CN113421459B - Ground traffic risk monitoring method and system caused by flight of unmanned aerial vehicle - Google Patents
Ground traffic risk monitoring method and system caused by flight of unmanned aerial vehicle Download PDFInfo
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Abstract
The utility model discloses a ground traffic risk monitoring method and system that unmanned aerial vehicle flight leads to, this method includes: acquiring flight plan data of the unmanned aerial vehicle to obtain road network information corresponding to a flight airspace; acquiring performance data of the unmanned aerial vehicle, and calculating the probability of accidental runaway of the unmanned aerial vehicle; and calculating a ground traffic risk value influenced by the accidental out-of-control unmanned aerial vehicle according to the probability of the accidental out-of-control unmanned aerial vehicle and the road network information. Through the technical scheme of this disclosure of embodiment, can monitor and assess the risk that unmanned aerial vehicle air flight caused ground traffic accurately high-efficiently.
Description
Technical Field
The invention relates to the technical field of safe flight of unmanned aerial vehicles, in particular to a method and a system for monitoring ground traffic risks caused by flight of an unmanned aerial vehicle.
Background
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 strong countries. The development of low-altitude general aviation is brought into the general aviation transportation plan in countries such as America and Europe, a European Private Aviation Transport System (EPATS) and a Small Aircraft Transport System (SATS) are started, and a new solution is provided for the urban traffic jam problem while the private aviation flight requirements are met.
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 realization is to the scientific management and control of unmanned aerial vehicle flight, is the important prerequisite of low latitude territory resource make full use of in the future. For unmanned aerial vehicle individuals, the flight risk assessment method is not perfect. Unmanned aerial vehicle operator, unmanned aerial vehicle aircraft itself and various safe influence key elements such as the change of environment (airspace structure, weather, topography etc.) and interact between them make unmanned aerial vehicle's low-altitude flight have great potential safety hazard, have increased the degree of difficulty of scientific management and control low-altitude flight.
Therefore, the safety of the unmanned aerial vehicle flying in the low-altitude airspace is of great importance, and how to scientifically evaluate and monitor the flight risk of the unmanned aerial vehicle is a major key problem in the low-altitude airspace navigation development of China.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for monitoring ground traffic risk caused by flight of an unmanned aerial vehicle, which can monitor and evaluate the risk of the unmanned aerial vehicle on the ground traffic, and at least partially solve the problems in the prior art.
To this end, the present disclosure discloses a method for monitoring ground traffic risk caused by flight of an unmanned aerial vehicle, the method comprising:
acquiring flight plan data of the unmanned aerial vehicle to obtain road network information corresponding to a flight airspace;
acquiring performance data of the unmanned aerial vehicle, and calculating the probability of accidental runaway of the unmanned aerial vehicle;
and calculating a ground traffic risk value influenced by the accidental out-of-control unmanned aerial vehicle according to the probability of the accidental out-of-control unmanned aerial vehicle and the road network information.
Correspondingly, in order to realize above-mentioned method, this disclosure still discloses a ground traffic risk monitoring system that unmanned aerial vehicle flight leads to, this system includes:
the information acquisition unit is used for acquiring flight plan data and road network data of the unmanned aerial vehicle;
the road network positioning unit is used for acquiring road network information corresponding to a flight airspace according to the flight plan data;
the performance monitoring unit is used for acquiring performance data of the unmanned aerial vehicle and calculating the probability of accidental runaway of the unmanned aerial vehicle;
and the risk evaluation unit is used for calculating the ground traffic risk value influenced by the accidental out-of-control unmanned aerial vehicle according to the probability of the accidental out-of-control unmanned aerial vehicle and the road network information.
Compared with the prior art, the ground traffic risk monitoring method and system caused by the flight of the unmanned aerial vehicle disclosed by the disclosure have the following technical effects:
by implementing the ground traffic risk monitoring method and system embodiment caused by unmanned aerial vehicle flight, the road network information corresponding to the flight area is extracted through the flight plan data and the road network data of the unmanned aerial vehicle, the probability of the unmanned aerial vehicle being out of control accidentally is calculated, and the ground traffic risk value influenced by the unmanned aerial vehicle being out of control accidentally is calculated by combining the road network information and the probability of the out of control, so that the flight risk of the unmanned aerial vehicle is accurately and efficiently monitored and evaluated, and the loss degree is analyzed and estimated.
