computer, adopt wireless network to communicate between unmanned aerial vehicle and the computer. Therefore, it is necessary to control not only the delay difference of the signal received by the single frame machine to be minimum, but also the delay speed of the instruction upload to be in the order of milliseconds. Therefore, the problem that the access requirement of thousands of levels of unmanned aerial vehicles is met and the communication of thousands of levels of unmanned aerial vehicles is timely is solved, and the problem becomes the key point of unmanned aerial vehicle cluster management.

In the prior art, in an unmanned aerial vehicle cluster performance, the main technologies adopted include a bluetooth technology, a zigbee (zigbee) technology, a wireless internet access (Wi-Fi), a third generation mobile communication (3rd-generation, 3G) technology of an operator, and a fourth generation mobile communication (4rd-generation, 4G) technology to realize transmission of control signals. However, bluetooth is limited by the transmission power of its modules, and the transmission distance is short; the transmission rate of the zigbee technology is too low; the number of access users of Wi-Fi is mainly limited by the hardware performance of a router and a self protocol, and the Wi-Fi is suitable for the flight of a small-scale unmanned aerial vehicle; the 3G technology is limited by the total base station rate of 14.4Mbps and 60 users carried by each carrier, and cannot meet the access and use requirements of batch unmanned aerial vehicles. The 4G technology can be used basically normally when a single carrier bears 120 users at present, and a 10-carrier scheme is expected to bear the cluster performance of thousands of unmanned aerial vehicles, but the multi-carrier scheme may face the problem of untimely inter-carrier pilot frequency switching and the problem of service equalization between carriers. Therefore, the performance of the existing unmanned aerial vehicle cluster cannot be guaranteed to be smoothly performed by the large-scale unmanned aerial vehicle cluster.

Disclosure of Invention

The application provides a method and a device for cluster performance of unmanned aerial vehicles, which can improve the success rate of large-scale unmanned aerial vehicle access under low delay, thereby ensuring smooth performance of large-scale unmanned aerial vehicle cluster performance.

In a first aspect, a method for unmanned aerial vehicle cluster performance is provided, including: acquiring flight data of an unmanned aerial vehicle cluster through testing, wherein the flight data comprises three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster and flight flow data of the unmanned aerial vehicle cluster, and the three-dimensional space network data comprises network data of a takeoff area of the unmanned aerial vehicle cluster and network data of a flight array area of the unmanned aerial vehicle cluster; determining a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster; determining key indexes of the unmanned aerial vehicle cluster performance according to flight data of the unmanned aerial vehicle cluster, wherein the key indexes comprise scheduling delay of the unmanned aerial vehicle cluster, downlink bearing perception rate of each unmanned aerial vehicle and the maximum number of Radio Resource Control (RRC) connection state users of the main coverage cell; optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance; and performing unmanned aerial vehicle cluster performance based on the optimized configuration.

The unmanned aerial vehicle cluster performance method solves the problem of airspace stereo test by using a stereo space network test means, realizes quick and accurate optimization, and greatly improves the working efficiency. Through the collection, the analysis to unmanned aerial vehicle cluster flight data, confirm the key direction of network guarantee to formulate accurate effectual network software and hardware dilatation scheme, utilize many times the color arrangement data to confirm the key evaluation index of unmanned aerial vehicle cluster performance, carry out configuration optimization according to the main cell that covers that unmanned aerial vehicle cluster performance corresponds at last according to the reference value of key index, improved the success rate that unmanned aerial vehicle of scale inserts under the low time delay, thereby guarantee going on smoothly of the large-scale cluster performance of unmanned aerial vehicle.

Optionally, the acquiring flight data of the drone cluster through the test includes: and performing three-dimensional dotting on the flight array area in a mode of binding the sampling terminal on the unmanned aerial vehicle, and acquiring the network data of the flight array area.

Optionally, the optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance includes: expanding the capacity of the baseband board of the main coverage cell; and/or reducing the power of the peripheral interference cells of the main coverage cell.

Optionally, the key indicator further comprises at least one of: the RRC connection establishment success rate of the primary coverage cell, the average utilization rate of an uplink Physical Resource Block (PRB) of the primary coverage cell or the average utilization rate of a downlink PRB of the primary coverage cell.

Optionally, the method further comprises: removing at least one of the following non-critical indicators from the flight data of the cluster of drones: the unmanned aerial vehicle cluster comprises the total flight flow, the RRC congestion rate, the average number of users in a cell, the uplink data retransmission rate, the downlink data retransmission rate or the downlink average path loss.

In a second aspect, there is provided an apparatus for a cluster performance of drones, comprising: the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring flight data of an unmanned aerial vehicle cluster, and the flight data comprises three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster and flight flow data of the unmanned aerial vehicle cluster, wherein the three-dimensional space network data comprises network data of a takeoff area of the unmanned aerial vehicle cluster and network data of a flight array area of the unmanned aerial vehicle cluster; the determining module is used for determining a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster; determining key indexes of the unmanned aerial vehicle cluster performance according to flight data of the unmanned aerial vehicle cluster, wherein the key indexes comprise scheduling delay of the unmanned aerial vehicle cluster, downlink bearing perception rate of each unmanned aerial vehicle and the maximum number of users in a Radio Resource Control (RRC) connection state of the main coverage cell; the processing module is used for optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance; and performing unmanned aerial vehicle cluster performance based on the optimized configuration.

