CN116494991A - Driving habit analysis system and method based on AI identification - Google Patents
Driving habit analysis system and method based on AI identification Download PDFInfo
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- 238000000034 method Methods 0.000 title claims description 19
- 230000002159 abnormal effect Effects 0.000 claims abstract description 48
- 230000009471 action Effects 0.000 claims abstract description 43
- 238000010195 expression analysis Methods 0.000 claims abstract description 25
- 230000006399 behavior Effects 0.000 claims description 135
- 238000000605 extraction Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 18
- 238000004891 communication Methods 0.000 claims description 16
- 230000005540 biological transmission Effects 0.000 claims description 13
- 230000008921 facial expression Effects 0.000 claims description 5
- 230000006855 networking Effects 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 description 6
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- 238000010586 diagram Methods 0.000 description 4
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- 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
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- G—PHYSICS
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- G—PHYSICS
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- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
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- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/089—Driver voice
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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Abstract
The invention discloses a driving habit analysis system based on AI identification, which comprises a driving habit analysis system, wherein a behavior analysis module is arranged in the driving habit analysis system. According to the invention, the action characteristics, the sound characteristics and the big data characteristics of the driver can be compared and analyzed through the action analysis module, the expression and the psychological state of the driver can be analyzed through the expression analysis module, whether the current psychological state of the driver is suitable for continuous driving is judged, the auxiliary driving actions can be analyzed through the auxiliary driving analysis module to prevent the influence on the driver, the abnormal driving actions of the driver can be prompted in the directions of an alarm bell and a voice prompt through the information prompt module, the danger caused by the abnormal driving actions is avoided, the abnormal driving actions of the driver are prompted to the associated user side through the association prompt module in a feedback manner, and the emergency contact person can timely find and know the abnormal conditions when the abnormal conditions exist.
Description
Technical Field
The invention relates to the technical field of driving behavior analysis, in particular to a driving habit analysis system and method based on AI identification.
Background
Artificial intelligence (Artificial Intelligence), english is abbreviated AI. It is sufficient to replace humans. It is a new technical science for researching, developing theory, method, technology and application system for simulating, extending and expanding human intelligence.
Artificial intelligence is a branch of computer science that attempts to understand the nature of intelligence and to produce a new intelligent machine that can react in a similar manner to human intelligence, research in this field including robotics, language recognition, image recognition, natural language processing, and expert systems. Since birth, the theory and technology are mature, and the application field is expanding, and it is supposed that the technological product brought by artificial intelligence in the future will be a "container" of human intelligence. Artificial intelligence can simulate the information process of consciousness and thinking of people. Artificial intelligence is not human intelligence, but can think like a human, and may also exceed human intelligence.
The driving behavior can be identified in an AI identification mode, and in the use process of the driving habit analysis system, the action of a driver is usually analyzed, so that the expression and psychological state of the driver are inconvenient to analyze, the analysis effect of the driving habit can be affected, and the abnormal driving behavior is not timely prompted. Therefore, we propose a driving habit analysis system and method based on AI recognition.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims to provide a driving habit analysis system and method based on AI identification, which can realize analysis of action behaviors and expression moods of a driver and has the effect of prompting abnormal driving behaviors in time.
In order to solve the problems, the invention adopts the following technical scheme:
the driving habit analysis system based on AI identification comprises a driving habit analysis system, wherein a behavior analysis module is arranged in the driving habit analysis system, an information prompt module is arranged in the driving habit analysis system, a system management module is arranged in the driving habit analysis system, the driving habit analysis system is electrically connected with the Internet through a communication network port, the driving habit analysis system is electrically connected with a cloud server through the communication network port, the driving habit analysis system is electrically connected with a data center through the communication network port, the driving habit analysis system is electrically connected with an information transmission module through the communication network port, and the information transmission module is electrically connected with a driving behavior acquisition module through the communication network port.
