CN117334066A - Risk analysis method, system, terminal and storage medium based on vehicle data - Google Patents
Risk analysis method, system, terminal and storage medium based on vehicle data Download PDFInfo
- Publication number
- CN117334066A CN117334066A CN202311218668.9A CN202311218668A CN117334066A CN 117334066 A CN117334066 A CN 117334066A CN 202311218668 A CN202311218668 A CN 202311218668A CN 117334066 A CN117334066 A CN 117334066A
- Authority
- CN
- China
- Prior art keywords
- data
- vehicle
- personnel
- information
- risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012502 risk assessment Methods 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000005484 gravity Effects 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000000007 visual effect Effects 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 239000011521 glass Substances 0.000 claims description 5
- 238000012423 maintenance Methods 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 5
- 239000000463 material Substances 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000002349 favourable effect Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 208000032140 Sleepiness Diseases 0.000 description 1
- 206010041349 Somnolence Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 230000008921 facial expression Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000000366 juvenile effect Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 235000007183 paleface Nutrition 0.000 description 1
- 230000037321 sleepiness Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096791—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is another vehicle
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
-
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0965—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages responding to signals from another vehicle, e.g. emergency vehicle
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Atmospheric Sciences (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention belongs to the technical field of vehicle expressway driving, and particularly relates to a risk analysis method, a system, a terminal and a storage medium based on vehicle data; wherein the risk analysis method based on vehicle data includes the steps of: acquiring real-time parameter information of a vehicle body; acquiring personnel data information in a vehicle; determining comparison data based on the implementation parameter information and the personnel data information; and acquiring the risk level of the vehicle according to the comparison data. The invention is beneficial to the running safety of the vehicle and improves the risk analysis efficiency, and can ensure the normal use of the expressway, thereby reducing or avoiding the loss of life and property and material property of personnel.
Description
Technical Field
The invention belongs to the technical field of vehicle expressway driving, and particularly relates to a risk analysis method, a system, a terminal and a storage medium based on vehicle data.
Background
In order to ensure safe running of vehicles, different vehicles on the road need to be capable of interacting with each other, and the road and vehicle conditions, such as front vehicle accidents, can be known through processing the data; even the accident can be predicted in advance, and then the warning is given to the driver, so that the driving strategy is changed.
At present, the information acquisition of the expressway mainly comprises the step that the high-speed police detects dangerous driving behaviors based on sensing data of point-type sensing equipment (such as a video detector, a laser radar, a millimeter wave radar and the like), so that the safety management of vehicle traffic is realized. However, this method cannot analyze the risk situation more comprehensively, and the detection and analysis result is inaccurate, and the operation efficiency is too low, so that the occurrence of accidents cannot be prevented indirectly.
Disclosure of Invention
The invention aims at: aiming at the defects of the prior art, a risk analysis method based on vehicle data is provided, and the risk analysis efficiency is improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a risk analysis method based on vehicle data, comprising the steps of:
acquiring real-time parameter information of a vehicle body;
acquiring personnel data information in a vehicle;
determining comparison data based on the implementation parameter information and the personnel data information;
and acquiring the risk level of the vehicle according to the comparison data.
Preferably, the step of acquiring real-time parameter information of the vehicle body includes the following steps:
using a visual scanning component to obtain real-time appearance data of the vehicle body; wherein the real-time appearance data comprises real-time frame data of the vehicle and real-time glass data of the vehicle;
a gravity sensing component is used to obtain real-time weight data of the vehicle body.
Preferably, the step of acquiring personnel data information in the vehicle includes the following steps:
a face scanning component is used to obtain information of personnel data in the vehicle.
Preferably, the face scanning component comprises a first face scanner and a second face scanner; the step of using the face scanning component to acquire the personnel data information in the vehicle comprises the following steps:
using the first face scanner to obtain face state data of a driver within the vehicle;
and using the second face scanner to acquire personnel architecture data in the vehicle.
Preferably, the step of determining the comparison data based on the implementation parameter information and the personnel data information includes the following steps:
comparing the real-time weight data of the vehicle body with the original weight data of the vehicle body of the model in a database to obtain a weight difference value;
and comparing the personnel architecture data in the vehicle with preset riding personnel architectures in a database to determine the actual personnel structure classification.