Optionally, the obtaining module is specifically configured to: and performing three-dimensional dotting on the flight array area in a mode of binding the sampling terminal on the unmanned aerial vehicle, and acquiring the network data of the flight array area.

Optionally, the processing module is specifically configured to: expanding the capacity of the baseband board of the main coverage cell; and/or reducing the power of the peripheral interference cells of the main coverage cell.

Optionally, the processing module is further configured to: removing at least one of the following non-critical indicators from the flight data of the cluster of drones: the total flight flow, the RRC congestion rate, the average number of users in a cell, the uplink data retransmission rate, or the downlink data retransmission rate or the downlink average path loss of the unmanned aerial vehicle cluster.

In a third aspect, there is provided an apparatus for cluster performance by drones, comprising a processor, coupled to a memory, and configured to execute instructions in the memory to implement the method in any one of the possible implementations of the first aspect.

Optionally, the apparatus further comprises a memory. Optionally, the apparatus further comprises a communication interface, the processor being coupled to the communication interface.

In one implementation, the apparatus for cluster performance of unmanned aerial vehicles. When the apparatus for unmanned aerial vehicle group performance is a data processing device, the communication interface may be a transceiver, or an input/output interface.

In another implementation, the apparatus for performing in a cluster of drones is a chip configured in a server. When the device for the cluster performance of the unmanned aerial vehicles is a chip configured in the server, the communication interface may be an input/output interface.

In a fourth aspect, a processor is provided, comprising: input circuit, output circuit and processing circuit. The processing circuit is configured to receive a signal via the input circuit and transmit a signal via the output circuit, so that the processor performs the method of any one of the possible implementations of the first aspect.

In a specific implementation process, the processor may be a chip, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a flip-flop, various logic circuits, and the like. The input signal received by the input circuit may be received and input by, for example and without limitation, a receiver, the signal output by the output circuit may be output to and transmitted by a transmitter, for example and without limitation, and the input circuit and the output circuit may be the same circuit that functions as the input circuit and the output circuit, respectively, at different times. The embodiment of the present application does not limit the specific implementation manner of the processor and various circuits.

In a fifth aspect, a processing apparatus is provided that includes a processor and a memory. The processor is configured to read instructions stored in the memory, and may receive signals via the receiver and transmit signals via the transmitter to perform the method of any one of the possible implementations of the first aspect.

Optionally, there are one or more processors and one or more memories.

Alternatively, the memory may be integrated with the processor, or provided separately from the processor.

In a specific implementation process, the memory may be a non-transient memory, such as a Read Only Memory (ROM), which may be integrated on the same chip as the processor, or may be separately disposed on different chips.

It will be appreciated that the associated data interaction process, for example, sending the indication information, may be a process of outputting the indication information from the processor, and receiving the capability information may be a process of receiving the input capability information from the processor. In particular, the data output by the processor may be output to a transmitter and the input data received by the processor may be from a receiver. The transmitter and receiver may be collectively referred to as a transceiver, among others.

The processing device in the fifth aspect may be a chip, the processor may be implemented by hardware or software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor implemented by reading software code stored in a memory, which may be integrated with the processor, located external to the processor, or stand-alone.

In a sixth aspect, there is provided a computer program product comprising: computer program (also called code, or instructions), which when executed, causes a computer to perform the method of any of the possible implementations of the first aspect described above.

In a seventh aspect, a computer-readable storage medium is provided, which stores a computer program (which may also be referred to as code or instructions) that, when executed on a computer, causes the computer to perform the method in any of the possible implementations of the first aspect.

Drawings

Fig. 1 is a schematic block diagram of a system architecture of a cluster performance of unmanned aerial vehicles according to an embodiment of the present application;

fig. 2 is a schematic flow chart of a method for performing a cluster of drones according to an embodiment of the present disclosure;

fig. 3 is a schematic block diagram of a communication structure between a drone and a server according to an embodiment of the present application;

fig. 4 is a graph of peak user data during a formal performance of a drone cluster according to an embodiment of the present application;

fig. 5 is a data diagram of scheduling delay during a formal performance of an unmanned aerial vehicle cluster according to an embodiment of the present application;

FIG. 6 is a graph of perceived rate data during a formal performance of a human-computer cluster according to an embodiment of the present application;

fig. 7 is a schematic block diagram of an apparatus for performing a cluster of unmanned aerial vehicles according to an embodiment of the present application;

fig. 8 is a schematic block diagram of another apparatus for performing unmanned aerial vehicle clustering provided in an embodiment of the present application.

Detailed Description

The technical solution in the present application will be described below with reference to the accompanying drawings.

Fig. 1 is a schematic block diagram of a system architecture 100 for unmanned aerial vehicle cluster performance provided in an embodiment of the present application, where the system architecture 100 includes: the drone 110, the server 120, the display control terminal 130, and the differential reference bar 140.