As a preferred scheme of the invention, the behavior analysis module comprises an action comparison module, a sound comparison module, an expression analysis module and a secondary driving analysis module, wherein the action comparison module is used for comparing and analyzing the acquired action image information characteristics with the characteristics of big data acquisition and learning, the sound comparison module is used for comparing and analyzing the acquired sound data information with the characteristics of big data acquisition and learning, the expression analysis module is used for analyzing facial expression information of an acquired user and analyzing psychological conditions of the user, the secondary driving analysis module is used for acquiring and analyzing secondary driving behaviors and judging whether the driving of a main driver is influenced or not, the secondary driving analysis module is electrically connected with the expression analysis module, the expression analysis module is electrically connected with the sound comparison module, and the action comparison module, the sound comparison module, the expression analysis module and the secondary driving analysis module are all electrically connected with the behavior analysis module and are electrically connected with a driving habit analysis system.
As a preferred scheme of the invention, the information prompt module comprises an alarm prompt module, a voice prompt module and an association prompt module, wherein the alarm prompt module is used for alarming and prompting abnormal dangerous driving behaviors of a user in an alarm mode, the voice prompt module is used for prompting abnormal operation behaviors of a driver in a voice playing mode, the association prompt module is used for prompting abnormal driving behaviors of the driver to an associated user side in a feedback mode, emergency contacts can find and know abnormal conditions in time when the abnormal conditions exist, the association prompt module is electrically connected with the voice prompt module, the voice prompt module is electrically connected with the alarm prompt module, and the alarm prompt module, the voice prompt module and the association prompt module are electrically connected with the information prompt module which is electrically connected with a driving habit analysis system.
As a preferred scheme of the invention, the system management module comprises a feature extraction module, an intelligent recognition module, a system setting module, an acquisition optimization module, a feature learning module and a behavior learning module, wherein the feature extraction module is used for extracting the features of driving actions and driver voice behaviors, judging whether drivers have abnormal driving behaviors, the intelligent recognition module is used for intelligently recognizing different drivers and switching different driving habits of different drivers, the system setting module is used for setting and changing various operation parameters of the system, the acquisition optimization module is used for optimizing acquired image information, reducing noise of acquired audio information so as to ensure the accuracy of driving behavior acquisition analysis, the feature learning module is used for storing driving habit information of the drivers in a mode of big data, learning the driving habit features of the drivers, analyzing and judging the driving behaviors of the users directly in the process of acquiring subsequent driving behaviors, and judging the behaviors with higher efficiency, the feature learning module is electrically connected with the feature extraction module, the electrical connection optimization module is electrically connected with the system setting module, the electrical property learning module is electrically connected with the system setting module, the system management module is electrically connected to the driving habit analysis system.
As a preferred scheme of the invention, the driving behavior acquisition module comprises a camera module, a voice receiving module and an identification prompting module, wherein the camera module is used for acquiring the behavior information of a driver through a camera, the voice receiving module is used for acquiring and receiving the voice audio information of the driver, the identification prompting module is used for prompting abnormal information affecting the driving behavior acquisition to ensure the integrity of the behavior acquisition, the identification prompting module is electrically connected with the voice receiving module, the voice receiving module is electrically connected with the camera module, the voice receiving module and the identification prompting module are electrically connected with the driving behavior acquisition module, and the driving behavior acquisition module is electrically connected with the information transmission module.
A using method of a driving habit analysis system based on AI identification comprises the following steps:
s1: starting a driving habit analysis system for networking analysis work of the system;
s2: the driving behavior acquisition module is used for acquiring behavior actions and sound information of a driver and a secondary driving;
s3: the starting information transmission module is used for transmitting and reporting the acquired information;
s4: the starting behavior analysis module is used for comparing and analyzing the characteristics of the acquired data and the action sound characteristics of the big data, and simultaneously analyzing the expression and the assistant driving of the driver;
s5: the starting information prompt module is used for prompting abnormal or dangerous driving behaviors;
s6: a system management module is started and used for learning management tasks of the system;
s7: starting a cloud server for backing up data information;
s8: and the starting data center is used for storing various working data information of the system.
As a preferred embodiment of the present invention, the driving behavior start acquisition module includes the following steps:
(1) Starting a camera module;
(2) Starting a voice receiving module;
(3) And starting to identify the prompting module.
As a preferred embodiment of the present invention, the start behavior analysis module includes the steps of:
(1) A start action comparison module;
(2) Starting a sound comparison module;
(3) Starting an expression analysis module;
(4) And starting a secondary driving analysis module.
As a preferred embodiment of the present invention, the start information prompting module includes the following steps:
(1) Starting an alarm bell prompting module;
(2) Starting a voice prompt module;
(3) And starting an association prompt module.
