CN110351752B - Unmanned ship, network optimization method and device thereof, and storage medium - Google Patents
Unmanned ship, network optimization method and device thereof, and storage medium Download PDFInfo
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- CN110351752B CN110351752B CN201910567516.7A CN201910567516A CN110351752B CN 110351752 B CN110351752 B CN 110351752B CN 201910567516 A CN201910567516 A CN 201910567516A CN 110351752 B CN110351752 B CN 110351752B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/12—Discovery or management of network topologies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract
The application is applicable to the technical field of communication, and provides an unmanned ship, a network optimization method and a network optimization device thereof, and a storage medium, wherein the network optimization method of the unmanned ship comprises the following steps: acquiring the running state of the unmanned ship and the current scene data of the unmanned ship; according to the running state of the unmanned ship and the current scene data of the unmanned ship, the topological structure of the scene where the unmanned ship is located in a preset time period after the current time is estimated; estimating the communication network quality between the base stations of different networks and the unmanned ship according to the estimated topological structure; and determining the network used by the unmanned ship in the preset time period according to the estimated network quality. The communication quality can be prevented from being reduced due to untimely network switching caused by sudden network quality change or communication link interruption caused by untimely network switching, real-time data transmission is facilitated, and therefore the instantaneity and the stability of unmanned ship data transmission can be improved.
Description
Technical Field
The application belongs to the field of communication, and particularly relates to an unmanned ship, a network optimization method and device thereof, and a storage medium.
Background
The unmanned ship is a small-sized water surface platform with autonomy, and has wide development prospect in the military field and the civil field. The unmanned ship working on the water surface needs to realize real-time communication with the shore-based terminal station and return real-time information of the state of the unmanned ship, so that the shore-based terminal can realize command issuing, data monitoring, evidence collection and the like.
Due to the diversity and complexity of the working water surface of the unmanned ship, the transmission of electromagnetic waves is often limited, the communication quality of a communication link between the unmanned ship and a shore-based station may be reduced, if switching is performed according to the calculated network quality, even communication link interruption may occur, the time delay of returned data is caused, and the real-time performance and the stability of the acquired data are poor.
Disclosure of Invention
In view of this, embodiments of the present application provide an unmanned surface vehicle, a network optimization method and apparatus thereof, and a storage medium, so as to solve the problems in the prior art that an unmanned surface vehicle may cause communication link interruption due to interference of external environmental factors, which may cause time delay of returned data, and poor instantaneity and stability of a data acquisition process.
A first aspect of an embodiment of the present application provides a network optimization method for an unmanned surface vehicle, where the network optimization method for the unmanned surface vehicle includes:
acquiring the running state of the unmanned ship and the current scene data of the unmanned ship;
according to the running state of the unmanned ship and the current scene data of the unmanned ship, the topological structure of the scene where the unmanned ship is located in a preset time period after the current time is estimated;
estimating the communication network quality between the base stations of different networks and the unmanned ship according to the estimated topological structure;
and determining the network used by the unmanned ship in the preset time period according to the estimated network quality.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the step of predicting, according to the driving state of the unmanned ship and current scene data of the unmanned ship, a topology of a scene where the unmanned ship is located within a predetermined time period after a current time includes:
estimating the dynamic position of the unmanned ship according to the running speed, the running direction and the running route of the unmanned ship;
estimating the dynamic position of the moving obstacle according to the moving speed and the moving direction of the moving obstacle in the scene;
and according to the dynamic position of the unmanned ship, the dynamic position of the moving obstacle, the position of the base station and the position of the fixed obstacle, predicting the topological structure of the scene where the unmanned ship is located in a preset time period after the current time.
With reference to the first aspect, in a second possible implementation manner of the first aspect, the predicting, according to the predicted topology, network quality of communication between base stations of different networks and the unmanned ship includes:
determining the dynamic influence range of the movement barrier influencing the base station signal according to the position of the base station and the estimated position of the movement barrier;
determining a static influence range of a fixed obstacle influencing a base station signal according to the position of a base station and the position of the fixed obstacle;
and determining the network quality of the communication between the unmanned ship and the base station according to the real-time position of the unmanned ship and by combining the dynamic influence range and the static influence range.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the predicting, according to the predicted topology structure, network quality of communication between base stations of different networks and the unmanned ship includes:
determining the position of the unmanned ship, the elevation angle and the orientation of the unmanned ship antenna, and the position and the height of the base station;
and determining the network quality of the communication between the unmanned ship and the base station according to the elevation angle and the orientation of the antenna at the unmanned ship position and the matching degree of the position and the height of the base station.
