CN109360137B - Vehicle accident assessment method, computer readable storage medium and server - Google Patents
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Abstract
The invention belongs to the technical field of computers, and particularly relates to a vehicle accident assessment method based on big data analysis, a computer-readable storage medium and a server. The method comprises the steps of receiving a vehicle accident assessment request, and extracting accident site position coordinates and license plate numbers from the vehicle accident assessment request; selecting the unmanned aerial vehicle with the shortest time consumption reaching the position coordinates of the accident scene from a preset unmanned aerial vehicle set as an investigation unmanned aerial vehicle; issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle; acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the surveying unmanned aerial vehicle; comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively to determine accident vehicles; and acquiring the image of the accident vehicle through the camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a vehicle accident assessment method, a computer-readable storage medium and a server.
Background
In the prior art, if a vehicle accident occurs on the road, an alarm needs to be given and then a traffic police is waited for in place to evaluate the accident, the workload of a traffic police department for giving out the alarm is large, the time consumption is long, traffic jam and even paralysis are easily caused, under the condition that the traffic jam occurs after the vehicle accident occurs, the traffic police even hardly enter the scene, and due to the complex conditions of distance, the road and the like, the traffic police cannot arrive at the scene in time and cannot be processed in time, so that the working efficiency is low, and the traffic paralysis is further aggravated.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a vehicle accident assessment method, a computer-readable storage medium, and a server, so as to solve the problem that traffic paralysis is easily caused when vehicle accident assessment is performed on an alarm site by a traffic police department.
A first aspect of an embodiment of the present invention provides a vehicle accident assessment method, which may include:
receiving a vehicle accident assessment request, and extracting accident site position coordinates and license plate numbers from the vehicle accident assessment request;
selecting the unmanned aerial vehicle with the shortest time consumption for reaching the position coordinates of the accident scene from a preset unmanned aerial vehicle set as an exploration unmanned aerial vehicle;
issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle;
after receiving a flight completion message sent by the survey unmanned aerial vehicle, acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the survey unmanned aerial vehicle;
comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively, and determining vehicles with successfully compared license plate numbers as accident vehicles;
and acquiring the image of the accident vehicle through the camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle.
A second aspect of embodiments of the present invention provides a computer-readable storage medium storing computer-readable instructions, which when executed by a processor implement the steps of:
receiving a vehicle accident assessment request, and extracting accident site position coordinates and license plate numbers from the vehicle accident assessment request;
selecting the unmanned aerial vehicle with the shortest time consumption reaching the position coordinates of the accident scene from a preset unmanned aerial vehicle set as an investigation unmanned aerial vehicle;
issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle;
after receiving a flight completion message sent by the survey unmanned aerial vehicle, acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the survey unmanned aerial vehicle;
comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively, and determining vehicles with successfully compared license plate numbers as accident vehicles;
and acquiring the image of the accident vehicle through the camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle.
A third aspect of the embodiments of the present invention provides a server, including a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, where the processor implements the following steps when executing the computer readable instructions:
receiving a vehicle accident assessment request, and extracting accident site position coordinates and license plate numbers from the vehicle accident assessment request;
selecting the unmanned aerial vehicle with the shortest time consumption reaching the position coordinates of the accident scene from a preset unmanned aerial vehicle set as an investigation unmanned aerial vehicle;
issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle;
after receiving a flight completion message sent by the survey unmanned aerial vehicle, acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the survey unmanned aerial vehicle;
comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively, and determining vehicles with successfully compared license plate numbers as accident vehicles;
and acquiring the image of the accident vehicle through the camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, after a vehicle accident assessment request is received, the unmanned aerial vehicle which has the shortest time consumption when reaching the position coordinate of the accident site is automatically selected to go to the accident site, after the unmanned aerial vehicle reaches the accident site, the license plate image of each vehicle is collected through the camera device of the unmanned aerial vehicle, the accident vehicle is determined through number comparison, and finally the image of the accident vehicle is collected through the camera device of the unmanned aerial vehicle, and the accident assessment is carried out according to the image. According to the embodiment of the invention, under the condition that the traffic police department is not required to give an alarm, the unmanned aerial vehicle acquires information at the accident site and carries out vehicle accident assessment, so that the efficiency of vehicle accident assessment is greatly improved, the assessment can be completed in the shortest time possible, and traffic jam and traffic paralysis possibly caused by long-time vehicle accident assessment are avoided.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart of one embodiment of a vehicle accident assessment method in accordance with embodiments of the present invention;
FIG. 2 is a schematic flow chart of selecting the unmanned aerial vehicle that takes the shortest time to reach the position coordinates of the accident site from the set of unmanned aerial vehicles;
FIG. 3 is a schematic flow chart of image enhancement processing of a license plate image;
FIG. 4 is a schematic flow diagram of accident assessment based on images of an accident vehicle;
FIG. 5 is a block diagram showing an embodiment of a vehicle accident evaluation apparatus according to an embodiment of the present invention;
fig. 6 is a schematic block diagram of a server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, an embodiment of a vehicle accident assessment method according to an embodiment of the present invention may include:
step S101, a vehicle accident assessment request is received, and accident site position coordinates and a license plate number are extracted from the vehicle accident assessment request.
