CN113359834A - Unmanned aerial vehicle operation monitoring method, system and monitoring platform - Google Patents
Unmanned aerial vehicle operation monitoring method, system and monitoring platform Download PDFInfo
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Abstract
The utility model discloses an unmanned aerial vehicle operation monitoring method, system and monitoring platform, wherein, this method includes: acquiring the operation parameters, flight scenes and flight control information of the unmanned aerial vehicle, calling a pre-established risk prediction model, and calculating the risk occurrence probability under the current scene; calling an accident evaluation model pre-established based on an accident scene, and calculating the accident severity of the unmanned aerial vehicle in the current flight scene and the running state; and calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity, and outputting the risk evaluation value. By adopting the monitoring method disclosed by the disclosure, a quantitative risk value causing ground personnel injury in unit time of unmanned aerial vehicle operation is calculated through a risk prediction model, and autonomous safe flight of the unmanned aerial vehicle is realized.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle operation monitoring method, a system and a monitoring platform.
Background
At present, the low-altitude airspace is an important natural resource, which contains huge economic value, and the reasonable development, utilization and management of the low-altitude airspace is an important way for the world countries to enter the aeronautical forcing countries. In China, with the continuous implementation of economic transformation and upgrading and high-quality development concepts, the potential and the prospect of the low-altitude high-quality high-altitude low-altitude traffic transportation system are gradually highlighted. The unmanned aerial vehicle is used as an important main body for running in a low-altitude airspace, realizes safe, orderly and efficient running, and makes the influence on ground facilities, public safety, manned aircrafts in the air and other unmanned aircrafts on the minimum important.
The safety of the unmanned aerial vehicle flying in the low-altitude airspace is crucial, the effective evaluation of the flight risk of the unmanned aerial vehicle is significant for realizing future high-density and free flying. Unmanned aerial vehicle itself, operator, and various safety influence factors such as environmental change and the interact between them for unmanned aerial vehicle's low-altitude flight has great potential safety hazard and has increased the degree of difficulty of scientific management and control low-altitude flight. Therefore, how to scientifically and efficiently manage and control the flight of the unmanned aerial vehicle and evaluate and monitor the flight risk of the low-altitude unmanned aerial vehicle still remains a major problem in the current development of low-altitude airspace navigation in China.
Disclosure of Invention
In view of the above, the invention provides an operation monitoring method, system and monitoring platform for an unmanned aerial vehicle, which can effectively analyze and evaluate risks in the operation process of the unmanned aerial vehicle, and at least partially solve the problems in the prior art.
To this end, the present disclosure discloses a method for monitoring the operation of an unmanned aerial vehicle, the method comprising: acquiring the operation parameters, flight scenes and flight control information of the unmanned aerial vehicle, calling a pre-established risk prediction model, and calculating the risk occurrence probability under the current scene; calling an accident evaluation model pre-established based on an accident scene, and calculating the accident severity of the unmanned aerial vehicle in the current flight scene and the running state; and calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity, and outputting the risk evaluation value.
Correspondingly, in order to realize above-mentioned method, this disclosure still discloses an unmanned aerial vehicle operation monitoring system, and this system includes: the performance monitoring equipment is used for monitoring the operation parameters of the unmanned aerial vehicle; the intelligent sensing equipment is used for detecting flight scene information of the unmanned aerial vehicle and identifying risk factors in the flight scene information; the risk analysis device is used for calling a risk analysis model, and calculating the risk occurrence probability of the unmanned aerial vehicle in the current operation scene by combining the flight control information, the operation parameters and the flight scene information; the accident evaluation device is used for calling an accident evaluation model which is pre-established based on an accident scene according to the flight control information, the operation parameters and the flight scene information and calculating the severity of the accident; and the comprehensive prediction device is used for calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity and outputting the risk evaluation value.
In addition, this disclosure still reveals an unmanned aerial vehicle operation monitoring platform, and this monitoring platform includes aforementioned unmanned aerial vehicle operation monitoring system for real time monitoring, demonstration unmanned aerial vehicle operation parameter information, airspace information, environmental information, geographic information and route course information, and control unmanned aerial vehicle's flight application, discernment pursuit target and early warning.
Compared with the prior art, the unmanned aerial vehicle operation monitoring method and the unmanned aerial vehicle operation monitoring system have the following technical effects:
according to the monitoring method disclosed by the disclosure, main factors influencing the flight safety of the unmanned aerial vehicle are extracted through factors influencing the flight safety of the unmanned aerial vehicle in a low-altitude airspace, a risk prediction model used for carrying out quantitative calculation on flight risks is established, a quantitative risk value causing ground personnel injury in the unit time of operation of the unmanned aerial vehicle is obtained through calculation of the risk prediction model, a specific safety target can be set, the obtained risk value can be further evaluated, scientific and efficient management and control of the unmanned aerial vehicle in low-altitude flight can be realized, and autonomous safe flight of the unmanned aerial vehicle is realized.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for monitoring the operation of an unmanned aerial vehicle according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a method for monitoring the operation of an unmanned aerial vehicle according to an embodiment of the present disclosure;
fig. 3 is a schematic view of the risk of collision between an unmanned aerial vehicle and an unmanned aerial vehicle in the embodiment of the present disclosure;
fig. 4 is a schematic view of a ground impact area of a vertically falling drone in an embodiment of the present disclosure;
fig. 5 is a schematic view of a ground collision increment area of a gliding falling drone in an embodiment of the present disclosure;
fig. 6 is a schematic composition diagram of an operation monitoring system of an unmanned aerial vehicle in an embodiment of the present disclosure.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, all other embodiments that can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort fall within the scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
The unmanned aerial vehicle flight risk assessment work is a precondition for guaranteeing the normal operation of the unmanned aerial vehicle, and the flight risk of the unmanned aerial vehicle is fully estimated, so that the operation risk of the unmanned aerial vehicle is guaranteed to meet an acceptable risk level. At present, the main risks faced by the operation of the unmanned aerial vehicle are the risk of collision of the unmanned aerial vehicle and the risk of self failure of the unmanned aerial vehicle. This is two problems that the unmanned aerial vehicle trade needs to solve most now, and only solved these two problems, the unmanned aerial vehicle trade just can obtain development.