Preferably, the step of acquiring the risk level of the vehicle according to the comparison data includes the steps of:
acquiring a risk level of the vehicle according to the face state data, the real-time appearance data, the weight difference value and the comparison data of the actual personnel structure classification; wherein the risk level comprises a common risk level, a medium risk level and a risk level;
the common risk level is that the face state data, the real-time appearance data, the weight difference value and the actual personnel structure classification are normal;
the medium risk level is that the face state data, the real-time appearance data and the weight difference value are all normal, and the actual personnel structure is classified as abnormal;
the risk level is an abnormality in any one or more of the face state data, the real-time appearance data, and the weight difference value.
Preferably, after the step of constructing a risk score model according to the risk factors and calculating the risk level of the vehicle, the method further includes:
acquiring data information of the road section where the vehicle is located; wherein the distance of the road section where the vehicle is located is within 60-100 Km; the data information comprises the congestion condition of a road, the condition of a road maintenance period and the frequency of occurrence of road accidents;
outputting early warning information according to the data information of the road section and the risk level of the vehicle; the early warning information comprises common early warning information, medium grade early warning information and danger early warning information.
The invention also discloses a risk analysis system based on the vehicle data, which comprises:
the data acquisition module is used for acquiring real-time parameter information of the vehicle body and acquiring personnel data information in the vehicle;
the comparison module is used for determining comparison data based on the implementation parameter information and the personnel data information;
and the analysis module is used for acquiring the risk level of the vehicle according to the comparison data.
The invention also discloses a risk analysis terminal based on the vehicle data, which comprises: the system comprises a memory, a processor and a vehicle data-based risk analysis program stored on the memory and executable on the processor, wherein the vehicle data-based risk analysis program realizes the steps of the vehicle data-based risk analysis method when being executed by the processor.
The invention also discloses a storage medium, wherein the storage medium is stored with a risk analysis program based on vehicle data, and the risk analysis program based on the vehicle data realizes the steps of the risk analysis method based on the vehicle data when being executed by a processor.
The method has the beneficial effects that the technical scheme firstly collects and acquires the real-time parameter information of the vehicle body, is beneficial to the subsequent judgment of the self-safety performance condition of the vehicle body, and can more comprehensively improve the accuracy of risk analysis data of vehicle running; then collecting and acquiring personnel data information in the vehicle to determine the rationality of the vehicle in the use process, so that the comprehensiveness and accuracy of analysis data of the vehicle on the expressway can be further ensured; the implementation parameter information and the personnel data information are subjected to safety comparison processing, and the difference between the actual data and the safety value of the vehicle is further determined; finally, the risk grade of the vehicle is obtained according to the comparison data, so that the safety coefficient of the vehicle can be determined and obtained, the driving safety of the vehicle is facilitated, the risk analysis efficiency is improved, the normal use of the expressway can be ensured, and the loss of lives and properties and material and properties of personnel is reduced or avoided.
Drawings
Features, advantages, and technical effects of exemplary embodiments of the present invention will be described below with reference to fig. 1 to 4.
FIG. 1 is a flow chart of a risk analysis method based on vehicle data according to an embodiment of the present invention;
FIG. 2 is a flow chart of a risk analysis method based on vehicle data according to an embodiment of the present invention;
FIG. 3 is a flow chart of a risk analysis system based on vehicle data according to an embodiment of the present invention;
fig. 4 is a block diagram illustrating an embodiment of a terminal for risk analysis based on vehicle data according to an embodiment of the present invention.
In the figure: 1001-a processor; 1002-a communication bus; 1003-user interface; 1004-a network interface; 1005-memory.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions.
In the description of the embodiments of the present application, the technical terms "first," "second," etc. are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the description of the embodiments of the present application, the term "and/or" merely describes association relationships of association objects based on a risk analysis method, a system, a terminal, and a storage medium of vehicle data, which indicates that three relationships may exist, for example, a and/or B may indicate: a alone, both a and B, and a plurality of cases alone. In addition, the character "/" herein generally indicates a relationship in which the front-rear association object is an or of the risk analysis method, system, terminal, and storage medium based on the vehicle data.
In the description of the embodiments of the present application, the term "plurality" refers to two or more (including two), and similarly, "plural sets" refers to two or more (including two), and "plural sheets" refers to two or more (including two).
In the description of the embodiments of the present application, the orientation or positional relationship indicated by the technical terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationship shown in the drawings, and are merely for convenience of describing the embodiments of the present application and for simplifying the description, rather than indicating or implying that the apparatus or element referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the embodiments of the present application.