Drawings
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 ground traffic risk caused by flight of an unmanned aerial vehicle according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a ground traffic risk monitoring system according to an embodiment of the present disclosure;
FIG. 3 is a schematic view of a flight area and a road network area in a route application mode of an unmanned aerial vehicle according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a flight area and a road network area in the airspace application mode of the unmanned aerial vehicle in the 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 method comprises the following steps:
in order to accurately estimate the risk and loss of the unmanned aerial vehicle on the ground road and traffic in the flying process, the disclosure discloses a method for monitoring the ground traffic risk caused by the flying of the unmanned aerial vehicle, as shown in fig. 1, the method comprises the following steps:
s100: acquiring flight plan data of the unmanned aerial vehicle to obtain road network information corresponding to a flight airspace;
s200: acquiring performance data of the unmanned aerial vehicle, and calculating the probability of accidental runaway of the unmanned aerial vehicle;
s300: and calculating a risk value of the unmanned aerial vehicle influencing the normal operation of the ground traffic due to the accidental out-of-control according to the probability of the accidental out-of-control of the unmanned aerial vehicle and the road network information.
The embodiment is through unmanned aerial vehicle's flight plan data and road network data, draws the road network information that the flight area corresponds, calculates the unexpected probability out of control of unmanned aerial vehicle to combine road network information and the probability out of control, calculate and obtain the unexpected possibility that out of control of unmanned aerial vehicle influences ground traffic normal operating, and then obtain the risk value that ground traffic receives its influence, thereby accurately, high-efficiently monitor and assess unmanned aerial vehicle's flight risk.
In this embodiment, it should be noted that:
1) the flight plan data may include flight information; the flight information may further include: the method comprises the steps of presetting a flight mode, a flight pre-use airspace and pre-flight height information.
In the risk assessment process brought to ground traffic by unmanned aerial vehicle flight, unmanned aerial vehicle flight plan data is used as basic information for risk assessment. For example: can follow the unmanned aerial vehicle model of acquireing in the unmanned aerial vehicle flight plan data, unmanned aerial vehicle flight intention (for example, take photo by plane, send flight modes such as takeaway), the unmanned aerial vehicle flight is in advance to use the airspace circumstances, basic information such as unmanned aerial vehicle flight height in advance.
2) The performance data of the drone may include: unmanned aerial vehicle model, self weight, fuselage size, system stability parameters, maximum flying height, maximum operating speed and battery duration.
For example: based on flight plan data, can acquire the performance data that corresponds unmanned aerial vehicle, the key information has: the self weight of the unmanned aerial vehicle, the system stability of the operation of the unmanned aerial vehicle, the maximum flying height of the unmanned aerial vehicle, the maximum operation speed of the unmanned aerial vehicle, the battery endurance time of the unmanned aerial vehicle and other basic information.
The corresponding road network information below the unmanned aerial vehicle operation airspace is a background material for analyzing ground traffic risk by the flight of the unmanned aerial vehicle, and corresponding road network information data should be acquired to serve as an important basis for flight risk assessment. As an optional implementation manner, S100 in the foregoing embodiment: according to the unmanned aerial vehicle flight plan data, obtaining road network information corresponding to a flight airspace, and further comprising the following processing steps:
s110: extracting flight information of the unmanned aerial vehicle from the flight plan data;
s120: determining a road network area covered by a flight airspace according to the flight information;
s130: obtaining the area and the road quantity of a road network area, and calculating the urban road area rate URAR:
wherein, the urban Road Area ratio URAR (Urban Road Area ratio) is used for describing the total density of roads in the city, the Road network Area has n roads in total, and the width of the ith Road is d i Having a length of l i Then, the area occupied by the ith road is: s i ＝l i d i 。
Optionally, in the foregoing embodiment, S120: determining a road network area covered by a flight airspace according to the flight information, and further comprising the following steps:
s121: extracting preset flight modes, flight pre-use airspace and pre-flight height information according to the flight information;
s122: when the flight mode is the air route application mode, the maximum horizontal flight speed v of the unmanned aerial vehicle is obtained according to the performance data of the unmanned aerial vehicle h Recording the time t of the unmanned aerial vehicle continuing to move according to inertia and moving in the air under the condition that the air height h is out of control unexpectedly a Then, the maximum horizontal extension distance of the unmanned aerial vehicle moving in the air is ρ:
the area of the road network area is S ═ ρ w, wherein w is the mean road width.