After a vehicle accident occurs, a vehicle owner can send a vehicle accident evaluation request to a server through a specified application program (APP) installed on a terminal device such as a mobile phone and a tablet personal computer. The vehicle accident assessment request carries the position coordinates of the accident scene acquired by the GPS module of the mobile terminal and the license plate number of the accident vehicle. After receiving the vehicle accident assessment request, the server can extract the position coordinates of the accident site and the license plate number from the vehicle accident assessment request.
And S102, selecting the unmanned aerial vehicle with the shortest time consumption reaching the position coordinates of the accident site from a preset unmanned aerial vehicle set as an investigation unmanned aerial vehicle.
As shown in fig. 2, step S102 may specifically include the following processes:
and S1021, respectively issuing a position coordinate query instruction to each unmanned aerial vehicle in the unmanned aerial vehicle set to obtain the current position coordinate of each unmanned aerial vehicle.
The unmanned aerial vehicle set comprises all unmanned aerial vehicles which are dispatched by the server and used for vehicle accident assessment. After receiving the position coordinate query instruction sent by the server, the unmanned aerial vehicle acquires the current position coordinate through a GPS module of the unmanned aerial vehicle and feeds the current position coordinate back to the server.
And step S1022, respectively querying a task list to be executed by each unmanned aerial vehicle.
The server can establish the task list that waits to execute respectively for every unmanned aerial vehicle in the in-process that the scheduling unmanned aerial vehicle carried out the vehicle accident and appraises, when the server has arranged new task for certain unmanned aerial vehicle, can add this task into the task list that this unmanned aerial vehicle waits to execute, and after this unmanned aerial vehicle has accomplished wherein a certain task, the server can remove this task from the task list that this unmanned aerial vehicle waits to execute. Each task in the task list includes the position coordinates of the task.
And step S1023, respectively calculating the time consumed by each unmanned aerial vehicle to reach the position coordinates of the accident site.
For example, the time taken for each drone to reach the accident site location coordinates may be calculated separately according to the following equation:
wherein DN is unmanned aerial vehicle's serial number, and is more than or equal to 0 DN less than or equal to DN, DN is unmanned aerial vehicle total number in the unmanned aerial vehicle set, KN dn The total number of the tasks in the task list to be executed by the nth unmanned aerial vehicle is represented by KN, the KN is the serial number of the tasks to be executed by the unmanned aerial vehicle, and the KN is more than or equal to 0 and less than or equal to KN dn ，(X dn,kn ,Y dn,kn ) Position coordinates of the kn-th task to be executed for the dn-th drone, in particular, (X) dn,0 ,Y dn,0 ) For the current position coordinates of the nth drone,
for the accident site position coordinates, stdtask time is a preset reference task time length, the value of which can be set according to the actual situation, for example, it can be set to 5 minutes, 10 minutes, 15 minutes or other values, stdVel is a preset reference flight speed, the value of which can be set according to the actual situation, for example, it can be set to 5 meters/second, 10 meters/second, 15 meters/second or other values, totalTime dn The time for the dn-th drone to reach the incident scene location coordinates.
And S1024, selecting the survey unmanned aerial vehicle.
For example, the survey drone may be selected according to the following equation:
TargetDrone＝argmin(TotalTime 1 ,TotalTime 2 ,TotalTime 3 ,...,TotalTime dn ,...,TotalTime DN ) Wherein, argmin is a minimum independent variable function, and TargetDrone is the serial number of the survey unmanned aerial vehicle.
And S103, issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle.
After the survey unmanned aerial vehicle is selected out, the server issues a flight instruction carrying the accident site position coordinates to the survey unmanned aerial vehicle, a new task is added into a task list to be executed by the survey unmanned aerial vehicle, and the survey unmanned aerial vehicle can fly to the accident site position coordinates after completing each task before the new task.
And S104, after receiving a flight completion message sent by the survey unmanned aerial vehicle, acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the survey unmanned aerial vehicle.
After the survey unmanned aerial vehicle arrives at the position coordinates of the accident scene, a flight completion message is sent to the server, and the server can control the survey unmanned aerial vehicle to survey the accident scene.
Because there may be a certain error in the GPS positioning of the terminal device that sends the vehicle accident assessment request, after the unmanned aerial vehicle arrives at the accident site, the server may collect its license plate images one by one for vehicles within a specified range through the camera device of the unmanned aerial vehicle, the specified range may be a circle center area that takes the accident site position coordinate as a circle center and takes a preset positioning error as a radius, and the positioning error may be set according to an actual situation, for example, it may be set to 10 meters, 15 meters, 20 meters, or other values.
And S105, comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively, and determining the vehicles with the license plate numbers successfully compared as accident vehicles.
