CN106933245B - Unmanned aerial vehicle remote danger avoiding method and device, cloud platform and system - Google Patents
Unmanned aerial vehicle remote danger avoiding method and device, cloud platform and system Download PDFInfo
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Abstract
本发明提供了一种无人机远程避险方法，包括：获取无人机的位置信息以及高度信息；发送所述位置信息以及所述高度信息至云平台侧；接收到所述云平台反馈的危险参数，基于所述危险参数的提示执行避险处理。本发明还提供了另外一种无人机远程避险方法、两种无人机远程避险装置以及一种无人机远程避险系统。
The present invention provides a remote hazard avoidance method for an unmanned aerial vehicle, including: acquiring the location information and altitude information of the unmanned aerial vehicle; sending the location information and the altitude information to the cloud platform side; receiving the feedback from the cloud platform Dangerous parameters, and a risk avoidance process is performed based on the prompts of the dangerous parameters. The invention also provides another method for remote avoidance of UAV, two kinds of remote avoidance devices for UAV, and a remote avoidance system for UAV.
Description
Technical Field
The invention relates to an unmanned aerial vehicle control technology, in particular to an unmanned aerial vehicle remote danger avoiding method, an unmanned aerial vehicle remote danger avoiding device, a cloud platform and a system.
Background
At present, because unmanned aerial vehicle characteristics itself lead to unmanned aerial vehicle to have certain danger when the executive task, except self factor, external multifactor also can cause unmanned aerial vehicle's out of control or fall, consequently keeps away dangerous management very important to unmanned aerial vehicle's long-range.
Most of the existing unmanned aerial vehicle control methods relate to the remote control problem of small unmanned aerial vehicles, and how to avoid risks is not related, when the unmanned aerial vehicles are located in regions with concentrated crowd and building group density, serious consequences can be caused once the unmanned aerial vehicles are out of control, fall and the like, and ground casualties, building damages, property losses and the like are caused, so that the invention provides the unmanned aerial vehicle remote risk avoiding method and system based on wireless communication, and the blank problem of the existing unmanned aerial vehicle in the aspect of remote risk avoiding management is solved.
Disclosure of Invention
In view of this, embodiments of the present invention are expected to provide a method, an apparatus, a cloud platform, and a system for remote risk avoidance of an unmanned aerial vehicle, which can implement remote risk avoidance of the unmanned aerial vehicle.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the embodiment of the invention provides an unmanned aerial vehicle remote risk avoiding method, which comprises the following steps:
acquiring position information and height information of the unmanned aerial vehicle;
sending the position information and the height information to the cloud platform side;
and receiving the danger parameters fed back by the cloud platform, and executing danger avoiding treatment based on the prompts of the danger parameters.
The embodiment of the invention also provides an unmanned aerial vehicle remote risk avoiding method, which comprises the following steps:
receiving position information and height information sent by an unmanned aerial vehicle;
determining danger parameters corresponding to the unmanned aerial vehicle based on the received position information and the received height information;
and sending the danger parameters to the unmanned aerial vehicle.
The embodiment of the invention also provides an unmanned aerial vehicle remote danger avoiding device, which comprises: an information acquisition module, an information sending module, a danger parameter receiving module and a danger avoiding module, wherein,
the information acquisition module is used for acquiring the position information and the height information of the unmanned aerial vehicle;
the information sending module is used for sending the position information and the height information to the cloud platform side;
the danger parameter receiving module is used for receiving the danger parameters fed back by the cloud platform;
and the risk avoiding module is used for executing risk avoiding processing based on the prompt of the risk parameters.
An embodiment of the present invention further provides a cloud platform, where the cloud platform includes: an information receiving module, a dangerous parameter calculating module and a dangerous parameter sending module, wherein,
the information receiving module is used for receiving position information and height information sent by the unmanned aerial vehicle;
the danger parameter calculation module is used for determining danger parameters corresponding to the unmanned aerial vehicle based on the received position information and the received height information;
and the danger parameter sending module is used for sending the danger parameters to the unmanned aerial vehicle.
The embodiment of the invention also provides an unmanned aerial vehicle remote danger avoiding system, which comprises: an unmanned aerial vehicle, a cloud platform, wherein,
the unmanned aerial vehicle is used for acquiring position information and height information of the unmanned aerial vehicle; sending the position information and the height information to the cloud platform side; receiving danger parameters fed back by the cloud platform, and executing danger avoiding processing based on the prompts of the danger parameters;
the cloud platform is used for receiving position information and height information sent by the unmanned aerial vehicle; determining danger parameters corresponding to the unmanned aerial vehicle based on the received position information and the received height information; and sending the danger parameters to the unmanned aerial vehicle.