Before the unmanned aerial vehicle 110 takes off, the display control terminal 130 transmits control information to the unmanned aerial vehicle 110 via the server 120, checks for abnormal states and uncontrollable unmanned aerial vehicles, and transmits the waypoint information of KB level to the unmanned aerial vehicle 110. The differential datum rod 140 sends differential data to the server 120, the server 120 forwards the received differential data to 1180 frames of unmanned aerial vehicles, the unmanned aerial vehicles 110 send self state information to the server 120 after receiving the differential data, so that the server 120 sends the received state information to the display control terminal 130, the display control terminal 130 judges whether the unmanned aerial vehicles 110 are in an abnormal state according to the self state information of the unmanned aerial vehicles 110, and sends an automatic return control command to the unmanned aerial vehicles 110 in the abnormal state.

It should be understood that the drone 110 may be a cluster of drones formed from a plurality of drones.

Along with the gradual maturity of unmanned aerial vehicle technique, the application of unmanned aerial vehicle cluster is more and more extensive. Nowadays, in some large-scale activities, often need a plurality of unmanned aerial vehicles to form an unmanned aerial vehicle cluster and carry out the performance, a plurality of unmanned aerial vehicles in the performance receive the unified regulation and control of a computer, adopt wireless network to communicate between unmanned aerial vehicle and the computer. Therefore, it is necessary to control not only the delay difference of the signal received by the single frame machine to be minimum, but also the delay speed of the instruction upload to be in the order of milliseconds. Therefore, the problem that the access requirement of thousands of levels of unmanned aerial vehicles is met and the communication of thousands of levels of unmanned aerial vehicles is timely is solved, and the problem becomes the key point of unmanned aerial vehicle cluster management.

The current unmanned aerial vehicle cluster performance method mainly comprises the following four steps:

1. bluetooth technology, unmanned aerial vehicle can utilize the access of Bluetooth technology, and the bluetooth agreement extensively is used for the communication between the equipment, and hardware configuration is simple.

2. The zigbee technology is obtained based on improvement of Bluetooth communication, so that access limitation in Bluetooth networking is relieved, and signal coverage is expanded. The devices in the Bluetooth network are limited to 8 devices, and zigbee can accommodate 1 master device at most and 254 slave devices by virtue of large-scale networking capability. Bluetooth communication is limited within 100 meters, and the Zigbee communication range can reach thousands of meters.

3. Wi-Fi technology, Wi-Fi focus communication technology are one of the selection of early unmanned aerial vehicle flight communication, and its advantage lies in that the frequency channel is open, and coverage is wide, and the bandwidth is big.

4. The 3/4G network technology operated by the mature operator has the advantages of mature technology, wide coverage range and high safety.

The four methods have the following defects in unmanned aerial vehicle cluster performance:

1. bluetooth is limited by the transmission power of its modules, and the transmission distance is short, generally only tens of meters. And the unmanned aerial vehicle flies in the high altitude of nearly 200 meters, and far exceeds the range of Bluetooth communication. Secondly, in the same Bluetooth network, at most 1 master device can be accommodated, and 7 additional slave devices are accommodated. In the face of the requirement that one control console is connected with thousands of unmanned aerial vehicles in actual performance, the control console can be called a water waggon. Finally, a safety issue. Bluetooth is vulnerable to denial of service attacks, eavesdropping, man-in-the-middle attacks, message modification, and resource abuse. The unmanned aerial vehicle shows as an important public program during the wealth forum, the number of field viewers is very large, and the possibility of deliberate attack by other people needs to be prevented.

2. The most fatal disadvantage of the zigbee technology is that the transmission rate is too low, and the transmission rate is only 250kb/s in the 2.4GHz band. Considering consumption in the actual transmission process, the actual transmission rate of the useful information is less than 100kb/s, and obviously, the transmission requirement of the communication information amount of the unmanned aerial vehicle cannot be met.

3. The Wi-Fi hotspot communication technology is very suitable for flight of small-scale unmanned aerial vehicles, the number of access users of a WIFI network is mainly limited by the hardware performance of a router and a protocol of the router, and generally does not exceed 100 users. At present, 2.4GHz public frequency bands are used in WIFI communication, 13 channels are arranged in the frequency bands, and the same frequency interference is serious.

4. The 3G technology of the operator is limited to the total base station rate of 14.4Mbps and 60 users carried by each carrier, and cannot meet the access and use requirements of batch unmanned aerial vehicles. The 4G technology of the operator can be used basically normally when a single carrier bears 120 users in the current common mobile phone user application mode. Supposing according to the existing mobile phone model, the 10-carrier scheme can be expected to bear the cluster performance of thousands of unmanned aerial vehicles. The multi-carrier scheme may face the problem of untimely inter-carrier pilot frequency switching and the problem of service equalization between carriers, and in addition, three operators do not have enough spectrum resources to open a 10-carrier scheme at present. The traditional operator network signal test focuses on road surface test and fixed point test, and the test optimization of a null area cannot be carried out.

In view of this, the present application provides a method and an apparatus for unmanned aerial vehicle cluster performance, which utilize a three-dimensional space network testing means to solve the problem of airspace three-dimensional testing, implement fast and accurate optimization, and greatly improve the working efficiency. The key direction of network guarantee is confirmed through collection and analysis of unmanned aerial vehicle cluster flight data, so that an accurate and effective network software and hardware capacity expansion scheme is formulated, a key evaluation index of unmanned aerial vehicle cluster performance is determined by using multiple color ranking data, and finally, a main coverage cell corresponding to the unmanned aerial vehicle cluster performance is configured and optimized according to a reference value of the key index, so that the unmanned aerial vehicle cluster performance is smoothly completed.