In this embodiment, license plate numbers in the license plate image may be extracted by an Optical Character Recognition (OCR) technology, and are respectively compared with the license plate numbers in the vehicle accident assessment request, and a vehicle with a successfully compared license plate number is an accident vehicle.
Preferably, before performing optical character recognition and license plate number comparison, the image enhancement processing may be performed on the license plate image through the process shown in fig. 3:
and S301, calculating the probability density of the license plate image gray distribution.
For example, the probability density of the license plate image gray distribution can be calculated according to the following formula:
wherein, P (k) is the probability density of the k-th gray level distribution, k is greater than or equal to 0 and less than or equal to L-1,L is the number of gray levels, and the value thereof can be set according to the actual situation, for example, it can be set to 256, 512, 1024 or other values, n is n k The number of pixels of the k-th gray scale in the vehicle image is shown, and N is the total number of pixels in the vehicle image.
And S302, calculating the probability density of the expanded gray distribution of the license plate image.
For example, the probability density of the expanded gray distribution of the license plate image may be calculated according to the following formula:
wherein q is a gray scale expansion factor, and 0<q<1, the value thereof can be set according to the actual situation, for example, it can be set to 0.2, 0.3, 0.5 or other values, P max Is the maximum value of the probability density of the gray level distribution of the license plate image, namely P max = max (P (1), P (2),. ·, P (k),. ·, P (L)), max being the function of the maximum, P (1) q (k) And the expanded k-th gray level probability density in the vehicle image.
And step S303, carrying out image enhancement on the vehicle image.
For example, the image enhancement is performed on the vehicle image according to the following formula:
and F (k) is a gray value of the kth level gray level in the vehicle image after image enhancement.
Through the process shown in fig. 3, the vehicle image is further enhanced, and the accuracy of subsequent optical character recognition can be greatly improved.
And S106, acquiring the image of the accident vehicle through the camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle.
In this embodiment, the accident type of the accident vehicle needs to be determined through accident assessment, wherein the accident type includes, but is not limited to: rear-end collision, scraping, parallel line collision, corner collision and other accident types.
Different accident types have different characteristics, for example, for a rear-end collision, the damaged parts of the accident vehicle are the front end and the rear end of the accident vehicle; for the accidents of doubling collision and scraping, the damaged parts of the accident vehicle are two sides of the accident vehicle; for a cornering crash accident, the damaged portions of the accident vehicle are one side and the front end of the accident vehicle, and therefore, the accident type can be determined by the vehicle damage position.
In addition, the relative position of the accident vehicle is also a strong evidence for judging the accident type, for example, if the accident vehicle is head-to-head connected, the accident type is rear-end collision; if the accident vehicles are parallel and the distance is very close, the accident type is scraping or parallel collision; if the head of the accident vehicle is connected with one side of the vehicle, the accident type is the corner collision.
In the present embodiment, the accident evaluation is performed by the procedure shown in fig. 4, taking these factors into consideration in combination:
and step S1061, extracting each evaluation parameter from the image of the accident vehicle, and constructing an evaluation vector.
The evaluation parameters include, but are not limited to, damaged parts of two vehicles in an accident and relative position information including, but not limited to: the included angle of the central axes of the two cars, the orientations of the car heads of the two cars, the distance between the central positions of the two cars and the like.
After extracting the respective evaluation parameters from the image of the accident vehicle, an evaluation vector may be constructed as follows:
IncVec＝(IncInf 1 ,IncInf 2 ,IncInf 3 ,...,IncInf m ,...,IncInf M )
wherein M is the serial number of the evaluation parameter, M is more than or equal to 1 and less than or equal to M, M is the total number of the evaluation parameters, incInf m And the value of the mth evaluation parameter is shown, and IncVec is the evaluation vector.
And step S1062, extracting sample vectors of various accident types from a preset accident sample set.
Any one of the sample vectors is as follows:
SpVec tn,sn ＝(SpInf tn,sn,1 ,SpInf tn,sn,2 ,SpInf tn,sn,3 ,...,SpInf tn,sn,m ,...,SpInf tn,sn,M )
wherein TN is the serial number of the accident type, TN is more than or equal to 1 and less than or equal to TN, TN is the total number of the accident type, SN is the serial number of the sample vector, and SN is more than or equal to 1 and less than or equal to SN tn ，SN tn The total number of sample vectors, spInf, for the tn th accident type tn,sn,m The value of the mth evaluation parameter, spVec, for the sn-th sample vector of the tn accident types tn,sn The sn-th sample vector for the tn-th accident type.