Risk is a combination of the likelihood of a risk factor causing an accident and the extent of injury as a consequence of the accident, in terms of risk definition. Unmanned aerial vehicle operation flight analysis will synthesize the risk value that considers all risk factors correspondences, fuses different risk values into comprehensive risk value, and this disclosure discloses a risk analysis model and injury degree evaluation model for realize that risk factor causes the possibility analysis of accident, the analysis and the aassessment of injury degree.
In the embodiments of the present disclosure, risk prediction refers to the overall process including risk identification, risk analysis, and risk assessment. The risk factor identification is used for identifying the risk factors in a specific risk management range and is a premise and a basis for risk prevention and control; risk analysis, namely analyzing and determining the probability of the accident possibly caused by the hazard factors and the severity of the consequence of the accident possibly occurring; the risk assessment is a process of determining whether the risk and the magnitude thereof can be accepted or allowed by comparing the risk analysis result with a risk criterion, and accordingly, whether a risk prevention and control measure, a prevention and control priority and the like need to be taken is judged.
It should be noted that the risk analysis scheme disclosed in the embodiments of the present disclosure may implement analysis of both the probability of an accident possibly caused by a risk and the severity of the consequences of the accident, and also include analysis of the coping control scheme for the risk and the uncertainty and sensitivity of the coping control scheme.
In addition, this disclosed embodiment combines unmanned aerial vehicle type and operation scene, discerns the risk factor, because unmanned aerial vehicle's physical behavior is completely different with the operation scene, consequently, the harm factor that different unmanned aerial vehicles correspond at different application scenes also because of the difference. For example, in the case of a large unmanned aerial vehicle running in a converged manner, the unmanned aerial vehicle may have an air collision with a civil aircraft, which would largely cause fatal injury to people on board the aircraft, and thus the air collision is the most important type of risk accident in this case. However, under the condition of low-altitude isolated operation of the small unmanned aerial vehicle, as only a single unmanned aerial vehicle operates in the designated airspace, the air collision is no longer a main risk accident under the operation condition; under this condition, because unmanned aerial vehicle is close with ground distance, unmanned aerial vehicle loses the striking of controlling the back to the people and will be main risk accident. Therefore, the risk identification work can be carried out according to specific scenes, and different hazard factor sets can be obtained under different operation scenes (referred to as risk assessment pre-classification) of different types of unmanned aerial vehicles.
It should be noted that the risk factors mainly include existing and potential hazard factors, wherein the hazard factors mainly relate to source-type hazard factors and derivative-type hazard factors, so the identification of the hazard factors should fully consider the identification of the following two types of hazard factors:
1) source type hazard factor identification
The source hazard factors include: unmanned aerial vehicle information, such as unmanned aerial vehicle flight altitude (gravitational potential energy), unmanned aerial vehicle flight speed (kinetic energy), unmanned aerial vehicle power system sources (electric energy, chemical energy, etc.); and whether a source of danger exists in the unmanned aerial vehicle flight airspace, such as: whether a high-voltage line exists in a target space, whether the high-voltage line exists around an airport, the density of a gas station nuclear power station, the coverage of a highway network and the like.
2) Identification of derived hazard factors:
the derived hazard factors mainly refer to defects on source hazard factor prevention barriers, and mainly relate to unsafe behaviors of people, unsafe states of objects, supervision defects and the like. Derived hazard factors are: the corresponding airspace situation of airborne vehicle operation, unmanned aerial vehicle positioning accuracy are poor, unmanned aerial vehicle continuation of the journey stability, ground danger area whether expose, unmanned and whether possess emergency facilities etc..
Method embodiment
In this embodiment, the risk assessment mainly includes that a qualitative or quantitative analysis conclusion of a corresponding flight risk level is given through risk analysis according to a preset acceptable risk level, that is, a safe operation Threshold (TSLs), and an acceptable risk value corresponding to the safe operation threshold may be compared with an operation risk value of the unmanned aerial vehicle, so as to finally determine whether a mitigating action is required to be taken for the risk.
First, the embodiment of the quantitative analysis method:
the operation monitoring scheme of the drone of the present disclosure is described below, starting with the disclosure of a quantitative risk prediction scheme, with reference to fig. 1 to 5:
as an optional implementation manner, an embodiment of the present disclosure discloses an operation monitoring method for an unmanned aerial vehicle, including the following steps:
s100: acquiring operation parameters, a flight scene and flight control information of the unmanned aerial vehicle;
s110: calling a pre-established risk prediction model, and calculating the risk occurrence probability under the current scene;
s120: calling an accident evaluation model which is pre-established based on an accident scene, and calculating the accident severity of the unmanned aerial vehicle in the current flight scene and the running state;
s130: and calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity, and outputting the risk evaluation value.
This embodiment extracts the main factor that influences unmanned aerial vehicle flight safety to influence unmanned aerial vehicle flight safety factor in the low-altitude airspace to establish corresponding evaluation index and carry out quantitative calculation to the flight risk, establish the risk prediction model promptly, reachs the grade of unmanned aerial vehicle flight risk through the risk prediction model, realize the evaluation to the airspace and the individual risk grade of unmanned aerial vehicle that unmanned aerial vehicle is located. By adopting the risk prediction model disclosed by the embodiment, the quantitative risk value of ground personnel injury caused in the unit time of the unmanned aerial vehicle operation can be calculated, and the obtained risk value can be further evaluated by setting a specific safety target. The method not only can provide partial theoretical and method support for scientific and efficient management and control of low-altitude flight and establishment of self-adaptive flight rules of the unmanned aerial vehicle, and realizes autonomous, safe, smooth and efficient flight of the unmanned aerial vehicle, but also has very important significance for low-altitude airspace safe operation management work according to the evaluation result, and lays a certain foundation for solving the problems of complex low-altitude operation safety, efficiency and the like in the future.