In the unmanned aerial vehicle remote danger avoiding method, the unmanned aerial vehicle remote danger avoiding device, the cloud platform and the unmanned aerial vehicle remote danger avoiding system, the unmanned aerial vehicle acquires current position information of the unmanned aerial vehicle and sends the position information to the cloud platform; the cloud platform determines a danger parameter of the current position of the unmanned aerial vehicle according to the received position information, and sends the danger parameter to the unmanned aerial vehicle; and the unmanned aerial vehicle carries out risk avoidance according to the received risk parameters. So, can make unmanned aerial vehicle can obtain the dangerous parameter of self position in real time, and then take effectual measure to keep away the danger, cause the loss of lives and property when avoiding unmanned aerial vehicle to fall.
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FIG. 1 is a flow chart of a unmanned remote risk avoidance method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a remote risk avoiding method for a unmanned aerial vehicle according to an embodiment of the invention;
fig. 3 is a schematic flow chart of a remote risk avoiding method for a third unmanned aerial vehicle according to an embodiment of the invention;
FIG. 4 is a schematic structural diagram of an unmanned remote risk avoiding device according to an embodiment of the present invention;
fig. 5 is a schematic structural view of a remote risk avoiding device of a second unmanned aerial vehicle according to an embodiment of the invention;
fig. 6 is a schematic structural view of a remote risk avoiding system of an unmanned aerial vehicle according to an embodiment of the present invention.
Detailed Description
In the embodiment of the invention, an unmanned aerial vehicle acquires the current position information of the unmanned aerial vehicle and sends the position information to a cloud platform; the cloud platform determines a danger parameter of the current position of the unmanned aerial vehicle according to the received position information, and sends the danger parameter to the unmanned aerial vehicle; and the unmanned aerial vehicle carries out risk avoidance according to the received risk parameters.
The following describes the implementation of the technical solution of the present invention in further detail with reference to the accompanying drawings and specific embodiments. Fig. 1 is a schematic flow chart of a unmanned aerial vehicle remote risk avoiding method according to an embodiment of the present invention, where the method is applied to an unmanned aerial vehicle side, and as shown in fig. 1, the unmanned aerial vehicle remote risk avoiding method according to the embodiment of the present invention includes the following steps:
step 101: acquiring position information and height information of the unmanned aerial vehicle;
step 102: sending the position information and the height information to the cloud platform side;
step 103: and receiving the danger parameters fed back by the cloud platform, and executing danger avoiding treatment based on the prompts of the danger parameters.
In the embodiment of the present invention, the location information includes: the current geographical position information and/or the information of the communication cell of the unmanned aerial vehicle; the unmanned aerial vehicle is provided with a plurality of SIM cards corresponding to different network operators, so that a plurality of communication cells in which the unmanned aerial vehicle is positioned are possible and respectively correspond to different operators;
in the embodiment of the invention, the unmanned aerial vehicle can acquire the geographical position information of the unmanned aerial vehicle from the GPS module of the unmanned aerial vehicle, acquire the altitude information of the unmanned aerial vehicle from the flight control module, and acquire the Cell ID of the communication Cell from the SIM card.
In the embodiment of the present invention, the risk parameters include, but are not limited to: population density, and/or building density, and/or overall hazard level, and/or alarm messages, etc. of the current location of the drone.
Specifically, population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position are transmitted to the ground remote control end of the unmanned aerial vehicle, so that the unmanned aerial vehicle control personnel can carry out danger avoiding precautionary measures according to the comprehensive danger level and corresponding alarm message.
Fig. 2 is a schematic flow chart of a remote risk avoiding method for an unmanned aerial vehicle according to an embodiment of the present invention, where the method is applied to a cloud platform side, and as shown in fig. 2, the remote risk avoiding method for an unmanned aerial vehicle according to the embodiment includes the following steps:
step 201: receiving position information and height information sent by an unmanned aerial vehicle;
step 202: determining danger parameters corresponding to the unmanned aerial vehicle based on the received position information and the received height information;
step 203: and sending the danger parameters to the unmanned aerial vehicle.
In the embodiment of the present invention, the location information includes: the current geographical position information and/or the information of the communication cell of the unmanned aerial vehicle; the risk parameters include, but are not limited to: population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position of the unmanned aerial vehicle;
correspondingly, the determining the risk parameter of the current position of the unmanned aerial vehicle according to the received position information includes: determining population density of the current position of the unmanned aerial vehicle according to the cell ID (cell ID) of the communication cell where the unmanned aerial vehicle is located; determining the building density of the current geographical position of the unmanned aerial vehicle according to the current geographical position information of the unmanned aerial vehicle; determining the comprehensive danger level of the current position of the unmanned aerial vehicle according to the population density and the building density of the current position of the unmanned aerial vehicle; determining alarm information according to population density, building density and comprehensive danger level of the current position of the unmanned aerial vehicle;
specifically, the determining, according to a cell id (cell id) of a communication cell in which the unmanned aerial vehicle is located, population density of a current location of the unmanned aerial vehicle includes: determining the network access quantity of SIM cards in a base station coverage area of a communication Cell according to the Cell ID of the communication Cell where the unmanned aerial vehicle is located; determining population density of the current position of the unmanned aerial vehicle according to the number of SIM cards in the coverage area of the communication cell base station;
in the embodiment of the invention, when a plurality of SIM cards exist in the unmanned aerial vehicle, the received Cell ID of the communication Cell where the unmanned aerial vehicle is located is the Cell ID of a plurality of communication cells of different operators, so that the population density information of the location can be accurately calculated by combining the network access quantity information of a plurality of operators.
