CN112802231B - Vehicle risk assessment method, device, equipment and medium based on GPS data - Google Patents
Vehicle risk assessment method, device, equipment and medium based on GPS data Download PDFInfo
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- 238000012502 risk assessment Methods 0.000 title claims abstract description 83
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 2
- 230000008859 change Effects 0.000 abstract description 8
- 230000008569 process Effects 0.000 description 8
- 230000006399 behavior Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 3
- 230000000737 periodic effect Effects 0.000 description 3
- 241000209202 Bromus secalinus Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
Abstract
The application discloses a vehicle risk assessment method, a device, equipment and a medium based on GPS data, which relate to the technical field of a wind control system based on a vehicle, and the method comprises the following steps: acquiring GPS historical data of a vehicle, and determining POI interest points according to the GPS historical data; generating a POI model according to data information corresponding to the POI interest points; and matching the acquired GPS real-time data of the vehicle with the POI model, and generating risk assessment information according to a matching result. The method can obtain the change of the user in the trip, timely generate the risk assessment information, find the change of the vehicle behavior in advance, provide important decision factors for the wind control center and improve the accuracy of the risk assessment.
Description
Technical Field
The application relates to the technical field of vehicle-based wind control systems, in particular to a vehicle risk assessment method, device, equipment and medium based on GPS data.
Background
In current wind control system, generally directly regard GPS data as the wind control means, after appearing the risk, carry out analysis management and control according to vehicle positioning data. For example, when the vehicle user is in debt or cheats and loan, the wind control personnel is informed to search and recover the vehicle according to the vehicle positioning, and the risk cannot be found in advance, so that the vehicle equipment is damaged.
Disclosure of Invention
The application aims to provide a vehicle risk assessment method, a vehicle risk assessment device, vehicle risk assessment equipment and a vehicle risk assessment medium based on GPS data, vehicle behavior changes can be found in advance, risk assessment information can be generated in time, and important decision factors are provided for a wind control center. The specific scheme is as follows:
in a first aspect, the present application discloses a vehicle risk assessment method based on GPS data, comprising:
acquiring GPS historical data of a vehicle, and determining POI interest points according to the GPS historical data;
generating a POI model according to the data information corresponding to the POI interest points;
and matching the acquired GPS real-time data of the vehicle with the POI model, and generating risk assessment information according to a matching result.
Optionally, determining POI interest points according to the GPS historical data includes:
and determining the POI interest points according to the speed information and the stay time in the GPS historical data.
Optionally, generating a POI model according to the data information corresponding to the POI interest points includes:
extracting the interest point type, the stay time and the stay duration of the POI interest point to obtain the travel habit data information of the user;
and taking the travel habit data information as the POI model.
Optionally, when the GPS real-time data of the vehicle does not match the POI model, before generating the risk assessment information, the method further includes:
judging whether the number of times that the GPS real-time data of the vehicle continuously deviates from the POI model exceeds a preset number of times;
if yes, executing a step of generating the risk assessment information;
and if not, adding the GPS real-time data of the vehicle into the POI model.
Optionally, before matching the acquired GPS real-time data of the vehicle with the POI model, the method further includes:
judging whether the building time of the POI model is greater than a preset period or not;
if yes, executing the step of matching the acquired GPS real-time data of the vehicle with the POI model;
and if not, adding the acquired GPS real-time data of the vehicle into the POI model.
Optionally, before matching the acquired GPS real-time data of the vehicle with the POI model, the method further includes:
judging whether the position information in the GPS real-time data of the vehicle is a sensitive point;
if yes, sending risk assessment information containing risks to a risk control center;
and if not, executing the step of matching the acquired GPS real-time data of the vehicle with the POI model.
In a second aspect, the present application discloses a vehicle risk assessment device based on GPS data, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring GPS data of a vehicle and determining POI interest points according to the GPS data;
the generating module is used for generating a POI model according to the data information corresponding to the POI interest points;
and the matching module is used for matching the acquired GPS real-time data of the vehicle with the POI model and generating risk assessment information according to a matching result.
Optionally, the obtaining module includes:
and the determining unit is used for determining the POI interest points according to the speed information and the stay time in the GPS historical data.
In a third aspect, the present application discloses an apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of the above-described GPS data based vehicle risk assessment method when executing the computer program.
In a fourth aspect, the present application discloses a medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method for GPS data based risk assessment of a vehicle as described above.