The flight scene information comprises environment, airspace and/or geographic information, existing risk factors in the flight scene are identified, and potential risk factors are identified; the accident scene comprises a system fault scene and a collision scene with a barrier, wherein the system fault scene comprises hardware faults and software fault scenes of an unmanned aerial vehicle body and a control console.
In this embodiment, unmanned aerial vehicle's operating parameter includes unmanned aerial vehicle type, key component like the performance parameter of power, navigation positioning device, and this embodiment accessible is gathered corresponding basic data that can reflect operation risk state and loss and is obtained.
For example: the drone operation risk assessment may contain several types of data related to drone operation:
unmanned aerial vehicle performance data, note: x is the number of1,x2,…
Unmanned aerial vehicle flight application data, note: y is1,y2,…
Airspace and geographic information data are recorded as: z is a radical of1,z2,…
In this embodiment, the risk severity corresponding to a certain risk state may be determined by flight control information such as flight plan information, maximum operation information of the unmanned aerial vehicle, and airspace geographic information. For example, the maximum operational performance of the unmanned aerial vehicle can depict the maximum lethality of the unmanned aerial vehicle, the flight plan information can give which areas the unmanned aerial vehicle may fly to, the geographical information can accurately give the exposure degree of the crowd in the risk, and factors such as the existence of sensitive information exist.
The method mainly comprises the steps of identifying hazard factors by adopting an empirical method and a system safety analysis method, wherein the empirical method needs to refer to a large amount of past historical accident data, the unmanned aerial vehicle operation risk assessment mainly adopts the system safety analysis method, and meanwhile, a large amount of unmanned aerial vehicle operation data are collected. The method comprises the following steps:
(1) airspace and terrain features: airspace structure, airspace volume, ground building and obstacle conditions, ground population density and the like;
(2) unmanned aerial vehicle equipment status: the physical size of the unmanned aerial vehicle, the mass of the unmanned aerial vehicle, the system failure rate of the unmanned aerial vehicle, the flight time of the unmanned aerial vehicle, the landing coverage area of the unmanned aerial vehicle, the ground impact kinetic energy of the unmanned aerial vehicle and the like;
(3) the working procedure is as follows: unmanned aerial vehicle driver management regulations, unmanned aerial vehicle flight plan management and approval, and the like.
It should be noted that the risk assessment of the present embodiment mainly includes risk occurrence probability analysis and accident severity analysis, wherein:
1) the risk occurrence probability refers to the possibility of occurrence of a risk state and is mainly determined by flight plan data and the flight performance of the unmanned aerial vehicle; for example, the complexity in the airspace at some future time may be predicted from flight planning data, and the likelihood of a collision in the air may be calculated using flight performance data.
2) The accident severity analysis refers to the possibility and degree of loss caused by the risk state, and is mainly determined by flight plan and geographic information data. For example, the probability of falling and injuring the unmanned aerial vehicle after losing control and the number of casualties are calculated according to the operation performance of the unmanned aerial vehicle and the ground surface crowd density.
As an optional implementation scheme, the method for monitoring the operation of the unmanned aerial vehicle in the embodiment may further include:
s140: judging whether the risk evaluation value is lower than a preset safe operation threshold value of the unmanned aerial vehicle;
s150: if the risk evaluation value is lower than the safe operation threshold value, returning to the step S100 and continuing monitoring;
s160: if the risk evaluation value reaches or is higher than the safe operation threshold value, starting risk early warning;
as an optional implementation scheme, the method for monitoring the operation of the unmanned aerial vehicle in the embodiment may further include:
s170: starting an automatic anti-collision system, calculating a conflict resolution route and re-planning a safe flight route or autonomous return;
as an optional implementation scheme, the method for monitoring the operation of the unmanned aerial vehicle in the embodiment may further include:
s180: the parachute is started when an automatic collision avoidance system and/or a power system of the unmanned aerial vehicle fail.
Above-mentioned embodiment, when unmanned aerial vehicle operation risk is too high, can start corresponding risk and alleviate the measure to:
1) reducing the possibility of risk state generation, such as enhancing the stability of the engine, reducing the possibility of the whole system being out of control, and the like;
2) reducing the likelihood of injury from a risk condition, such as modifying a flight path, following a path of rare human smoke, etc.;
3) reducing the degree of danger, for example, starting a parachute after the unmanned aerial vehicle is out of control, and enabling air resistance to consume the gravitational potential energy of the unmanned aerial vehicle;
after corresponding relieving measures are taken, the flight risk of the unmanned aerial vehicle is evaluated again, and the process is repeated.
Optionally, in the above embodiment, current position information, a route trajectory, a performance index of the fuselage, and/or risk factor information monitored in real time of the unmanned aerial vehicle may also be displayed.
As an optional implementation scheme, in any of the above embodiments, the risk occurrence probability P (C) includes a system failure rate P (C)1) Air collision rate P (C)2) And/or the air crash rate P (C)3). The risk prediction model C further comprises: system failure rate submodel C1Air collision rate submodel C2And/or air crash rate submodel C3；
Wherein, the system failure rate submodel C1Probability for calculating accidents caused by system faults of unmanned aerial vehicle based on fault scene of unmanned aerial vehicle systemRate; aerial collision rate submodel C2Calculating the probability of accidents caused by collision among the unmanned aerial vehicles based on the aerial collision scene of the unmanned aerial vehicles; air collision rate submodel C3And calculating the probability of accidents caused by collision of the unmanned aerial vehicle and the obstacle based on the collision scene of the unmanned aerial vehicle and the obstacle.