The embodiment of the application is completed under the condition that each unmanned aerial vehicle in the unmanned aerial vehicle cluster participating in the performance is completely the same. Because unmanned aerial vehicle service type is complicated, the condition that the unmanned aerial vehicle of different usage of different producers used the network also is different, for simplifying actual service environment, can make the following hypothesis to each unmanned aerial vehicle in the unmanned aerial vehicle cluster of participating in the performance:

1. the unmanned aerial vehicles are all devices of the same manufacturer, and the network access mode of the unmanned aerial vehicles belongs to the same network operator;

2. the unmanned aerial vehicle performs cluster flight performance within a preset time, and the flight performance time cannot be changed randomly;

3. the unmanned aerial vehicle performs cluster flight performance in a relatively fixed range, and the flight performance area cannot be changed randomly;

4. the unmanned aerial vehicle only carries out network interaction related to flight performance and does not carry out large-flow services such as real-time video return and the like;

5. the weather condition is good when the unmanned aerial vehicle performs in flight, and severe weather such as thunderstorm and strong wind does not appear;

6. malicious interference signals influencing the work of the mobile network do not appear in the flight period and the flight area of the unmanned aerial vehicle;

7. no large user congregation should be found in the unmanned plane takeoff area and array area within 150 meters.

Before introducing the method and apparatus for unmanned aerial vehicle group performance provided in the embodiments of the present application, the following description is made.

First, in the embodiments shown below, terms and english abbreviations such as the area of the flight array, the primary coverage cell, and the key indicators are exemplary examples given for convenience of description, and should not limit the present application in any way. This application is not intended to exclude the possibility that other terms may exist or may be defined in the future that achieve the same or similar functionality.

Second, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, and c, may represent: a, or b, or c, or a and b, or a and c, or b and c, or a, b and c, wherein a, b and c can be single or multiple.

The method 200 for performing a cluster of drones according to the embodiment of the present application is described in detail below with reference to fig. 2. The method in this embodiment of the present application may be executed by a back-end management device (also referred to as a management device or a processing device), or may be executed by a chip in the back-end management device (or the management device or the processing device), which is not limited in this embodiment of the present application.

Fig. 2 is a schematic flow chart of a method 200 for performing a cluster of drones according to an embodiment of the present application. As shown in fig. 2, the method 200 may include the following steps:

s201, acquiring flight data of the unmanned aerial vehicle cluster through testing, wherein the flight data comprises three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster and flight flow data of the unmanned aerial vehicle cluster.

S202, determining a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster. It should be understood that the primary coverage cell may also be referred to as a primary serving cell, which is not limited in this application.

S203, determining key indicators of the unmanned aerial vehicle cluster performance according to the flight data of the unmanned aerial vehicle cluster, where the key indicators include scheduling delay of the unmanned aerial vehicle cluster, downlink bearer sensing rate of each unmanned aerial vehicle, and the maximum number of Radio Resource Control (RRC) connected users in a primary coverage cell.

And S204, optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance.

And S205, performing unmanned aerial vehicle cluster performance based on the optimized configuration.

According to the unmanned aerial vehicle cluster performance method and the unmanned aerial vehicle cluster performance device, the problem of airspace stereo test is solved by using a stereo space network test means, rapid and accurate optimization is realized, and the working efficiency is greatly improved. Through the collection, the analysis to unmanned aerial vehicle cluster flight data, confirm the key direction of network guarantee to formulate accurate effectual network software and hardware dilatation scheme, utilize many times the color arrangement data to confirm the key evaluation index of unmanned aerial vehicle cluster performance, carry out configuration optimization according to the main cell that covers that unmanned aerial vehicle cluster performance corresponds at last according to the reference value of key index, improved the success rate that unmanned aerial vehicle of scale inserts under the low time delay, thereby guarantee going on smoothly of the large-scale cluster performance of unmanned aerial vehicle.

It should be understood that, in the embodiment of the present application, the above-mentioned obtaining of flight data of the unmanned aerial vehicle cluster through testing may be obtaining of flight data of the unmanned aerial vehicle cluster through multiple rehearsal. The stereoscopic network data may include network data of a takeoff area of the unmanned aerial vehicle cluster and network data of a flight array area of the unmanned aerial vehicle cluster.

Exemplarily, the three-dimensional space data may be acquired by a three-dimensional space network signal test means, where the network data of the takeoff area of the unmanned aerial vehicle cluster may be tested by a conventional test means, for example, a manual test, that is, a worker carries a terminal device capable of being networked to move back and forth in the takeoff area of the unmanned aerial vehicle cluster to acquire the network data of the takeoff area, and the specific test method is not limited in this application.

It should be understood that each unmanned aerial vehicle of the above unmanned aerial vehicle cluster has a number corresponding to each unmanned aerial vehicle, and may be referred to as a flight number of the unmanned aerial vehicle, and flight path data of the unmanned aerial vehicle cluster may be obtained by tracking a flight number of all or a part of unmanned aerial vehicles during a plurality of rounds of the unmanned aerial vehicle cluster.