With reference to the first aspect, in a fourth possible implementation manner of the first aspect, the predicting, according to the predicted topology structure, network quality of communication between base stations of different networks and the unmanned ship includes:
determining the dynamic position of the unmanned ship within a preset time period after the current time according to the running speed and the running direction of the unmanned ship;
and determining the interfered network in the unmanned ship driving track according to the corresponding relation between the preset interference frequency spectrum type and the interference area.
With reference to the first aspect, in a fifth possible implementation manner of the first aspect, the step of predicting network quality of communication between base stations of different networks and the unmanned ship according to the predicted topology further includes:
when no obstacle exists between the unmanned ship and the base station, acquiring the distance between the unmanned ship and the base station;
and estimating the communication network quality of the base station and the unmanned ship according to the distance.
With reference to the first aspect, in a sixth possible implementation manner of the first aspect, the network includes one or more of a wireless image transmission network, a beidou communication network, a mobile communication network, and a public network.
A second aspect of an embodiment of the present application provides a network optimization device for an unmanned surface vehicle, including:
the data acquisition unit is used for acquiring the running state of the unmanned ship and the current scene data of the unmanned ship;
the topological structure estimation unit is used for estimating the topological structure of the scene where the unmanned ship is located in a preset time period after the current time according to the running state of the unmanned ship and the current scene data of the unmanned ship;
the network quality estimation unit is used for estimating the network quality of communication between base stations of different networks and the unmanned ship according to the estimated topological structure;
and the network selection unit is used for determining the network used by the unmanned ship in the preset time period according to the estimated network quality.
A third aspect of embodiments of the present application provides an unmanned surface vehicle, including a processor, a memory, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the method for network optimization of an unmanned surface vehicle according to any one of the first aspect.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for network optimization of an unmanned boat according to any one of the first aspects.
Compared with the prior art, the embodiment of the application has the advantages that: according to the driving state of the unmanned ship and the current scene data of the unmanned ship, the topological structure of the scene where the unmanned ship is located in a preset time period after the current time is estimated, the network quality of different networks of the unmanned ship is estimated according to the estimated topological structure, and the network to be used is selected according to the estimated network quality, so that the situation that the communication quality is reduced due to untimely network switching caused by sudden network quality change or communication link interruption caused by untimely network switching can be avoided, real-time data transmission is facilitated, and the real-time performance and the stability of unmanned ship data transmission can be improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an implementation of a network optimization method for an unmanned surface vehicle according to an embodiment of the present application;
fig. 2 is a schematic flow chart of an implementation of determining a topology of a scene where an unmanned surface vehicle is located according to an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating an implementation of a method for estimating network quality of an unmanned ship according to an embodiment of the present application;
fig. 4 is a schematic flow chart illustrating an implementation of a method for determining network quality according to an interference spectrum according to an embodiment of the present application;
fig. 5 is a schematic diagram of a network optimization device of an unmanned ship according to an embodiment of the present application;
fig. 6 is a schematic view of an unmanned boat provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation process of a network optimization method for an unmanned surface vehicle according to an embodiment of the present application, which is detailed as follows:
in step S101, a driving state of the unmanned surface vehicle and current scene data of the unmanned surface vehicle are acquired;
specifically, the driving state of the unmanned ship may include a current driving speed, a current driving direction, and the like of the unmanned ship. Alternatively, the driving state of the unmanned vehicle may further include an elevation angle and an orientation of an antenna of the unmanned vehicle, and the bearing of the received communication signal may be determined according to the orientation and the elevation angle of the unmanned vehicle, thereby facilitating estimation of the network quality of the wireless communication network.
