CN111651664A - Accident vehicle positioning method and device based on accident position point, storage medium and terminal - Google Patents
Accident vehicle positioning method and device based on accident position point, storage medium and terminal Download PDFInfo
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Abstract
The invention discloses an accident vehicle positioning method, device, storage medium and terminal based on accident position points, wherein the method comprises the following steps: acquiring a coordinate point of an accident position in an electronic map; matching historical vehicle running data based on the coordinate points of the accident positions to generate a suspect vehicle set; sorting all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a sorted suspected vehicle set; obtaining historical driving data of each suspect vehicle in the sorted suspect vehicle set; and determining the target suspect vehicle based on the historical driving data of the suspect vehicles. Therefore, by adopting the embodiment of the application, the accuracy rate of positioning the accident vehicle can be improved.
Description
Technical Field
The invention relates to the technical field of vehicle safety, in particular to an accident vehicle positioning method and device based on an accident position point, a storage medium and a terminal.
Background
With the rapid development of social economy, vehicles become indispensable vehicles in people's lives, great convenience is brought to users, meanwhile, the occurrence rate of traffic accidents inevitably increases, and after the traffic accidents happen, finding out suspect vehicles quickly is the key point for law enforcement departments to solve the traffic accidents.
When a suspect vehicle causing an accident is found at present, a large amount of hardware equipment such as a vehicle event recorder, an early warning device, a traffic monitor and a cloud server is usually relied on by a relevant law enforcement department. And the related law enforcement departments play back the historical driving video data through hardware equipment so as to locate the suspect vehicle. Due to the fact that the cost of hardware equipment is high, a part of the hardware equipment is not required to be installed by national hard vehicles, a part of vehicles do not have related hardware equipment, and law enforcement departments may fail in acquiring driving video data, and therefore the accuracy rate of suspect vehicle positioning is reduced.
Disclosure of Invention
The embodiment of the application provides an accident vehicle positioning method and device based on an accident position point, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides an accident vehicle positioning method based on an accident location point, where the method includes:
acquiring a coordinate point of an accident position in an electronic map;
matching historical vehicle running data based on the coordinate points of the accident positions to generate a suspect vehicle set;
sorting all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a sorted suspected vehicle set;
obtaining historical driving data of each suspect vehicle in the sorted suspect vehicle set;
and determining the target suspect vehicle based on the historical driving data of the suspect vehicles.
Optionally, after determining the target suspect vehicle based on the historical driving data of the suspect vehicles, the method further includes:
and drawing a 3D vehicle driving model aiming at the target suspect vehicle at a preset time granularity position point, and sending the target suspect vehicle and the 3D vehicle driving model to an accident monitoring platform.
Optionally, the matching of the historical vehicle driving data based on the coordinate point of the accident position to generate a suspect vehicle set includes:
when a region division instruction for the coordinate point is received, performing region division based on the region division instruction to generate an accident region;
and matching historical vehicle running data in a preset time interval according to the accident area to generate a suspect vehicle set.
Optionally, the sorting the suspected vehicles in the suspected vehicle set according to the matching weight rule to generate a sorted suspected vehicle set includes:
calculating the suspicion probability of each suspect vehicle in the suspect vehicle set according to the matching weights;
and performing descending order arrangement on the suspected vehicles according to the suspected probability to generate a sorted suspected vehicle set.
Optionally, the plurality of matching weights include:
a first matching weight, which is determined according to whether a suspected vehicle has a collision alarm;
a second matching weight determined according to whether the suspect vehicle has a negative acceleration;
and determining the third matching weight according to the number of the matching position points of the suspected vehicle.
Optionally, the determining a target suspect vehicle based on the historical driving data of the suspect vehicles includes:
acquiring the driving-in time and the driving-in speed, the driving-out time and the driving-out speed as well as the staying time of each suspected vehicle for the accident area in the historical driving data;
calculating the acceleration corresponding to each suspect vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the staying time aiming at the accident area;
calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
calculating and generating a reference route according to the diameter of the accident area and the preset road grade;
and when the driving distance is greater than the reference distance, determining a first target suspect vehicle set.
Optionally, after determining the first set of target suspect vehicles, the method further includes:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the driving speed and the driving time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of each position point;
calculating and generating the driving distance of each suspected vehicle based on the acceleration between the position points;
calculating and generating a reference route according to the diameter of the accident area and the preset road grade;
and when the driving distance is greater than the reference distance, determining the vehicle as a target suspect vehicle.
In a second aspect, the present application provides an accident vehicle positioning apparatus based on an accident location point, the apparatus including:
the coordinate point acquisition module is used for acquiring coordinate points of accident positions in the electronic map;
the first set generation module is used for matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspect vehicle set;
the second set generation module is used for sequencing all suspected vehicles in the suspected vehicle set according to a matching weight rule and generating a sequenced suspected vehicle set;
the data acquisition module is used for acquiring historical driving data of each suspect vehicle in the sorted suspect vehicle set;
and the vehicle determining module is used for determining the vehicles as target suspected vehicles based on the historical driving data of the suspected vehicles.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, a user terminal firstly obtains a coordinate point of an accident position in an electronic map, then matches historical vehicle driving data based on the coordinate point of the accident position to generate a suspect vehicle set, then sorts all suspect vehicles in the suspect vehicle set according to a matching weight rule to generate a sorted suspect vehicle set, then obtains all suspect vehicle historical driving data in the sorted suspect vehicle set, and finally determines a target suspect vehicle based on all suspect vehicle historical driving data. According to the scheme, based on the combination of Beidou positioning service and map information (accident site), historical position information of vehicles in China is matched, then 3D model track backtracking is carried out on matched suspect vehicles, the actual travelling track details of the vehicles at millisecond level are preset through an accident point and are used for restoring an accident scene and finally positioning the accident vehicles, and therefore the accuracy of positioning the accident vehicles is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic flow chart of an accident vehicle positioning method based on an accident location point according to an embodiment of the present application;
FIG. 2 is a scene diagram of an implementation scenario of accident vehicle positioning based on an accident location point according to an embodiment of the present application;
FIG. 3 is a schematic flow chart diagram illustrating another accident vehicle positioning method based on accident location points according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of an apparatus for locating an accident vehicle based on an accident location point according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
To date, for locating a suspected vehicle of an accident, when the suspected vehicle causing the accident is found at present, a large amount of hardware equipment, such as a vehicle data recorder, an early warning device, a traffic monitor and a cloud server, is usually relied on by a relevant law enforcement department. And the related law enforcement departments play back the historical driving video data through hardware equipment so as to locate the suspect vehicle. Due to the fact that the cost of hardware equipment is high, a part of the hardware equipment is not required to be installed by national hard vehicles, a part of vehicles do not have related hardware equipment, and law enforcement departments may fail in acquiring driving video data, and therefore the accuracy rate of suspect vehicle positioning is reduced. Therefore, the application provides an accident vehicle positioning method, device, storage medium and terminal based on an accident location point, so as to solve the problems in the related art. In the technical scheme provided by the application, because this scheme combines together based on big dipper location service and map information (accident site), through matching the historical position information to national vehicle, then carry out 3D model orbit backtracking to the suspect vehicle of matching, observe the vehicle and predetermine the true orbit details of traveling of millisecond rank through the accident point, be used for restoring the accident scene, the ultimate location accident vehicle to the rate of accuracy of location accident vehicle has been improved, adopt the exemplary embodiment to explain in detail below.
The following describes in detail an accident vehicle positioning method based on accident location points according to an embodiment of the present application with reference to fig. 1 to 3. The method may be implemented in dependence on a computer program operable on an accident location point based accident vehicle locating device based on the von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application. The accident vehicle positioning device based on the accident location point in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Referring to fig. 1, a flow chart of an accident vehicle positioning method based on an accident location point is provided for the embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the steps of:
s101, acquiring a coordinate point of an accident position in an electronic map;
the electronic map is map data information provided by an electronic map manufacturer based on Beidou positioning service. The accident position point is a longitude and latitude point after the vehicle has a traffic accident. The coordinate points are latitude and longitude data used to describe the location point of the accident.
Generally, the Beidou positioning service is equipment forcibly installed by the country when the vehicle leaves a factory, running data of the vehicle can be acquired after being positioned through the Beidou positioning service, the vehicle position information reported by the Beidou positioning service has a uniform and definite protocol format, the difficulty is reduced for data analysis, and the consumption of service resources can be reduced due to the fact that the occupied space of data storage is small.