For example, the flight traffic data of the drone cluster may include the upstream traffic, the downstream traffic of each drone, the total traffic generated by the drone cluster during each test, and the average traffic.

Illustratively, the table one details the traffic data acquired by the background management device and generated by all flying drones during the rehearsal period.

Watch 1

Number of aircraft	Downstream flow (KB)	Upstream flow (KB)	Total flow (KB)	Mean flow (KB)
					1015	4468605	3794954	8263559	8141

As can be seen from the flow data shown in table one: although the communication frequency is high during the flight of the unmanned aerial vehicle, the consumed traffic is not large, and the downlink traffic is slightly higher than the uplink traffic.

It should be understood that the overall process of drone swarm performance may mainly include three communication phases: firstly, network access is performed when the unmanned aerial vehicle is started; secondly, issuing an instruction before the unmanned aerial vehicle takes off, wherein the instruction comprises flight data and performance data; and thirdly, data interaction between the unmanned aerial vehicle and the server in the flight process. Therefore, during flight performance of the unmanned aerial vehicle cluster, frequent information interaction exists between the unmanned aerial vehicle and the mobile network. This makes clear that the demands of the drone cluster performance on the network are concentrated on latency and capacity.

It should be understood that the key indexes of the unmanned aerial vehicle cluster performance may be obtained by the background management device through the network operating platform. The background management equipment can determine the network coverage condition, the signal strength, the signal quality and the like of a main coverage cell of the unmanned aerial vehicle cluster performance according to the flight data acquired in the earlier stage, and then the background management equipment can determine the key indexes of the unmanned aerial vehicle cluster performance by observing the corresponding parameters of the main coverage cell on the network platform. Meanwhile, the background management equipment can also determine the parameter values of key indexes of the unmanned aerial vehicle cluster flight performance according to the statistical results of multiple chromatic rows and the network operation platform.

For example, the scheduling delay may be derived from a relationship between a delivery distance and a time length of the unmanned aerial vehicle issued by the government. Specifically, the average flying speed of the unmanned aerial vehicle is calculated to be about 16 m/s according to the relationship between the express delivery distance and the time length of the unmanned aerial vehicle, the response time delay of the unmanned aerial vehicle is assumed to be 0, the unmanned aerial vehicle is in a non-static state in a flying state (the time of accelerating the unmanned aerial vehicle from a static state to the average speed is ignored) most of the time, and the position of the unmanned aerial vehicle is changed by 0.16 m and is about 10 ms. Therefore, if the unmanned aerial vehicle needs to make an accurate arrangement shape, strives for a position error smaller than 0.2 m, and considers that the minimum time delay of the server is 2ms (2 ms-15 ms), the scheduling time delay of network response can be calculated to be not more than 10ms so as to avoid influencing the effect of the arrangement shape.

As an optional embodiment, the obtaining of flight data of the cluster of drones through testing includes: the method comprises the steps that three-dimensional dotting is conducted on the flight array area in a mode that the sampling terminal is bound on the unmanned aerial vehicle, and network data of the flight array area are obtained.

It should be understood that the flight array area may also be referred to as an air performance area, since the flight array is located in high air and the air area signals are cluttered, reducing surrounding base station cell power is desirable to reduce interference. Traditional collection mode that artifical was walked is difficult to satisfy high altitude test requirement, consequently, this application embodiment is through adopting the mode of fixing the signal sampling terminal who will open the test function of dotting on unmanned aerial vehicle, by the cooperation of on-the-spot network management personnel with unmanned aerial vehicle performance engineer jointly, thereby carries out the network data that the space was dotted in the air performance region and is acquireed flight array area.

For example, during the test, the drone may be affected by the battery endurance of the drone, so the back-office management device may choose to test for 5 minutes in a 100 meter air performance area and 5 minutes in a 170 meter area.

The final test effect of the acquisition mode of fixing the sampling terminal on the unmanned aerial vehicle can show that the innovative method for testing the three-dimensional space wireless signals can find the actual network problem that the sunless drive test and the fixed-point test cannot be positioned, and can be widely applied to the test of unreachable or inconvenient back and forth areas, such as river surfaces, high-rise buildings, highways and tunnel portals with repeated test requirements and the like. If need to the above-mentioned unreachable or inconvenient area that makes a round trip to traverse the test, adopt traditional artifical collection of walking to need about two days test time, adopt the above-mentioned collection mode of fixing sampling terminal on unmanned aerial vehicle to accomplish in tens of minutes high-efficiently, greatly promoted efficiency of software testing, guaranteed test effect.

The method of unmanned aerial vehicle cluster performance of this application embodiment through fixing the mode on unmanned aerial vehicle with sampling terminal, carries out the solid to above-mentioned flight array region and gets ready, can discover the actual network problem that traditional test and fixed point test failed the location, compares traditional measurement mode, uses this mode can accomplish the test in tens of minutes high-efficiently, has greatly promoted efficiency of software testing, has guaranteed test effect simultaneously.

As an optional embodiment, the optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance includes: expanding the capacity of a baseband board of a main coverage cell; and/or reducing the power of the peripheral interference cells of the main coverage cell.