The determining, according to the information of the current geographical location of the unmanned aerial vehicle, the building density of the current geographical location of the unmanned aerial vehicle includes: and determining the building density of the current geographical position of the unmanned aerial vehicle according to the information of the current geographical position of the unmanned aerial vehicle and the satellite electronic map of the geographical position.
In the embodiment of the invention, other parameter information such as height information and the like sent by the unmanned aerial vehicle can be received for assisting in more accurate calculation. For example, when building density calculation is performed, calculation can be performed by combining position information and height information of the current unmanned aerial vehicle, so as to obtain a more accurate calculation result. Specifically, building density information of the position height of the unmanned aerial vehicle is calculated and calculated corresponding to the three-dimensional panoramic satellite electronic map through the position information and the flight height information of the unmanned aerial vehicle. The method comprises the steps of obtaining space coordinate three-dimensional information of the unmanned aerial vehicle through the real-time geographic position (such as longitude and latitude) and the flight height of the unmanned aerial vehicle, marking the coordinate where the unmanned aerial vehicle is located on a three-dimensional map, and calculating building density information within a certain range of the coordinate on the plane by combining the existing information of the map on the plane with the corresponding height of the coordinate.
The comprehensive danger level of the current position of the unmanned aerial vehicle can be determined according to population density and building density of the current position of the unmanned aerial vehicle according to the following method:
firstly, parameters are set:
d: a risk grade, wherein D belongs to [0, T ], wherein T is a self-defined limit risk value;
p: population density, P ∈ [0, T ]
B: building Density, B ∈ [0, T ]
X: extensible unknown factor parameter, X ∈ [0, T ]
Alpha is population density weighting coefficient, alpha belongs to [0,1]
Beta: building Density weighting coefficient, beta ∈ [0,1]
γ: extensible weighting factor for unknown factors, gamma is in the range of 0,1
D＝α*P+β*B+γ*X
Wherein, the alpha + beta + gamma is 1, and the alpha, beta and gamma coefficients are checked in time according to the proportion of each factor;
from the above process, the risk level is formed by fusing population density factors, building density factors and other extensible unknown factors, wherein the extensible unknown factors can be other risk factors that can influence the flight of the unmanned aerial vehicle, for example, the wind speed of the position where the unmanned aerial vehicle is located, and the extensible unknown factors can be multiple.
And (3) specifying the population density P, the building density B and other extensible unknown factor parameters X in a numerical range from 0 to T, so that the danger level D is in a certain planning range, and the reference of related flight remote control personnel is facilitated.
The determining alarm information according to population density, building density and comprehensive danger level of the current position of the unmanned aerial vehicle comprises: and triggering corresponding alarm messages when the population density, the building density and the comprehensive danger level are greater than the corresponding preset threshold values. For example, when the population density is greater than a preset threshold value, generating a population density alarm message; and generating a building density alarm message when the building density is greater than a preset threshold value, and generating a comprehensive danger level alarm message when the comprehensive danger level is greater than the preset threshold value.
Fig. 3 is a schematic flow chart of a remote risk avoiding method for a third unmanned aerial vehicle according to an embodiment of the present invention, where the method is applied to a cloud platform side, and as shown in fig. 3, the remote risk avoiding method for the unmanned aerial vehicle according to the embodiment includes the following steps:
step 301: the unmanned aerial vehicle acquires the current position information of the unmanned aerial vehicle and sends the position information to the cloud platform;
in the embodiment of the present invention, the location information includes: the current geographical position information and/or the information of the communication cell of the unmanned aerial vehicle; the unmanned aerial vehicle is provided with a plurality of SIM cards corresponding to different network operators, so that a plurality of communication cells in which the unmanned aerial vehicle is positioned are possible and respectively correspond to different operators;
in the embodiment of the invention, the unmanned aerial vehicle can acquire the geographical position information of the unmanned aerial vehicle from the GPS module of the unmanned aerial vehicle, acquire the altitude information of the unmanned aerial vehicle from the flight control module, and acquire the Cell ID of the communication Cell from the SIM card.