In the description of the embodiments of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; or may be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to the specific circumstances.
The invention provides a risk analysis method, a risk analysis system, a risk analysis terminal and a risk analysis storage medium based on vehicle data.
As shown in fig. 4, fig. 4 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, or can be a mobile terminal device with a display function, such as a smart phone, a tablet personal computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III, dynamic image expert compression standard audio layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, dynamic image expert compression standard audio layer 3) player, a portable computer and the like.
As shown, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and so on. Among other sensors, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal moves to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile terminal is stationary, and the mobile terminal can be used for recognizing the gesture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and the like, which are not described herein.
It will be appreciated by those skilled in the art that the terminal structure shown in the drawings does not constitute a limitation of the terminal and may include more or less components than those illustrated, or may combine certain components, or may be arranged in different components.
Referring to fig. 1, one embodiment of a risk analysis method, apparatus, terminal and medium based on vehicle data according to the present invention provides a risk analysis method based on vehicle data, as shown in fig. 1, the risk analysis method based on vehicle data includes:
s1, acquiring real-time parameter information of a vehicle body;
s2, acquiring personnel data information in the vehicle;
s3, determining comparison data based on the implementation parameter information and the personnel data information;
and S4, acquiring the risk level of the vehicle according to the comparison data.
In the embodiment, the technical scheme firstly acquires the real-time parameter information of the vehicle body, is favorable for subsequent judgment of the self-safety performance condition of the vehicle body, and can more comprehensively improve the accuracy of risk analysis data of vehicle running; then collecting and acquiring personnel data information in the vehicle to determine the rationality of the vehicle in the use process, so that the comprehensiveness and accuracy of analysis data of the vehicle on the expressway can be further ensured; the implementation parameter information and the personnel data information are subjected to safety comparison processing, and the difference between the actual data and the safety value of the vehicle is further determined; finally, the risk grade of the vehicle is obtained according to the comparison data, so that the safety coefficient of the vehicle can be determined and obtained, the driving safety of the vehicle is facilitated, the risk analysis efficiency is improved, the normal use of the expressway can be ensured, and the loss of lives and properties and material and properties of personnel is reduced or avoided.
Specifically, in some embodiments, in the step S1, the step of acquiring real-time parameter information of the vehicle body includes the following steps:
a visual scanning component is used to obtain real-time appearance data of the vehicle body. Wherein the real-time appearance data comprises real-time frame data of the vehicle and real-time glass data of the vehicle.
That is, in this embodiment, during the running process of the expressway and because the vehicle is in a state of running at a high speed, the vehicle is liable to be splashed into the cockpit or the passenger cabin by external objects, so that it is required to acquire real-time glass data of the vehicle and real-time frame data of the vehicle, so as to avoid the user from driving the vehicle with potential safety hazard into the expressway, thereby improving the use safety of the vehicle, further improving the data accuracy of risk analysis, and improving the risk analysis efficiency.
Wherein in some embodiments, a plurality of visual scanning components (wherein the visual scanning components may be scanning cameras) are used for the left and right directions of the vehicle body and the 45-degree angular direction in the front direction, the 45-degree angular direction in the rear direction, the 45-degree angular direction in the left direction, and the 45-degree angular direction in the right direction. The real-time appearance data of the vehicle body are collected through a plurality of angles and a plurality of directions, so that more accurate data information can be obtained, the ordered performance of subsequent risk analysis is facilitated, and the risk analysis efficiency is improved. In addition, as the vehicle body does not need to be additionally provided with the visual scanning components at the front and the rear in the forward running process, the investment of the visual scanning components is effectively reduced; is beneficial to reducing the cost.
Specifically, in some embodiments, in the step S1, the step of acquiring real-time parameter information of the vehicle body further includes the following steps:
a gravity sensing component is used to obtain real-time weight data of the vehicle body.
That is, in the present embodiment, during the running of the expressway and due to the state of the vehicle running at a high speed, the vehicle that is overweight is driven for a long time is liable to have accidents (such as rollover, rear-end collision, guardrail collision, etc.) of different degrees; therefore, it is necessary to acquire real-time weight data of the vehicle; the method is favorable for orderly carrying out subsequent risk analysis, and improves the risk analysis efficiency.