The embodiment of the application can optimize the main coverage cell by adopting two optimization modes, so that the actual value of the key index of the unmanned aerial vehicle cluster performance meets the requirement of a reference value.

Mode one, carry out the dilatation to the baseband board of above-mentioned main cell that covers to solve unmanned aerial vehicle's communication time delay and perception rate's problem. The communication time delay of the unmanned aerial vehicle is mainly divided into wireless side time delay and server side time delay, and the total control time delay is equal to the sum of the wireless side time delay and the server side time delay.

Fig. 3 shows a schematic block diagram of a communication structure between a drone and a server provided by an embodiment of the present application. The communication structure includes: a wireless side and a wired side. Wherein, the wireless side includes: a base station and an unmanned aerial vehicle; the wired side includes: the system comprises a core network, a Proxy Gateway (PGW), an Internet of things access platform and a server. Thereby unmanned aerial vehicle can realize the communication of unmanned aerial vehicle and server through basic station, core network, PGW access thing networking platform.

Illustratively, in the rehearsal process before formal performance, when the number of the unmanned aerial vehicles which are accessed is increased from 10 to 1000, the scheduling delay is deteriorated from less than 3ms to 17ms to 19ms, and the downlink perception rate is deteriorated from 10Mbps to less than 1 Mbps. Therefore, the network needs to be expanded to meet the problems of communication delay and sensing rate. The embodiment of the application solves the problems through expanding the base band board of the main coverage cell, and the base band boards corresponding to the main service cell of the unmanned aerial vehicle cluster in the color ranking process are all UBBPd5 with a plurality of cells.

Illustratively, the configuration parameters of the baseband boards of various models are described in detail below with reference to table two.

Watch two

As can be seen from table three, the maximum number of RRC connected users of the UBBPd5 baseband board is 3600 users, but the problem of insufficient capability of the baseband board is revealed in the color space. In order to guarantee the performance effect, the expansion of the baseband board of the main coverage cell can be carried out, and after the adjustment of the expansion of the baseband board for many times, the finally expanded baseband board can directly solve the problems of network delay and the number of users accessing to a large scale. The specific expansion manner is not limited in this embodiment.

And secondly, reducing the power of the peripheral interference cell of the main coverage cell, thereby solving the interference of the peripheral coverage cell to the main coverage cell.

Exemplarily, according to the test result of the takeoff area network data, there are 3 peripheral coverage cells interfering with the main coverage cell; according to the test result of the flight array airspace network data, 7 peripheral coverage cells interfere with a main coverage cell; according to the tracking result of the flight path of the unmanned aerial vehicle, 1 peripheral coverage cell interferes with the main coverage cell. In order to solve the interference of the peripheral coverage cell to the main coverage cell, the embodiment of the present application reduces the power of the peripheral interference cell of the main coverage cell, and completes the optimization processing on the main coverage cell. And (4) testing the optimized main coverage cell again, wherein the test result shows that the Reference Signal Received Quality (RSRQ) occupied by the unmanned aerial vehicle in the air is obviously improved, and the interference problem of the surrounding coverage cells is solved.

Exemplarily, the following describes, with reference to table three, the above optimized primary coverage cell is tested again to obtain a test result.

Watch III

It will be appreciated that RSRQ represents the reference signal received quality of the communication system, and such a metric primarily ranks different candidate cells according to signal quality. RSRQ achieves an efficient way to report the effect of combining signal strength and interference. The RSRQ value range is generally: -3 to-19.5, and the smaller the absolute value, the better. As shown in table three, the RSRQ mean of the optimized main coverage cells is smaller than the RSRQ mean before optimization.

As an optional embodiment, the key indicator further includes at least one of the following: an RRC connection establishment success rate of the primary coverage cell, an average utilization rate of an uplink Physical Resource Block (PRB) of the primary coverage cell, or an average utilization rate of a downlink PRB of the primary coverage cell.

Illustratively, table four lists examples of reference values for key indicators of the drone swarm performance of the present application.

Watch four

Serial number	Index item	Reference value	Color rank value	Performance value
					1	Scheduling delay ms	Less than 10	19.2	3.2
2	Per user downlink bearing sensing rate (Mbps)	Greater than 5	0.5	12.4
					3	Maximum number of users (number) in RRC connection state	Less than 3600	965	2471
4	RRC establishment success ratio (%)	Greater than 98	48.4	98.4
					5	Average utilization (%) -of upstream PRBs	Less than 60	26.9	48.2
6	Average utilization ratio (%) of downlink PRB	Less than 60	46.7	44.2

From the data for the color ranking values and performance values shown in table four, it can be seen that: the unmanned aerial vehicle cluster performance method can optimize key index values in the formal performance of the unmanned aerial vehicle cluster within the reference value range of the key index.

As an optional embodiment, the method further includes: removing at least one of the following non-critical indicators from the flight data of the unmanned aerial vehicle cluster: the total flight flow, the RRC congestion rate, the average number of users in a cell, the uplink data retransmission rate, the downlink data retransmission rate or the downlink average path loss of the unmanned aerial vehicle cluster.