As a preferred embodiment of the present invention, the start system management module includes the steps of:
(1) Starting a feature extraction module;
(2) Starting an intelligent recognition module;
(3) Starting a system setting module;
(4) Starting an acquisition optimization module;
(5) Starting a feature learning module;
(6) And a start behavior learning module.
The invention has the advantages that: according to the invention, the action characteristics, the sound characteristics and the big data characteristics of the driver can be compared and analyzed through the action analysis module, the expression and the psychological state of the driver can be analyzed through the expression analysis module, whether the current psychological state of the driver is suitable for continuous driving is judged, the auxiliary driving actions can be analyzed through the auxiliary driving analysis module to prevent the influence on the driver, the abnormal driving actions of the driver can be prompted in the directions of an alarm bell and a voice prompt through the information prompt module, the danger caused by the abnormal driving actions is avoided, the abnormal driving actions of the driver are prompted to the associated user side through the association prompt module in a feedback manner, and the emergency contact person can timely find and know the abnormal conditions when the abnormal conditions exist.
Drawings
FIG. 1 is a schematic block diagram of a driving habit analysis system based on AI identification of the present invention;
FIG. 2 is a workflow diagram of a driving habit analysis system based on AI identification of the present invention;
FIG. 3 is a flow chart of a driving behavior acquisition module of the AI-based driving habit analysis system of the present invention;
FIG. 4 is a workflow diagram of a behavior analysis module of the AI-based driving habit analysis system of the present invention;
FIG. 5 is a flow chart of the information prompt module of the driving habit analysis system based on AI identification of the present invention;
fig. 6 is a system management module workflow diagram of a driving habit analysis system based on AI identification of the present invention.
The reference numerals in the figures illustrate:
1. a driving habit analysis system; 2. a behavior analysis module; 3. an information prompt module; 4. a system management module; 5. a driving behavior acquisition module; 6. an information transmission module; 7. the Internet; 8. a cloud server; 9. a data center; 21. the action comparison module; 22. a sound comparison module; 23. expression analysis module; 24. the auxiliary driving analysis module; 31. an alarm bell prompting module; 32. a voice prompt module; 33. an association prompt module; 41. a feature extraction module; 42. an intelligent identification module; 43. a system setting module; 44. the acquisition optimization module; 45. a feature learning module; 46. a behavior learning module; 51. a camera module; 52. a voice receiving module; 53. and identifying a prompt module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments, and that all other embodiments obtained by persons of ordinary skill in the art without making creative efforts based on the embodiments in the present invention are within the protection scope of the present invention.
In the description of the present invention, it should be noted that the positional or positional relationship indicated by the terms such as "upper", "lower", "inner", "outer", "top/bottom", etc. are based on the positional or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "configured to," "engaged with," "connected to," and the like are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Examples: referring to fig. 1-6, a driving habit analysis system based on AI identification includes a driving habit analysis system 1, a behavior analysis module 2 is provided in the driving habit analysis system 1, an information prompt module 3 is provided in the driving habit analysis system 1, a system management module 4 is provided in the driving habit analysis system 1, the driving habit analysis system 1 is electrically connected with an internet 7 through a communication network port, the driving habit analysis system 1 is electrically connected with a cloud server 8 through the communication network port, the driving habit analysis system 1 is electrically connected with a data center 9 through the communication network port, the driving habit analysis system 1 is electrically connected with an information transmission module 6 through the communication network port, and the information transmission module 6 is electrically connected with a driving behavior acquisition module 5 through the communication network port.