In one possible implementation mode, after the relevant department door receives the early warning of the occurrence of the traffic accident, the longitude and latitude coordinate point of the occurrence of the accident is searched in the electronic map through the position point of the occurrence of the accident, and the detailed data information near the position point of the accident is checked.
S102, matching historical vehicle running data based on the coordinate points of the accident positions to generate a suspect vehicle set;
the historical vehicle running data is historical data information which is uploaded to a server side by Beidou positioning service installed on the vehicle through real-time acquisition of the running data of the vehicle. The suspect vehicle is a set of vehicles matched to the accident position point according to the set time range value.
In the embodiment of the application, when a user matches historical vehicle driving data, the user needs to perform area division according to position points firstly, when the user performs area division through triggering a terminal key, a division instruction is generated and sent to a user terminal, when the user terminal receives the area division instruction for a coordinate point, the user terminal performs area division based on the area division instruction to generate an accident area, and finally, historical vehicle driving data in a preset time interval are matched according to the accident area to generate a suspect vehicle set.
In a possible implementation mode, a user terminal firstly acquires a coordinate point of an accident position, then divides an accident area based on an accident area division instruction sent by a user to generate the accident area, and finally the user terminal matches historical vehicle driving data according to the accident area divided by the user and set time to generate a plurality of suspected vehicles after matching.
For example, after an accident occurs, the xiaoming receives traffic accident warning information, searches the coordinates of the accident occurrence position point and the nearby information of the coordinates of the position point on the electronic map according to the received warning information, after the xiaoming searches the accident position point, plans the fence according to the accident position point, for example, draws a circular fence (or a polygon) with the radius of 1000 meters on the traffic accident occurrence position point of the electronic map, when the nearby traffic terrain is more complex and the traffic intersection is more, draws a plurality of fences, and then marks the approximate time that the accident vehicle may pass by for each fence, for example, 15:59:27 in 4/2/2020 to 17:59:41 in 4/2/2020, when the fence planning and the time marking are finished, the xiaoming sends a vehicle matching instruction, when the user terminal receives the matching instruction sent by the xiaoming, the user terminal matches the vehicle historical position information in the designated time according to the planned fence and the marked time on the electronic map, several suspect vehicle data are generated.
Specifically, the functions realized by the matching algorithm used in the historical vehicle position information within the specified time at least comprise the steps of obtaining the historical vehicle position information within the specified time of the vehicle, traversing whether the distance between each track point and the incident area is less than 1000 meters, and recording the matched position information.
S103, sorting all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a sorted suspected vehicle set;
the matching weight rule (the unit of the matching weight coefficient a-ij is W) comprises a first matching weight, a second matching weight and a third matching weight. And determining the first matching weight according to whether the suspected vehicle has a collision alarm, determining the second matching weight according to whether the suspected vehicle has a negative acceleration, and determining the third matching weight according to the number of matching position points of the suspected vehicle.
In the embodiment of the application, the user terminal calculates the suspicion probability of each suspect vehicle in the suspect vehicle set according to the first matching weight, the second matching weight and the third matching weight, and then obtains a plurality of suspect vehicles arranged from large to small after performing descending order on the suspect vehicles according to the suspicion probability.
In a possible implementation mode, a user terminal firstly judges whether each suspected vehicle in a suspected vehicle set has a collision alarm or not, when the collision alarm exists, the suspected vehicles are subjected to weight matching according to the vehicle collision alarm, when the collision alarm exists, the suspicion is larger, the matching weight can be 0.5W, then whether each suspected vehicle has negative acceleration or not is judged, if the vehicle runs in an accident planning area, the vehicle has a deceleration behavior, according to the matching weight of the negative acceleration of each suspected vehicle, the smaller the negative acceleration is, the larger the suspicion is, the larger the matching weight is, the matching weight can be 0.3W, finally, the number of position points of each suspected vehicle in the accident planning area is judged, and according to the number of the position points of each suspected vehicle, the larger the number of the position points is, the larger the staying time is, and the larger the suspicion is. And after the three condition matching weights are finished, generating the suspicion probability of each suspect vehicle, and performing descending order arrangement on the suspect vehicles according to the generated suspicion probability to obtain a plurality of suspect vehicles arranged from large to small.
It should be noted that the weighting factor in the embodiment of the present application may be determined according to actual situations, and is not limited herein.
S104, obtaining historical driving data of each suspect vehicle in the sorted suspect vehicle set;
in one possible implementation manner, when the ranked suspect vehicles are generated based on step S103, the user terminal sequentially acquires the historical driving data of each suspect vehicle from the vehicle with the largest suspicion. The historical driving data comprises the driving-in time, the driving-out time, the positive and negative acceleration, the driving direction, the vehicle speed and the number of position points of each suspected vehicle in the sorted suspected vehicle set aiming at the accident area.
And S105, determining the target suspect vehicle based on the historical driving data of the suspect vehicles.
In the embodiment of the application, a user terminal firstly obtains the entry time and the entry speed, the exit time and the exit speed and the stay time of each suspected vehicle for an accident area in historical travel data of each suspected vehicle, then calculates the corresponding acceleration of each suspected vehicle based on the entry time and the entry speed, the exit time and the exit speed and the stay time of each suspected vehicle for the accident area, then calculates and generates the travel distance of each suspected vehicle according to the corresponding acceleration of each suspected vehicle, then calculates and generates a reference route according to the diameter of the accident area and a preset road grade, then determines a first target suspected vehicle set when the travel distance is greater than the reference route, finally obtains a position point set of each suspected vehicle historical travel data in the first target suspected vehicle set for the accident area, and then obtains the travel speed and the travel time of each position point in the position point set, and calculating and generating acceleration between the position points according to the driving speed and the driving time of each position point, calculating and generating the driving distance of each suspected vehicle according to the acceleration between the position points, calculating and generating a reference route according to the diameter of the accident area and the preset road grade, and determining the suspected vehicle as the target vehicle when the driving distance is greater than the reference route.
In a possible implementation mode, the user terminal firstly obtains the driving time t of each suspect vehicle in the sequenced suspect vehicle set aiming at the accident areaiSpeed v corresponding to the time of entryiTime t of departureoSpeed v corresponding to the time of departureo. According to the time t of entryiAnd a time t of entry and exitoThe staying time delta t-t of the suspected vehicle in the accident area can be calculatedo-tiBased on Δ t and the entry time tiSpeed v corresponding to the time of entryiThe acceleration a ═ v (v) of each suspect vehicle can be calculatedo-vi)/(to-ti) Finally, the acceleration a, delta t and v of each suspect vehicle are usedi、aeCan be according to the formula S ═ viΔt+1/2(a*ae) The Δ t2 is used to obtain the driving distance S of each suspected vehicle, if S>Sp, suspected. Wherein a iseThe method is characterized in that ten-million-level vehicle acceleration empirical coefficients are accumulated in the industry, Sp is a reference distance, the calculation formula of Sp is that Sp is equal to (the diameter of a fence 2000+ the diameter of the fence 2000 p) m, p is determined according to different road grades (20% of high speed, 15% of cities and 10% of cities and countryside), and a way of directly acquiring the road grades is provided at present.
Obtaining a plurality of vehicles with each suspected vehicle driving distance S larger than the reference distance according to the above, then obtaining all position points of the plurality of vehicles passing through the accident area, and respectively calculating the acceleration of the front and rear two position points, for example, the matching position is p1、p2、p3、p4Corresponding vehicle speed v1、v2、v3、v4Corresponding to the running time t1、t2、t3、t,4，Respectively calculate p1To p2Acceleration, p2To p3The acceleration a passing through the accident area is calculated by referring to the formula in the previous step12、a23、a34. For example a12＝(v1-v1)/(t1-t1). Performing time division equally according to the front position point and the rear position point (10-50 milliseconds is one time window, namely 100-20 time division windows can be cut one second), and calculating the running distance S of the vehicle machine in each time window according to the respective acceleration11、S12...S1100The calculation formula is as follows: s11＝v1Δt+1/2(a12*ae) Δ t2, and so on, calculating the total distance STotal ═ totalS11+S12+…+SnnIf S isGeneral assembly>Sp, drawing a 3D vehicle driving model according to the 10-50 millisecond time granularity position points under suspicion, and pushing the suspicion maximum vehicle after secondary screening and the corresponding 3D backtracking model to a traffic law enforcement department.
For example, as shown in fig. 2, three points and a half point are reached in the morning of 5.4.2019, traffic accidents occur at the intersection of the sunny road and the national road, and big data analysis shows that A, B, C vehicles pass through the intersection in the time range, a and B have the same number of matched track points, but B has no track point with negative acceleration, which indicates that the suspicion of the vehicle a is greater than that of the vehicle B, and the suspicion of the vehicle C is minimum.
In the embodiment of the application, a user terminal firstly obtains a coordinate point of an accident position in an electronic map, then matches historical vehicle driving data based on the coordinate point of the accident position to generate a suspect vehicle set, then sorts all suspect vehicles in the suspect vehicle set according to a matching weight rule to generate a sorted suspect vehicle set, then obtains all suspect vehicle historical driving data in the sorted suspect vehicle set, and finally determines a target suspect vehicle based on all suspect vehicle historical driving data. According to the scheme, based on the combination of Beidou positioning service and map information (accident site), historical position information of vehicles in China is matched, then 3D model track backtracking is carried out on matched suspect vehicles, the actual travelling track details of the vehicles at millisecond level are preset through an accident point and are used for restoring an accident scene and finally positioning the accident vehicles, and therefore the accuracy of positioning the accident vehicles is improved.
Please refer to fig. 3, which is a flowchart illustrating an accident vehicle positioning method based on an accident location point according to an embodiment of the present application. The present embodiment is exemplified in that an accident vehicle locating method based on an accident location point is applied to a user terminal. The accident vehicle positioning method based on the accident position point can comprise the following steps:
s201, acquiring a coordinate point of an accident position in an electronic map;
s202, when a region division instruction for the coordinate point is received, performing region division based on the region division instruction to generate an accident region;
s203, matching historical vehicle running data in a preset time interval according to the accident area to generate a suspect vehicle set;
s204, calculating the suspicion probability of each suspicion vehicle in the suspicion vehicle set according to the matching weights;
s205, performing descending order arrangement on the suspected vehicles according to the suspected probability to generate a sorted suspected vehicle set;
s206, obtaining historical driving data of each suspect vehicle in the sorted suspect vehicle set;
s207, determining a target suspect vehicle based on the historical driving data of the suspect vehicles;
s208, drawing a 3D vehicle driving model aiming at the target suspect vehicle at a preset time granularity position point, and sending the target suspect vehicle and the 3D vehicle driving model to an accident monitoring platform.
In the embodiment of the application, a user terminal firstly obtains a coordinate point of an accident position in an electronic map, then matches historical vehicle driving data based on the coordinate point of the accident position to generate a suspect vehicle set, then sorts all suspect vehicles in the suspect vehicle set according to a matching weight rule to generate a sorted suspect vehicle set, then obtains all suspect vehicle historical driving data in the sorted suspect vehicle set, and finally determines a target suspect vehicle based on all suspect vehicle historical driving data. According to the scheme, based on the combination of Beidou positioning service and map information (accident site), historical position information of vehicles in China is matched, then 3D model track backtracking is carried out on matched suspect vehicles, the actual travelling track details of the vehicles at millisecond level are preset through an accident point and are used for restoring an accident scene and finally positioning the accident vehicles, and therefore the accuracy of positioning the accident vehicles is improved.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 4, a schematic structural diagram of an accident vehicle locating device based on an accident location point according to an exemplary embodiment of the present invention is shown. The accident vehicle locating device based on the accident position point can be realized by software, hardware or a combination of the two to form all or part of the terminal. The apparatus 1 includes a coordinate point acquisition module 10, a first set generation module 20, a second set generation module 30, a data acquisition module 40, and a vehicle determination module 50.
A coordinate point obtaining module 10, configured to obtain a coordinate point of an accident position in an electronic map;
the first set generation module 20 is configured to match historical vehicle driving data based on the coordinate points of the accident positions to generate a suspect vehicle set;
the second set generation module 30 is used for sorting the suspected vehicles in the suspected vehicle set according to the matching weight rule and generating a sorted suspected vehicle set;
the data acquisition module 40 is configured to acquire historical driving data of each suspect vehicle in the sorted suspect vehicle set;
and a vehicle determination module 50 for determining the suspected vehicle as the target suspected vehicle based on the historical driving data of the suspected vehicles.
It should be noted that, when the accident vehicle positioning device based on the accident location point provided in the above embodiment executes the accident vehicle positioning method based on the accident location point, the division of the function modules is merely exemplified, and in practical applications, the function distribution may be completed by different function modules according to needs, that is, the internal structure of the device may be divided into different function modules, so as to complete all or part of the functions described above. In addition, the accident vehicle positioning device based on the accident position point and the accident vehicle positioning method based on the accident position point provided by the embodiment belong to the same concept, and the detailed implementation process is shown in the method embodiment and is not described herein.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, a user terminal firstly obtains a coordinate point of an accident position in an electronic map, then matches historical vehicle driving data based on the coordinate point of the accident position to generate a suspect vehicle set, then sorts all suspect vehicles in the suspect vehicle set according to a matching weight rule to generate a sorted suspect vehicle set, then obtains all suspect vehicle historical driving data in the sorted suspect vehicle set, and finally determines a target suspect vehicle based on all suspect vehicle historical driving data. According to the scheme, based on the combination of Beidou positioning service and map information (accident site), historical position information of vehicles in China is matched, then 3D model track backtracking is carried out on matched suspect vehicles, the actual travelling track details of the vehicles at millisecond level are preset through an accident point and are used for restoring an accident scene and finally positioning the accident vehicles, and therefore the accuracy of positioning the accident vehicles is improved.
The present invention also provides a computer readable medium having stored thereon program instructions that, when executed by a processor, implement the accident location point based accident vehicle locating method provided by the various method embodiments described above.
The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the method for accident location point based accident vehicle localization as described in the various method embodiments above.
Please refer to fig. 5, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 5, the terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 5, the memory 1005, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and an accident vehicle location application based on the accident location point.
In the terminal 1000 shown in fig. 5, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke an accident location point-based accident vehicle locating application stored in the memory 1005, and specifically perform the following operations:
acquiring a coordinate point of an accident position in an electronic map;
matching historical vehicle running data based on the coordinate points of the accident positions to generate a suspect vehicle set;
sorting all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a sorted suspected vehicle set;
obtaining historical driving data of each suspect vehicle in the sorted suspect vehicle set;
and determining the target suspect vehicle based on the historical driving data of the suspect vehicles.
In one embodiment, the processor 1001, after performing the determining of the target suspect vehicle based on the historical traveling data of the suspect vehicles, further performs the following:
and drawing a 3D vehicle driving model aiming at the target suspect vehicle at a preset time granularity position point, and sending the target suspect vehicle and the 3D vehicle driving model to an accident monitoring platform.
In one embodiment, when the matching of the coordinate points based on the accident position to the historical vehicle driving data is performed to generate a suspect vehicle set, the processor 1001 specifically performs the following operations:
when a region division instruction for the coordinate point is received, performing region division based on the region division instruction to generate an accident region;
and matching historical vehicle running data in a preset time interval according to the accident area to generate a suspect vehicle set.
In one embodiment, when the processor 1001 performs the sorting of the suspicious vehicles in the set of suspicious vehicles according to the matching weight rule and generates a sorted set of suspicious vehicles, the following operations are specifically performed:
calculating the suspicion probability of each suspect vehicle in the suspect vehicle set according to the matching weights;
and performing descending order arrangement on the suspected vehicles according to the suspected probability to generate a sorted suspected vehicle set.
In one embodiment, when the processor 1001 determines the target suspect vehicle based on the historical driving data of the suspect vehicles, the following operations are specifically performed:
acquiring the driving-in time and the driving-in speed, the driving-out time and the driving-out speed as well as the staying time of each suspected vehicle for the accident area in the historical driving data;
calculating the acceleration corresponding to each suspect vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the staying time aiming at the accident area;
calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
calculating and generating a reference route according to the diameter of the accident area and the preset road grade;
and when the driving distance is greater than the reference distance, determining a first target suspect vehicle set.
In one embodiment, the processor 1001, when performing the determining the first set of target suspect vehicles, further performs the following:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the driving speed and the driving time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of each position point;
calculating and generating the driving distance of each suspected vehicle based on the acceleration between the position points;
calculating and generating a reference route according to the diameter of the accident area and the preset road grade;
and when the driving distance is greater than the reference distance, determining the vehicle as a target suspect vehicle.