As an optional implementation scheme, in the foregoing embodiment, the invoking an accident assessment model pre-established based on an accident scenario, and calculating the severity of the accident further includes:
calculating to obtain the casualty rate of the unmanned aerial vehicle to ground personnel according to the operation monitoring parameters of the unmanned aerial vehicle;
according to the population density of the unmanned aerial vehicle region, calculating to obtain the casualty number of the unmanned aerial vehicle caused by the ground personnel for evaluating the accident severity E caused by the crash of the unmanned aerial vehicle:
wherein:
e is the severity of the accident, and the number of casualties on the ground caused by each accident;
Aexpthe area of the area affected by the impact of the unmanned aerial vehicle on the ground;
rho is population density of the ground area impacted by the unmanned aerial vehicle;
Eimpactthe kinetic energy of the unmanned aerial vehicle impacting the ground.
As an optional implementation, in the foregoing embodiment, the calculating a risk estimation value according to the risk occurrence probability and the accident severity further includes:
the risk assessment R ═ E × p (c), i.e.:
and the risk estimated value R is a quantitative risk value of ground casualties caused in the unit time of the unmanned aerial vehicle operation.
As an optional implementation scheme, the operation monitoring method for the unmanned aerial vehicle in the embodiment may further include:
s20: configuring a parameter database for a risk prediction model and an accident assessment model aiming at different unmanned aerial vehicle models and operation scenes;
s21: and when the risk prediction model and the accident assessment model are called, corresponding related parameters are called from the parameter database according to the model and the flight environment of the unmanned aerial vehicle, and the parameter configuration of the risk prediction model and the accident assessment model is initialized.
Optionally, the method of the foregoing embodiment may further include:
s22: and dynamically configuring parameters of a risk prediction model and an accident assessment model according to the change of the operation scene.
The quantitative monitoring method of the above embodiment is further explained herein with reference to fig. 2-5, in conjunction with a specific example:
the unmanned aerial vehicle system mainly comprises three parts: a control station, an aircraft, and a communication link. Failure of any one of these parts can result in failure of the drone and a fall. According to classification statistics (shown in the following table 1) of reasons of system accidents/accident signs (2010-. The human factors and the environmental factors are difficult to analyze in a mathematical modeling mode, and can only be obtained through related statistical data at present. However, the flight accident data of the unmanned aerial vehicle is in short supply, so that the flight accident rate caused by human factors and environmental factors cannot be effectively measured temporarily. Therefore, the influence of human factors and environmental factors on the flight accidents of the unmanned aerial vehicles is not considered for the moment. Therefore, modeling quantitative analysis is mainly performed on three key factors, namely the failure rate of the unmanned aerial vehicle system, the air collision probability of the unmanned aerial vehicle and the collision probability of the unmanned aerial vehicle with a protection area.
TABLE 1 FAA civil unmanned aerial vehicle System Accident/Accident (2010-
Assuming that the failure rate of the unmanned aerial vehicle parts is constant after the early failure period and before the loss failure period; for mechanical and electromechanical parts, early faults can be eliminated through running-in, and ageing faults can be avoided through preventive maintenance; all these measures will cause the drone to exhibit a constant failure rate. According to the reliability theory, the occurrence of the system fault of the unmanned aerial vehicle obeys exponential distribution, and the probability of the fault is only related to the service time of the fault. Therefore, in the quantitative calculation of the unmanned aerial vehicle system safety evaluation, the fault rate calculated according to the exponentially distributed probability model can effectively represent the performance of the unmanned aerial vehicle in the service life period.
Therefore, in the present embodiment, based on the risk cause analysis of the unmanned aerial vehicle, the simplicity and the practicability of the index are considered, and three unmanned aerial vehicle operation risk probability indexes, namely, the system failure rate of the unmanned aerial vehicle, the air collision probability of the unmanned aerial vehicle, and the collision probability of the unmanned aerial vehicle with the protected area, are established, so the risk occurrence probability may include the system failure rate, the air collision rate, and the air collision rate, which are specifically defined as follows:
1. probability of occurrence of risk
1) Failure rate of unmanned aerial vehicle system
The failure rate of the unmanned aerial vehicle system is as follows: the average failure occurrence probability of an unmanned aerial vehicle of a certain type in the airspace in each flight hour in the service life period of the unmanned aerial vehicle, and the system failure rate index of the unmanned aerial vehicle is used for measuring the failure rate of the unmanned aerial vehicle running in the airspace and evaluating the running risk possibility of the airspace. In this example, the following method may be used to calculate the failure rate of the drone system:
the probability P of the unmanned aerial vehicle failing at the time t is calculated according to the following formula:
P＝1-e-λt (1-1)
in the formula, e is the base of natural logarithm, and lambda is the failure rate of the unmanned aerial vehicle and is a constant value; t is the usage time (i.e. the time of danger) of the drone.
The formula (1-1) is developed using Taylor's formula:
thus, when λ t is small (typically λ t ≦ 0.1), P ═ 1-e may be approximately replaced with P ═ λ t-λtNamely:
SF＝P＝λt (1-3)
then, in the process of establishing the unmanned aerial vehicle system fault falling probability index, the system fault rate is only influenced by the failure rate lambda and the service time t of the unmanned aerial vehicle, so that the obtained unmanned aerial vehicle system fault falling probability P (C)1) Comprises the following steps:
P(C1)＝SF＝λt
2) aerial collision rate, i.e. probability of aerial collision between unmanned aerial vehicle and unmanned aerial vehicle
The air collision rate is as follows: probability of collision and falling of two unmanned aerial vehicles in the air. The aerial collision rate index is used for measuring collision failure rate between unmanned aerial vehicles operating in an airspace and evaluating the operating risk possibility of the airspace. The reason that unmanned aerial vehicle produces the air collision lies in that the distance between the unmanned aerial vehicle is less than unmanned aerial vehicle's size, assumes that unmanned aerial vehicle obeys normal distribution along the positioning error of a certain direction, in certain space, if only have in certain time under two miniature unmanned aerial vehicle's the condition, probability that two machines collided is very little. The collision probability results obtained for different types of unmanned aerial vehicles and different numbers of unmanned aerial vehicles in the same airspace are different.