In this embodiment, the behavior analysis module 2 includes an action comparison module 21, a sound comparison module 22, an expression analysis module 23 and a secondary driving analysis module 24, where the action comparison module 21 is configured to perform a comparison analysis on the collected characteristic of the action image information and the characteristic of the big data acquisition learning, the sound comparison module 22 is configured to perform a comparison analysis on the collected sound data information and the characteristic of the big data acquisition learning, the expression analysis module 23 is configured to perform an analysis on the facial expression information of the collected user, analyze the psychological condition of the user, and the secondary driving analysis module 24 is configured to perform an acquisition analysis on the secondary driving behavior, and determine whether to affect the driving of the main driver; the information prompt module 3 comprises an alarm prompt module 31, a voice prompt module 32 and an association prompt module 33, wherein the alarm prompt module 31 is used for alarming and prompting abnormal dangerous driving behaviors of a user in an alarm mode, the voice prompt module 32 is used for prompting abnormal operation behaviors of a driver in a voice playing mode, the association prompt module 33 is used for prompting abnormal driving behaviors of the driver to an associated user side in a feedback mode, and emergency contacts can find and know abnormal conditions in time when the abnormal conditions exist; the system management module 4 comprises a feature extraction module 41, an intelligent identification module 42, a system setting module 43, an acquisition optimization module 44, a feature learning module 45 and a behavior learning module 46, wherein the feature extraction module 41 is used for extracting features of driving actions and sound behaviors of a driver and judging whether the driver has road anger driving abnormal behaviors, the intelligent identification module 42 is used for intelligently identifying different drivers and switching different driving habits of the different drivers, the system setting module 43 is used for setting and changing various operation parameters of the system, the acquisition optimization module 44 is used for optimizing acquired image information and reducing noise of the acquired audio information so as to ensure the accuracy of driving behavior acquisition analysis, the feature learning module 45 is used for carrying out learning of driving behavior features in a big data mode, the behavior learning module 46 is used for storing driving habit information of the driver and learning the driving behavior habit features of the driver, and can directly analyze and judge the driving behaviors of the user in the subsequent driving behavior acquisition process, so that the efficiency of behavior judgment is higher; the driving behavior acquisition module 5 comprises a camera module 51, a voice receiving module 52 and an identification prompt module 53, wherein the camera module 51 is used for acquiring behavior information of a driver through a camera, the voice receiving module 52 is used for acquiring and receiving voice audio information of the driver, and the identification prompt module 53 is used for prompting abnormal information affecting driving behavior acquisition and ensuring the integrity of behavior acquisition.
Specifically, the behavior analysis module 2 includes a motion comparison module 21, a sound comparison module 22, an expression analysis module 23 and a secondary driving analysis module 24, where the motion comparison module 21 is used for comparing and analyzing the collected motion image information features with the features learned by big data acquisition, the sound comparison module 22 is used for comparing and analyzing the collected sound data information with the features learned by big data acquisition, the expression analysis module 23 is used for analyzing facial expression information of a collected user and analyzing psychological conditions of the user, the secondary driving analysis module 24 is used for collecting and analyzing secondary driving behaviors and judging whether the driving of a main driver is affected, the secondary driving analysis module 24 is electrically connected to the expression analysis module 23, the expression analysis module 23 is electrically connected to the sound comparison module 22, the sound comparison module 22 is electrically connected to the motion comparison module 21, and the motion comparison module 21, the sound comparison module 22, the expression analysis module 23 and the secondary driving analysis module 24 are all electrically connected to the behavior analysis module 2, and the behavior analysis module 2 is electrically connected to the driving habit analysis system 1.
In this embodiment, the behavior analysis module 2 includes a motion comparison module 21, a sound comparison module 22, an expression analysis module 23 and a secondary driving analysis module 24, where the motion comparison module 21 is used for comparing the collected motion image information features with the features of big data acquisition learning, the sound comparison module 22 is used for comparing the collected sound data information with the features of big data acquisition learning, the expression analysis module 23 is used for analyzing facial expression information of a collected user and analyzing psychological conditions of the user, and the secondary driving analysis module 24 is used for collecting and analyzing secondary driving behaviors and judging whether the driving of a main driver is affected.
Specifically, the information prompt module 3 includes an alarm prompt module 31, a voice prompt module 32 and an association prompt module 33, the alarm prompt module 31 is used for alarming and prompting the abnormal dangerous driving behavior of the user through an alarm mode, the voice prompt module 32 is used for prompting the abnormal operation behavior of the driver through a voice playing mode, the association prompt module 33 is used for prompting the abnormal driving behavior feedback of the driver to an associated user terminal, emergency contacts can find out and know the abnormal condition in time when the abnormal condition exists, the association prompt module 33 is electrically connected with the voice prompt module 32, the voice prompt module 32 is electrically connected with the alarm prompt module 31, the voice prompt module 32 and the association prompt module 33 are electrically connected with the information prompt module 3, and the information prompt module 3 is electrically connected with the driving habit analysis system 1.