In the embodiment of the application, a user terminal firstly obtains a coordinate point of an accident position in an electronic map, then matches historical vehicle driving data based on the coordinate point of the accident position to generate a suspect vehicle set, then sorts all suspect vehicles in the suspect vehicle set according to a matching weight rule to generate a sorted suspect vehicle set, then obtains all suspect vehicle historical driving data in the sorted suspect vehicle set, and finally determines a target suspect vehicle based on all suspect vehicle historical driving data. According to the scheme, based on the combination of Beidou positioning service and map information (accident site), historical position information of vehicles in China is matched, then 3D model track backtracking is carried out on matched suspect vehicles, the actual travelling track details of the vehicles at millisecond level are preset through an accident point and are used for restoring an accident scene and finally positioning the accident vehicles, and therefore the accuracy of positioning the accident vehicles is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. An accident vehicle locating method based on accident location points, the method comprising:
acquiring a coordinate point of an accident position in an electronic map;
matching historical vehicle running data based on the coordinate points of the accident positions to generate a suspect vehicle set;
sorting all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a sorted suspected vehicle set;
obtaining historical driving data of each suspect vehicle in the sorted suspect vehicle set;
and determining the target suspect vehicle based on the historical driving data of the suspect vehicles.
2. The method of claim 1, wherein after determining the target suspect vehicle based on the historical driving data of each suspect vehicle, further comprising:
and drawing a 3D vehicle driving model aiming at the target suspect vehicle at a preset time granularity position point, and sending the target suspect vehicle and the 3D vehicle driving model to an accident monitoring platform.
3. The method of claim 1, wherein matching historical vehicle travel data based on the coordinate points of the accident location to generate a set of suspect vehicles comprises:
when a region division instruction for the coordinate point is received, performing region division based on the region division instruction to generate an accident region;
and matching historical vehicle running data in a preset time interval according to the accident area to generate a suspect vehicle set.
4. The method of claim 1, wherein the sorting the suspect vehicles in the suspect vehicle set according to the matching weight rule to generate a sorted suspect vehicle set comprises:
calculating the suspicion probability of each suspect vehicle in the suspect vehicle set according to the matching weights;
and performing descending order arrangement on the suspected vehicles according to the suspected probability to generate a sorted suspected vehicle set.
5. The method of claim 4, wherein the plurality of matching weights comprises:
a first matching weight, which is determined according to whether a suspected vehicle has a collision alarm;
a second matching weight determined according to whether the suspect vehicle has a negative acceleration;
and determining the third matching weight according to the number of the matching position points of the suspected vehicle.
6. The method according to claim 4 or 5, wherein the determining a target suspect vehicle based on the historical driving data of each suspect vehicle comprises:
acquiring the driving-in time and the driving-in speed, the driving-out time and the driving-out speed as well as the staying time of each suspected vehicle for the accident area in the historical driving data;
calculating the acceleration corresponding to each suspect vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the staying time aiming at the accident area;
calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
calculating and generating a reference route according to the diameter of the accident area and the preset road grade;
and when the driving distance is greater than the reference distance, determining a first target suspect vehicle set.
7. The method of claim 6, wherein after determining the first set of target suspect vehicles, further comprising:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the driving speed and the driving time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of each position point;
calculating and generating the driving distance of each suspected vehicle based on the acceleration between the position points;
calculating and generating a reference route according to the diameter of the accident area and the preset road grade;
and when the driving distance is greater than the reference distance, determining the vehicle as a target suspect vehicle.
8. An accident vehicle locating apparatus based on an accident location point, the apparatus comprising:
the coordinate point acquisition module is used for acquiring coordinate points of accident positions in the electronic map;
the first set generation module is used for matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspect vehicle set;
the second set generation module is used for sequencing all suspected vehicles in the suspected vehicle set according to a matching weight rule and generating a sequenced suspected vehicle set;
the data acquisition module is used for acquiring historical driving data of each suspect vehicle in the sorted suspect vehicle set;
and the vehicle determining module is used for determining the vehicles as target suspected vehicles based on the historical driving data of the suspected vehicles.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
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