S123: when the flight mode is an airspace application mode, acquiring a corresponding airspace area according to the airspace information used in flight in advance; and determining the area of the corresponding road network area according to the area of the airspace.
As an optional implementation manner, in the above embodiment, S200: calculating the probability of accidental runaway of the unmanned aerial vehicle, and further comprising the following steps:
s210: acquiring a system stability parameter lambda according to the performance data of the unmanned aerial vehicle;
s220: according to flight plan data, acquiring preset flight time t of the unmanned aerial vehicle, and calculating to obtain the probability that the unmanned aerial vehicle is out of control unexpectedly in the flight plan at this time as P, wherein P is lambda t.
As an optional implementation manner, in the above embodiment, S300: calculating the ground traffic risk value influenced by the accidental out-of-control unmanned aerial vehicle, and further comprising the following steps of:
s310: acquiring a corresponding urban road area rate URAR according to the road network information;
s320: acquiring the probability P of accidental runaway of a corresponding unmanned aerial vehicle according to the operation performance data of the unmanned aerial vehicle;
s330: according to the urban road area rate URAR and the probability P of the unmanned aerial vehicle out of control, calculating the possibility L of the unmanned aerial vehicle out of control affecting the normal operation of the road network: l ═ λ t 5 URAR. And the risk value R of the unmanned aerial vehicle accidental runaway influencing the normal operation of the road network is L multiplied by SE, wherein SE is the average severity grade of the loss of the unmanned aerial vehicle accidental runaway to the ground road.
As an optional implementation manner, based on the method for monitoring ground traffic risk caused by flight of the unmanned aerial vehicle disclosed in any of the foregoing embodiments, the method further includes the following steps:
s400: calculating the energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground according to the flight information of the unmanned aerial vehicle;
s500: acquiring the size of the body of the unmanned aerial vehicle according to the performance data of the unmanned aerial vehicle, thereby determining the visual field range beta of the unmanned aerial vehicle for shielding a driver of a road vehicle;
s600: extracting road speed limit information in the road network area, and calling a corresponding road structure coefficient alpha from a preset road structure coefficient group according to the road speed limit information k ；
S700: according to the energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground, the visual field range beta of the unmanned aerial vehicle for shielding the driver, and the road structure coefficient alpha k Calculating the severity level SE of the loss of the ground road caused by the accidental runaway of the unmanned aerial vehicle: SE ═ Σ α k SE k ；
Wherein SE k ＝SE k (β,E)，SE k The severity level of the k-th road loss caused by the accident is determined by the visual field range beta of the vehicle driver shielding the road surface and the energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground.
As an optional implementation manner, in the above-mentioned embodiment S700, the road structure coefficient α k The calculation process of (2) includes:
according to the road information in the road network area, the road structure coefficient alpha is set according to the classification of the speed limit condition of the road k ；
Wherein the road structure coefficient alpha k The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
further comprising: sigma k a k ＝1
Wherein S is j The area s of the ith road corresponding to the area of the jth road i Comprises the following steps: s i ＝l i d i ，Γ k A set of k-th class roads; road structure coefficient alpha k Has a value range of [0,1 ]]，α k Further comprising:
class 1 road structure coefficient alpha 1 The value range is [0,1 ]]Corresponding road 1 The speed limit is below 60 KM/h;
class 2 road structure coefficient alpha 2 The value range is [0,1 ]]Corresponding road 2 The speed limit is more than 60KM/h and less than 100 KM/h;
class 3 road structure coefficient alpha 3 The value range is [0,1 ]]Corresponding road 3 The speed limit is above 100 KM/h.