Step 302: the cloud platform determines a danger parameter of the current position of the unmanned aerial vehicle according to the received position information, and sends the danger parameter to the unmanned aerial vehicle;
in the embodiment of the present invention, the risk parameters include, but are not limited to: population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position of the unmanned aerial vehicle;
correspondingly, the cloud platform determining the danger parameters of the current position of the unmanned aerial vehicle according to the received current position information includes: the cloud platform determines population density of the current position of the unmanned aerial vehicle according to the cell ID (cell ID) of the communication cell where the unmanned aerial vehicle is located; determining the building density of the current geographical position of the unmanned aerial vehicle according to the current geographical position information of the unmanned aerial vehicle; determining the comprehensive danger level of the current position of the unmanned aerial vehicle according to the population density and the building density of the current position of the unmanned aerial vehicle; and determining the type of the alarm information according to the comprehensive danger level of the current position of the unmanned aerial vehicle.
Specifically, the determining, according to a cell id (cell id) of a communication cell in which the unmanned aerial vehicle is located, population density of a current location of the unmanned aerial vehicle includes: the cloud platform determines the network access quantity of SIM cards in a base station coverage area of a communication Cell according to the Cell ID of the communication Cell where the unmanned aerial vehicle is located; determining population density of the current position of the unmanned aerial vehicle according to the number of SIM cards in the coverage area of the communication cell base station;
in the embodiment of the invention, when a plurality of SIM cards exist in the unmanned aerial vehicle, the received Cell ID of the communication Cell where the unmanned aerial vehicle is located is the Cell ID of a plurality of communication cells of different operators, so that the population density information of the location can be accurately calculated by combining the network access quantity information of a plurality of operators.
The determining, according to the information of the current geographical location of the unmanned aerial vehicle, the building density of the current geographical location of the unmanned aerial vehicle includes: and the cloud platform determines the building density of the current geographical position of the unmanned aerial vehicle according to the current geographical position information of the unmanned aerial vehicle and the satellite electronic map of the geographical position.
In the embodiment of the invention, other parameter information such as height information and the like sent by the unmanned aerial vehicle can be received for assisting in more accurate calculation. For example, when building density calculation is performed, calculation can be performed by combining position information and height information of the current unmanned aerial vehicle, so as to obtain a more accurate calculation result. Specifically, building density information of the position height of the unmanned aerial vehicle is calculated and calculated corresponding to the three-dimensional panoramic satellite electronic map through the position information and the flight height information of the unmanned aerial vehicle. The method comprises the steps of obtaining space coordinate three-dimensional information of the unmanned aerial vehicle through the real-time geographic position (such as longitude and latitude) and the flight height of the unmanned aerial vehicle, marking the coordinate where the unmanned aerial vehicle is located on a three-dimensional map, and calculating building density information within a certain range of the coordinate on the plane by combining the existing information of the map on the plane with the corresponding height of the coordinate.
The comprehensive danger level of the current position of the unmanned aerial vehicle can be determined according to population density and building density of the current position of the unmanned aerial vehicle according to the following method:
firstly, parameters are set:
d: a risk grade, wherein D belongs to [0, T ], wherein T is a self-defined limit risk value;
p: population density, P ∈ [0, T ]
B: building Density, B ∈ [0, T ]
X: extensible unknown factor parameter, X ∈ [0, T ]
Alpha is population density weighting coefficient, alpha belongs to [0,1]
Beta: building Density weighting coefficient, beta ∈ [0,1]
γ: extensible weighting factor for unknown factors, gamma is in the range of 0,1
D＝α*P+β*B+γ*X
Wherein, the alpha + beta + gamma is 1, and the alpha, beta and gamma coefficients are checked in time according to the proportion of each factor;
from the above process, the risk level is formed by fusing population density factors, building density factors and other extensible unknown factors, wherein the extensible unknown factors can be other risk factors that can influence the flight of the unmanned aerial vehicle, for example, the wind speed of the position where the unmanned aerial vehicle is located, and the extensible unknown factors can be multiple.
And (3) specifying the population density P, the building density B and other extensible unknown factor parameters X in a numerical range from 0 to T, so that the danger level D is in a certain planning range, and the reference of related flight remote control personnel is facilitated.
The determining alarm information according to population density, building density and comprehensive danger level of the current position of the unmanned aerial vehicle comprises: and triggering corresponding alarm messages when the population density, the building density and the comprehensive danger level are greater than the corresponding preset threshold values. For example, when the population density is greater than a preset threshold value, generating a population density alarm message; and generating a building density alarm message when the building density is greater than a preset threshold value, and generating a comprehensive danger level alarm message when the comprehensive danger level is greater than the preset threshold value.
Step 303: and the unmanned aerial vehicle carries out risk avoidance according to the received risk parameters.
Specifically, population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position are transmitted to the ground remote control end of the unmanned aerial vehicle, so that the unmanned aerial vehicle control personnel can carry out danger avoiding precautionary measures according to the comprehensive danger level and corresponding alarm message.
An embodiment of the present invention further provides an unmanned aerial vehicle remote risk avoiding device, where the device is located at an unmanned aerial vehicle end, fig. 4 is a schematic structural diagram of an unmanned aerial vehicle remote risk avoiding device according to an embodiment of the present invention, and as shown in fig. 4, the device includes: an information acquisition module 41, an information sending module 42, a danger parameter receiving module 43, and a danger avoiding module 44, wherein,
the information acquisition module 41 is configured to acquire position information and altitude information of the unmanned aerial vehicle;
in this embodiment of the present invention, in the embodiment of the present invention, the location information includes: the current geographical position information and/or the information of the communication cell of the unmanned aerial vehicle; the unmanned aerial vehicle is provided with a plurality of SIM cards corresponding to different network operators, so that a plurality of communication cells in which the unmanned aerial vehicle is positioned are possible and respectively correspond to different operators;
in this embodiment of the present invention, the information obtaining module 41 may obtain its own geographic location information from its own GPS module 45, its own altitude information from the flight control module 46, and the Cell ID of the communication Cell obtained from the SIM card.
The information sending module 42 is configured to send the position information and the altitude information to the cloud platform side;
the danger parameter receiving module 43 is configured to receive a danger parameter fed back by the cloud platform;
in the embodiment of the present invention, the risk parameters include, but are not limited to: population density, and/or building density, and/or overall hazard level, and/or alarm messages, etc. of the current location of the drone.
And the risk avoiding module 44 is configured to execute risk avoiding processing based on the prompt of the risk parameter.
Specifically, the risk avoiding module 44 transmits population density, and/or building density, and/or comprehensive risk level, and/or alarm message of the current position to the ground remote control end of the unmanned aerial vehicle, so that the unmanned aerial vehicle control personnel can perform risk avoiding precautionary measures according to the comprehensive risk level and corresponding alarm message.
To sum up, the information input by the remote danger avoiding device of the unmanned aerial vehicle according to the embodiment of the present invention includes: the current geographical position information of the unmanned aerial vehicle is acquired from a GPS module 45 of the unmanned aerial vehicle, the flight altitude information of the unmanned aerial vehicle is acquired from a flight control module 46, and the communication cell information is acquired from an SIM card; information such as population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position of the unmanned aerial vehicle received from the cloud platform;
the information of unmanned aerial vehicle long-range danger avoiding device output includes: the system comprises geographic position information, height information and located communication cell information of the current unmanned aerial vehicle, which are sent to a cloud platform, and population density, and/or building density, and/or comprehensive danger level and/or alarm message of the current location, which are sent to a ground remote control end of the unmanned aerial vehicle.
The embodiment of the present invention further provides an unmanned aerial vehicle remote risk avoiding device, the device is located on a cloud platform, fig. 5 is a schematic structural diagram of a second unmanned aerial vehicle remote risk avoiding device according to the embodiment of the present invention, as shown in fig. 5, the device includes: an information receiving module 51, a danger parameter calculating module 52, a danger parameter transmitting module 53, wherein,
the information receiving module 51 is configured to receive position information and altitude information sent by the unmanned aerial vehicle;
the danger parameter calculation module 52 is configured to determine a danger parameter corresponding to the unmanned aerial vehicle based on the received position information and altitude information;
in the embodiment of the present invention, the location information includes: the current geographical position information and/or the information of the communication cell of the unmanned aerial vehicle; the risk parameters include, but are not limited to: population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position of the unmanned aerial vehicle;
in the embodiment of the present invention, the risk parameter calculation module includes a population density calculation submodule 521, a building density calculation submodule 522, a risk level calculation submodule 523, and an alarm submodule 524,
the population density calculation sub-module 521 is configured to determine the population density of the current position of the unmanned aerial vehicle according to the cell id (cell id) of the communication cell in which the unmanned aerial vehicle is located; the building density calculation submodule 522 is configured to determine, according to the information of the current geographical position of the unmanned aerial vehicle, the building density of the current geographical position of the unmanned aerial vehicle; the risk level calculation submodule 523 is configured to determine a comprehensive risk level of the current position of the unmanned aerial vehicle according to population density and building density of the current position of the unmanned aerial vehicle; the warning submodule 524 is configured to determine warning information according to population density, building density, and comprehensive risk level of the current location of the unmanned aerial vehicle.
Specifically, the population density calculation submodule 521 is specifically configured to: determining the network access quantity of SIM cards in a base station coverage area of a communication Cell according to the Cell ID of the communication Cell where the unmanned aerial vehicle is located; determining population density of the current position of the unmanned aerial vehicle according to the number of SIM cards in the coverage area of the communication cell base station;
in the embodiment of the invention, when a plurality of SIM cards exist in the unmanned aerial vehicle, the received Cell ID of the communication Cell where the unmanned aerial vehicle is located is the Cell ID of a plurality of communication cells of different operators, so that the population density information of the location can be accurately calculated by combining the network access quantity information of a plurality of operators.
The building density calculation submodule 522 is specifically configured to: and determining the building density of the current geographical position of the unmanned aerial vehicle according to the information of the current geographical position of the unmanned aerial vehicle and the satellite electronic map of the geographical position.
In the embodiment of the invention, other parameter information such as height information and the like sent by the unmanned aerial vehicle can be received for assisting in more accurate calculation. For example, when building density calculation is performed, calculation can be performed by combining position information and height information of the current unmanned aerial vehicle, so as to obtain a more accurate calculation result. Specifically, building density information of the position height of the unmanned aerial vehicle is calculated and calculated corresponding to the three-dimensional panoramic satellite electronic map through the position information and the flight height information of the unmanned aerial vehicle. The method comprises the steps of obtaining space coordinate three-dimensional information of the unmanned aerial vehicle through the real-time geographic position (such as longitude and latitude) and the flight height of the unmanned aerial vehicle, marking the coordinate where the unmanned aerial vehicle is located on a three-dimensional map, and calculating building density information within a certain range of the coordinate on the plane by combining the existing information of the map on the plane with the corresponding height of the coordinate.
The risk level calculation submodule 523 determines, according to population density and building density of the current position of the unmanned aerial vehicle, a comprehensive risk level of the current position of the unmanned aerial vehicle according to the following method:
firstly, parameters are set:
d: a risk grade, wherein D belongs to [0, T ], wherein T is a self-defined limit risk value;
p: population density, P ∈ [0, T ]
B: building Density, B ∈ [0, T ]
X: extensible unknown factor parameter, X ∈ [0, T ]
Alpha is population density weighting coefficient, alpha belongs to [0,1]
Beta: building Density weighting coefficient, beta ∈ [0,1]
γ: extensible weighting factor for unknown factors, gamma is in the range of 0,1
D＝α*P+β*B+γ*X
Wherein, the alpha + beta + gamma is 1, and the alpha, beta and gamma coefficients are checked in time according to the proportion of each factor;
from the above process, the risk level is formed by fusing population density factors, building density factors and other extensible unknown factors, wherein the extensible unknown factors can be other risk factors that can influence the flight of the unmanned aerial vehicle, for example, the wind speed of the position where the unmanned aerial vehicle is located, and the extensible unknown factors can be multiple.
And (3) specifying the population density P, the building density B and other extensible unknown factor parameters X in a numerical range from 0 to T, so that the danger level D is in a certain planning range, and the reference of related flight remote control personnel is facilitated.
The alarm sub-module 524 is specifically configured to trigger a corresponding alarm message when the population density, the building density, and the comprehensive risk level are greater than corresponding preset thresholds.
If the population density is larger than a preset threshold value, generating a population density alarm message; and generating a building density alarm message when the building density is greater than a preset threshold value, and generating a comprehensive danger level alarm message when the comprehensive danger level is greater than the preset threshold value.
In the embodiment of the present invention, the apparatus further includes another factor expansion submodule 525, configured to calculate an influence of another factor on risk avoidance of the unmanned aerial vehicle.
And the danger parameter sending module 53 is configured to send the danger parameter to the unmanned aerial vehicle.
To sum up, the information input by the information receiving module 51 according to the embodiment of the present invention includes: receiving current geographic position information, altitude information and communication cell information sent by the unmanned aerial vehicle; the output information includes: the current geographical position information, the altitude information and the communication cell information sent to the risk parameter calculation module 52;
the information input by the risk parameter sending module 53 includes: the population density, the building density, the comprehensive danger level and the alarm message of the current position sent by the danger parameter calculation module 52; the output information includes: the population density, the building density, the comprehensive danger level and the alarm message of the current position of the unmanned aerial vehicle are sent;
the information input by the population density calculation sub-module 521 includes: cell information (Cell ID) of a communication Cell in which the unmanned aerial vehicle is located, which is received from the information receiving module 51; the output information includes: the information of the number of SIM card-inserted networks sent to the risk level calculation submodule 523 according to the cell base station coverage area, the information of the number of SIM card-inserted networks combined with multiple network operators, and the calculated population density;
the information input by the building density calculation sub-module 522 includes: the information receiving module 51 receives the current altitude information and the current geographical position information of the unmanned aerial vehicle; the output information includes: the building density of the height of the unmanned aerial vehicle at the position, which is sent to the risk level calculation submodule 523 and can be calculated corresponding to the three-dimensional panoramic satellite electronic map according to the geographical position information and the height information of the unmanned aerial vehicle;
the information input by the risk level calculation submodule 523 includes: population density of the current position output by the population density calculation sub-module 521 and building density of the current position output by the building density calculation sub-module 522; the output information includes: the calculated composite risk level sent to the alert submodule 524, and the received population density, building density.
The information input by the alarm sub-module 524 includes: the received comprehensive risk level, population density and building density sent by the risk level calculation submodule 523; the output information includes: population density warning messages, building density warning messages, and integrated hazard level warnings sent to the hazard parameter sending module 53.
An embodiment of the present invention further provides an unmanned aerial vehicle remote risk avoiding system, fig. 6 is a schematic structural diagram of the unmanned aerial vehicle remote risk avoiding system according to the embodiment of the present invention, and as shown in fig. 6, the system includes: a drone 61, a cloud platform 62, wherein,
the unmanned aerial vehicle 61 is used for acquiring the current position information of the unmanned aerial vehicle and sending the position information to the cloud platform;
in the embodiment of the present invention, the location information includes: the current geographical position information and/or the information of the communication cell of the unmanned aerial vehicle; a plurality of SIM cards exist in the drone 61 and correspond to different network operators, so that a plurality of communication cells in which the drone 61 is located may also be provided, and correspond to different operators respectively;
in the embodiment of the present invention, the unmanned aerial vehicle 61 may obtain its own geographical location information from its own GPS module, its own altitude information from the flight control module, and a Cell ID of a communication Cell in which it is located from the SIM card.
In the embodiment of the present invention, the unmanned aerial vehicle 61 is further configured to: carrying out risk avoidance according to the received risk parameters;
specifically, population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position are transmitted to the ground remote control end of the unmanned aerial vehicle, so that the unmanned aerial vehicle control personnel can carry out danger avoiding precautionary measures according to the comprehensive danger level and corresponding alarm message.
And the cloud platform 62 is configured to determine a danger parameter of the current position of the unmanned aerial vehicle according to the received position information, and send the danger parameter to the unmanned aerial vehicle.
In the embodiment of the present invention, the risk parameters include, but are not limited to: population density, and/or building density, and/or comprehensive danger level, and/or alarm message of the current position of the unmanned aerial vehicle;
accordingly, the cloud platform 62 is specifically configured to: determining population density of the current position of the unmanned aerial vehicle according to the cell ID (cell ID) of the communication cell where the unmanned aerial vehicle is located; determining the building density of the current geographical position of the unmanned aerial vehicle according to the current geographical position information of the unmanned aerial vehicle; determining the comprehensive danger level of the current position of the unmanned aerial vehicle according to the population density and the building density of the current position of the unmanned aerial vehicle; and determining the type of the alarm information according to the comprehensive danger level of the current position of the unmanned aerial vehicle.
Specifically, the cloud platform 62 is specifically configured to: determining the network access quantity of SIM cards in a base station coverage area of a communication Cell according to the Cell ID of the communication Cell where the unmanned aerial vehicle is located; determining population density of the current position of the unmanned aerial vehicle according to the number of SIM cards in the coverage area of the communication cell base station;
in the embodiment of the invention, when a plurality of SIM cards exist in the unmanned aerial vehicle, the received Cell ID of the communication Cell where the unmanned aerial vehicle is located is the Cell ID of a plurality of communication cells of different operators, so that the population density information of the location can be accurately calculated by combining the network access quantity information of a plurality of operators.
The cloud platform 62 is specifically configured to: and determining the building density of the current geographical position of the unmanned aerial vehicle according to the information of the current geographical position of the unmanned aerial vehicle and the satellite electronic map of the geographical position.
In the embodiment of the invention, other parameter information such as height information and the like sent by the unmanned aerial vehicle can be received for assisting in more accurate calculation. For example, when building density calculation is performed, calculation can be performed by combining position information and height information of the current unmanned aerial vehicle, so as to obtain a more accurate calculation result. Specifically, building density information of the position height of the unmanned aerial vehicle is calculated and calculated corresponding to the three-dimensional panoramic satellite electronic map through the position information and the flight height information of the unmanned aerial vehicle. The method comprises the steps of obtaining space coordinate three-dimensional information of the unmanned aerial vehicle through the real-time geographic position (such as longitude and latitude) and the flight height of the unmanned aerial vehicle, marking the coordinate where the unmanned aerial vehicle is located on a three-dimensional map, and calculating building density information within a certain range of the coordinate on the plane by combining the existing information of the map on the plane with the corresponding height of the coordinate.
The cloud platform 62 may determine the comprehensive risk level of the current position of the unmanned aerial vehicle according to the population density and the building density of the current position of the unmanned aerial vehicle, according to the following method:
firstly, parameters are set:
d: a risk grade, wherein D belongs to [0, T ], wherein T is a self-defined limit risk value;
p: population density, P ∈ [0, T ]
B: building Density, B ∈ [0, T ]
X: extensible unknown factor parameter, X ∈ [0, T ]
Alpha is population density weighting coefficient, alpha belongs to [0,1]
Beta: building Density weighting coefficient, beta ∈ [0,1]
γ: extensible weighting factor for unknown factors, gamma is in the range of 0,1
D＝α*P+β*B+γ*X
Wherein, the alpha + beta + gamma is 1, and the alpha, beta and gamma coefficients are checked in time according to the proportion of each factor;
from the above process, the risk level is formed by fusing population density factors, building density factors and other extensible unknown factors, wherein the extensible unknown factors can be other risk factors that can influence the flight of the unmanned aerial vehicle, for example, the wind speed of the position where the unmanned aerial vehicle is located, and the extensible unknown factors can be multiple.
And (3) specifying the population density P, the building density B and other extensible unknown factor parameters X in a numerical range from 0 to T, so that the danger level D is in a certain planning range, and the reference of related flight remote control personnel is facilitated.
In the embodiment of the present invention, the cloud platform 62 is specifically configured to: and triggering corresponding alarm messages when the population density, the building density and the comprehensive danger level are greater than the corresponding preset threshold values. For example, when the population density is greater than a preset threshold value, generating a population density alarm message; and generating a building density alarm message when the building density is greater than a preset threshold value, and generating a comprehensive danger level alarm message when the comprehensive danger level is greater than the preset threshold value.
The specific working process of the unmanned aerial vehicle remote danger avoiding system provided by the embodiment of the invention is as follows:
a: the information acquisition module 41 of the unmanned aerial vehicle acquires real-time geographical position information of the unmanned aerial vehicle from the GPS module 45, and acquires real-time altitude information of the unmanned aerial vehicle from the flight control module 46.
B: the information acquisition module 41 sends real-time geographic position information, altitude information, Cell ID information of a communication Cell where the unmanned aerial vehicle is located and the like to the information receiving module 51 of the cloud platform;
c: the information receiving module 51 sends the Cell ID information of the Cell where the unmanned aerial vehicle is located to the population density calculating sub-module 521; sending the real-time altitude and geographical location information of the drone to the building density calculation sub-module 522; other factor information influencing the flight of the unmanned aerial vehicle is transmitted to the other factor expansion submodule 525; the population density calculation submodule 521 calculates population density; the building density calculation submodule 522 calculates the building density; the other factor calculation expansion sub-module 525 performs the calculation of other expandable unknown factor parameters.
D: the population density calculation submodule 521 transmits the population density and building density calculation submodule 522 to transmit the building density and other factor calculation expansion submodule 525 to the risk level calculation submodule 523 to calculate the risk level.
E: the risk level calculation submodule 523 transmits the calculated real-time flight comprehensive risk level, population density, building density, and the like to the alarm submodule 524, and triggers a corresponding alarm message when the population density, the building density, and the comprehensive risk level are greater than a certain threshold value.
F: the risk level calculation submodule 523 transmits the calculated real-time flight comprehensive risk level, population density and building density to the risk parameter sending module 53; the alarm sub-module 524 transmits the population density alarm message, the building density alarm message, and the integrated risk level alarm message to the risk parameter sending module 53.
G: the risk parameter transmitting module 53 transmits population density, building density, integrated risk level, various warning messages, etc. to the risk parameter receiving module 43.
H: the danger parameter receiving module 43 transmits population density, building density, comprehensive danger level, various warning messages and the like to the danger avoiding module 44.
I: the risk avoiding module 44 transmits population density, building density, comprehensive danger level, various alarm messages and the like to the ground remote control end of the unmanned aerial vehicle; make unmanned aerial vehicle control personnel can keep away dangerous precautionary measure according to this comprehensive dangerous grade, corresponding warning message.
The implementation functions of the processing modules in the unmanned aerial vehicle remote danger avoiding device shown in fig. 4 and 5 can be understood by referring to the related description of the unmanned aerial vehicle remote danger avoiding method. Those skilled in the art should understand that the functions of each processing module in the unmanned aerial vehicle remote risk avoiding device shown in fig. 4 and 5 can be implemented by a program running on a processor, and can also be implemented by specific logic circuits, such as: may be implemented by a Central Processing Unit (CPU), Microprocessor (MPU), Digital Signal Processor (DSP), or Field Programmable Gate Array (FPGA).
In the embodiments provided in the present invention, it should be understood that the disclosed method and apparatus can be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the modules is only one logical functional division, and other division manners may be implemented in practice, such as: multiple modules or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the communication connections between the components shown or discussed may be through interfaces, indirect couplings or communication connections of devices or modules, and may be electrical, mechanical or other.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, that is, may be located in one place, or may be distributed on a plurality of network modules; some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional modules in the embodiments of the present invention may be integrated into one processing module, or each module may be separately used as one module, or two or more modules may be integrated into one module; the integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated module according to the embodiment of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The method and the device for remotely avoiding risks of the unmanned aerial vehicle described in the embodiment of the present invention are only examples of the above embodiments, but are not limited thereto, and those skilled in the art should understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (7)
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