The current scene data of the unmanned ship can comprise the position of the unmanned ship, the number of moving objects such as other ships around the unmanned ship, the moving speed and the moving direction of other moving objects, the position of a fixed obstacle, the position of a wireless communication base station at the position of the unmanned ship and the like, or can also comprise the size of the moving objects or the fixed obstacle, so that the communication range of the unmanned ship and the base station is influenced according to the size of the obstacle. Of course, the current scene data of the unmanned ship may also include information such as the course of the unmanned ship, and the water flow speed of the course. Alternatively, the scene data of the unmanned ship can also comprise the height of the base station and the like, so as to more accurately determine the communication quality between the unmanned ship and the base station.
In step S102, according to the driving state of the unmanned surface vehicle and the current scene data of the unmanned surface vehicle, estimating a topological structure of a scene where the unmanned surface vehicle is located within a predetermined time period after the current time;
the process of determining the topology of the scene in which the unmanned ship is located may be as shown in fig. 2, and includes:
in step S201, estimating a dynamic position of the unmanned ship according to a driving speed, a driving direction, and a driving route of the unmanned ship;
through the driving state of the unmanned ship, such as the driving speed and the driving direction of the unmanned ship, the corresponding relation between the position of the unmanned ship and the time within a preset time period after the current time, such as 30 minutes after the current time, can be effectively estimated. Of course, the planned routes of the unmanned ship can be combined to obtain more accurate data of the corresponding relation between the position and the time, so that the position of the unmanned ship corresponding to any time in the preset time period can be estimated, and the dynamic position of the unmanned ship can be estimated.
In step S202, a dynamic position of the moving obstacle is estimated according to a moving speed and a moving direction of the moving obstacle in the scene;
when the scene of the unmanned ship includes a moving object, namely a moving obstacle, the driving track of the moving obstacle can be estimated according to the speed and the driving direction of the moving obstacle, and the dynamic position of the moving obstacle is determined, so that the dynamic position of the moving obstacle can be estimated.
In step S203, the topological structure of the scene where the unmanned surface vehicle is located in a predetermined time period after the current time is estimated according to the dynamic position of the unmanned surface vehicle, the dynamic position of the moving obstacle, the position of the base station, and the position of the fixed obstacle.
And generating a topological structure of a dynamic scene according to the determined dynamic position of the unmanned ship and the dynamic position of the moving obstacle and by combining the fixed base station position and the fixed obstacle position. Namely, at any moment, the estimated positions of the unmanned ship and the movement obstacles can be inquired in the dynamic topological structure. Thereby facilitating the analytical calculation of network quality based on the queried location.
The network can comprise one or more of a wireless image transmission network, a Beidou communication network, a mobile communication network and a public network.
In step S103, estimating the network quality of the communication between the base stations of different networks and the unmanned ship according to the estimated topology;
the step of predicting the network quality of the communication between the base stations of different networks and the unmanned ship according to the predicted topological structure may specifically include any one of the following manners, or a combination of the following manners, which is detailed as follows:
the first method is as follows:
as shown in fig. 3, the process of estimating the network quality of the communication between the base stations of different networks and the unmanned ship includes:
in step S301, determining a dynamic influence range of a movement obstacle affecting a base station signal according to a base station position and an estimated movement obstacle position;
since the position of a moving obstacle such as a ship is changing, the communication quality influence area due to the change in the position of the moving obstacle also changes. After the position of the on-shore base station is determined, according to the position of the moving obstacle, the far base station side area of the obstacle can be determined as an affected area. The size of the affected area is related to the size of the blocking surface of the moving obstacle. Generally, the shielding surface reflects the size of the movement barrier, and the larger the movement barrier is, the larger the generated affected area is.
In addition, the same size of moving obstacle has different influences on different base stations, and the influence on a low-power mapping communication network is generally larger than that on a high-power mapping communication network.
In step S302, determining a static influence range of the fixed obstacle on the base station signal according to a base station position and a fixed obstacle position;
during the running process of the unmanned ship, a fixed obstacle including an island and the like may exist on the water surface, and for the influence range of the fixed obstacle, after the position of the base station is determined, the affected area generated by the fixed obstacle can be determined, namely, the side area far away from the fixed obstacle is the affected area. Also, the size of the affected area is related to the size of the blocking surface of the fixed obstacle, the material of the fixed obstacle, the type of the base station, and the like.