Wherein in some embodiments, at least four gravity sensing components are used (wherein the gravity sensing components are gravity sensors) distributed in an array to obtain fulcrum weight data at wheels in the vehicle body;
acquiring air pressure condition data of wheels of the vehicle body according to the fulcrum weight data;
and acquiring real-time weight data of the vehicle body according to the fulcrum weight data.
That is, the weight sensors at four points up, down, left and right are used to obtain tire pressure conditions of four wheels of a common car so as to obtain actual real-time weight data of the car; the method is favorable for orderly carrying out subsequent risk analysis, and improves the risk analysis efficiency.
Specifically, in some embodiments, in the step S2, the step of acquiring the personnel data information in the vehicle includes the following steps:
a face scanning component is used to obtain information of personnel data in the vehicle.
Wherein, in some embodiments, the face scanning component comprises a first face scanner and a second face scanner; and the step of using the face scanning component to acquire the personnel data information in the vehicle comprises the following steps:
using the first face scanner to obtain face state data of a driver within the vehicle;
and using the second face scanner to acquire personnel architecture data in the vehicle.
That is, the first face scanner is used for determining the facial expression data of the driver, so that whether the driving state of the driver is in a fatigue state or not is obtained, further, the comprehensive analysis of the data in the process of being fed back to the terminal later is facilitated, and the risk analysis efficiency is improved. In addition, a second face scanner is used to acquire personnel architecture data within the vehicle; the structure condition of the age bracket of the driver is confirmed, and the risk analysis is facilitated to be carried out according to the framework; for example, a second face scanner is used to learn that five persons are in the vehicle, two of which are young, two of which are elderly, and one of which is juvenile.
Specifically, in some embodiments, in the step S3, the step of determining the comparison data based on the implementation parameter information and the personnel data information includes the following steps:
comparing the real-time weight data of the vehicle body with the original weight data of the vehicle body of the model in a database to obtain a weight difference value;
and comparing the personnel architecture data in the vehicle with preset riding personnel architectures in a database to determine the actual personnel structure classification.
In some embodiments, all the fulcrum weight data are compared with each other to obtain a fulcrum difference value between the fulcrum weight data;
and determining whether the wheels of the vehicle body are abnormal according to the fulcrum difference value. That is, the gravity acting conditions between the wheels are compared to determine the pressure conditions of the vehicle, so as to determine whether the wheels of the vehicle are inflated or whether faults such as air leakage occur; thereby being beneficial to acquiring the condition of the vehicle body and improving the risk analysis efficiency.
Specifically, in some embodiments, in the step S4, the step of acquiring the risk level of the vehicle according to the comparison data includes the steps of:
acquiring a risk level of the vehicle according to the face state data, the real-time appearance data, the weight difference value, the fulcrum difference value and the comparison data of the actual personnel structure classification; the risk level comprises a common risk level, a medium risk level and a dangerous risk level.
In some embodiments, the common risk level is that the face state data, the real-time appearance data, the weight difference value, the fulcrum difference value, and the actual person structure categorization are all normal;
the middle-level risk level is that the face state data, the real-time appearance data, the weight difference value and the fulcrum difference value are all normal, and the actual personnel structure is classified as abnormal; the actual personnel structure classification abnormality comprises that the personnel in the vehicle are all old people or the personnel in the vehicle are all teenagers, or the proportion of the old people and the teenagers is more than sixty percent. The facial state data anomalies include pale faces, obvious sleepiness of the faces, reddening of the faces, and the like. The weight differential numerical anomaly includes an actual weight of the vehicle itself being greater than one hundred fifty percent of the weight of the original vehicle itself. The fulcrum differential numerical anomaly includes a weight differential of each fulcrum of between more than fifteen percent and twenty percent.
The dangerous risk level is that any one or more of the face state data, the real-time appearance data, the weight difference value and the fulcrum difference value are abnormal.
Specifically, in some embodiments, as shown in fig. 2, the risk analysis method based on the vehicle data further includes:
s5, acquiring data information of the road section where the vehicle is located;
and S6, outputting early warning information according to the data information of the road section and the risk level of the vehicle.
That is, after the step of constructing a risk scoring model according to the risk factors and calculating the risk level of the vehicle, the method further comprises:
s5, acquiring data information of the road section where the vehicle is located; wherein the distance of the road section where the vehicle is located is within 60-100 Km; the data information comprises the congestion condition of a road, the condition of a road maintenance period and the frequency of occurrence of road accidents;
and S6, outputting early warning information according to the data information of the road section and the risk level of the vehicle. The early warning information comprises common early warning information, medium grade early warning information and danger early warning information.