In this embodiment, the information prompt module 3 includes an alarm prompt module 31, a voice prompt module 32 and an association prompt module 33, where the alarm prompt module 31 is configured to prompt an abnormal dangerous driving behavior of a user in an alarm manner, the voice prompt module 32 is configured to prompt an abnormal operation behavior of a driver in a voice play manner, and the association prompt module 33 is configured to feedback a prompt to an associated user terminal for the abnormal driving behavior of the driver, so that an emergency contact person can find and learn about an abnormal situation in time when the abnormal situation exists.
Specifically, the system management module 4 includes a feature extraction module 41, an intelligent recognition module 42, a system setting module 43, a collection optimization module 44, a feature learning module 45 and a behavior learning module 46, where the feature extraction module 41 is used to extract features of driving actions and driver voice actions, determine whether a driver has road anger driving abnormal actions, the intelligent recognition module 42 is used to perform intelligent recognition work on different drivers and can switch different driving habits of different drivers, the system setting module 43 is used to set and change various operation parameters of the system, the collection optimization module 44 is used to optimize collected image information, reduce noise of collected audio information, and ensure accuracy of driving behavior collection analysis, the feature learning module 45 is used for learning driving behavior features in a big data manner, the behavior learning module 46 is used for storing driving habit information of a driver, learning driving behavior habit features of the driver, directly analyzing and judging driving behaviors of the user in a subsequent driving behavior acquisition process, and is higher in efficiency of behavior judgment, the behavior learning module 46 is electrically connected to the feature learning module 45, the feature learning module 45 is electrically connected to the acquisition optimizing module 44, the acquisition optimizing module 44 is electrically connected to the system setting module 43, the system setting module 43 is electrically connected to the intelligent recognition module 42, the intelligent recognition module 42 is electrically connected to the feature extraction module 41, and the feature extraction module 41, the intelligent recognition module 42, the system setting module 43 and the acquisition optimizing module 44, the feature learning module 45 and the behavior learning module 46 are electrically connected to the system management module 4, and the system management module 4 is electrically connected to the driving habit analysis system 1.
In this embodiment, the system management module 4 includes a feature extraction module 41, an intelligent recognition module 42, a system setting module 43, an acquisition optimization module 44, a feature learning module 45 and a behavior learning module 46, where the feature extraction module 41 is used to extract features of driving actions and driver voice behaviors, determine whether a driver has road anger driving abnormal behaviors, the intelligent recognition module 42 is used to perform intelligent recognition work on different drivers, and can switch different driving habits of different drivers, the system setting module 43 is used to set and change various operation parameters of the system, the acquisition optimization module 44 is used to optimize acquired image information, reduce noise of acquired audio information, so as to ensure accuracy of driving behavior acquisition and analysis, the feature learning module 45 is used to perform learning work of driving behavior features in a mode of big data, and the behavior learning module 46 is used to store driving habit information of the driver, learn driving behavior habit features of the driver, and can directly analyze and determine driving behaviors of the user in a subsequent driving behavior acquisition process, so that the efficiency of behavior determination is higher.
Specifically, the driving behavior acquisition module 5 includes a camera module 51, a voice receiving module 52 and a recognition prompt module 53, the camera module 51 is used for acquiring behavior information of a driver through the camera, the voice receiving module 52 is used for acquiring and receiving voice audio information of the driver, the recognition prompt module 53 is used for prompting abnormal information affecting driving behavior acquisition to ensure the integrity of behavior acquisition, the recognition prompt module 53 is electrically connected with the voice receiving module 52, the voice receiving module 52 is electrically connected with the camera module 51, the voice receiving module 52 and the recognition prompt module 53 are electrically connected with the driving behavior acquisition module 5, and the driving behavior acquisition module 5 is electrically connected with the information transmission module 6.
In this embodiment, the driving behavior acquisition module 5 includes a camera module 51, a voice receiving module 52 and an identification prompt module 53, where the camera module 51 is configured to acquire behavior information of a driver through a camera, the voice receiving module 52 is configured to acquire and receive voice audio information of the driver, and the identification prompt module 53 is configured to prompt abnormal information affecting driving behavior acquisition, so as to ensure integrity of behavior acquisition.