In this example, the following method may be used to calculate the air collision rate: assuming that the positioning error of the unmanned aerial vehicle along a certain direction follows normal distribution, the calculation formula is shown as formula (1-4):
in the formula (I), the compound is shown in the specification,
for unmanned aerial vehicle along a certain pathThe probability that the actual position of the direction is d from the satellite or radar fix. Rotating the formula (1-4) by 360 degrees to obtain the two-dimensional probability distribution of the positioning error of the unmanned aerial vehicle, as shown in the formula (1-5):
according to the 3 σ criterion, the maximum radius of the unmanned aerial vehicle positioning error zone can be assumed to be 3 σ. Then, normalizing equation (1-5) can obtain the probability distribution of the circular positioning error region D of the drone, as shown in equation (1-6):
thus, taking two drones as an example (independent of each other), one of the drones UAV is provided1The positioning point of the satellite or the radar is the coordinate origin (0,0), and the actual position is (x)1,y1) (ii) a Another unmanned aerial vehicle UAV2The coordinates of the positioning point are (x)0,y0) The actual position is (x)2-x0,y2-y0) (ii) a Distance between actual positions of two unmanned aerial vehicles
Then the calculation formula of the unmanned aerial vehicle air collision probability is shown as the formula (1-7):
since the formula (1-7) is difficult to calculate, the position distribution of the unmanned aerial vehicle is discretized by adopting a gridding method. If the unmanned aerial vehicle positioning error area D can be divided into N grids, the actual position of the unmanned aerial vehicle is located in the grid NiProbability of center point P (N)i) Is composed of
Thus, the equations (1-7) can be simplified as:
in the formula, Dis (N)UAV1,NUAV2) The distance between the grid central points of the actual positions of the two unmanned aerial vehicles. As shown in fig. 3, it is a schematic diagram of the risk of air collision between the unmanned aerial vehicle and the unmanned aerial vehicle.
Then, in the process of establishing the probability index of the unmanned aerial vehicle air collision crash, P (N)i) For the actual position of the unmanned plane in grid NiProbability of center Point, Dis (N)UAV1,NUAV2) The distance between the grid central points of the actual positions of the two unmanned aerial vehicles. Therefore, the probability of falling of unmanned aerial vehicle in air collision P (C)2) Comprises the following steps:
3) air collision rate, i.e. probability of collision of unmanned aerial vehicle with protected area
The air collision rate refers to the probability that an unmanned aircraft and a protective area in the air collide and fall. The collision probability index of the unmanned aerial vehicle and the protected area is used for measuring the collision rate of the unmanned aerial vehicle operating in the airspace and the protected area and evaluating the operating risk possibility of the airspace. In this example, the following method may be used to calculate the air collision rate:
the collision between the unmanned aerial vehicle and the protection area also belongs to a kind of air collision, and according to the analysis, the protection area can be divided into a plurality of grids in the same way, and then the collision probability between the unmanned aerial vehicle and the protection area can be obtained by the formula (1-9):
in the formula, Dis (N)UAV,Nboundary) The distance between the central point of the mesh of the actual position of the unmanned aerial vehicle and the central point of the boundary mesh of the protected area is obtained.
Then, in the process of establishing the collision and falling probability index of the unmanned aerial vehicle and the protection area under the collision and falling scene of the unmanned aerial vehicle and the protection area, the modeling mode is the same as the air collision principle of the unmanned aerial vehicle, P (N)i) For the actual position of the unmanned plane in grid NiProbability of center Point, Dis (N)UAV,Nboundary) The distance between the central point of the mesh of the actual position of the unmanned aerial vehicle and the central point of the boundary mesh of the protection area, so the air collision rate P (C)3) Namely:
2、severity of accident
The severity of the accident refers to severity indexes of consequences caused by risk occurrence, in this example, three unmanned aerial vehicle ground risk assessment indexes, namely a ground coverage area, a ground impact kinetic energy and a ground population density, are constructed on the basis of actual monitoring data of the UTMISS system and partial data acquired by the public network of the unmanned aerial vehicle in the world, and are specifically defined as follows:
1) floor area of coverage
The floor coverage area is: unmanned aircraft are dropped from the air, impacting the area coverage of the ground due to system failure, air crash, etc. The ground coverage area index is used for calculating the coverage area of the unmanned aerial vehicle falling to the ground from the air due to system failure or air collision. This index mainly depends on the size of unmanned aerial vehicle size, and if unmanned aerial vehicle size is big more, unmanned aerial vehicle's the area of coverage that falls to the ground is also big more, also is big more to subaerial personnel's influence. This index is simple, easily understands, can reflect unmanned aerial vehicle's ground risk betterly. In the Specific Operation Risk Assessment (SORA) of an unmanned system rule making united body (JARUS), the maximum characteristic dimension of an unmanned aerial vehicle serves as one of important indexes for measuring the ground risk level of the unmanned aerial vehicle, and the ground risk of the unmanned aerial vehicle is influenced by the fact that the landing coverage area of the unmanned aerial vehicle is directly influenced by the size of the unmanned aerial vehicle. Because unmanned aerial vehicle size data comparatively acquires easily, the unmanned aerial vehicle ground risk management of being convenient for. The floor coverage area can be calculated by adopting the following method:
(1) scene for ground impact of vertically falling unmanned aerial vehicle
In this scenario, the impact area is as shown in fig. 4, and the size of the fuselage frontal area of the drone can be r1Indicating that the area actually affected during impact with the ground is approximately the fuselage frontal area r of the drone1Reamplifying a small region to r2. The actual area of influence is greater than the fuselage frontal area of the drone and the area needs to be augmented because the person on the ground is itself wide, about 0.5m, although the fuselage frontal area of the drone is only r1However, in practice the area threatened by the falling of the unmanned aerial vehicle is r2I.e. AexpIs equal to r2The area of (a).