Product example:
as shown in fig. 2, to implement the above method embodiment, the present disclosure also discloses a ground traffic risk monitoring system embodiment, which includes the following components:
the information acquisition unit is used for acquiring flight plan data and road network data of the unmanned aerial vehicle;
the road network positioning unit is used for acquiring road network information corresponding to a flight airspace according to the flight plan data;
the performance monitoring unit is used for acquiring performance data of the unmanned aerial vehicle and calculating the probability of accidental runaway of the unmanned aerial vehicle;
and the risk evaluation unit is used for calculating a risk value of the unmanned aerial vehicle influencing the normal operation of the ground traffic due to the accidental out-of-control according to the probability of the accidental out-of-control of the unmanned aerial vehicle and the road network information.
The ground traffic risk monitoring system disclosed in the above embodiment extracts the road network information corresponding to the flight area through the flight plan data and the road network data of the unmanned aerial vehicle, calculates the probability of the unmanned aerial vehicle being out of control accidentally, and calculates to obtain the ground traffic risk value influenced by the unmanned aerial vehicle being out of control accidentally by combining the road network information and the probability of the out of control, thereby accurately and efficiently monitoring and evaluating the flight risk of the unmanned aerial vehicle.
As an optional implementation manner, the ground traffic risk monitoring system may further include the following components:
the energy estimation unit is used for calculating the energy E and the visual field range beta of the shielded driver when the unmanned aerial vehicle lands on the ground according to the flight information and the performance data of the unmanned aerial vehicle;
a speed limit statistical unit for calling the corresponding road structure coefficient alpha from the preset road structure coefficient group according to the road speed limit information in the road network area k ；
A loss evaluation unit for evaluating the loss of the unmanned aerial vehicle based on the energy E of the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground, the visual field range beta of the unmanned aerial vehicle covering the driver, and the road structure coefficient alpha k Calculating the severity level SE of the loss of the ground road caused by the accidental runaway of the unmanned aerial vehicle: SE ═ Σ α k SE k ；
Wherein SE k ＝SE k (β,E)，SE k The severity level of the k-th road loss caused by the accident is determined by the visual field range beta of the vehicle driver shielding the road surface and the energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground.
The ground traffic risk monitoring system disclosed in the above embodiment calculates the loss of the unmanned aerial vehicle caused by unexpected runaway to the ground respectively for different scenes of deducing the movement trend of the unmanned aerial vehicle for controlling falling, thereby accurately and efficiently analyzing and estimating the loss degree.
Optionally, the road network positioning unit may further include:
the positioning subunit is used for extracting the flight information of the unmanned aerial vehicle from the flight plan data and determining a road network area covered by a flight airspace according to the flight information;
the calculating subunit is used for acquiring the area of the road network area and the number of roads, and calculating the urban road area rate URAR:
wherein, the road network region has n roads, the width of the ith road is d i Having a length of l i Then, the area occupied by the ith road is: s i ＝l i d i 。
The embodiment of the present invention has the same inventive concept as the embodiment of the method, and the detailed implementation can refer to the embodiment described above, which is not described herein again.
The risk monitoring method and system are further described below with reference to a specific example:
1. basic data acquisition
Assuming that the time duration of the flight time period required by the unmanned aerial vehicle is t, if the system stability parameter of the unmanned aerial vehicle is p (which represents the probability of accidental runaway occurring in unit time during the flight of the unmanned aerial vehicle), then it can be known that: the probability that the unmanned aerial vehicle is accidentally out of control in the whole flight process is tp.
Assuming that the unmanned aerial vehicle loses a power system and does free-fall movement after being out of control unexpectedly, the position of the unmanned aerial vehicle possibly appearing in the whole flight process is defined as the region omega of the road network structure analysis according to the operation performance and flight plan data of the unmanned aerial vehicle. According to the road network information in the region omega, the possibility L that the unmanned aerial vehicle is out of control accidentally and influences the normal operation of the road network is known as follows: l is tp and URAR. Then, the risk value R that the unmanned aerial vehicle is accidentally out of control and affects normal operation of the road network is L × SE, where SE is the average severity level of the loss of the ground road caused by the unmanned aerial vehicle being accidentally out of control.