That is, after the risk level of the vehicle is obtained, the road section within the preset distance to be driven by the vehicle is collected and monitored by the external monitoring component; meanwhile, after the risk level of the vehicle is acquired by combining the monitored data information, the data early warning information of the driver is provided for the driver to refer to, so that the driving safety is improved.
The common early warning information may be: the vehicle and the road in front are good, the driver can drive safely, the driver can feel good, and the driver can pay attention to the driving; the medium-level early warning information can be: "old people on the vehicle are in most, please pay attention to driving", "children on the vehicle are in most, please pay attention to driving", etc.; the danger warning information may be: "vehicle is at risk of failure, recommended to service and then run", "do not drive too much fatigue", "weight of vehicle or glass may be at risk of failure, recommended to service and then run", etc. That is, by giving the driver some warning or advice, the safety driving on the expressway can be facilitated, and the occurrence of accidents can be reduced or avoided.
The invention further provides a risk analysis system based on the vehicle data.
Specifically, as shown in fig. 3, the risk analysis system based on vehicle data includes:
a data acquisition module 610; the data acquisition module 610 is configured to acquire real-time parameter information of a vehicle body and acquire personnel data information in the vehicle;
a comparison module 620; the comparison module 620 is configured to determine comparison data based on the implementation parameter information and the personnel data information;
an analysis module 630; the analysis module 630 is configured to obtain a risk level of the vehicle according to the comparison data.
In addition, an embodiment of the present invention also proposes a computer-readable storage medium having stored thereon a risk analysis program based on vehicle data, which when executed by a processor, implements the operations of:
acquiring real-time parameter information of a vehicle body;
acquiring personnel data information in a vehicle;
determining comparison data based on the implementation parameter information and the personnel data information;
and acquiring the risk level of the vehicle according to the comparison data.
Further, the step of acquiring real-time parameter information of the vehicle body includes the following steps:
using a visual scanning component to obtain real-time appearance data of the vehicle body;
a gravity sensing component is used to obtain real-time weight data of the vehicle body.
Further, the step of using a gravity sensing component to obtain real-time weight data of the vehicle body includes:
using at least four gravity sensing components distributed in an array to obtain fulcrum weight data at wheels in the vehicle body;
acquiring air pressure condition data of wheels of the vehicle body according to the fulcrum weight data;
and acquiring real-time weight data of the vehicle body according to the fulcrum weight data.
Further, the step of acquiring personnel data information in the vehicle comprises the following steps:
a face scanning component is used to obtain information of personnel data in the vehicle.
Further, the face scanning component comprises a first face scanner and a second face scanner; and the step of using the face scanning component to acquire the personnel data information in the vehicle comprises the following steps:
using the first face scanner to obtain face state data of a driver within the vehicle;
and using the second face scanner to acquire personnel architecture data in the vehicle.
Further, the step of determining the comparison data based on the implementation parameter information and the personnel data information includes the following steps:
comparing the real-time weight data of the vehicle body with the original weight data of the vehicle body of the model in a database to obtain a weight difference value;
and comparing the personnel architecture data in the vehicle with preset riding personnel architectures in a database to determine the actual personnel structure classification.
Further, in the step S4, the step of acquiring the risk level of the vehicle according to the comparison data includes the steps of:
acquiring a risk level of the vehicle according to the face state data, the real-time appearance data, the weight difference value, the fulcrum difference value and the comparison data of the actual personnel structure classification; the risk level comprises a common risk level, a medium risk level and a dangerous risk level.
Further, after the step of constructing a risk score model according to the risk factors and calculating the risk level of the vehicle, the method further comprises:
s5, acquiring data information of the road section where the vehicle is located; wherein the distance of the road section where the vehicle is located is within 60-100 Km; the data information comprises the congestion condition of a road, the condition of a road maintenance period and the frequency of occurrence of road accidents;
and S6, outputting early warning information according to the data information of the road section and the risk level of the vehicle. The early warning information comprises common early warning information, medium grade early warning information and danger early warning information.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the embodiments of the disclosure may be suitably combined to form other embodiments as will be understood by those skilled in the art.