It should be understood that the non-critical indicators may be indicators that are not associated with the flight performance effect, and in order to avoid the distraction of the network guarantee, the critical network indicators are focused, so that the influence of the non-critical indicators can be ignored.

The network guarantee method for the unmanned aerial vehicle cluster performance in the embodiment of the application cannot be used for developing four important works: firstly, actively communicating with customers, and defining the corresponding relation between flight requirements and network requirements; secondly, a batch downloading mode is innovated, so that the network burden is reduced; thirdly, the accurate application of the network guarantee means is completed by stepping on the basic work one by one; and fourthly, innovating the application of means such as three-dimensional test dotting in airspace, tracking logs of thousands of unmanned aerial vehicles and the like.

In one possible implementation, before the cluster of drones performs, network parameters need to be checked. The method mainly comprises the steps of checking an RRC connection user number license (license) and checking a PUSCH nominal P0 value.

Table five shows the relevant information of PUSCH nominal P0 values in the network parameters. From table five, the recommended value for PUSCH nominal P0 value is-67.

Watch five

Based on the unmanned aerial vehicle cluster performance method provided by the embodiment of the application, the application provides a test example. The test example is to count the related data during the formal performance of the drone, and the test result of the related data is described in detail below with reference to fig. 4 to 6.

Fig. 4 shows the peak number of users during a drone cluster official performance, including drone terminals and surrounding spectators. During formal performance, the number of peak users exceeds the number of users in weekday periods and the number of users in rehearsal periods, and through statistics on network data, each network parameter is all normal, so that the unmanned aerial vehicle cluster performance method provided by the embodiment of the application can completely meet the requirement of multi-user access.

Fig. 5 shows the scheduling delay during the formal performance of the drone swarm, as shown in fig. 5, with the abscissa representing the performance time and the ordinate representing the scheduling delay (ms). The scheduling delay can be kept below 5ms during the formal performance of the unmanned aerial vehicle cluster, and no network congestion occurs. The unmanned aerial vehicle cluster performance method provided by the embodiment of the application can ensure that the optimized mobile network meets the requirement of scheduling delay.

Fig. 6 shows the perception rate during the formal performance of the drone swarm, as shown in fig. 6, the abscissa represents the performance time, and the ordinate represents the downlink bearer perception rate (mbps). The downlink bearing perception rate is kept above 10Mbps during the formal performance of the unmanned aerial vehicle cluster, and network congestion does not occur. The unmanned aerial vehicle cluster performance method provided by the embodiment of the application can guarantee that the optimized mobile network meets the requirement of perception rate.

A method for performing a cluster of drones according to an embodiment of the present application is described in detail above with reference to fig. 2, and an apparatus for performing a cluster of drones according to an embodiment of the present application is described in detail below with reference to fig. 7 to 8.

Fig. 7 shows an apparatus 700 for a cluster performance of drones according to an embodiment of the present application, where the apparatus 700 includes: an acquisition module 710, a determination module 720, and a processing module 730.

The acquiring module 710 is configured to acquire flight data of an unmanned aerial vehicle cluster, where the flight data includes three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster, and flight flow data of the unmanned aerial vehicle cluster, where the three-dimensional space network data includes network data of a takeoff area of the unmanned aerial vehicle cluster and network data of a flight array area of the unmanned aerial vehicle cluster;

a determining module 720, configured to determine a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster; determining key indexes of unmanned aerial vehicle cluster performance according to flight data of the unmanned aerial vehicle cluster, wherein the key indexes comprise scheduling delay of the unmanned aerial vehicle cluster, downlink bearing perception rate of each unmanned aerial vehicle and the maximum number of users in RRC connection state of the main coverage cell;

the processing module 730 is configured to perform optimization processing on the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance; and the unmanned aerial vehicle cluster performance is performed based on the optimized configuration.

Optionally, the obtaining module 710 is specifically configured to: the method comprises the steps that a sampling terminal is bound on an unmanned aerial vehicle, three-dimensional dotting is conducted on a flight array area, and network data of the flight array area are obtained.

Optionally, the processing module 730 is specifically configured to: expanding the capacity of the baseband board of the main coverage cell; and/or reducing the power of the peripheral interference cells of the main coverage cell.

Optionally, the apparatus 700 further includes: a culling module, the culling module to: removing at least one of the following non-critical indicators from the flight data of the unmanned aerial vehicle cluster: the total flight flow, the RRC congestion rate, the average number of users in a cell, the uplink data retransmission rate, the downlink data retransmission rate or the downlink average path loss of the unmanned aerial vehicle cluster.

It should be appreciated that the apparatus 700 herein is embodied in the form of functional modules. The term module herein may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (e.g., a shared, dedicated, or group processor) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an optional example, it may be understood by those skilled in the art that the apparatus 700 may be specifically a background management device in the foregoing embodiment, or functions of the background management device in the foregoing embodiment may be integrated in the apparatus 700, and the apparatus 700 may be configured to execute each procedure and/or step corresponding to the background management device in the foregoing method embodiment, and in order to avoid repetition, details are not described here again.

The device 700 has a function of implementing corresponding steps executed by the background management device in the method; the above functions may be implemented by hardware, or may be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions described above. For example, the obtaining module 710 may be a communication interface, such as a transceiver interface.

In an embodiment of the present application, the apparatus 700 in fig. 7 may also be a chip or a chip system, for example: system on chip (SoC). Correspondingly, the obtaining module 710 may be a transceiver circuit of the chip, and is not limited herein.

Fig. 8 shows another apparatus 800 for performing a cluster of drones according to an embodiment of the present application. The apparatus 800 includes a processor 810, a memory 820, and a transceiver 830. Wherein, the processor 810, the memory 820 and the transceiver 830 communicate with each other through an internal connection path, the memory 820 is used for storing instructions, and the processor 810 is used for executing the instructions stored by the memory 820 to control the transceiver 830 to transmit and/or receive signals.

It should be understood that the apparatus 800 may be embodied as a data processing device in the foregoing embodiments, or the functions of the data processing device in the foregoing embodiments may be integrated in the apparatus 800, and the apparatus 800 may be configured to execute each step and/or flow corresponding to the data processing device in the foregoing method embodiments. Alternatively, the memory 820 may include a read-only memory and a random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information. The processor 810 may be configured to execute the instructions stored in the memory, and when the processor executes the instructions, the processor may perform the steps and/or processes corresponding to the data processing apparatus in the above method embodiments.

It should be understood that, in the embodiments of the present application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in a processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor executes instructions in the memory, in combination with hardware thereof, to perform the steps of the above-described method. To avoid repetition, it is not described in detail here.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, 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 system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, 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. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The 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.

In addition, functional units in the embodiments of the present application 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 functions, if implemented in the form of software functional units 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 application or portions thereof that substantially contribute to the prior art may 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 application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of drone swarm performance, comprising:

acquiring flight data of an unmanned aerial vehicle cluster through testing, wherein the flight data comprises three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster and flight flow data of the unmanned aerial vehicle cluster, and the three-dimensional space network data comprises network data of a takeoff area of the unmanned aerial vehicle cluster and network data of a flight array area of the unmanned aerial vehicle cluster;

determining a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster;

determining key indexes of the unmanned aerial vehicle cluster performance according to flight data of the unmanned aerial vehicle cluster, wherein the key indexes comprise scheduling delay of the unmanned aerial vehicle cluster, downlink bearing perception rate of each unmanned aerial vehicle and the maximum number of Radio Resource Control (RRC) connection state users of the main coverage cell;

optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance;

and performing unmanned aerial vehicle cluster performance based on the optimized configuration.

2. The method of claim 1, wherein the obtaining flight data for the cluster of drones through the test comprises:

and performing three-dimensional dotting on the flight array area in a mode of binding the sampling terminal on the unmanned aerial vehicle, and acquiring the network data of the flight array area.

3. The method according to claim 1 or 2, wherein the optimizing the primary coverage cell according to the reference value of the key index of the drone swarm performance comprises:

expanding the capacity of the baseband board of the main coverage cell; and/or the presence of a gas in the gas,

and reducing the power of the peripheral interference cells of the main coverage cell.

4. The method according to any one of claims 1 to 3, wherein the key indicators further comprise at least one of:

the RRC connection establishment success rate of the primary coverage cell, the average utilization rate of an uplink Physical Resource Block (PRB) of the primary coverage cell or the average utilization rate of a downlink PRB of the primary coverage cell.

5. The method according to any one of claims 1 to 4, further comprising:

removing at least one of the following non-critical indicators from the flight data of the cluster of drones:

the total flight flow, the RRC congestion rate, the average number of users in a cell, the uplink data retransmission rate, or the downlink data retransmission rate or the downlink average path loss of the unmanned aerial vehicle cluster.

6. An apparatus for cluster performance by unmanned aerial vehicles, comprising:

the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring flight data of an unmanned aerial vehicle cluster, and the flight data comprises three-dimensional space network data, flight path data of the unmanned aerial vehicle cluster and flight flow data of the unmanned aerial vehicle cluster, wherein the three-dimensional space network data comprises network data of a takeoff area of the unmanned aerial vehicle cluster and network data of a flight array area of the unmanned aerial vehicle cluster;

the determining module is used for determining a main coverage cell of the unmanned aerial vehicle cluster according to the flight path data of the unmanned aerial vehicle cluster; determining key indexes of the unmanned aerial vehicle cluster performance according to flight data of the unmanned aerial vehicle cluster, wherein the key indexes comprise scheduling delay of the unmanned aerial vehicle cluster, downlink bearing perception rate of each unmanned aerial vehicle and the maximum number of users in a Radio Resource Control (RRC) connection state of the main coverage cell;

the processing module is used for optimizing the main coverage cell according to the reference value of the key index of the unmanned aerial vehicle cluster performance; and performing unmanned aerial vehicle cluster performance based on the optimized configuration.

7. The apparatus of claim 6, wherein the obtaining module is specifically configured to:

8. The apparatus according to claim 6 or 7, wherein the processing module is specifically configured to:

9. An apparatus for cluster performance by unmanned aerial vehicles, comprising: a processor coupled with a memory for storing a computer program that, when invoked by the processor, causes the apparatus to perform the method of any of claims 1 to 5.

10. A computer-readable storage medium for storing a computer program comprising instructions for implementing the method of any one of claims 1 to 5.