In step S303, the network quality of the communication between the unmanned surface vehicle and the base station is determined according to the real-time position of the unmanned surface vehicle and by combining the dynamic influence range and the static influence range.
After the dynamic influence range and the static influence range are determined, the time and/or the position of the communication quality which is possibly influenced by the obstacle in the driving process of the unmanned ship can be estimated by combining the dynamic position of the unmanned ship, and when the unmanned ship drives to the estimated influenced position, the network corresponding to other unaffected base stations is switched to, or when the unmanned ship drives to the estimated influenced time, the network corresponding to other unaffected base stations is switched to.
The second method comprises the following steps:
as shown in fig. 4, the method for estimating the network quality of the unmanned ship communication according to the preset interference spectrum specifically includes:
in step S401, determining a dynamic position of the unmanned ship within a predetermined time period after the current time according to the driving speed and the driving direction of the unmanned ship;
according to the current running speed and running direction of the unmanned ship, the dynamic position in a future preset time period can be estimated. Or, the dynamic position of the unmanned ship can be determined by combining the air route of the unmanned ship and the water condition information of the air route, such as water flow speed, tide information and the like, so that the dynamic position of the unmanned ship can be estimated more accurately, and network switching can be performed more accurately in the follow-up process.
In step S402, according to a preset correspondence between the interference spectrum type and the interference region, an interfered network in the unmanned ship driving trajectory is determined.
The corresponding relationship between the interference spectrum type and the interference region may be counted in advance, that is, the corresponding relationship between the spectrum type and the interference region may be counted in advance, for example, an interference spectrum of the spectrum a exists in a certain location region, but the spectrum B and the spectrum C can normally communicate, and then the communication network corresponding to the spectrum a may be interfered by the spectrum in the location region, so as to affect the communication quality of the communication network corresponding to the spectrum a.
On the premise that other influence conditions are the same, if it is determined that the unmanned ship is interfered by the frequency spectrum a in a certain area, the unmanned ship can be switched to other communication networks which are not subjected to the frequency spectrum type before entering the area.
The third method comprises the following steps:
because the horizontal positions of the base stations are different, the installation elevation angles and the orientation directions of the receiving antennas of the unmanned boats are also different, and in order to effectively determine the influence of the receiving antennas of the unmanned boats and the base stations, the positions of the unmanned boats, the elevation angles and the orientation directions of the unmanned boat antennas and the positions and the heights of the base stations can be obtained to determine the matching degree of the antennas of the unmanned boats and the base stations.
According to the position, the antenna orientation and the elevation angle of the unmanned ship, the matching degree of the signal receiving angle of the unmanned ship can be calculated by combining the position and the height of the base station, and the communication quality corresponding to the antenna state of the unmanned ship in the current scene can be determined according to the influence of different pre-counted matching degrees on the communication quality.
On the premise that other influence conditions are the same, if the influence of the matching degree of the base station A and the antenna of the unmanned ship on the communication quality is large, other base stations with higher matching degree with the antenna of the unmanned ship can be selected for communication.
The method is as follows:
when no obstacle exists between the unmanned ship and the base station, whether the distance can affect the effective communication between the base station and the unmanned ship or not can be determined according to the distance between the base station and the unmanned ship in the topological structure. Generally, the size of the influence of the distance on the communication quality can be determined according to different base station types, and a communication network corresponding to a base station with better communication quality is selected for communication according to the size of the influence of the communication quality.
Of course, in the above four modes, two or more modes may be combined for consideration, and the base station with better communication quality corresponding to the dynamic position of the unmanned ship is estimated comprehensively according to the estimated influence of each mode, so that data communication can be performed more reliably and in real time.
In step S104, the network used by the unmanned ship in the predetermined period is determined according to the estimated network quality.