In addition to the urban road area rate, another very critical factor is the speed limit condition of the road, and the speed of the road inside the cell is usually required to be very low, for example, within 40KM/h, in this case, if the unmanned aerial vehicle is unexpectedly out of control, the driver in driving can quickly take corresponding response measures to avoid severe traffic accidents such as severe collision or rear-end collision with multiple vehicles. However, if the speed of a fast-running automobile is usually 120KM/h on a tolway, if the unmanned aerial vehicle suddenly and unexpectedly runs away at the moment, a driver is difficult to avoid strong impact, and the sudden braking of the unmanned aerial vehicle also easily causes multiple rear-end collisions, and even threatens the life safety of several people.
Therefore, the speed limit condition of each road is an important parameter for unmanned aerial vehicle flight risk assessment. All roads in the area (denoted road) 1 ,…,road n ) The classification according to the road speed-limiting condition (note that if there are multiple sections in a road speed-limiting condition, it is determined as a combination of multiple roads), the classification criteria are:
road i And the speed limit is below 60KM/h, then the road corresponds to the 1 st type and is recorded as: road i ∈Γ 1 ；
Road i The speed limit is above 60KM/h and below 100KM/h, then the road is classified as type 2: road i ∈Γ 2 ；
Road i The speed limit is above 100KM/h (e.g. the speed limit is 120KM/h), then the road is classified as 3, which is recorded as: road i ∈Γ 3
Defining a set of road structure coefficients alpha k K is 1,2,3 as follows:
wherein the content of the first and second substances,
further comprising: sigma k a k ＝1.
2. Risk probability analysis and assessment
Assuming that the time duration of the flight time period required by the unmanned aerial vehicle is t, if the system stability parameter of the unmanned aerial vehicle is p (which represents the probability of accidental runaway occurring in unit time during the flight of the unmanned aerial vehicle), then it can be known that: the probability that the unmanned aerial vehicle is accidentally out of control in the whole flight process is tp.
Assuming that the unmanned aerial vehicle loses a power system and does free-fall movement after being out of control unexpectedly, the position of the unmanned aerial vehicle possibly appearing in the whole flight process is defined as the region omega of the road network structure analysis according to the operation performance and flight plan data of the unmanned aerial vehicle. According to the road network information in the region omega, the possibility L that the unmanned aerial vehicle is out of control accidentally and influences the normal operation of the road network is known as follows: l tp and URAR. The risk value R-L × SE, where SE is the average severity level of the loss of the ground road caused by the accidental loss of the drone.
1) Airline application mode
As shown in fig. 3, if the flight plan information submitted by the unmanned aerial vehicle is the airline application mode, the unmanned aerial vehicle appliesPlease find that the flying height is h, and learn that the maximum horizontal flying speed of the unmanned aerial vehicle is v according to the operation performance of the unmanned aerial vehicle h . It can be known that the unmanned aerial vehicle will continue to move according to inertia in case of unexpected runaway, and because the unmanned aerial vehicle will accelerate according to the gravity acceleration g in the vertical direction, the unmanned aerial vehicle exists in the air for the time t a Satisfies the following conditions:
by the above formula, can be solved
Therefore, the maximum distance of horizontal extension of the unmanned aerial vehicle is as follows:
at this moment, it can be known that the region of interest is shown in fig. 3, which is a region map based on the unmanned aerial vehicle route, the central broken line segment in fig. 3 is the route applying line, and the region corresponding to the annular boundary around the broken line is the region of road network information acquisition.
2) Airspace application mode
If the unmanned aerial vehicle applies for airspace to perform free flight, then, according to the analysis, it can be known that the corresponding region of interest is as shown in fig. 4, where the region located at the innermost part is the airspace applied for the flight plan, and the region corresponding to the region in the line outside the airspace is the region where road network information acquisition is required.
3. Loss severity grade analysis
When unmanned aerial vehicle accident was out of control, the loss degree and the road structure that unmanned aerial vehicle brought ground traffic, unmanned aerial vehicle physical properties, unmanned aerial vehicle flight information three are all closely relevant. In general, the level of severity of the loss is estimated and calculated by the energy E possessed by the drone when landing, the range of view β (the size of the drone) of the drone shielding the driver, and the maximum driving speed v of the vehicle on the road. For example:
1) the kinetic energy of the unmanned aerial vehicle when falling to the ground is large enough, so that the vehicle is damaged after being hit, and further subsequent risk events can be caused;
2) if unmanned aerial vehicle's size is great, even then unmanned aerial vehicle is very light, though unmanned aerial vehicle can not bring catastrophic destruction to automobile body itself, nevertheless, because it shields driver's field of vision for driver's unable normal reasonable action of taking. Safety accidents such as multi-vehicle collision are likely to be caused. For example, a newspaper floating in the air tends to stick directly to the driver's front window, resulting in the driver being completely obscured;
3) the higher the vehicle running speed, the higher the possibility of rear-end collision between the front and rear vehicles. When the car flies on the highway, even find the place ahead unmanned aerial vehicle after promptly the emergency braking, avoided the direct collision with unmanned aerial vehicle out of control. But still likely to cause a rear vehicle to rear-end a front vehicle. Causing severe losses.
The extent of loss SE caused by an accidental loss of the drone should be determined by the desired value of the extent of loss it falls on the different classes of roads, namely:
suppose that the air operation height of the unmanned aerial vehicle is h, the mass of the unmanned aerial vehicle is m, and the maximum operation speed is v h Then, the physics knowledge shows that the maximum kinetic energy of the unmanned aerial vehicle when the unmanned aerial vehicle is out of control and lands on the ground is:
then, the degree of road surface loss is classified and analyzed as follows: because the reaction time length from the time when the driver finds the unexpected dangerous event to the time when the driver responds is almost a fixed value, the faster the automobile runs, the smaller the time margin for avoiding and relieving the injury of the driver is; moreover, the greater the speed of the vehicle, the poorer the stability of the whole system, and the more serious the consequences of a small-error operation. Therefore, the model of the degree of loss should be discussed in a classification according to the traveling speed of the automobile.
In the following, the loss degree is discussed according to the road speed limit condition, so as to objectively reflect the maximum speed of the vehicle on the road surface, and further establish the upper limit of the loss degree. In this example, the energy carried by the drone can be divided into the following categories:
high energy-it means that the energy carried by the drone is sufficient to destroy an automobile and cause the personnel on the automobile to face fatal injury;
the medium energy-no-man can cause the damage of the automobile function, but can not cause the injury of the personnel on the automobile
Low-grade energy means that the unmanned aerial vehicle can not cause functional damage to the automobile even if colliding with the automobile
1) In this embodiment, the loss severity level is greatly estimated according to the full load of the car, and 5 people are loaded on each car. Then, in this case, the loss severity level SE 1 This is given by the following table:
2) medium speed situation
Under the condition of medium speed, firstly, the rear vehicle and the front vehicle collide with each other only under the condition that the front vehicle is completely out of control or the view field of a driver is completely shielded; secondly, due to the speed, only the front and rear occupants of the rear vehicle are seriously injured after a rear-end collision occurs, and the loss degree SE is reduced 2 This is given by the following table:
| height of | In | Is low in | |
| Big (a) | 7 | 5 | 5 |
| In | 7 | 5 | 10 -1 |
| Small | 7 | 5 | 10 -3 |
3) High speed condition
In contrast to the low speed situation, the size of an unmanned aerial vehicle that is accidentally out of control has a severe impact on the extent of loss during the course of a vehicle traveling at very high speeds. The size of the unmanned aerial vehicle is divided into a large situation, a medium situation, a small situation and a micro situation:
large-the size of the drone is sufficient to cover the entire front window of the car, the driver's field of view will be completely covered
The size of the unmanned aerial vehicle is larger than half of the front window of the automobile, and the visual field of the driver is influenced
The binocular lines of the small-driver cannot be simultaneously shielded by the unmanned aerial vehicle, and the visual field of the driver is hardly influenced at the moment