A using method of a driving habit analysis system based on AI identification comprises the following steps:
s1: starting a driving habit analysis system for networking analysis work of the system;
s2: the driving behavior acquisition module is used for acquiring behavior actions and sound information of a driver and a secondary driving;
s3: the starting information transmission module is used for transmitting and reporting the acquired information;
s4: the starting behavior analysis module is used for comparing and analyzing the characteristics of the acquired data and the action sound characteristics of the big data, and simultaneously analyzing the expression and the assistant driving of the driver;
s5: the starting information prompt module is used for prompting abnormal or dangerous driving behaviors;
s6: a system management module is started and used for learning management tasks of the system;
s7: starting a cloud server for backing up data information;
s8: and the starting data center is used for storing various working data information of the system.
Specifically, the driving behavior starting acquisition module includes the following steps:
(1) Starting a camera module;
(2) Starting a voice receiving module;
(3) And starting to identify the prompting module.
Specifically, the starting behavior analysis module includes the following steps:
(1) A start action comparison module;
(2) Starting a sound comparison module;
(3) Starting an expression analysis module;
(4) And starting a secondary driving analysis module.
Specifically, the start information prompting module includes the following steps:
(1) Starting an alarm bell prompting module;
(2) Starting a voice prompt module;
(3) And starting an association prompt module.
Specifically, the starting system management module includes the following steps:
(1) Starting a feature extraction module;
(2) Starting an intelligent recognition module;
(3) Starting a system setting module;
(4) Starting an acquisition optimization module;
(5) Starting a feature learning module;
(6) And a start behavior learning module.
The present invention is not limited to the above-mentioned embodiments, and any person skilled in the art, based on the technical solution of the present invention and the improved concept thereof, can be equivalently replaced or changed within the scope of the present invention.
Claims (10)
1. The utility model provides a driving habit analysis system based on AI discernment, includes driving habit analysis system (1), its characterized in that, the inside of driving habit analysis system (1) is provided with action analysis module (2), the inside of driving habit analysis system (1) is provided with information prompt module (3), the inside of driving habit analysis system (1) is provided with system management module (4), driving habit analysis system (1) has internet (7) through communication net gape electric connection, driving habit analysis system (1) has cloud server (8) through communication net gape electric connection, driving habit analysis system (1) has data center (9) through communication net gape electric connection, driving habit analysis system (1) has information transmission module (6) through communication net gape electric connection, information transmission module (6) has driving action to gather module (5) through communication net gape electric connection.
2. The driving habit analysis system based on AI recognition according to claim 1, wherein the behavior analysis module (2) comprises an action comparison module (21), a sound comparison module (22), an expression analysis module (23) and a co-driving analysis module (24), the action comparison module (21) is used for comparing the acquired characteristics of the action image information with the characteristics learned by acquiring big data, the sound comparison module (22) is used for comparing the acquired characteristics learned by acquiring the sound data information with the big data, the expression analysis module (23) is used for analyzing facial expression information of an acquired user and analyzing psychological conditions of the user, the co-driving analysis module (24) is used for acquiring and analyzing the co-driving behavior and judging whether the driving of a main driver is influenced, the co-driving analysis module (24) is electrically connected with the expression analysis module (23), the expression analysis module (23) is electrically connected with the sound comparison module (22), the sound comparison module (22) is electrically connected with the action comparison module (21), and the expression analysis module (21), the co-driving analysis module (22) and the behavior analysis module (24) are electrically connected with the behavior analysis module (2).
3. The driving habit analysis system based on AI recognition according to claim 1, wherein the information prompt module (3) comprises an alarm prompt module (31), a voice prompt module (32) and an association prompt module (33), the alarm prompt module (31) is used for prompting abnormal dangerous driving behaviors of a user in an alarm mode, the voice prompt module (32) is used for prompting abnormal driving behaviors of the driver in a voice playing mode, the association prompt module (33) is used for prompting abnormal driving behaviors of the driver to an associated user side in a feedback mode, emergency contacts can find out an abnormal situation in time when the abnormal situation exists, the association prompt module (33) is electrically connected with the voice prompt module (32), the voice prompt module (32) is electrically connected with the alarm prompt module (31), the voice prompt module (32) and the association prompt module (33) are electrically connected with the information prompt module (3), and the information prompt module (3) is electrically connected with the driving habit analysis system (1).