The application provides a vehicle risk assessment method based on GPS data, which comprises the following steps: acquiring GPS historical data of a vehicle, and determining POI interest points according to the GPS historical data; generating a POI model according to the data information corresponding to the POI interest points; and matching the acquired GPS real-time data of the vehicle with the POI model, and generating risk assessment information according to a matching result.
Therefore, the method and the device for the risk assessment of the vehicle generate the interest points of the user, namely POI interest points, by acquiring the GPS historical data of the vehicle, further obtain a POI model, match the GPS real-time data of the vehicle with the POI model, and obtain the risk assessment information according to the matching result; this application is through establishing the POI model promptly, match the GPS real-time data of vehicle and POI model, when not matching, can draw user's trip and change, timely generation risk assessment information discovers the vehicle behavior change in advance, provide important decision factor for the wind control center, when the vehicle appearance is unusual among the correlation technique, just seek the vehicle and retrieve, lead to the defect that the vehicle has damaged, this application combines GPS data and user's POI interest point to jointly as risk assessment factor in addition, can improve the accuracy of risk assessment, improve user experience. This application still provides a vehicle risk assessment device, equipment and medium based on GPS data simultaneously, has above-mentioned beneficial effect, and it is no longer repeated here.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for risk assessment of a vehicle based on GPS data according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a POI model updating and matching process in an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a vehicle risk assessment device based on GPS data according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but 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 application.
In current wind control system, what take generally directly regards GPS data as the wind control means, after the risk appears, carry out analysis management and control according to vehicle positioning data. However, when the vehicle user is in debt or cheats and loan, the wind control personnel searches for the recovered vehicle, the vehicle may be damaged and disassembled, and the risk cannot be found out in advance. Based on the above technical problems, the present embodiment provides a vehicle risk assessment method based on GPS data, which finds a vehicle behavior change in advance, generates risk assessment information in time, and provides an important decision factor for a wind control center, specifically please refer to fig. 1, where fig. 1 is a flowchart of a vehicle risk assessment method based on GPS data provided in the present embodiment, and specifically includes:
s101, GPS historical data of the vehicle are obtained, and POI interest points are determined according to the GPS historical data.
The data size of the acquired GPS historical data is not limited in this embodiment, and may be a data size of a week, a data size of a month, or a data size of other time periods. It can be understood that the more the data volume of the acquired GPS historical data is, the more beneficial the POI interest point and the subsequent POI model are to be created, and the more accurate the POI model is to be created. The GPS history data may include information such as speed, online status, direction angle, whether effective positioning is available, and the number of positioning satellites. It is also understood that the POI interest points are locations where a vehicle user (user) often stays, and may be a car 4S store, a restaurant, a company, a community, a school, etc. The embodiment does not limit the specific way of determining the POI interest points, and may be according to the length of the vehicle stay, the number of stay times, or other.
In a specific embodiment, determining POI interest points according to GPS history data may include:
and determining POI interest points according to the speed information and the stay time in the GPS historical data.
The mode of determining POI interest points in this embodiment is determined according to the speed information and the stay time in the GPS history data. The rule for determining whether the POI is the POI interest point according to the speed information and the stay time in the GPS history data may be that the speed is 0, and the stay time exceeds a preset value, and the present embodiment does not limit the specific size of the preset value, and may be 180s, or may be another value. In order to determine the result more accurately, if the speed of consecutive data points (for example, consecutive 3 points) before the GPS data point with the speed of 0 is not 0, the GPS data point is determined to be the POI interest point. For example, the POI may be a POI interest point, which is a point where 3 consecutive points have a speed, the speed is not 0, the data is not uploaded suddenly offline, the parking time is more than 180 seconds, and the last point is taken as a parking point.
And S102, generating a POI model according to the data information corresponding to the POI interest points.
It is understood that the POI is a POI type, which may be an automobile service, an automobile sales service, a food and beverage service, a shopping service, a healthcare service, an accommodation service, and the like. In this embodiment, the data information corresponding to the POI interest point may be referred to as POI data, and may include location information of the location point, such as longitude and latitude, address information, address category, and the like. The effective travel habits can be extracted by extracting the POI data, namely classifying and analyzing the data information corresponding to the POI interest points of the user, so as to generate the POI model. It can be understood that, in the present embodiment, the process of generating the POI model according to the data information of the POI interest points may be to store the data information of the POI interest points in a database or a storage container in a record form, where all data records of the vehicle are the generated POI model. Specifically, the information in the record may include three data information, i.e., a type of the point of interest, a stay time and a stay time, and the POI points of interest satisfying the conditions are stored in the database in a record form, and each stored record is the POI model. In other words, the POI model may be statistics of the sum of the superimposed generation of all POI data, e.g., vehicle a, point of interest: school (1), residence time: 20 minutes, generally at 8:00-9:00 residence time; and (3) interest points: mall (2 times), residence time: for 80 minutes, generally from 19:00 to 21: 00.
In a specific embodiment, generating a POI model according to data information corresponding to a POI interest point may include:
extracting the interest point type, the stay time and the stay duration of the POI interest point to obtain the travel habit data information of the user;
and taking the travel habit data information as a POI model.
That is, in this embodiment, the travel habit data information of the user is obtained by combining the stay time and the stay duration according to the type of the POI interest point of the POI. It can be understood that the travel habit data information records the behavior habit of the user, and is prepared for later alarming. The specific content of the travel habit data information is not limited in this embodiment, and the POI data of the user may be stored in a superimposed manner, and may be stored according to actual situations. For example, user A may start 8 am to go to the company, go home 6 pm, go to the movie on weekends, and the corresponding weight number (how many times it appears).
S103, matching the acquired GPS real-time data of the vehicle with the POI model, and generating risk assessment information according to a matching result.
It can be understood that, in the matching process of the GPS real-time data of the vehicle and the POI model in this embodiment, the GPS real-time data may be compared with each record or Excel file in the database or the storage container, and whether the GPS real-time data matches with a certain record in the POI model or not may be determined. In a specific embodiment, the matching process may be performed within the same time range and occur at the same interest point, and then the matching process is determined to be matched, otherwise, the matching process is determined to be not matched. In the embodiment, the acquired GPS real-time data of the vehicle is matched with the POI model, and the result of successful matching and the result of unsuccessful matching are obtained, so that corresponding risk assessment information is generated. The embodiment does not limit the specific content of the risk assessment information, and may generate risk assessment information containing risks when the risk assessment information is not matched; and if the matching result is matched, generating risk assessment information without risk or continuing monitoring or other operations as the risk assessment information corresponding to the matching result. That is, in the embodiment, when there is no match, that is, when the GPS real-time data does not match with the records in the POI model, the server is triggered to perform the operation of generating risk assessment information containing risks. For example, if the vehicle a stays at the mortgage point for 10 minutes and fails to hit, i.e., fails to match the POI model of the vehicle, there is an anomaly.
It can also be understood that in this embodiment, the GPS real-time data may be matched with the POI model as long as the GPS real-time data is acquired, or the GPS real-time data acquired for a certain period may be matched with the POI model; the embodiment does not limit the specific size of the period, and may be 3 days or one week.
The embodiment does not limit the specific process of matching, and may be that whether the GPS real-time data of the vehicle deviates from the POI model is determined, and if so, risk assessment information containing risks is generated; and if the data are not deviated, adding the acquired GPS real-time data into the POI model or continuously monitoring.
In a specific embodiment, when the GPS real-time data of the vehicle does not match the POI model, before generating the risk assessment information, the method may further include:
judging whether the frequency of continuous deviation of the GPS real-time data of the vehicle from the POI model exceeds a preset frequency or not;
if yes, executing a step of generating risk assessment information;
and if not, adding the GPS real-time data of the vehicle into the POI model.
In this embodiment, when the GPS real-time data of the vehicle is not matched with the POI model, before the risk assessment information is generated, it is also determined whether the number of times that the GPS real-time data of the vehicle continuously deviates from the POI model exceeds a preset number of times, and if so, the risk assessment information is generated; and if not, adding the GPS real-time data of the vehicle into the POI model. The specific number of consecutive deviations from the POI model is not limited in this embodiment, and may be 3, 4, or another value. It will be appreciated that if the acquired real-time GPS data for the vehicle is the first time it deviates from the POI model, it may be the case that the user arrives at a new destination and is unable to generate risk assessment information including risks and inform the central wind control based on this particular case. In a specific embodiment, if the deviation is from the POI model for the first time, the acquired GPS real-time data of a certain period can be marked independently without adding the POI model, and the next verification is waited; if the deviation is continuously caused from the POI model for the second time, the acquired GPS real-time data with a certain period can be immediately notified to a risk control center without adding the POI model; if the current period does not deviate from the POI model for the second time, the GPS real-time data of the current period and the GPS real-time data of the previous period can be added into the POI model.
It will be appreciated that the POI model functions to provide a judgment to inform the vehicle of the real-time status (at risk or not), and that the model build is constantly growing. In a specific embodiment, in order to effectively improve the stability and accuracy of the POI model, before matching the acquired GPS real-time data of the vehicle with the POI model, the method may further include:
judging whether the building time of the POI model is greater than a preset period or not;
if so, executing a step of matching the acquired GPS real-time data of the vehicle with the POI model;
and if not, adding the acquired GPS real-time data of the vehicle into the POI model.
The specific size of the preset period is not limited in this embodiment, and may be set according to actual conditions. In the embodiment, whether the building time of the POI model is greater than a preset period is judged, and when the building time of the POI model is greater than the preset period, matching is performed; when the data is not larger than the preset value, the POI model is required to be updated, namely the acquired GPS real-time data of the vehicle is added into the POI model. The stability and accuracy of the POI model can be effectively improved. For example, when the time for establishing the POI model is greater than or equal to 12 times the time of the periodic real-time data (i.e., when GPS real-time data of a certain period is acquired, the GPS real-time data is matched with the POI model), the operation of matching the acquired GPS real-time data of the vehicle with the POI model is performed. And when the building time of the POI model is less than 12 times of the periodic real-time data time, directly superposing the real-time periodic data to the POI model. It can be understood that the GPS data of the vehicle is real-time, and the POI model establishment is a process, assuming that the user a buys a new vehicle, the vehicle does not have any data at this time, the first model data is established based on the GPS data of the previous 3 days, and after 12 times of the cycle data is acquired, that is, 12 × 3=36 days, a complete travel model of the user a, that is, the POI model is established. Fig. 2 is a schematic flow chart of an embodiment of the present embodiment.
In a specific embodiment, in order to find the abnormal information of the vehicle early and prevent the occurrence of the condition other than the vehicle, before matching the acquired GPS real-time data of the vehicle with the POI model, the method further comprises:
judging whether the position information in the GPS real-time data of the vehicle is a sensitive point;
if yes, sending risk assessment information containing risks to a risk control center;
and if not, executing a step of matching the acquired GPS real-time data of the vehicle with the POI model.
That is, before performing model matching, the present embodiment also determines whether the location information in the GPS real-time data is a sensitive point (e.g., a mortgage point, a loan center, etc.), and if so, immediately sends risk assessment information containing a risk to the risk control center; and if the point is a non-sensitive point, performing subsequent operation to match the acquired GPS real-time data of the vehicle with the POI model. The behavior change of the vehicle can be captured in time, the vehicle can be searched and recovered in time, and the situation that the vehicle is damaged by mortgage can be prevented.
Based on the technical scheme, the GPS real-time data of the vehicle is matched with the POI model by creating the POI model, when the GPS real-time data of the vehicle is not matched with the POI model, the trip change of the user can be obtained, the risk assessment information can be generated timely, the behavior change of the vehicle can be found in advance, an important decision factor is provided for a wind control center, the GPS data and the POI interest points of the user are combined together to serve as the risk assessment factor, and the accuracy of risk assessment can be improved.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a vehicle risk assessment device based on GPS data according to an embodiment of the present disclosure, which includes:
in some specific embodiments, the method specifically includes:
the acquisition module 301 is configured to acquire GPS data of a vehicle and determine a POI interest point according to the GPS data;
a generating module 302, configured to generate a POI model according to data information corresponding to a POI interest point;
and the matching module 303 is configured to match the acquired GPS real-time data of the vehicle with the POI model, and generate risk assessment information according to a matching result.
In some specific embodiments, the obtaining module 301 includes:
and the determining unit is used for determining the POI interest points according to the speed information and the stay time in the GPS historical data.
In some specific embodiments, the generating module 302 includes:
extracting the interest point type, the stay time and the stay duration of the POI interest point to obtain the travel habit data information of the user;
and taking the travel habit data information as a POI model.
In some specific embodiments, the method further comprises:
the first judgment module is used for judging whether the frequency of continuous deviation of the GPS real-time data of the vehicle from the POI model exceeds a preset frequency or not;
and the adding module is used for adding the GPS real-time data of the vehicle into the POI model if the GPS real-time data of the vehicle is not in the POI model.
In some specific embodiments, the method further comprises:
and the second judgment module is used for judging whether the building time of the POI model is more than a preset period.
In some specific embodiments, the method further comprises:
the third judgment module is used for judging whether the position information in the GPS real-time data of the vehicle is a sensitive point;
and the sending module is used for sending the risk evaluation information containing the risk to the risk control center if the risk evaluation information contains the risk.
Since the embodiment of the vehicle risk assessment device part based on GPS data corresponds to the embodiment of the vehicle risk assessment method part based on GPS data, please refer to the description of the embodiment of the vehicle risk assessment method part based on GPS data, which is not repeated herein.
In the following, a device provided by an embodiment of the present application is described, and the device described below and the vehicle risk assessment method based on GPS data described above may be referred to correspondingly.
The present application also discloses an apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for vehicle risk assessment based on GPS data as described above when executing a computer program.
Since the embodiment of the device portion corresponds to the embodiment of the vehicle risk assessment method portion based on GPS data, please refer to the description of the embodiment of the vehicle risk assessment method portion based on GPS data for the embodiment of the device portion, and details thereof are not repeated here.
The following describes a medium provided by an embodiment of the present application, and the medium described below and the vehicle risk assessment method based on GPS data described above may be referred to correspondingly.
The present application also discloses a medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described GPS data-based vehicle risk assessment method.
Since the embodiment of the medium part corresponds to the embodiment of the vehicle risk assessment method part based on GPS data, please refer to the description of the embodiment of the vehicle risk assessment method part based on GPS data for the embodiment of the medium part, which is not repeated here.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The vehicle risk assessment method, device, equipment and medium based on the GPS data provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (8)
1. A vehicle risk assessment method based on GPS data is characterized by comprising the following steps:
acquiring GPS historical data of a vehicle, and determining POI interest points according to the GPS historical data;
generating a POI model according to the data information corresponding to the POI interest points;
matching the acquired GPS real-time data of the vehicle with the POI model, and generating risk assessment information according to a matching result;
before the step of matching the acquired GPS real-time data of the vehicle with the POI model, the method further comprises the following steps:
judging whether the position information in the GPS real-time data of the vehicle is a sensitive point;
if yes, sending risk assessment information containing risks to a risk control center;
if not, executing the step of matching the acquired GPS real-time data of the vehicle with the POI model;
when the GPS real-time data of the vehicle does not match the POI model, before generating the risk assessment information, further comprising:
judging whether the number of times that the GPS real-time data of the vehicle continuously deviates from the POI model exceeds a preset number of times;
if yes, executing a step of generating the risk assessment information;
and if not, adding the GPS real-time data of the vehicle into the POI model.
2. The GPS data based vehicle risk assessment method of claim 1, wherein determining POI points of interest from the GPS historical data comprises:
and determining the POI interest points according to the speed information and the stay time in the GPS historical data.
3. The vehicle risk assessment method based on GPS data according to claim 1, wherein generating a POI model according to the data information corresponding to the POI interest points comprises:
extracting the interest point type, the stay time and the stay duration of the POI interest point to obtain the travel habit data information of the user;
and taking the travel habit data information as the POI model.
4. The GPS data based vehicle risk assessment method of claim 1, further comprising, prior to matching the acquired GPS real-time data of the vehicle with the POI model:
judging whether the building time of the POI model is greater than a preset period or not;
if yes, executing the step of matching the acquired GPS real-time data of the vehicle with the POI model;
and if not, adding the acquired GPS real-time data of the vehicle into the POI model.
5. A vehicle risk assessment device based on GPS data, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring GPS data of a vehicle and determining POI interest points according to the GPS data;
the generating module is used for generating a POI model according to the data information corresponding to the POI interest points;
the matching module is used for matching the acquired GPS real-time data of the vehicle with the POI model and generating risk assessment information according to a matching result;
before the step of matching the acquired GPS real-time data of the vehicle with the POI model, the method further comprises the following steps:
judging whether the position information in the GPS real-time data of the vehicle is a sensitive point;
if yes, sending risk assessment information containing risks to a risk control center;
if not, executing the step of matching the acquired GPS real-time data of the vehicle with the POI model;
when the GPS real-time data of the vehicle does not match the POI model, before generating the risk assessment information, further comprising:
judging whether the number of times that the GPS real-time data of the vehicle continuously deviates from the POI model exceeds a preset number of times;
if yes, executing a step of generating the risk assessment information;
and if not, adding the GPS real-time data of the vehicle into the POI model.
6. The GPS data based vehicle risk assessment device of claim 5, wherein said acquisition module comprises:
and the determining unit is used for determining the POI interest points according to the speed information and the stay time in the GPS historical data.
7. An apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the GPS data based vehicle risk assessment method according to any one of claims 1 to 4 when executing said computer program.
8. A medium, characterized in that it has stored thereon a computer program which, when being executed by a processor, carries out the steps of the GPS data based vehicle risk assessment method according to any one of claims 1 to 4.
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