In the process that the car goes at the hypervelocity, no matter how the energy that unmanned aerial vehicle carried, car self speed is very big, and two cars are easily rear-ended around, and the person of the rear-end automobile and automobile body can receive corresponding injury. Degree of loss SE 3 This is given by the following table:
| height of | In | Is low in | |
| Big (a) | 10 | 10 | 10 |
| In | 10 | 10 | 1 |
| Small | 10 | 10 | 10 -3 |
The above description is only for the specific embodiment 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 (7)
1. A ground traffic risk monitoring method caused by flight of an unmanned aerial vehicle is characterized by comprising the following steps:
acquiring flight plan data of the unmanned aerial vehicle to obtain road network information corresponding to a flight airspace;
acquiring performance data of the unmanned aerial vehicle, and calculating the probability of accidental runaway of the unmanned aerial vehicle;
calculating a risk value of the unmanned aerial vehicle influencing ground traffic due to the accidental runaway according to the probability of the accidental runaway of the unmanned aerial vehicle and the road network information;
wherein, the calculating the risk value of the ground traffic influenced by the accidental runaway of the unmanned aerial vehicle comprises the following steps:
acquiring a corresponding urban road area rate URAR according to the road network information;
acquiring the probability P of the unmanned aerial vehicle going out of control accidentally according to the operation performance data of the unmanned aerial vehicle;
calculating the possibility L that the unmanned aerial vehicle is out of control accidentally to influence the normal operation of a road network according to the urban road area rate URAR and the probability P that the unmanned aerial vehicle is out of control accidentally: l ═ P · URAR; the risk value R of the unmanned aerial vehicle which is accidentally out of control and influences the normal operation of a road network is L multiplied by SE, wherein SE is the average severity level of the loss of the unmanned aerial vehicle to the ground road caused by the accidental out of control;
calculating the average severity level of the loss of the ground road caused by the accidental runaway of the unmanned aerial vehicle, wherein the average severity level comprises the following steps:
calculating energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground according to the flight information of the unmanned aerial vehicle;
acquiring the size of the unmanned aerial vehicle body according to the performance data of the unmanned aerial vehicle, so as to determine the visual field range beta of the unmanned aerial vehicle for shielding a road vehicle driver;
extracting road speed limit information in the road network area, and obtaining a preset road structure coefficient according to the road speed limit informationIn the group, the corresponding road structure coefficient alpha is called k ；
According to the energy E possessed by the unmanned aerial vehicle when falling to the ground, the visual field range beta of the unmanned aerial vehicle for shielding the driver, and the road structure coefficient alpha k And calculating the severity level SE of the loss of the ground road caused by the accidental runaway of the unmanned aerial vehicle:
wherein SE k ＝SE k (β,E)，SE k The severity level of the k-th road loss caused by the accident is determined by the visual field range beta of the vehicle driver shielding the road surface and the energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground.
2. The method for monitoring the ground traffic risk caused by the flight of the unmanned aerial vehicle according to claim 1, wherein the obtaining of the flight plan data of the unmanned aerial vehicle to obtain the road network information corresponding to the flight airspace further comprises:
extracting flight information of the unmanned aerial vehicle from the flight plan data;
determining a road network area covered by a flight airspace according to the flight information;
acquiring the area and the road quantity of the road network area, and calculating the urban road area rate URAR:
wherein, the road network region has n roads in total, and the width of the ith road is recorded as d i Having a length of l i Then, the area occupied by the ith road is: s i ＝l i d i And S represents an area of the road network region.
3. The method of claim 2, wherein determining a road network area covered by a flight airspace according to the flight information further comprises:
extracting preset flight modes, flight pre-use airspace and pre-flight height information according to the flight information;
when the flight mode is the air route application mode, acquiring the maximum horizontal flight speed v of the unmanned aerial vehicle according to the performance data of the unmanned aerial vehicle h Recording the time t of the unmanned aerial vehicle continuing to move according to inertia and moving in the air under the condition that the air height h is unexpectedly out of control a And g is gravity acceleration, the maximum horizontal extension distance of the unmanned aerial vehicle moving in the air is rho:
when the flight mode is an airspace application mode, acquiring a corresponding airspace area according to the flight pre-use airspace information; and calculating the area of the corresponding road network area according to the airspace area.
4. The method for monitoring ground traffic risk caused by flight of the unmanned aerial vehicle according to claim 2 or 3, wherein the calculating of the probability of accidental runaway of the unmanned aerial vehicle further comprises:
acquiring a system stability parameter lambda according to the performance data of the unmanned aerial vehicle;
and acquiring the preset flight duration t of the unmanned aerial vehicle according to the flight plan data, and calculating to obtain the probability P that the unmanned aerial vehicle is out of control unexpectedly in the flight plan at this time, wherein the probability P is lambda multiplied by t.
5. The method for monitoring ground traffic risk caused by unmanned aerial vehicle flight according to claim 1, wherein the road structure coefficient α is k The calculation process of (2) includes:
according to the road information in the road network area, the road structure coefficient alpha is set according to the classification of the road speed limit condition k ；
WhereinThe road structure coefficient α k The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
further comprising: sigma k a k ＝1
Wherein S is j The area s of the ith road corresponding to the area of the jth road i Comprises the following steps: s i ＝l i di，Γ k A set of k-th class roads; alpha is alpha k Has a value range of [0,1 ]]；
The road structure coefficient alpha k The method comprises the following steps:
class 1 road structure coefficient alpha 1 Corresponding road 1 The speed limit is below 60 KM/h;
class 2 road structure coefficient alpha 2 Corresponding road 2 The speed limit is more than 60KM/h and less than 100 KM/h;
class 3 road structure coefficient alpha 3 Corresponding road 3 The speed limit is above 100 KM/h.
6. A ground traffic risk monitoring system that unmanned aerial vehicle flight leads to, its characterized in that includes:
the information acquisition unit is used for acquiring flight plan data and road network data of the unmanned aerial vehicle;
the road network positioning unit is used for acquiring road network information corresponding to a flight airspace according to the flight plan data;
the performance monitoring unit is used for acquiring performance data of the unmanned aerial vehicle and calculating the probability of accidental runaway of the unmanned aerial vehicle;
the risk evaluation unit is used for calculating the ground traffic risk influenced by the accidental out-of-control of the unmanned aerial vehicle according to the probability of the accidental out-of-control of the unmanned aerial vehicle and the road network information;
wherein, the calculating the ground traffic risk influenced by the accidental out-of-control unmanned aerial vehicle comprises:
acquiring a corresponding urban road area rate URAR according to the road network information;
acquiring the probability P of accidental runaway of the corresponding unmanned aerial vehicle according to the operation performance data of the unmanned aerial vehicle;
calculating the possibility L that the unmanned aerial vehicle is out of control accidentally to influence the normal operation of a road network according to the urban road area rate URAR and the probability P that the unmanned aerial vehicle is out of control accidentally: l ═ P · URAR; the risk value R of the unmanned aerial vehicle which is accidentally out of control and influences the normal operation of a road network is L multiplied by SE, wherein SE is the average severity level of the loss of the unmanned aerial vehicle to the ground road caused by the accidental out of control;
the system further comprises:
the energy estimation unit is used for calculating the energy E possessed by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground and the visual field range beta of a driver of a vehicle covering the road surface according to the flight information and the performance data of the unmanned aerial vehicle;
a speed limit statistic unit for calling corresponding road structure coefficient alpha from a preset road structure coefficient group according to the road speed limit information in the road network area k ；
A loss evaluation unit for evaluating the loss according to the energy E of the unmanned aerial vehicle when landing, the visual field range beta of the unmanned aerial vehicle for shielding the road vehicle driver, and the road structure coefficient alpha k And calculating the severity level SE of the loss of the ground road caused by the accidental runaway of the unmanned aerial vehicle:
wherein SE k ＝SE k (β,E)，SE k The severity level of the k-th road loss caused by accidents is determined by the energy E of the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground and the visual field range beta of the unmanned aerial vehicle for shielding the road vehicle driver.
7. The system of claim 6, wherein the road network positioning unit further comprises:
the positioning subunit is used for extracting the flight information of the unmanned aerial vehicle from the flight plan data and determining a road network area covered by a flight airspace according to the flight information;
a calculating subunit, configured to obtain the area of the road network region and the number of roads, and calculate an urban road area rate URAR:
wherein, the road network region has n roads in total, and the width of the ith road is recorded as d i Having a length of l i Then, the area occupied by the ith road is: s i ＝l i d i And S represents an area of the road network region.
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