The area of a circle made by taking the maximum size in all directions of measurement of the unmanned aerial vehicle and the average width of a human body as the diameter represents an area which can cause personnel injury after the unmanned aerial vehicle collides with the ground. The calculation formula is as follows:
d1measuring the maximum size of the unmanned aerial vehicle in each direction;
d2is the average width of a human body.
(2) Scene of ground collision for gliding falling unmanned aerial vehicle
In this scenario, the collision increment area is as shown in fig. 5, when the drone slides and falls, a standing person with a height of h is located at a position d away from the collision position in the figure, and at this time, the drone has collided with the person, so that in fact, the real area threatened by the fact that the drone collides with the ground is the fuselage front area of the drone plus an annular area with an enlarged radius of d.
AexpThe ground coverage area of the unmanned aerial vehicle is the square of a meter;
wuavis the span or transverse dimension of the drone;
Luavthe length or longitudinal dimension of the unmanned aerial vehicle body;
Hpersonis the average adult height.
glide is the glide angle
2) Kinetic energy of ground impact
In this example, the ground impact kinetic energy refers to: the unmanned aircraft falls from the air due to system failure, air crash, etc., and the amount of kinetic energy when striking the ground. This example uses a logical growth variable model from the impact kinetic energy EimpAnd occlusion factor PsTo establish an outcome severity model P of the crash of the unmanned aerial vehicle.
Wherein, ground striking kinetic energy index has reflected unmanned aerial vehicle to ground personnel's risk from the angle of energy. When unmanned aerial vehicle's the striking kinetic energy that falls is big more, the accident hazard that unmanned aerial vehicle caused is also big more, produces secondary disasters such as conflagration, explosion, collapse easily. When unmanned aerial vehicle striking kinetic energy is big enough, can destroy the building even, brought huge potential safety hazard for ground personnel. Therefore, this embodiment sets up an assessment index of being convenient for to calculate from the angle of striking kinetic energy, considers momentum conservation theorem and energy conservation theorem, only needs to combine together real-time supervision data and unmanned aerial vehicle model performance data, can approximately solve the striking kinetic energy to ground when unmanned aerial vehicle falls, comparatively simply just assesses unmanned aerial vehicle ground risk effectively.
In this example, the ground impact kinetic energy can be calculated by the following method:
Eimpactthe kinetic energy of ground impact is joule;
muavthe flight mass of the unmanned aerial vehicle;
mpis of adult quality;
vimpactthe speed of the unmanned aerial vehicle when in collision;
vuavthe flight speed of the unmanned aerial vehicle;
huavis the flight altitude of the drone;
g is the acceleration of gravity.
3) Ground population density
The ground population density refers to the number of people per unit land area under the unmanned aerial vehicle operation airspace. Population density has decided the population quantity that unmanned aerial vehicle striking area influences, and different population density areas receive the number of people that the striking threatened also different at the striking within range to the influence causes the number of people of injury.
In this example, the ground population density may be calculated as follows:
rho is population/area (1-14)
The ground population density index can be regarded as a constant in a certain area, and for different areas, the statistical data of the population density rho in the local statistical bureau can be directly adopted. Optionally, the present example may also approximate the population density level (people/square kilometer) of the target area by table 2:
TABLE 2 population Density
Because unmanned aerial vehicle is not manned attribute, the consequence severity that unmanned aerial vehicle and transportation aviation aircraft lead to when the bumps is different, even unmanned aerial vehicle takes place to become invalid and causes to fall, if do not have personnel and property in the region that falls so unmanned aerial vehicle fall just can not cause the consequence severity. The consequence severity can be obtained only when personnel exist in a falling area, so that the calculation of the crash consequence severity of the unmanned aerial vehicle is the calculation of the casualty situation of the personnel on the ground after the crash occurs; the higher the casualty rate, the higher the severity of the consequences, and the lower the casualty rate, the lower the severity of the consequences.
The severity of the consequence of the unmanned aerial vehicle crash can be calculated by using a model (1-15), E is the number of casualties caused by the ground impact of the unmanned aerial vehicle, N isexpIs the number of people who appear in the impact area, and P is the casualty rate caused by the crash of the unmanned aerial vehicle to the ground.
E＝Nexp·P (1-15)
Assuming that the population density in the impact area of the drone with the ground is uniform, AexpThe land coverage area is defined as rho is the ground population density; both values can be calculated from the risk severity index. N is a radical ofexpThe following can be calculated by (1-15):
Nexp＝Aexp·ρ (1-15)
the casualty rate P caused by the crash of the unmanned aerial vehicle on the ground can be calculated by using the models (1-16)):
wherein E isimpactThe ground impact kinetic energy can be calculated by index formulas (1-13).
In conclusion, according to the models (1-15), the severity model of the consequences of the ground impact casualties of the unmanned aerial vehicle is as follows:
according to the operation parameters of the unmanned aerial vehicle, the severity of the casualty rate consequence of the falling unmanned aerial vehicle to ground personnel can be obtained; and then the severity of the casualty number consequence caused by the falling of the unmanned aerial vehicle to ground personnel can be obtained according to the population density condition of the falling area, and finally the severity evaluation of the operation crash consequence of the unmanned aerial vehicle is realized.
Second, qualitative analysis method embodiment:
besides the above quantitative analysis method for monitoring the operation of the unmanned aerial vehicle, an embodiment of the present disclosure further discloses an implementation scheme as an optional implementation scheme, and the method for monitoring the operation of the unmanned aerial vehicle of the above embodiment may further include:
s30: setting risk probability grades aiming at different unmanned aerial vehicle models and operation scenes in advance;
s31: setting accident severity levels aiming at serious consequences of risk occurrence in advance;
s32: generating a risk evaluation matrix model according to the risk probability grade and the accident severity grade;
s33: and qualitatively analyzing the current model and the risk level under the flight environment according to the risk evaluation matrix model.
In this embodiment, the risk is evaluated according to a risk evaluation matrix method. The risk matrix is an effective tool for risk assessment, which utilizes a short-matrix graph approach. When the risk evaluation matrix is adopted for risk evaluation, the severity of the consequences of the risk event is qualitatively divided into a plurality of levels relatively, the probability of the risk event is qualitatively divided into a plurality of levels relatively, then the severity is taken as a table column, the probability is taken as a table row, a matrix table is made, and qualitative weighting indexes are given at the intersection points of the rows and the columns. All weighted indices form a matrix, and each index represents a risk level. Through the risk assessment matrix diagram, the height and the distribution condition of the risk of the organization can be visually displayed, and the risk level can be determined. And determining which hazard factors do not need to be processed, which hazard factors need to be further analyzed, which hazard factors need to be preferentially processed and the like by determining the area of the identified hazard factors in the matrix.
The monitoring method for qualitative risk analysis is further described below with reference to a specific example:
different from an unmanned aerial vehicle operation risk evaluation model, the risk evaluation matrix carries out qualitative classification on the risk occurrence possibility and the consequence severity respectively, and then establishes the evaluation matrix to determine the final risk grade.
First, the risk occurrence probability is qualitatively analyzed according to the probability or frequency of the risk occurrence, and the present example adopts the risk probability ranking shown in table 3 below:
TABLE 3 common Risk potential ratings
And, the severity of the risk occurrence is qualitatively analyzed in terms of severity of the outcome of the risk occurrence, this example using the risk severity ratings as shown in table 4 below:
TABLE 4 common Risk severity ratings
From the qualitative ranking of risk likelihood and severity, a risk assessment matrix is generated, as shown in table 5:
TABLE 5 Risk assessment matrix
In the qualitative analysis, the model and the operation scene of the unmanned aerial vehicle are combined, and the risk assessment grade of the qualitative analysis is obtained through the risk assessment matrix, in this example, the risk assessment grade can be divided into three grades, i.e., acceptable, tolerable and unacceptable, where:
1) acceptable, representing low risk, requiring risk monitoring and operating normally;
2) tolerable, representing medium risk, needing to take control measures for the risk and being capable of normally operating; .
3) Unacceptable, representing a high risk, requires immediate risk control measures to be taken and the operation stopped until the risk falls to an acceptable level.
Product implementationExample (b)
In the above, the embodiment of the method for monitoring the operation of the unmanned aerial vehicle is explained, and the following further introduces an unmanned aerial vehicle operation monitoring system disclosed by the present disclosure, where the monitoring system includes:
the performance monitoring equipment is used for monitoring the operation parameters of the unmanned aerial vehicle;
the intelligent sensing equipment is used for detecting flight scene information of the unmanned aerial vehicle and identifying risk factors in the flight scene information;
the risk analysis device is used for calling a risk analysis model, and calculating the risk occurrence probability of the unmanned aerial vehicle in the current operation scene by combining the flight control information, the operation parameters and the flight scene information;
the accident evaluation device is used for calling an accident evaluation model which is pre-established based on an accident scene according to the flight control information, the operation parameters and the flight scene information and calculating the severity of the accident;
and the comprehensive prediction device is used for calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity and outputting the risk evaluation value.
As an optional implementation scheme, the operation monitoring system for an unmanned aerial vehicle in the above embodiment may further include: and the early warning device is used for monitoring whether the risk assessment value is lower than a preset safe operation threshold value or not, and starting risk early warning when the risk assessment value reaches or is higher than the safe operation threshold value.
As an optional implementation scheme, the operation monitoring system for an unmanned aerial vehicle in the above embodiment may further include: and the automatic collision avoidance system is used for calculating a conflict resolution route when the risk evaluation value reaches or is higher than a safe operation threshold value, and replanning a safe flight route to complete a flight control command or autonomous return flight.
As an optional implementation scheme, the operation monitoring system for an unmanned aerial vehicle in the above embodiment may further include: a fall protection device for starting the parachute when the unmanned aerial vehicle power system fails.
As an optional implementation scheme, the operation monitoring system for an unmanned aerial vehicle in the above embodiment may further include: and the parameter database is configured with operation parameter sets of the risk prediction model and the accident assessment model under different unmanned aerial vehicle models and operation scenes and used for initializing and/or updating the risk prediction model and the accident assessment model.
As an optional implementation scheme, the operation monitoring system for an unmanned aerial vehicle in the above embodiment may further include: and the qualitative analysis device is used for establishing a risk assessment matrix model in advance and qualitatively analyzing the current model and the risk level under the flight environment according to the risk assessment matrix model.
As an optional implementation scheme, the operation monitoring system for an unmanned aerial vehicle in the above embodiment may further include: the positioning display equipment is used for displaying the current position information, the air route track, the fuselage performance index and/or the risk factor information monitored in real time of the unmanned aerial vehicle;
the flight scene information comprises environment, airspace and/or geographic information, existing risk factors in the flight scene are identified, and potential risk factors are identified;
the accident scene comprises a system fault scene and a collision scene with a barrier, wherein the system fault scene comprises hardware faults and software fault scenes of an unmanned aerial vehicle body and a control console.
In addition, as an optional implementation scheme, the present disclosure further discloses an operation monitoring platform for an unmanned aerial vehicle, where the monitoring platform includes the operation monitoring system for an unmanned aerial vehicle disclosed in any of the foregoing embodiments, and is configured to monitor and display operation parameter information, airspace information, environmental information, geographic information, and airway information of the unmanned aerial vehicle in real time, and monitor a flight application, identify a tracked target, and perform an early warning of the unmanned aerial vehicle.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An unmanned aerial vehicle operation monitoring method is characterized by comprising the following steps:
acquiring the operation parameters, flight scenes and flight control information of the unmanned aerial vehicle, calling a pre-established risk prediction model, and calculating the risk occurrence probability under the current scene;
calling an accident evaluation model pre-established based on an accident scene, and calculating the accident severity of the unmanned aerial vehicle in the current flight scene and the running state;
and calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity, and outputting the risk evaluation value.