Preferably, the setting process of the accident sample set may include the following steps:
firstly, extracting images of each group of accident vehicles with the evaluation results of the tn-th accident types from a preset historical accident database, extracting each evaluation parameter from the images of each group of accident vehicles, and respectively constructing the evaluation parameters into the following evaluation vectors:
HsIncVec tn,hn ＝(HsIncInf tn,hn,1 ,HsIncInf tn,hn,2 ,HsIncInf tn,hn,3 ,...,HsIncInf tn,hn,m ,...,HsIncInf tn,hn,M )
wherein HN is the serial number of the accident vehicle, HN is more than or equal to 1 and less than or equal to HN tn ，HN tn For the evaluation result to be the firsttotal number of accident vehicles of tn accident types, hsIncInf tn,hn,m The value of the mth evaluation parameter of the hn group accident vehicle with the evaluation result of the tn accident types is HsIncVec tn,hn The evaluation vector of the hn-th group of accident vehicles whose evaluation result is the tn-th accident type is obtained.
Then, the priority index of the evaluation vector of each group of accident vehicles whose evaluation results are the tn th accident types is calculated according to the following formula:
wherein abs is an absolute value function, exp is a natural index function, priIdx tn,hn And the priority index of the evaluation vector of the hn group accident vehicle with the evaluation result of the tn type accident.
Finally, selecting front SN with highest priority index from evaluation vectors of all groups of accident vehicles with the evaluation results of the tn-th accident types tn And taking the evaluation vector as a sample vector of the tn th accident type, and adding the sample vector into the accident sample set.
Wherein, SN tn ＝min(max(μ×HN tn MinNum), maxNum), μ is a preset proportionality coefficient, the value of which can be set according to the actual situation, for example, it can be set to 0.1, 0.2, 0.5 or other values, minNum is a preset minimum number of accident samples, the value of which can be set according to the actual situation, for example, it can be set to 100, 200, 500 or other values, maxNum is a preset minimum number of accident samples, the value of which can be set according to the actual situation, for example, it can be set to 1000, 2000, 5000 or other values, min is a minimum-value-solving function, and max is a maximum-value-solving function.
And step S1063, calculating the average distance between the evaluation vector and the sample vector of each accident type respectively.
For example, the average distance between the evaluation vector and the sample vector for each accident type is calculated separately according to:
wherein, avDis tn The average distance between the evaluation vector and the sample vector of the tn accident types is calculated;
and step S1064, determining the accident type of the accident vehicle.
For example, the accident type of the accident vehicle may be determined according to the following formula:
TargetType＝argmin(AvDis 1 ,AvDis 2 ,AvDis 3 ,...,AvDis tn ,...,AvDis TN )
wherein argmin is a minimum independent variable function, and TargetType is a serial number of the accident type of the accident vehicle.
In summary, after receiving a vehicle accident assessment request, the embodiment of the invention automatically selects the unmanned aerial vehicle which has the shortest time consumption when reaching the position coordinate of the accident site to go to the accident site, acquires the license plate image of each vehicle through the camera device of the unmanned aerial vehicle after the unmanned aerial vehicle reaches the accident site, determines the accident vehicle through number comparison, and finally acquires the image of the accident vehicle through the camera device of the unmanned aerial vehicle, and performs accident assessment according to the image. According to the embodiment of the invention, under the condition that no police are required to be issued by a traffic police department, the unmanned aerial vehicle acquires information at the accident site and evaluates the vehicle accident, so that the efficiency of evaluating the vehicle accident is greatly improved, the evaluation can be completed in as short a time as possible, and traffic jam and traffic paralysis possibly caused by long-time evaluation of the vehicle accident are avoided.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 5 is a block diagram of an embodiment of a vehicle accident assessment apparatus according to an embodiment of the present invention, which corresponds to the vehicle accident assessment method described in the above embodiments.
In this embodiment, a vehicle accident evaluation apparatus may include:
an accident assessment request receiving module 501, configured to receive a vehicle accident assessment request, and extract an accident site location coordinate and a license plate number from the vehicle accident assessment request;
an investigation unmanned aerial vehicle selection module 502 for selecting an unmanned aerial vehicle with the shortest time consumption to reach the position coordinate of the accident site from a preset unmanned aerial vehicle set as an investigation unmanned aerial vehicle;
a flight instruction issuing module 503, configured to issue a flight instruction carrying the accident site position coordinates to the survey unmanned aerial vehicle;
the license plate image acquisition module 504 is used for acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the surveying unmanned aerial vehicle after receiving a flight completion message sent by the surveying unmanned aerial vehicle;
a license plate number comparison module 505, configured to compare license plate numbers in the license plate images with license plate numbers in the vehicle accident assessment request, respectively, and determine a vehicle with successfully compared license plate numbers as an accident vehicle;
an accident vehicle image acquisition module 506, configured to acquire an image of the accident vehicle through a camera of the survey drone;
and an accident evaluation module 507, configured to perform accident evaluation according to the image of the accident vehicle.