According to one or more of the introduced modes, the estimated network corresponding to the dynamic position of the unmanned ship can effectively switch base stations before the unmanned ship enters an area where the communication quality is possibly affected, and other networks with better quality are selected for communication, so that the interruption of a communication link can be effectively avoided, and the stability of the real-time performance of data return is favorably improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 5 is a schematic structural diagram of a network optimization device of an unmanned surface vehicle according to an embodiment of the present application, which is detailed as follows:
the network optimization device of unmanned ship includes:
the data acquisition unit 501 is configured to acquire a driving state of the unmanned ship and current scene data of the unmanned ship;
a topological structure estimation unit 502, configured to estimate a topological structure of a scene where the unmanned ship is located within a predetermined time period after current time according to a driving state of the unmanned ship and current scene data of the unmanned ship;
a network quality estimation unit 503, configured to estimate network quality of communication between base stations of different networks and the unmanned ship according to the estimated topology structure;
a network selecting unit 504, configured to determine, according to the estimated network quality, a network used by the unmanned ship in the predetermined period.
The data obtaining unit 501 sends the obtained driving state of the unmanned ship and current scene data of the unmanned ship to the topological structure estimating unit 502, the topological structure estimating unit 502 estimates the topological structure of the scene where the unmanned ship is located, and sends the estimated topological structure to the network quality estimating unit 503, the network estimating unit 503 estimates the network quality of communication between base stations of different networks and the unmanned ship according to the topological structure, and sends the estimated network quality to the network selecting unit 504, so that the network selecting unit 504 determines the network used by the unmanned ship in the preset time period according to the estimated network quality.
The network optimization device for the unmanned surface vehicle shown in fig. 5 corresponds to the network optimization method for the unmanned surface vehicle shown in fig. 1.
Fig. 6 is a schematic view of an unmanned boat provided by an embodiment of the present application. As shown in fig. 6, the unmanned boat 6 of this embodiment includes: a processor 60, a memory 61 and a computer program 62, such as a network optimization program for an unmanned boat, stored in said memory 61 and operable on said processor 60. The processor 60, when executing the computer program 62, implements the steps in the network optimization method embodiments of each of the drones described above. Alternatively, the processor 60 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 62.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 62 in the drones 6. For example, the computer program 62 may be divided into:
the data acquisition unit 501 is configured to acquire a driving state of the unmanned ship and current scene data of the unmanned ship;
a topological structure estimation unit 502, configured to estimate a topological structure of a scene where the unmanned ship is located within a predetermined time period after current time according to a driving state of the unmanned ship and current scene data of the unmanned ship;
a network quality estimation unit 503, configured to estimate network quality of communication between base stations of different networks and the unmanned ship according to the estimated topology structure;
a network selecting unit 504, configured to determine, according to the estimated network quality, a network used by the unmanned ship in the predetermined period.
The unmanned surface vehicle 6 may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is merely an example of an unmanned boat 6, and does not constitute a limitation of unmanned boat 6, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the unmanned boat may also include input-output devices, network access devices, buses, etc.
The Processor 60 may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or any conventional processor or the like.
The memory 61 may be an internal storage unit of the unmanned boat 6, such as a hard disk or a memory of the unmanned boat 6. The Memory 61 may also be an external storage device of the unmanned surface vehicle 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Memory (SD) Card, a Flash Memory Card (Flash Card), and the like, which are equipped on the unmanned surface vehicle 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the unmanned boat 6. The memory 61 is used for storing the computer program 62 and other programs and data required by the drones. The memory 61 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
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.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, 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 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 modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A network optimization method of an unmanned ship is characterized by comprising the following steps:
acquiring the running state of the unmanned ship and the current scene data of the unmanned ship;
the running state of the unmanned ship comprises the current running speed and running direction of the unmanned ship, and the elevation angle and orientation of an antenna of the unmanned ship;
the current scene data of the unmanned ship comprises the position of the unmanned ship, the number of other moving objects around the unmanned ship, the moving speed and the moving direction of the other moving objects and the position of a fixed obstacle;
according to the running state of the unmanned ship and the current scene data of the unmanned ship, the topological structure of the scene where the unmanned ship is located in a preset time period after the current time is estimated;
estimating the communication network quality between the base stations of different networks and the unmanned ship according to the estimated topological structure;
and determining the network used by the unmanned ship in the preset time period according to the estimated network quality.