Variations and modifications of the above embodiments will occur to those skilled in the art to which the invention pertains from the foregoing disclosure and teachings. Therefore, the present invention is not limited to the above-described embodiments, but is intended to be capable of modification, substitution or variation in light thereof, which will be apparent to those skilled in the art in light of the present teachings. In addition, although specific terms are used in the present specification, these terms are for convenience of description only and do not limit the present invention in any way.
Claims (10)
1. A risk analysis method based on vehicle data, characterized by: the method comprises the following steps:
acquiring real-time parameter information of a vehicle body;
acquiring personnel data information in a vehicle;
determining comparison data based on the implementation parameter information and the personnel data information;
and acquiring the risk level of the vehicle according to the comparison data.
2. The risk analysis method based on vehicle data according to claim 1, wherein: the step of acquiring the real-time parameter information of the vehicle body comprises the following steps:
using a visual scanning component to obtain real-time appearance data of the vehicle body; wherein the real-time appearance data comprises real-time frame data of the vehicle and real-time glass data of the vehicle;
a gravity sensing component is used to obtain real-time weight data of the vehicle body.
3. The risk analysis method based on vehicle data according to claim 2, wherein: the step of acquiring personnel data information in the vehicle comprises the following steps of:
a face scanning component is used to obtain information of personnel data in the vehicle.
4. A risk analysis method based on vehicle data according to claim 3, characterized in that: the face scanning component comprises a first face scanner and a second face scanner; the step of using the face scanning component to acquire the personnel data information in the vehicle comprises the following steps:
using the first face scanner to obtain face state data of a driver within the vehicle;
and using the second face scanner to acquire personnel architecture data in the vehicle.
5. The risk analysis method based on vehicle data according to claim 4, wherein: the step of determining comparison data based on the implementation parameter information and the personnel data information comprises the following steps:
comparing the real-time weight data of the vehicle body with the original weight data of the vehicle body of the model in a database to obtain a weight difference value;
and comparing the personnel architecture data in the vehicle with preset riding personnel architectures in a database to determine the actual personnel structure classification.
6. The vehicle data-based risk analysis method according to claim 5, wherein: the step of acquiring the risk level of the vehicle according to the comparison data comprises the following steps:
acquiring a risk level of the vehicle according to the face state data, the real-time appearance data, the weight difference value and the comparison data of the actual personnel structure classification; wherein the risk level comprises a common risk level, a medium risk level and a risk level;
the common risk level is that the face state data, the real-time appearance data, the weight difference value and the actual personnel structure classification are normal;
the medium risk level is that the face state data, the real-time appearance data and the weight difference value are all normal, and the actual personnel structure is classified as abnormal;
the risk level is an abnormality in any one or more of the face state data, the real-time appearance data, and the weight difference value.
7. The risk analysis method based on vehicle data according to claim 1, wherein: after the step of constructing the risk scoring model according to the risk factors and calculating the risk grade of the vehicle, the method further comprises the following steps:
acquiring data information of a road section where the vehicle is located; wherein the distance of the road section where the vehicle is located is within 60-100 Km; the data information comprises the congestion condition of a road, the condition of a road maintenance period and the frequency of occurrence of road accidents;
outputting early warning information according to the data information of the road section and the risk level of the vehicle; the early warning information comprises common early warning information, medium grade early warning information and danger early warning information.
8. A risk analysis system based on vehicle data, characterized by: comprising the following steps:
the data acquisition module is used for acquiring real-time parameter information of the vehicle body and acquiring personnel data information in the vehicle;
the comparison module is used for determining comparison data based on the implementation parameter information and the personnel data information;
and the analysis module is used for acquiring the risk level of the vehicle according to the comparison data.
9. A terminal for risk analysis based on vehicle data, characterized in that the terminal for risk analysis based on vehicle data comprises: memory, a processor and a vehicle data based risk analysis program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the vehicle data based risk analysis method of any one of claims 1 to 7.