Further, the survey drone selection module may include:
the position coordinate acquisition unit is used for respectively issuing position coordinate query instructions to each unmanned aerial vehicle in the unmanned aerial vehicle set to acquire the current position coordinates of each unmanned aerial vehicle;
the task list query unit is used for respectively querying task lists to be executed by all the unmanned aerial vehicles;
the time consumption calculation unit is used for calculating the time consumption of each unmanned aerial vehicle reaching the position coordinates of the accident scene according to the following formula:
wherein DN is unmanned aerial vehicle's serial number, and is more than or equal to 0 DN less than or equal to DN, DN is unmanned aerial vehicle total number in the unmanned aerial vehicle set, KN dn The total number of the tasks in the task list to be executed by the nth unmanned aerial vehicle is represented by KN, the KN is the serial number of the tasks to be executed by the unmanned aerial vehicle, and the KN is greater than or equal to 0 and less than or equal to KN dn ，(X dn,kn ,Y dn,kn ) Position coordinates of the kn-th task to be performed for the dn-th drone, in particular, (X) dn,0 ,Y dn,0 ) For the current position coordinates of the nth drone,
for the accident site position coordinates, stdTaskTime is a preset reference task time length, stdVel is a preset reference flight speed, and TotalTime dn The time spent for the dn-th unmanned aerial vehicle to reach the position coordinate of the accident scene;
an unmanned aerial vehicle survey selection unit for selecting the unmanned aerial vehicle survey according to the following formula:
TargetDrone＝argmin(TotalTime 1 ,TotalTime 2 ,TotalTime 3 ,...,TotalTime dn ,...,TotalTime DN ) Wherein, argmin is a minimum independent variable function, and TargetDrone is the serial number of the survey unmanned aerial vehicle.
Further, the incident evaluation module may include:
an evaluation vector construction unit for extracting each evaluation parameter from the image of the accident vehicle and constructing an evaluation vector as shown below:
IncVec＝(IncInf 1 ,IncInf 2 ,IncInf 3 ,...,IncInf m ,...,IncInf M )
wherein M is the serial number of the evaluation parameter, M is more than or equal to 1 and less than or equal to M, M is the total number of the evaluation parameters, incInf m Taking the value of the mth evaluation parameter, wherein IncVec is the evaluation vector;
the sample vector extraction unit is used for extracting sample vectors of various accident types from a preset accident sample set, wherein any sample vector is as follows:
SpVec tn,sn ＝(SpInf tn,sn,1 ,SpInf tn,sn,2 ,SpInf tn,sn,3 ,...,SpInf tn,sn,m ,...,SpInf tn,sn,M )
wherein TN is the serial number of the accident type, TN is more than or equal to 1 and is less than or equal to TN, TN is the total number of the accident type, SN is the serial number of the sample vector, and SN is more than or equal to 1 and is less than or equal to SN and is less than or equal to SN tn ，SN tn Total number of sample vectors, spInf, for the tn th accident type tn,sn,m The value of the mth evaluation parameter, spVec, for the sn-th sample vector of the tn accident types tn,sn The sn-th sample vector of the tn-th accident type;
an average distance calculation unit for calculating average distances between the evaluation vector and sample vectors of the respective accident types, respectively, according to the following formula:
wherein, avDis tn The average distance between the evaluation vector and the sample vector of the tn accident types;
an accident type determination unit for determining the accident type of the accident vehicle according to the following formula:
TargetType＝argmin(AvDis 1 ,AvDis 2 ,AvDis 3 ,...,AvDis tn ,...,AvDis TN )
wherein argmin is a minimum independent variable function, and TargetType is a serial number of the accident type of the accident vehicle.
Further, the vehicle accident evaluation apparatus may further include:
the historical evaluation vector construction module is used for extracting images of each group of accident vehicles with the evaluation results of the tn th accident types from a preset historical accident database, extracting each evaluation parameter from the images of each group of accident vehicles, and respectively constructing the evaluation vectors as follows:
HsIncVec tn,hn ＝(HsIncInf tn,hn,1 ,HsIncInf tn,hn,2 ,HsIncInf tn,hn,3 ,...,HsIncInf tn,hn,m ,...,HsIncInf tn,hn,M ) Wherein HN is the serial number of the accident vehicle, HN is more than or equal to 1 and less than or equal to HN tn ，HN tn HsIncInf the total number of accident vehicles with the evaluation result of the tn th accident type tn,hn,m The evaluation result is the value of the mth evaluation parameter of the hn group accident vehicle with the tn accident types, hsIncVec tn,hn An evaluation vector of the hn group accident vehicle of which the evaluation result is the tn th accident type;
and the priority index calculation module is used for calculating the priority indexes of the evaluation vectors of the groups of accident vehicles with the evaluation results of the tn accident types according to the following formula:
wherein abs is an absolute value function, exp is a natural index function, priIdx tn,hn A priority index of an evaluation vector for an hn-th group of accident vehicles whose evaluation result is the tn-th accident type;
a sample vector selection module for selecting the front SN with the highest priority index from the evaluation vectors of each group of accident vehicles with the evaluation result of the tn-th accident type tn And taking the evaluation vector as a sample vector of the tn accident type, and adding the sample vector into the accident sample set.
Further, the vehicle accident evaluation apparatus may further include:
a first probability density calculating module, configured to calculate a probability density of the license plate image gray distribution according to the following formula:
wherein P (k) is the probability density of the k-th level gray distribution, k is more than or equal to 0 and less than or equal to L, L is the number of gray levels, n k The number of pixels of the k-th gray level in the vehicle image is N, and the N is the total number of pixels in the vehicle image;
the second probability density calculation module is used for calculating the probability density of the expanded gray distribution of the license plate image according to the following formula:
wherein q is a gray scale expansion factor, and 0<q<1，P max Is the maximum value of the probability density of the gray level distribution of the license plate image, namely P max = max (P (1), P (2),. Eta., P (k),. Eta., P (L)), max being a function of the maximum value, P (1), P (2),. Eta., P (L)), max being a function of the maximum value q (k) The probability density after the expansion of the k-th level gray scale in the vehicle image is obtained;
an image enhancement module for image enhancing the vehicle image according to:
wherein, floor is a downward value-taking function, and F (k) is a gray value of the kth level gray in the vehicle image after image enhancement.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, modules and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Fig. 6 shows a schematic block diagram of a server provided in an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown.
In this embodiment, the server 6 may include: a processor 60, a memory 61, and computer readable instructions 62 stored in the memory 61 and operable on the processor 60, such as computer readable instructions to perform the vehicle accident assessment method described above. The processor 60, when executing the computer readable instructions 62, implements the steps in the various vehicle accident assessment method embodiments described above, such as steps S101-S106 shown in fig. 1. Alternatively, the processor 60, when executing the computer readable instructions 62, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 501 to 507 shown in fig. 5.
Illustratively, the computer readable instructions 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules/units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution of the computer-readable instructions 62 in the server 6.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 61 may be an internal storage unit of the server 6, such as a hard disk or a memory of the server 6. The memory 61 may also be an external storage device of the server 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the server 6. Further, the memory 61 may also include both an internal storage unit of the server 6 and an external storage device. The memory 61 is used to store the computer readable instructions and other instructions and data required by the server 6. The memory 61 may also be used to temporarily store data that has been output or is to be output.
Each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of computer readable instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, which can store computer readable instructions.
The above-mentioned embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A vehicle accident assessment method, comprising:
receiving a vehicle accident assessment request, and extracting accident site position coordinates and a license plate number from the vehicle accident assessment request;
selecting the unmanned aerial vehicle with the shortest time consumption reaching the position coordinates of the accident scene from a preset unmanned aerial vehicle set as an investigation unmanned aerial vehicle;
issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle;
after receiving a flight completion message sent by the survey unmanned aerial vehicle, acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the survey unmanned aerial vehicle;
comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively, and determining the vehicles with successfully compared license plate numbers as accident vehicles;
acquiring an image of the accident vehicle through a camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle;
the unmanned aerial vehicle that selects to reach from predetermined unmanned aerial vehicle set accident scene position coordinate consuming time is the shortest includes as survey unmanned aerial vehicle:
respectively issuing a position coordinate query instruction to each unmanned aerial vehicle in the unmanned aerial vehicle set to obtain the current position coordinate of each unmanned aerial vehicle;
respectively inquiring task lists to be executed by all unmanned aerial vehicles;
respectively calculating the time spent by each unmanned aerial vehicle on reaching the position coordinates of the accident scene according to the following formula:
wherein DN is unmanned aerial vehicle's serial number, and is more than or equal to 0 DN less than or equal to DN, DN is unmanned aerial vehicle total number in the unmanned aerial vehicle set, KN dn The total number of the tasks in the task list to be executed by the nth unmanned aerial vehicle is represented by KN, the KN is the serial number of the tasks to be executed by the unmanned aerial vehicle, and the KN is more than or equal to 0 and less than or equal to KN dn ，(X dn,kn ,Y dn,kn ) Position coordinates of the kn-th task to be performed for the dn-th drone, in particular, (X) dn,0 ,Y dn,0 ) For the current position coordinates of the nth drone,
for the position coordinates of the accident scene, stdTaskTime is a preset reference task time length, stdVel is a preset reference flight speed, and TotalTime dn The time consumed for the dn-th unmanned aerial vehicle to reach the position coordinate of the accident scene is taken;
selecting the survey drone according to the following formula:
TargetDrone＝argmin(TotalTime 1 ,TotalTime 2 ,TotalTime 3 ,...,TotalTime dn ,...,TotalTime DN )
wherein, argmin is a minimum independent variable function, and TargetDrone is a serial number of the survey unmanned aerial vehicle;
the accident evaluation according to the image of the accident vehicle comprises:
extracting various evaluation parameters from the image of the accident vehicle, and constructing an evaluation vector as follows:
IncVec＝(IncInf 1 ,IncInf 2 ,IncInf 3 ,...,IncInf m ,...,IncInf M )
wherein M is the serial number of the evaluation parameter, M is more than or equal to 1 and less than or equal to M, M is the total number of the evaluation parameters, incInf m Taking the value of the mth evaluation parameter, wherein IncVec is the evaluation vector;
sample vectors of various accident types are extracted from a preset accident sample set, and any sample vector is as follows:
SpVec tn,sn ＝(SpInf tn,sn,1 ,SpInf tn,sn,2 ,SpInf tn,sn,3 ,...,SpInf tn,sn,m ,...,SpInf tn,sn,M )
wherein TN is the serial number of the accident type, TN is more than or equal to 1 and is less than or equal to TN, TN is the total number of the accident type, SN is the serial number of the sample vector, and SN is more than or equal to 1 and is less than or equal to SN and is less than or equal to SN tn ，SN tn Total number of sample vectors, spInf, for the tn th accident type tn,sn,m The value of the mth evaluation parameter, spVec, of the sn-th sample vector for the tn-th accident type tn,sn The sn-th sample vector of the tn-th accident type;
calculating the average distance between the evaluation vector and the sample vector of each accident type according to the following formula respectively:
wherein, avDis tn The average distance between the evaluation vector and the sample vector of the tn accident types;
determining the accident type of the accident vehicle according to the following formula:
TargetType＝argmin(AvDis 1 ,AvDis 2 ,AvDis 3 ,...,AvDis tn ,...,AvDis TN )
wherein argmin is a minimum independent variable function, and TargetType is a serial number of an accident type of the accident vehicle.
2. The vehicle accident assessment method according to claim 1, wherein the setting process of the accident sample set comprises:
extracting images of each group of accident vehicles with the evaluation result of the tn-th accident type from a preset historical accident database, extracting each evaluation parameter from the images of each group of accident vehicles, and respectively constructing the evaluation parameters into the following evaluation vectors:
HsIncVec tn,hn ＝(HsIncInf tn,hn,1 ,HsIncInf tn,hn,2 ,HsIncInf tn,hn,3 ,...,HsIncInf tn,hn,m ,...,HsIncInf tn,hn,M )
wherein HN is the serial number of the accident vehicle, and HN is more than or equal to 1 and less than or equal to HN tn ，HN tn To evaluate the total number of accident vehicles with the result of the tn accident type, hsIncInf tn,hn,m The evaluation result is the value of the mth evaluation parameter of the hn group accident vehicle with the tn accident types, hsIncVec tn,hn An evaluation vector of the hn group accident vehicle of which the evaluation result is the tn th accident type;
calculating the priority index of the evaluation vector of each group of accident vehicles with the evaluation result of the tn th accident type according to the following formula:
wherein abs is an absolute value function, exp is a natural index function, priIdx tn,hn A priority index of an evaluation vector for an hn-th group of accident vehicles whose evaluation result is the tn-th accident type;
selecting front SN with highest priority index from evaluation vectors of all groups of accident vehicles with the evaluation results of the tn-th accident types tn And taking the evaluation vector as a sample vector of the tn th accident type, and adding the sample vector into the accident sample set.
3. The vehicle accident assessment method according to any one of claims 1 to 2, wherein before comparing the license plate numbers in the license plate image with the license plate numbers in the vehicle accident assessment request, respectively, the method further comprises:
calculating the probability density of the license plate image gray distribution according to the following formula:
wherein P (k) is the probability density of the k-th level gray distribution, k is more than or equal to 0 and less than or equal to L, L is the number of gray levels, n k The number of pixels of the k-th gray level in the vehicle image is N, and the N is the total number of pixels in the vehicle image;
calculating the probability density of the expanded gray distribution of the license plate image according to the following formula:
wherein q is a gray scale expansion factor, and 0<q<1，P max Is the maximum value of the probability density of the gray level distribution of the license plate image, namely P max = max (P (1), P (2),. ·, P (k),. ·, P (L)), max being the function of the maximum, P (1) q (k) The probability density after the expansion of the k-th level gray scale in the vehicle image is obtained;
image enhancement of the vehicle image according to:
and F (k) is a gray value of the kth level gray level in the vehicle image after image enhancement.
4. A computer readable storage medium storing computer readable instructions, wherein the computer readable instructions, when executed by a processor, implement the steps of the vehicle accident assessment method of any of claims 1 to 3.
5. A server comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor when executing the computer readable instructions performs the steps of:
receiving a vehicle accident assessment request, and extracting accident site position coordinates and license plate numbers from the vehicle accident assessment request;
selecting the unmanned aerial vehicle with the shortest time consumption for reaching the position coordinates of the accident scene from a preset unmanned aerial vehicle set as an exploration unmanned aerial vehicle;
issuing a flight instruction carrying the position coordinates of the accident scene to the survey unmanned aerial vehicle;
after receiving a flight completion message sent by the survey unmanned aerial vehicle, acquiring license plate images of all vehicles at the position coordinates of the accident scene through a camera device of the survey unmanned aerial vehicle;
comparing the license plate numbers in the license plate images with the license plate numbers in the vehicle accident assessment request respectively, and determining vehicles with successfully compared license plate numbers as accident vehicles;
acquiring an image of the accident vehicle through a camera device of the survey unmanned aerial vehicle, and performing accident assessment according to the image of the accident vehicle;
the unmanned aerial vehicle that selects to reach from predetermined unmanned aerial vehicle set accident scene position coordinate consuming time is the shortest includes as survey unmanned aerial vehicle:
respectively issuing a position coordinate query instruction to each unmanned aerial vehicle in the unmanned aerial vehicle set to obtain the current position coordinate of each unmanned aerial vehicle;
respectively inquiring task lists to be executed by all unmanned aerial vehicles;
respectively calculating the time spent by each unmanned aerial vehicle for reaching the position coordinates of the accident scene according to the following formula:
wherein DN is the serial number of the unmanned aerial vehicle, DN is more than or equal to 0 and less than or equal to DN, DN is the total number of the unmanned aerial vehicles in the unmanned aerial vehicle set, KN dn The total number of the tasks in the task list to be executed by the nth unmanned aerial vehicle is represented by KN, the KN is the serial number of the tasks to be executed by the unmanned aerial vehicle, and the KN is greater than or equal to 0 and less than or equal to KN dn ，(X dn,kn ,Y dn,kn ) Position coordinates of the kn-th task to be performed for the dn-th drone, in particular, (X) dn,0 ,Y dn,0 ) For the current position coordinates of the nth drone,
for the accident site position coordinates, stdTaskTime is a preset reference task time length, stdVel is a preset reference flight speed, and TotalTime dn The time spent for the dn-th unmanned aerial vehicle to reach the position coordinate of the accident scene;
selecting the survey drone according to the following formula:
TargetDrone＝argmin(TotalTime 1 ,TotalTime 2 ,TotalTime 3 ,...,TotalTime dn ,...,TotalTime DN )
wherein, argmin is a minimum independent variable function, and TargetDrone is a serial number of the survey unmanned aerial vehicle;
the performing accident assessment according to the image of the accident vehicle comprises:
extracting various evaluation parameters from the image of the accident vehicle, and constructing an evaluation vector as follows:
IncVec＝(IncInf 1 ,IncInf 2 ,IncInf 3 ,...,IncInf m ,...,IncInf M )
wherein M is the serial number of the evaluation parameter, M is more than or equal to 1 and less than or equal to M, M is the total number of the evaluation parameters, incInf m Taking the value of the mth evaluation parameter, wherein IncVec is the evaluation vector;
sample vectors of various accident types are extracted from a preset accident sample set, and any sample vector is as follows:
SpVec tn,sn ＝(SpInf tn,sn,1 ,SpInf tn,sn,2 ,SpInf tn,sn,3 ,...,SpInf tn,sn,m ,...,SpInf tn,sn,M )
wherein TN is the serial number of the accident type, TN is more than or equal to 1 and less than or equal to TN, TN is the total number of the accident type, SN is the serial number of the sample vector, and SN is more than or equal to 1 and less than or equal to SN tn ，SN tn The total number of sample vectors, spInf, for the tn th accident type tn,sn,m The value of the mth evaluation parameter, spVec, of the sn-th sample vector for the tn-th accident type tn,sn The sn-th sample vector of the tn-th accident type;
calculating the average distance between the evaluation vector and the sample vector of each accident type according to the following formula respectively:
wherein, avDis tn The average distance between the evaluation vector and the sample vector of the tn accident types is calculated;
determining the accident type of the accident vehicle according to the following formula:
TargetType＝argmin(AvDis 1 ,AvDis 2 ,AvDis 3 ,...,AvDis tn ,...,AvDis TN )
wherein argmin is a minimum independent variable function, and TargetType is a serial number of an accident type of the accident vehicle.
6. The server according to claim 5, wherein the setting process of the accident sample set comprises:
extracting images of each group of accident vehicles with the evaluation results of the tn th accident types from a preset historical accident database, extracting each evaluation parameter from the images of each group of accident vehicles, and respectively constructing the evaluation parameters into the following evaluation vectors:
HsIncVec tn,hn ＝(HsIncInf tn,hn,1 ,HsIncInf tn,hn,2 ,HsIncInf tn,hn,3 ,...,HsIncInf tn,hn,m ,...,HsIncInf tn,hn,M )
wherein HN is the serial number of the accident vehicle, and HN is more than or equal to 1 and less than or equal to HN tn ，HN tn HsIncInf the total number of accident vehicles with the evaluation result of the tn th accident type tn,hn,m The value of the mth evaluation parameter of the hn group accident vehicle with the evaluation result of the tn accident types is HsIncVec tn,hn An evaluation vector of an hn group of accident vehicles of which the evaluation result is the tn th accident type;
calculating the priority index of the evaluation vector of each group of accident vehicles with the evaluation result of the tn th accident type according to the following formula:
wherein abs is an absolute value function, exp is a natural index function, prIdx tn,hn A priority index of an evaluation vector for an hn-th group of accident vehicles whose evaluation result is the tn-th accident type;
selecting front SN with highest priority index from evaluation vectors of all groups of accident vehicles with the evaluation results of the tn-th accident types tn And taking the evaluation vector as a sample vector of the tn th accident type, and adding the sample vector into the accident sample set.
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