2. The network optimization method of the unmanned surface vehicle according to claim 1, wherein the step of predicting the topology of the scene where the unmanned surface vehicle is located within a predetermined time period after the current time according to the driving state of the unmanned surface vehicle and the current scene data of the unmanned surface vehicle comprises:
estimating the dynamic position of the unmanned ship according to the running speed, the running direction and the running route of the unmanned ship;
estimating the dynamic position of the moving obstacle according to the moving speed and the moving direction of the moving obstacle in the scene;
and according to the dynamic position of the unmanned ship, the dynamic position of the moving obstacle, the position of the base station and the position of the fixed obstacle, predicting the topological structure of the scene where the unmanned ship is located in a preset time period after the current time.
3. The method of claim 1, wherein the step of predicting the network quality of the communication between the base stations of the different networks and the unmanned vehicle based on the predicted topology comprises:
determining the dynamic influence range of the movement barrier influencing the base station signal according to the position of the base station and the estimated position of the movement barrier;
determining a static influence range of a fixed obstacle influencing a base station signal according to the position of a base station and the position of the fixed obstacle;
and determining the network quality of the communication between the unmanned ship and the base station according to the real-time position of the unmanned ship and by combining the dynamic influence range and the static influence range.
4. The method of claim 1, wherein the step of predicting the network quality of the communication between the base stations of the different networks and the unmanned vehicle based on the predicted topology comprises:
determining the position of the unmanned ship, the elevation angle and the orientation of the unmanned ship antenna, and the position and the height of the base station;
and determining the network quality of the communication between the unmanned ship and the base station according to the elevation angle and the orientation of the antenna at the unmanned ship position and the matching degree of the position and the height of the base station.
5. The method of claim 1, wherein the step of predicting the network quality of the communication between the base stations of the different networks and the unmanned vehicle based on the predicted topology comprises:
determining the dynamic position of the unmanned ship within a preset time period after the current time according to the running speed and the running direction of the unmanned ship;
and determining the interfered network in the unmanned ship driving track according to the corresponding relation between the preset interference frequency spectrum type and the interference area.
6. The method of network optimization for an unmanned vehicle of claim 1, wherein the step of predicting network quality of communications between base stations of different networks and the unmanned vehicle based on the predicted topology further comprises:
when no obstacle exists between the unmanned ship and the base station, acquiring the distance between the unmanned ship and the base station;
and estimating the communication network quality of the base station and the unmanned ship according to the distance.
7. The network optimization method for the unmanned ship according to claim 1, wherein the network comprises one or more of a wireless image transmission network, a Beidou communication network, a mobile communication network and a public network.
8. An unmanned under vehicle network optimization device, comprising:
the data acquisition unit (501) is used for acquiring the running state of the unmanned ship and the current scene data of the unmanned ship;
the running state of the unmanned ship comprises the current running speed and running direction of the unmanned ship, and the elevation angle and orientation of an antenna of the unmanned ship;
the current scene data of the unmanned ship comprises the position of the unmanned ship, the number of other moving objects around the unmanned ship, the moving speed and the moving direction of the other moving objects and the position of a fixed obstacle;
the topological structure estimation unit (502) is used for estimating the topological structure of the scene where the unmanned ship is located in a preset time period after the current time according to the running state of the unmanned ship and the current scene data of the unmanned ship;
a network quality estimation unit (503) for estimating the network quality of the communication between the base stations of different networks and the unmanned ship according to the estimated topological structure;
a network selection unit (504) for determining a network used by the unmanned vehicle for the predetermined period of time based on the estimated network quality.
9. An unmanned surface vehicle comprising a processor (60), a memory (61) and a computer program (62) stored in the memory (61) and operable on the processor (60), characterized in that the processor (60) when executing the computer program (62) implements the steps of the method of network optimization of an unmanned surface vehicle according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for network optimization of an unmanned boat according to any one of claims 1 to 7.
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| CN112564954B (en) * | 2020-11-30 | 2023-03-21 | 锐捷网络股份有限公司 | Network quality prediction method and device |
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