10. A storage medium having stored thereon a vehicle data based risk analysis program which when executed by a processor implements the steps of the vehicle data based risk analysis method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311218668.9A CN117334066A (en) | 2023-09-20 | 2023-09-20 | Risk analysis method, system, terminal and storage medium based on vehicle data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311218668.9A CN117334066A (en) | 2023-09-20 | 2023-09-20 | Risk analysis method, system, terminal and storage medium based on vehicle data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117334066A true CN117334066A (en) | 2024-01-02 |
Family
ID=89294396
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311218668.9A Pending CN117334066A (en) | 2023-09-20 | 2023-09-20 | Risk analysis method, system, terminal and storage medium based on vehicle data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117334066A (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130016606A (en) * | 2011-08-08 | 2013-02-18 | 한국과학기술원 | Advanced driver assistance system for safety driving using driver adaptive irregular behavior detection |
| WO2014207558A2 (en) * | 2013-06-27 | 2014-12-31 | Scope Technologies Holdings Limited | Onboard vehicle accident detection and damage estimation system and method of use |
| US9014920B1 (en) * | 2012-07-02 | 2015-04-21 | Ricardo Torres | Vehicle occupants alert and alarm system |
| CN105046766A (en) * | 2015-08-27 | 2015-11-11 | 孙健 | Freight vehicle traveling safety monitoring system and method |
| CN105225509A (en) * | 2015-10-28 | 2016-01-06 | 努比亚技术有限公司 | A kind of road vehicle intelligent early-warning method, device and mobile terminal |
| WO2017219319A1 (en) * | 2016-06-23 | 2017-12-28 | 驭势科技（北京）有限公司 | Automatic vehicle driving method and automatic vehicle driving system |
| CN109872554A (en) * | 2019-03-22 | 2019-06-11 | 山西省交通科学研究院有限公司 | A kind of expressway fog zone promotes the bus or train route early warning system of traffic safety |
| CN110033614A (en) * | 2019-03-22 | 2019-07-19 | 山西省交通科学研究院有限公司 | A kind of road hazard cargo transport dynamic risk early warning system based on technology of Internet of things |
| US20190384870A1 (en) * | 2018-06-13 | 2019-12-19 | Toyota Jidosha Kabushiki Kaisha | Digital twin for vehicle risk evaluation |
| US20200094820A1 (en) * | 2018-09-21 | 2020-03-26 | Element Ai Inc. | Automatically assessing and reducing vehicular incident risk |
| US20210107421A1 (en) * | 2018-06-26 | 2021-04-15 | Denso Corporation | Vehicle control method, vehicle control system, vehicle control device, passenger watching-over method, passenger watching-over system, and passenger watching-over device |
| CN113715837A (en) * | 2020-05-25 | 2021-11-30 | 沃尔沃汽车公司 | Vehicle potential safety hazard management system and method |
| CN114419880A (en) * | 2021-12-28 | 2022-04-29 | 深圳市航天无线通信技术有限公司 | Driving behavior analysis method and system based on human, vehicle and environment |
| CN115131894A (en) * | 2022-06-28 | 2022-09-30 | 武汉科技大学 | Motor vehicle remote monitoring system based on block chain technology |
| US20220341248A1 (en) * | 2021-04-26 | 2022-10-27 | Ford Global Technologies, Llc | Systems And Methods Of Interior Sensor-Based Vehicle Liftgate Actuation |
| WO2022227129A1 (en) * | 2021-04-27 | 2022-11-03 | 山东交通学院 | Road transportation risk assessment method and system for hazardous chemical |
| JP2023007334A (en) * | 2021-06-30 | 2023-01-18 | 株式会社ユピテル | Information processing systems, programs and the like |
| CN116052475A (en) * | 2023-03-06 | 2023-05-02 | 山东高速股份有限公司 | Vehicle risk early warning method, system, storage medium and device |
-
2023
- 2023-09-20 CN CN202311218668.9A patent/CN117334066A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130016606A (en) * | 2011-08-08 | 2013-02-18 | 한국과학기술원 | Advanced driver assistance system for safety driving using driver adaptive irregular behavior detection |
| US9014920B1 (en) * | 2012-07-02 | 2015-04-21 | Ricardo Torres | Vehicle occupants alert and alarm system |
| WO2014207558A2 (en) * | 2013-06-27 | 2014-12-31 | Scope Technologies Holdings Limited | Onboard vehicle accident detection and damage estimation system and method of use |
| CN105046766A (en) * | 2015-08-27 | 2015-11-11 | 孙健 | Freight vehicle traveling safety monitoring system and method |
| CN105225509A (en) * | 2015-10-28 | 2016-01-06 | 努比亚技术有限公司 | A kind of road vehicle intelligent early-warning method, device and mobile terminal |