4. The AI-recognition-based driving habit analysis system as defined in claim 1, wherein the system management module (4) comprises a feature extraction module (41), an intelligent recognition module (42), a system setting module (43), a collection optimization module (44), a feature learning module (45) and a behavior learning module (46), the feature extraction module (41) is used for extracting features of driving actions and driver voice behaviors to judge whether drivers have abnormal road-anger driving behaviors, the intelligent recognition module (42) is used for intelligently recognizing different drivers, different driving habits of different drivers can be switched, the system setting module (43) is used for setting and changing various operation parameters of the system, the collection optimization module (44) is used for optimizing collected image information, noise reduction is performed on collected audio information to ensure accuracy of driving behavior collection and analysis, the feature learning module (45) is used for learning the driving behavior features in a mode of big data, the behavior learning module (46) is used for storing driving habit information of the drivers, the learning behavior learning module (46) can be used for directly analyzing the characteristics of the collected by the drivers in a mode of learning behavior of the drivers, the learning module (46) can be used for judging the characteristics of the driving habits of the drivers directly after the driving habits of the drivers are connected with the user (46), the feature learning module (45) is electrically connected with the acquisition optimization module (44), the acquisition optimization module (44) is electrically connected with the system setting module (43), the system setting module (43) is electrically connected with the intelligent recognition module (42), the intelligent recognition module (42) is electrically connected with the feature extraction module (41), the intelligent recognition module (42), the system setting module (43), the acquisition optimization module (44), the feature learning module (45) and the behavior learning module (46) are electrically connected with the system management module (4), and the system management module (4) is electrically connected with the driving habit analysis system (1).
5. The driving habit analysis system based on AI recognition according to claim 1, wherein the driving behavior acquisition module (5) comprises a camera module (51), a voice receiving module (52) and a recognition prompt module (53), the camera module (51) is used for acquiring behavior information of a driver through a camera, the voice receiving module (52) is used for acquiring and receiving voice audio information of the driver, the recognition prompt module (53) is used for prompting abnormal information affecting driving behavior acquisition to ensure the integrity of behavior acquisition, the recognition prompt module (53) is electrically connected with the voice receiving module (52), the voice receiving module (52) is electrically connected with the camera module (51), the voice receiving module (52) and the recognition prompt module (53) are electrically connected with the driving behavior acquisition module (5), and the driving behavior acquisition module (5) is electrically connected with the information transmission module (6).
6. The method of using an AI-recognition-based driving habit analysis system of any one of claims 1-5, comprising the steps of:
s1: starting a driving habit analysis system for networking analysis work of the system;
s2: the driving behavior acquisition module is used for acquiring behavior actions and sound information of a driver and a secondary driving;
s3: the starting information transmission module is used for transmitting and reporting the acquired information;
s4: the starting behavior analysis module is used for comparing and analyzing the characteristics of the acquired data and the action sound characteristics of the big data, and simultaneously analyzing the expression and the assistant driving of the driver;
s5: the starting information prompt module is used for prompting abnormal or dangerous driving behaviors;
s6: a system management module is started and used for learning management tasks of the system;
s7: starting a cloud server for backing up data information;
s8: and the starting data center is used for storing various working data information of the system.
7. The method of claim 6, wherein the starting driving behavior acquisition module comprises the steps of:
(1) Starting a camera module;
(2) Starting a voice receiving module;
(3) And starting to identify the prompting module.
8. The method of claim 6, wherein the start behavior analysis module comprises the steps of:
(1) A start action comparison module;
(2) Starting a sound comparison module;
(3) Starting an expression analysis module;
(4) And starting a secondary driving analysis module.
9. The method of claim 6, wherein the start information prompting module includes the steps of:
(1) Starting an alarm bell prompting module;
(2) Starting a voice prompt module;
(3) And starting an association prompt module.
10. The method of claim 6, wherein the starting system management module comprises the steps of:
(1) Starting a feature extraction module;
(2) Starting an intelligent recognition module;
(3) Starting a system setting module;
(4) Starting an acquisition optimization module;
(5) Starting a feature learning module;
(6) And a start behavior learning module.
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