2. The method of claim 1, further comprising:
judging whether the risk assessment value is lower than a preset safe operation threshold value of the unmanned aerial vehicle, and if the risk assessment value is lower than the safe operation threshold value, continuing monitoring;
and if the risk estimated value reaches or is higher than the safe operation threshold value, starting a risk early warning and/or automatic anti-collision system, calculating a conflict resolution route and re-planning a safe flight route or autonomous return voyage.
3. The drone operation monitoring method of claim 1 or 2, wherein the risk occurrence probability P (C) includes a system failure rate P (C)1) Air collision rate P (C)2) And/or the air crash rate P (C)3) (ii) a The risk prediction model C further comprises: system failure rate submodel C1Air collision rate submodel C2And/or air crash rate submodel C3；
Wherein the system failure rate submodel C1Calculating the probability of accidents caused by system faults of the unmanned aerial vehicle based on the fault scene of the unmanned aerial vehicle system; the aerial collision rate submodel C2Calculating inter-unmanned aerial vehicle distribution based on unmanned aerial vehicle air collision sceneProbability of a collision causing an accident; the air collision rate sub-model C3And calculating the probability of accidents caused by collision of the unmanned aerial vehicle and the obstacle based on the collision scene of the unmanned aerial vehicle and the obstacle.
4. The method of claim 3, wherein the invoking of the pre-established accident assessment model based on the accident scenario, and the calculating of the severity of the accident further comprises:
calculating to obtain the casualty rate of the unmanned aerial vehicle to ground personnel according to the operation monitoring parameters of the unmanned aerial vehicle;
calculating to obtain the number of casualties of the unmanned aerial vehicle caused by the ground personnel according to the population density of the unmanned aerial vehicle region, and evaluating the severity E of the accident caused by the crash of the unmanned aerial vehicle:
wherein:
e is the severity of the accident, and the number of casualties on the ground caused by each accident;
Aexpthe area of the area affected by the impact of the unmanned aerial vehicle on the ground;
rho is population density of the ground area impacted by the unmanned aerial vehicle;
Eimpactthe kinetic energy of the unmanned aerial vehicle impacting the ground.
5. The method of claim 4, wherein calculating a risk estimate based on the probability of occurrence of risk and the severity of the accident further comprises:
the risk estimate, R ═ E × p (c), i.e.:
and the risk estimated value R is a quantitative risk value of ground casualties caused in the unit time of the unmanned aerial vehicle operation.
6. An operation monitoring method for a drone according to any one of claims 1 to 5, characterised in that the method further comprises:
configuring a parameter database for the risk prediction model and the accident assessment model according to different unmanned aerial vehicle models and operation scenes; when the risk prediction model and the accident assessment model are called, corresponding related parameters are called from the parameter database according to the model and the flight environment of the unmanned aerial vehicle, the parameter configuration of the risk prediction model and the accident assessment model is initialized, and/or the parameters of the risk prediction model and the accident assessment model are dynamically configured according to the change of the operation scene;
and/or the presence of a gas in the gas,
setting risk probability grades aiming at different unmanned aerial vehicle models and operation scenes in advance; setting accident severity levels aiming at serious consequences of risk occurrence in advance; generating a risk evaluation matrix model according to the risk probability grade and the accident severity grade; and qualitatively analyzing the current model and the risk level under the flight environment according to the risk assessment matrix model.
7. An unmanned aerial vehicle operation monitoring system, its characterized in that includes:
the performance monitoring equipment is used for monitoring the operation parameters of the unmanned aerial vehicle;
the intelligent sensing equipment is used for detecting flight scene information of the unmanned aerial vehicle and identifying risk factors in the flight scene information;
the risk analysis device is used for calling a risk analysis model, and calculating the risk occurrence probability of the unmanned aerial vehicle in the current operation scene by combining the flight control information, the operation parameters and the flight scene information;
the accident evaluation device is used for calling an accident evaluation model which is pre-established based on an accident scene according to the flight control information, the operation parameters and the flight scene information and calculating the severity of the accident;
and the comprehensive prediction device is used for calculating to obtain a risk evaluation value according to the risk occurrence probability and the accident severity and outputting the risk evaluation value.
8. The unmanned aerial vehicle operation monitoring system of claim 7, further comprising:
the early warning device is used for monitoring whether the risk assessment value is lower than a preset safe operation threshold value or not, and starting risk early warning when the risk assessment value reaches or is higher than the safe operation threshold value; and/or
The automatic collision avoidance system is used for calculating a conflict resolution route when the risk evaluation value reaches or is higher than the safe operation threshold value, and replanning a safe flight route to complete a flight control instruction or autonomous return flight; and/or
And the falling protection device is used for starting the parachute when the unmanned aerial vehicle power system fails.
9. An unmanned aerial vehicle operation monitoring system according to claim 7 or 8, wherein the system further comprises:
the parameter database is configured with operation parameter sets of the risk prediction model and the accident assessment model under different unmanned aerial vehicle models and operation scenes and used for initializing and/or updating the risk prediction model and the accident assessment model; and/or
The qualitative analysis device is used for establishing a risk assessment matrix model in advance and qualitatively analyzing the current model and the risk level under the flight environment according to the risk assessment matrix model; and/or
The positioning display equipment is used for displaying the current position information, the air route track, the fuselage performance index and/or the risk factor information monitored in real time of the unmanned aerial vehicle;
the flight scene information comprises environment, airspace and/or geographic information, existing risk factors in the flight scene are identified, and potential risk factors are identified;
the accident scene comprises a system fault scene and a collision scene with an obstacle, wherein the system fault scene comprises hardware faults and software fault scenes of an unmanned aerial vehicle body and a control console.
10. An unmanned aerial vehicle operation monitoring platform, comprising the unmanned aerial vehicle operation monitoring system according to any one of claims 7 to 9, for monitoring, displaying the unmanned aerial vehicle operation parameter information, airspace information, environmental information, geographic information and airway information in real time, and monitoring the unmanned aerial vehicle for flight application, target recognition, tracking and early warning.
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