| WO2017219319A1 (en) * | 2016-06-23 | 2017-12-28 | 驭势科技（北京）有限公司 | Automatic vehicle driving method and automatic vehicle driving system |
| US20190384870A1 (en) * | 2018-06-13 | 2019-12-19 | Toyota Jidosha Kabushiki Kaisha | Digital twin for vehicle risk evaluation |
| US20210107421A1 (en) * | 2018-06-26 | 2021-04-15 | Denso Corporation | Vehicle control method, vehicle control system, vehicle control device, passenger watching-over method, passenger watching-over system, and passenger watching-over device |
| US20200094820A1 (en) * | 2018-09-21 | 2020-03-26 | Element Ai Inc. | Automatically assessing and reducing vehicular incident risk |
| CN110033614A (en) * | 2019-03-22 | 2019-07-19 | 山西省交通科学研究院有限公司 | A kind of road hazard cargo transport dynamic risk early warning system based on technology of Internet of things |
| CN109872554A (en) * | 2019-03-22 | 2019-06-11 | 山西省交通科学研究院有限公司 | A kind of expressway fog zone promotes the bus or train route early warning system of traffic safety |
| CN113715837A (en) * | 2020-05-25 | 2021-11-30 | 沃尔沃汽车公司 | Vehicle potential safety hazard management system and method |
| US20220341248A1 (en) * | 2021-04-26 | 2022-10-27 | Ford Global Technologies, Llc | Systems And Methods Of Interior Sensor-Based Vehicle Liftgate Actuation |
| WO2022227129A1 (en) * | 2021-04-27 | 2022-11-03 | 山东交通学院 | Road transportation risk assessment method and system for hazardous chemical |
| JP2023007334A (en) * | 2021-06-30 | 2023-01-18 | 株式会社ユピテル | Information processing systems, programs and the like |
| CN114419880A (en) * | 2021-12-28 | 2022-04-29 | 深圳市航天无线通信技术有限公司 | Driving behavior analysis method and system based on human, vehicle and environment |
| CN115131894A (en) * | 2022-06-28 | 2022-09-30 | 武汉科技大学 | Motor vehicle remote monitoring system based on block chain technology |
| CN116052475A (en) * | 2023-03-06 | 2023-05-02 | 山东高速股份有限公司 | Vehicle risk early warning method, system, storage medium and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11557207B1 (en) | Vehicle collision alert system and method for detecting driving hazards | |
| CN110770084B (en) | Vehicle monitoring system and vehicle monitoring method | |
| CN109466488B (en) | Automatic vehicle collision rescue alarm system | |
| US9122933B2 (en) | After market driving assistance system | |
| US20190035279A1 (en) | Information processing apparatus, information processing method, program, and vehicle | |
| CN110316198A (en) | A kind of safe-guard system and operation method for highway speed-raising | |
| CN104599443A (en) | Vehicle-mounted forewarning terminal for driving behaviors based on information fusion and forewarning method thereof | |
| CN103723096A (en) | Driving assistance system with wireless communication function | |
| CN107219330A (en) | Vehicle exhaust monitoring method, system and computer-readable recording medium | |
| CN108922181A (en) | A kind of intelligent traffic monitoring system of multi-faceted monitoring driving vehicle | |
| CN113352989B (en) | Intelligent driving safety auxiliary method, product, equipment and medium | |
| CN114360210A (en) | Vehicle fatigue driving early warning system | |
| CN115384540A (en) | Vehicle-mounted safe driving detection device | |
| CN114973212A (en) | Fatigue driving stopping method based on visual features, steering wheel operation detection and active intervention | |
| CN117334066A (en) | Risk analysis method, system, terminal and storage medium based on vehicle data | |
| Pardhe et al. | Alcohol Detection and Traffic Sign Board Recognition for Vehicle Acceleration Using CNN | |
| CN117334047A (en) | Dangerous driving behavior detection method and device | |
| CN116630945A (en) | Driving distraction reminding method, device, equipment and readable storage medium | |
| KR20170004141A (en) | Apparatus and System for estimating insurance rate using LDWS and BSD and method thereof | |
| CN110060507A (en) | One kind anti-knock into the back prior-warning device and method for early warning | |
| CN107415688A (en) | A kind of overspeed of vehicle reminding method and relevant device | |
| CN211979500U (en) | Vehicle-mounted information collecting and processing system | |
| CN106600427A (en) | Novel vehicle insurance system | |
| CN111252063A (en) | Novel automobile active safety control system and method | |
| CN117995021B (en) | Vehicle safety early warning method and system based on multi-source data fusion algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |