CN116409331B - Data analysis processing system and method based on intelligent photoelectric sensing technology - Google Patents
Data analysis processing system and method based on intelligent photoelectric sensing technology Download PDFInfo
- Publication number
- CN116409331B CN116409331B CN202310395508.5A CN202310395508A CN116409331B CN 116409331 B CN116409331 B CN 116409331B CN 202310395508 A CN202310395508 A CN 202310395508A CN 116409331 B CN116409331 B CN 116409331B
- Authority
- CN
- China
- Prior art keywords
- night vision
- vehicle
- data
- low
- vision mode
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000005516 engineering process Methods 0.000 title claims abstract description 25
- 238000007405 data analysis Methods 0.000 title claims abstract description 24
- 238000012545 processing Methods 0.000 title claims abstract description 15
- 230000004297 night vision Effects 0.000 claims abstract description 210
- 230000004044 response Effects 0.000 claims abstract description 7
- 230000008859 change Effects 0.000 claims description 85
- 230000003044 adaptive effect Effects 0.000 claims description 34
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 24
- 230000008569 process Effects 0.000 claims description 22
- 230000004300 dark adaptation Effects 0.000 claims description 12
- 238000003672 processing method Methods 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 230000006978 adaptation Effects 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000000712 assembly Effects 0.000 claims description 6
- 238000000429 assembly Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 abstract description 5
- 230000006870 function Effects 0.000 description 12
- 230000004438 eyesight Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
Classifications
-
- 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
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R1/00—Optical viewing arrangements; Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
- B60R1/20—Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
- B60R1/30—Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles providing vision in the non-visible spectrum, e.g. night or infrared vision
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- 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
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
- B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
- B60W2050/0005—Processor details or data handling, e.g. memory registers or chip architecture
-
- 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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Mechanical Engineering (AREA)
- Transportation (AREA)
- Human Computer Interaction (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of photoelectric sensing, in particular to a data analysis processing system and method based on an intelligent photoelectric sensing technology. According to the invention, the influence of the switching time of the night vision mode of the low-light camera module on the driver is considered, the self-adaptive adjustment is carried out on the judging threshold value of the switching of the night vision mode according to the response time of the driver, whether the night vision mode of the low-light camera module needs to be switched or not is judged in advance, the visual field reference picture of the road surface in front is provided for the user in advance, the driving risk is reduced, and the driving safety of the driver is ensured.
Description
Technical Field
The invention relates to the technical field of photoelectric sensing, in particular to a data analysis processing system and method based on an intelligent photoelectric sensing technology.
Background
The good view is the basic guarantee of safe driving, and traffic accidents occurring at night or under other conditions with low visibility occupy a large proportion in all traffic accidents according to statistics; with the development of information technology, low-light night vision technology is an important means for night auxiliary observation. The low-light level works in visible light and near infrared bands, the resolution is high, the formed image accords with the visual characteristics of human eyes, and the method has wide application prospect and good auxiliary driving effect in the field of vehicle night auxiliary driving.
The vision-based automobile driving assistance technology uses a vision sensor arranged outside an automobile body to acquire scene information around the automobile body, outputs images around the automobile in real time, and improves the environment perception of the automobile. However, the size of the single lens field of view cannot meet the actual needs, and then the sensing of the surrounding environment of the vehicle in various scenes needs to be realized through the micro-light camera module.
In the existing data analysis processing system based on the intelligent photoelectric sensing technology, ambient light around a vehicle is obtained in real time through a low-light camera module, the brightness of the monitored ambient environment around the vehicle is compared with a preset night vision mode switching threshold value, and cameras which correspondingly operate different night vision modes of the low-light camera module are switched;
However, in the prior art, the switching duration of the night vision mode of the low-light camera module is not considered, the influence of the switching of the night vision mode of the low-light camera module on a driver is ignored (under the condition of abrupt change of ambient light, the eyes of the driver also have a self-adaptive process, if the night vision mode of the low-light camera module is also in a switching state, a user does not have a view reference picture of a road surface in front, the driving risk is easy to occur, the potential safety hazard is large), and the switching of the night vision mode cannot be performed in advance according to the response of the driver; if the threshold value set during switching of the night vision mode is simply reduced, the situation of error switching of the night vision mode occurs, and the influence of different driving habits (time for early reaction of the situation of abrupt change of the ambient light) of different drivers on the switching of the night vision mode is not considered, so that the prior art has larger defects.
Disclosure of Invention
The invention aims to provide a data analysis processing system and method based on an intelligent photoelectric sensing technology, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a data analysis processing method based on intelligent photoelectric sensing technology comprises the following steps:
S1, acquiring vehicle information corresponding to different time points of a vehicle carrying a low-light-level camera assembly in historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light-level camera assembly; selecting a change time node of each night vision mode change of the vehicle in the historical data, vehicle information between the corresponding change time node and a previous change time node and a driving behavior characteristic array of a corresponding driver, and constructing a data reference set corresponding to the corresponding change time node;
s2, dividing types corresponding to the data reference sets according to vehicle information in the data reference sets, wherein each data reference set type corresponds to a night vision switching judgment standard, and analyzing the data reference sets respectively corresponding to the same vehicle in each change time node in the historical data to obtain night vision mode change characteristics of the corresponding low-light camera assembly of the corresponding vehicle under the condition of different night vision switching judgment standards;
s3, taking the current time as a reference node, acquiring vehicle information between the reference node and a previous change time node of the vehicle and a driving behavior feature array of a corresponding driver, and acquiring a judgment threshold value for switching a night vision mode of a camera in a low-light camera component of the vehicle by combining a night vision switching judgment standard of the vehicle when the reference node is used;
S4, acquiring a judging threshold value of the camera in the adjusted low-light camera component for switching the night vision mode, comparing the obtained threshold value judging section with vehicle information corresponding to a reference node, and controlling the night vision mode of the camera in the low-light camera component on the vehicle.
Further, vehicle information corresponding to different time points of a vehicle carrying the low-light camera assembly in the historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light camera assembly are obtained through a sensor, the sensor obtains the vehicle information once every a first threshold time t0, and the first threshold time t0 is a preset constant in a database;
the vehicle information comprises the vehicle speed and the brightness value of the surrounding environment of the vehicle monitored by a brightness sensor carried by the vehicle;
the driving behavior characteristics of the driver comprise a brake state coefficient and an accelerator state coefficient, wherein the corresponding ranges of the brake state coefficient and the accelerator state coefficient are 0 and 1, each brake state coefficient corresponds to a unique brake stepping degree, each accelerator state coefficient corresponds to a unique accelerator stepping degree, and the brake state coefficient/the accelerator state coefficient is obtained through corresponding values corresponding to the brake stepping degree/the accelerator stepping degree in a database;
The night vision mode of the low-light camera component comprises a bright light state W1 and a dark light state W2, the change of the night vision mode is a process of switching the bright light state W1 and the dark light state W2, the night vision mode comprises a process of W1-W2 and W2-W1, the camera component running in the bright light state of the low-light camera component is different from the camera component running in the dark light state of the low-light camera component,
the data reference set type comprises a first data reference set type and a second data reference set type, the corresponding night vision switching judgment standards are a constant value judgment standard and an adaptive judgment standard respectively, and the constant value judgment standard represents the preset beta of the environment light brightness value in the database when the brightness state and the darkness state of the micro-light camera component are switched; the self-adaptive judging standard indicates that the corresponding ambient light brightness value is a value obtained after self-adaptive adjustment of beta when the brightness state and the darkness state of the glimmer camera component are switched;
the night vision switching judgment standard is a fixed value judgment standard and an adaptive judgment standard respectively, wherein the fixed value judgment standard refers to that the night vision mode is switched only when the corresponding ambient light brightness value in the acquired vehicle information reaches a judgment threshold value (the threshold value is beta), but for the adaptive judgment standard, the influence of the use process of a user in the switching process is avoided by considering the switching duration of the night vision mode of the low-light camera module (under the condition that the ambient light is suddenly changed, the eyes of the driver also have an adaptive process, and if the night vision mode of the low-light camera module is also in a switching state, the user does not have a visual field reference picture of a road surface in front, the driving risk is easy to occur, the potential safety hazard is large), the adaptive adjustment is carried out on the judgment threshold value, and whether the night vision mode of the low-light camera module needs to be switched is judged in advance; in the fixed value judgment standard, when W1- & gt W2, the corresponding brightness value of the last vehicle information in the corresponding data reference set is smaller than or equal to beta, and when W2- & gt W1, the corresponding brightness value of the last vehicle information in the corresponding data reference set is larger than or equal to beta; in the self-adaptive judging standard, when W1- & gt W2, the corresponding brightness value of the last vehicle information in the corresponding data reference set is larger than or equal to beta, and when W2- & gt W1, the corresponding brightness value of the last vehicle information in the corresponding data reference set is smaller than or equal to beta;
Recording a change time node of a vehicle with a night vision mode change at the ith time in the historical data as Ti, constructing a data reference set corresponding to the change time node Ti, wherein when the data reference set corresponding to the change time node Ti is constructed, when Ti-T (i-1) > T1, a time interval corresponding to an element in the data reference set is [ Ti-T1, ti ], and when Ti-T (i-1) < T1, a time interval corresponding to the element in the data reference set is [ T (i-1), ti ], wherein T1 represents a preset constant in a database;
the first element in the data reference set is a vehicle information interval array, each element in the vehicle information interval array corresponds to vehicle information acquired by a time point sensor, the second element in the data reference set is a driving behavior feature array, and each element in the driving behavior feature array corresponds to driving behavior features acquired by a time point sensor;
the night vision modes corresponding to the glimmer camera assemblies of the corresponding vehicles in the same data reference set at different time points are the same.
Further, when the night vision mode change characteristics of the low-light camera assemblies corresponding to the corresponding vehicles under the condition of different night vision switching judgment standards are obtained in the step S2, data reference sets corresponding to the same vehicle at each change time node in the historical data are obtained, and the data reference set corresponding to the jth change time node of the same vehicle is marked as Cj;
The night vision mode change characteristic of the low-light camera component of the vehicle is recorded as { G11, G12, G2}, and G1 represents a bright adaptation behavior response characteristic function of a driver of the vehicle under the condition that a night vision switching judgment standard is a fixed value judgment standard; g12 represents a dark adaptation behavior reaction characteristic function of a vehicle driver under the condition that the night vision switching judgment standard is a fixed value judgment standard; and G2 represents the maximum time length of the mutual switching between W1 and W2 in the night vision mode of the low-light camera component in the historical data.
Dark adaptation in the present invention: when a person suddenly enters a dark place from a bright place, the visual perceptibility is low at the beginning, the person cannot see something clearly, after a period of time, the vision gradually recovers, and the person can recognize objects in the dark, and the process is called dark adaptation; the difference of dark adaptation time between people is large, and it is self-evident that the safety of driving at night or passing through a tunnel is lower for people with long dark adaptation time; the method is characterized by comprising the following steps: when a person suddenly enters a very bright environment from darkness, the light is perceived as glaring and the eyes also have a habit and vision recovery process, which is called explicit adaptation. This phenomenon occurs if the driver just comes out of the long tunnel to see the sun; in the invention, the corresponding process of W1-W2 is a dark adaptation process, and the corresponding process of W2-W1 is a bright adaptation process.
Further, the adaptive time length corresponding to the data reference set Cj is calculated and is denoted as Dtj, and Dtj =t1 Cj TCj in which TCj represents the point in time of minimum abnormal behavior feature in the data reference set Cj, T1 Cj Representing the maximum time point of the time interval corresponding to the element in the data reference set Cj;
the method of acquiring TCj includes the steps of:
s21, acquiring each abnormal behavior feature in the data reference set Cj, and obtaining a time point corresponding to each abnormal behavior feature to T1 Cj In the formed abnormal time interval, the vehicle speeds corresponding to different time points monotonically decrease, the comprehensive evaluation value of the driving behavior characteristics from the time point corresponding to the abnormal behavior characteristics to any point in the corresponding abnormal time interval is less than 0,
the comprehensive evaluation value of the driving behavior characteristic between the time point a1 corresponding to the abnormal behavior characteristic and the time point a2 in the corresponding abnormal time interval is marked as F (a1，a2) ，F (a1，a2) ＝∑ x＝a1/t0 x1 (r1×P1x-r2×P2x)×t0，
Wherein, P1x represents a brake state coefficient, P2x represents an accelerator state coefficient, x1= [ a2/t0] represents a value of an integer part corresponding to a2/t0, r1 is a first coefficient preset in a database, and r2 is a second coefficient preset in the database;
s22, taking the abnormal behavior characteristic time point with the minimum corresponding time point as TCj;
The adaptive duration Dtj corresponding to the data reference set Cj is acquired,
when the data reference set type of Cj is the first data reference set type,
if the night vision mode corresponding to the data reference set Cj is W1, a first type data pair (VCj, dtj) is constructed, and if the night vision mode corresponding to the data reference set Cj is W2, a second type data pair (VCj, dtj) is constructed, wherein VCj represents the vehicle speed corresponding to TCj in the data reference set Cj;
when the data reference set type of Cj is the second data reference set type, the data pair is not constructed;
recording a plurality of first type data pairs with the same vehicle speed value and the minimum self-adaptive time length as reference data pairs under the corresponding vehicle speed, and determining each coordinate point corresponding to each reference data pair in a first plane rectangular coordinate system, wherein the first plane rectangular coordinate system is a coordinate system of the vehicle speed and the self-adaptive time length; according to a broken line formed by sequentially connecting the coordinate points in the first plane rectangular coordinate system, if the function corresponding to the obtained broken line is that the night vision switching judgment standard is a fixed value judgment standard, the explicit adaptation behavior of the vehicle driver reacts to the characteristic function G11;
recording a plurality of second type data pairs with the same vehicle speed value and the minimum self-adaptive time length as reference data pairs under the corresponding vehicle speed, and determining each coordinate point corresponding to each reference data pair in a second plane rectangular coordinate system, wherein the second plane rectangular coordinate system is a coordinate system of the vehicle speed and the self-adaptive time length; and according to a broken line formed by sequentially connecting the coordinate points in the second plane rectangular coordinate system, when the function corresponding to the obtained broken line is that the night vision switching judgment standard is a fixed value judgment standard, the dark adaptation behavior of the vehicle driver reacts to the characteristic function G12.
The night vision mode change characteristics { G11, G12, G2} of the low-light camera assembly of the vehicle are obtained, so that the judgment threshold value for switching the night vision mode of the camera in the low-light camera assembly of the vehicle is adaptively generated in combination with the acquisition information corresponding to the current time in the subsequent steps, the effective management of the night vision mode of the camera in the low-light camera assembly on the vehicle is realized, and the driving safety of a driver is ensured.
Further, in the step S3, the minimum time point of the corresponding abnormal behavior feature in the vehicle information between the vehicle reference node and the previous change time node and the driving behavior feature array of the corresponding driver is recorded as H;
the method for adjusting the judging threshold value of the camera switching night vision mode in the low-light camera component of the vehicle in the S3 comprises the following steps:
s31, acquiring vehicle information between a vehicle reference node and a previous change time node and brightness values LH of surrounding environments of the vehicle corresponding to the vehicle speeds VH and H in a driving behavior feature array of a corresponding driver, night vision modes and time periods tm corresponding to the current time from H,
s32, obtaining a judging threshold value of the camera switching night vision mode in the low-light camera component of the vehicle, which is marked as g1,
When the night vision switching judgment standard corresponding to the reference node is a fixed value judgment standard, g1=beta;
when the night vision handover decision criterion corresponding to the reference node is an adaptive decision criterion,
if the night vision mode corresponding to H is W2,
if G11 (VH). Gtoreq.tm+G2, the equation G11 (VH)/[ G11 (VH) -G2] = [ β -LH ]/[ β -G ], g= [ (β -LH). Times.G2+LH.times.G11 (VH) ]/G11 (VH), g1=min { G, G3, β },
if G11 (VH) < tm+G2, then the equation G11 (VH)/[ G11 (VH) -tm ] = [ β -LH ]/[ β -G ], g= [ (β -LH) x tm+LH x G11 (VH) ]/G11 (VH), g1=min { G, G3, β }; g11 (VH) represents a value corresponding to an adaptive time period when the vehicle speed is VH in G11, G11 (VH) > tm, tm > 0, and β > G, G represents a predicted value of G1 when the night vision switching criterion corresponding to the reference node is W2 in the case where the night vision switching criterion corresponding to the reference node is the adaptive criterion, G1 is equal to the minimum value of G and β, G3 represents an average value of the respective determination thresholds corresponding to the respective data reference sets of VH in the history data in the case where the night vision switching criterion is the adaptive criterion and the night vision mode is W2;
if the night vision mode corresponding to H is W1,
if G12 (VH). Gtoreq.tm+G2, the equation G12 (VH)/[ G12 (VH) -G2] = [ LH- β ]/[ G2- β ], g2= [ (β -LH). Times.G2+LHXG12 (VH) ]/G12 (VH), g1=max { G2, G4, β },
If G12 (VH) < tm+G2, then the equation G12 (VH)/[ G12 (VH) -tm ] = [ LH- β ]/[ G2- β ], g2= [ (β -LH). Times.tm+LH×G12 (VH) ]/G12 (VH), g1=min { G2, G4, β }; g12 (VH) represents a value corresponding to the adaptive time period when the vehicle speed is VH in G12, G12 (VH) > tm, tm > 0, and β < G2, G2 represents a predicted value of G1 when the night vision switching criterion corresponding to the reference node is the adaptive criterion and the night vision mode corresponding to H is W1; and g4, in the historical data, when the night vision switching judgment standard is the adaptive judgment standard and the night vision mode is W1, the average value of the judgment thresholds corresponding to the data reference sets of VH corresponding to the vehicle speed with the minimum time point of the abnormal behavior characteristics is shown.
Further, in the step S4, when the night vision mode of the camera in the low-light camera assembly on the vehicle is controlled, the judgment threshold g1 for switching the night vision mode of the camera in the low-light camera assembly on the vehicle is obtained,
acquiring the brightness value of the surrounding environment of the vehicle in the vehicle information after the reference node in real time, recording the acquired brightness value as Lf,
if the night vision mode corresponding to H is W2,
if Lf is more than or equal to g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be changed into W2-W1; if Lf is less than g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be unchanged, wherein the night vision mode is still W2;
If the night vision mode corresponding to H is W1,
if Lf is less than or equal to g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be changed into W1-W2; if Lf is more than g1, controlling the night vision mode of the camera in the low-light camera component on the vehicle to be unchanged, and still being W1.
A data analysis processing system based on intelligent photoelectric sensing technology, the system comprising the following modules: an information acquisition module, a data reference set construction module, a night vision mode change characteristic analysis module, a judgment threshold adjustment module and a mode management module,
the information acquisition module acquires vehicle information corresponding to different time points of a vehicle carrying the low-light-level camera assembly in the historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light-level camera assembly;
the data reference set construction module selects a change time node of each night vision mode change of the vehicle in the historical data, vehicle information between the corresponding change time node and the previous change time node and a driving behavior characteristic array of a corresponding driver, and constructs a data reference set corresponding to the corresponding change time node;
the night vision mode change feature analysis module divides types corresponding to data reference sets according to vehicle information in the data reference sets, each data reference set type corresponds to a night vision switching judgment standard, and analyzes data reference sets respectively corresponding to the same vehicle at each change time node in historical data to obtain night vision mode change features of the low-light camera assembly corresponding to the corresponding vehicle under different night vision switching judgment standards;
The judging threshold value adjusting module takes the current time as a reference node, acquires vehicle information between the vehicle reference node and a previous change time node and a driving behavior characteristic array of a corresponding driver, and acquires a judging threshold value of a camera in a low-light camera component of the vehicle for switching a night vision mode by combining a night vision switching judging standard of the vehicle when the reference node is used;
the mode management module acquires a judging threshold value of the camera in the adjusted low-light camera component for switching the night vision mode of the vehicle, compares the obtained threshold value judging section with vehicle information corresponding to a reference node, and controls the night vision mode of the camera in the low-light camera component on the vehicle.
Further, in the historical data in the information acquisition module, vehicle information corresponding to different time points of the running process of the vehicle carrying the low-light camera assembly, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light camera assembly are acquired through a sensor, the sensor acquires the information once every a first threshold time t0, and the first threshold time t0 is a constant preset in a database; the vehicle information comprises the vehicle speed and the brightness value of the surrounding environment of the vehicle monitored by a brightness sensor carried by the vehicle; the driving behavior characteristics of the driver comprise a brake state coefficient and an accelerator state coefficient, the corresponding ranges of the brake state coefficient and the accelerator state coefficient are 0 and 1, each brake state coefficient corresponds to a unique brake stepping degree, each accelerator state coefficient corresponds to a unique accelerator stepping degree, and the brake state coefficient/the accelerator state coefficient is obtained through corresponding values corresponding to the brake stepping degree/the accelerator stepping degree in a database.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the influence of the switching time of the night vision mode of the low-light camera module on the driver is considered, the driving habit factors (the time for early response to the condition of sudden change of the ambient light) of the driver are considered, the self-adaptive adjustment is carried out on the judging threshold value of the switching of the night vision mode according to the response time of the driver, whether the night vision mode of the low-light camera module needs to be switched or not is judged in advance, the visual field reference picture of the road surface in front is provided for the user in advance, the driving risk is reduced, and the driving safety of the driver is ensured.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a data analysis processing method based on intelligent photoelectric sensing technology;
FIG. 2 is a schematic flow chart of a data analysis processing system based on intelligent photoelectric sensing technology;
FIG. 3 is an envelope of a housing of a low-light camera module in a data analysis processing system based on intelligent photoelectric sensing technology according to the present invention;
In the figure: 1. a housing of the low-light camera assembly; 2. a wide-angle low-light camera protection window; 3. the auxiliary low-light camera 1 protects a window; 4. a main micro-light camera protection window; 5. the auxiliary glimmer camera 2 protects the window.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the present invention provides the following technical solutions:
if shown in fig. 1, a data analysis processing method based on intelligent photoelectric sensing technology includes the following steps:
s1, acquiring vehicle information corresponding to different time points of a vehicle carrying a low-light-level camera assembly in historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light-level camera assembly; selecting a change time node of each night vision mode change of the vehicle in the historical data, vehicle information between the corresponding change time node and a previous change time node and a driving behavior characteristic array of a corresponding driver, and constructing a data reference set corresponding to the corresponding change time node;
The vehicle information corresponding to different time points of the vehicle carrying the low-light camera assembly in the historical data, the driving behavior characteristics of the corresponding driver and the night vision mode of the low-light camera assembly are obtained through a sensor, the sensor obtains once every first threshold time t0, and the first threshold time t0 is a preset constant in a database;
the vehicle information comprises the vehicle speed and the brightness value of the surrounding environment of the vehicle monitored by a brightness sensor carried by the vehicle;
the driving behavior characteristics of the driver comprise a brake state coefficient and an accelerator state coefficient, wherein the corresponding ranges of the brake state coefficient and the accelerator state coefficient are 0 and 1, each brake state coefficient corresponds to a unique brake stepping degree, each accelerator state coefficient corresponds to a unique accelerator stepping degree, and the brake state coefficient/the accelerator state coefficient is obtained through corresponding values corresponding to the brake stepping degree/the accelerator stepping degree in a database;
in this embodiment, when the brake is not stepped, the corresponding brake state coefficient is 0, and when the brake is stepped to the maximum degree, the corresponding brake state coefficient is 1; in the embodiment, the corresponding accelerator state coefficient when the accelerator is not stepped is 0, and the corresponding accelerator state coefficient when the accelerator is stepped to the maximum is 1;
The night vision mode of the low-light camera component comprises a bright light state W1 and a dark light state W2, the change of the night vision mode is a process of switching the bright light state W1 and the dark light state W2, the night vision mode comprises a process of W1-W2 and W2-W1, the camera component operated by the low-light camera component in the bright light state is different from the camera component operated by the low-light camera component in the dark light state, and the components operated by the low-light camera component in the different modes are preset;
the data reference set type comprises a first data reference set type and a second data reference set type, the corresponding night vision switching judgment standards are a constant value judgment standard and an adaptive judgment standard respectively, and the constant value judgment standard represents the preset beta of the environment light brightness value in the database when the brightness state and the darkness state of the micro-light camera component are switched; the self-adaptive judging standard indicates that the corresponding ambient light brightness value is a value obtained after self-adaptive adjustment of beta when the brightness state and the darkness state of the glimmer camera component are switched;
recording a change time node of a vehicle with a night vision mode change at the ith time in the historical data as Ti, constructing a data reference set corresponding to the change time node Ti, wherein when the data reference set corresponding to the change time node Ti is constructed, when Ti-T (i-1) > T1, a time interval corresponding to an element in the data reference set is [ Ti-T1, ti ], and when Ti-T (i-1) < T1, a time interval corresponding to the element in the data reference set is [ T (i-1), ti ], wherein T1 represents a preset constant in a database;
The first element in the data reference set is a vehicle information interval array, each element in the vehicle information interval array corresponds to vehicle information acquired by a time point sensor, the second element in the data reference set is a driving behavior feature array, and each element in the driving behavior feature array corresponds to driving behavior features acquired by a time point sensor;
the night vision modes corresponding to the glimmer camera assemblies of the corresponding vehicles in the same data reference set at different time points are the same.
S2, dividing types corresponding to the data reference sets according to vehicle information in the data reference sets, wherein each data reference set type corresponds to a night vision switching judgment standard, and analyzing the data reference sets respectively corresponding to the same vehicle in each change time node in the historical data to obtain night vision mode change characteristics of the corresponding low-light camera assembly of the corresponding vehicle under the condition of different night vision switching judgment standards;
when night vision mode changing characteristics of the low-light camera assemblies corresponding to the corresponding vehicles under the condition of different night vision switching judging standards are obtained in the S2, data reference sets corresponding to the same vehicle at each changing time node in the historical data are obtained, and the data reference set corresponding to the j-th changing time node of the same vehicle is marked as Cj;
The night vision mode change characteristic of the low-light camera component of the vehicle is recorded as { G11, G12, G2}, and G1 represents a bright adaptation behavior response characteristic function of a driver of the vehicle under the condition that a night vision switching judgment standard is a fixed value judgment standard; g12 represents a dark adaptation behavior reaction characteristic function of a vehicle driver under the condition that the night vision switching judgment standard is a fixed value judgment standard; g2 represents the maximum time length of the mutual switching between W1 and W2 in a night vision mode of the low-light camera component in the historical data;
in the embodiment, the corresponding procedure from W1 to W2 is a dark adaptation process, and the corresponding procedure from W2 to W1 is a bright adaptation process;
calculating an adaptive time length corresponding to the data reference set Cj, which is denoted as Dtj, wherein Dtj =t1 Cj TCj in which TCj represents the point in time of minimum abnormal behavior feature in the data reference set Cj, T1 Cj Representing the maximum time point of the time interval corresponding to the element in the data reference set Cj;
the method of acquiring TCj includes the steps of:
s21, acquiring each abnormal behavior feature in the data reference set Cj, and obtaining a time point corresponding to each abnormal behavior feature to T1 Cj In the formed abnormal time interval, the vehicle speeds corresponding to different time points monotonically decrease, the comprehensive evaluation value of the driving behavior characteristics from the time point corresponding to the abnormal behavior characteristics to any point in the corresponding abnormal time interval is less than 0,
The comprehensive evaluation value of the driving behavior characteristic between the time point a1 corresponding to the abnormal behavior characteristic and the time point a2 in the corresponding abnormal time interval is marked as F (a1，a2) ，F (a1，a2) ＝∑ x＝a1/t0 x1 (r1×P1x-r2×P2x)×t0，
Wherein, P1x represents a brake state coefficient, P2x represents an accelerator state coefficient, x1= [ a2/t0] represents a value of an integer part corresponding to a2/t0, r1 is a first coefficient preset in a database, and r2 is a second coefficient preset in the database;
s22, taking the abnormal behavior characteristic time point with the minimum corresponding time point as TCj;
the adaptive duration Dtj corresponding to the data reference set Cj is acquired,
when the data reference set type of Cj is the first data reference set type,
if the night vision mode corresponding to the data reference set Cj is W1, a first type data pair (VCj, dtj) is constructed, and if the night vision mode corresponding to the data reference set Cj is W2, a second type data pair (VCj, dtj) is constructed, wherein VCj represents the vehicle speed corresponding to TCj in the data reference set Cj;
when the data reference set type of Cj is the second data reference set type, the data pair is not constructed;
recording a plurality of first type data pairs with the same vehicle speed value and the minimum self-adaptive time length as reference data pairs under the corresponding vehicle speed, and determining each coordinate point corresponding to each reference data pair in a first plane rectangular coordinate system, wherein the first plane rectangular coordinate system is a coordinate system of the vehicle speed and the self-adaptive time length; according to a broken line formed by sequentially connecting the coordinate points in the first plane rectangular coordinate system, if the function corresponding to the obtained broken line is that the night vision switching judgment standard is a fixed value judgment standard, the explicit adaptation behavior of the vehicle driver reacts to the characteristic function G11;
Recording a plurality of second type data pairs with the same vehicle speed value and the minimum self-adaptive time length as reference data pairs under the corresponding vehicle speed, and determining each coordinate point corresponding to each reference data pair in a second plane rectangular coordinate system, wherein the second plane rectangular coordinate system is a coordinate system of the vehicle speed and the self-adaptive time length; and according to a broken line formed by sequentially connecting the coordinate points in the second plane rectangular coordinate system, when the function corresponding to the obtained broken line is that the night vision switching judgment standard is a fixed value judgment standard, the dark adaptation behavior of the vehicle driver reacts to the characteristic function G12.
S3, taking the current time as a reference node, acquiring vehicle information between the reference node and a previous change time node of the vehicle and a driving behavior feature array of a corresponding driver, and acquiring a judgment threshold value for switching a night vision mode of a camera in a low-light camera component of the vehicle by combining a night vision switching judgment standard of the vehicle when the reference node is used;
the S3 obtains vehicle information between a vehicle reference node and a previous change time node and a corresponding minimum time point of abnormal behavior characteristics in a driving behavior characteristic array of a corresponding driver, and marks the minimum time point as H;
The method for adjusting the judging threshold value of the camera switching night vision mode in the low-light camera component of the vehicle in the S3 comprises the following steps:
s31, acquiring vehicle information between a vehicle reference node and a previous change time node and brightness values LH of surrounding environments of the vehicle corresponding to the vehicle speeds VH and H in a driving behavior feature array of a corresponding driver, night vision modes and time periods tm corresponding to the current time from H,
s32, obtaining a judging threshold value of the camera switching night vision mode in the low-light camera component of the vehicle, which is marked as g1,
when the night vision switching judgment standard corresponding to the reference node is a fixed value judgment standard, g1=beta;
when the night vision handover decision criterion corresponding to the reference node is an adaptive decision criterion,
if the night vision mode corresponding to H is W2,
if G11 (VH). Gtoreq.tm+G2, the equation G11 (VH)/[ G11 (VH) -G2] = [ β -LH ]/[ β -G ], g= [ (β -LH). Times.G2+LH.times.G11 (VH) ]/G11 (VH), g1=min { G, G3, β },
if G11 (VH) < tm+G2, then the equation G11 (VH)/[ G11 (VH) -tm ] = [ β -LH ]/[ β -G ], g= [ (β -LH) x tm+LH x G11 (VH) ]/G11 (VH), g1=min { G, G3, β }; g11 (VH) represents a value corresponding to an adaptive time period when the vehicle speed is VH in G11, G11 (VH) > tm, tm > 0, and β > G, G represents a predicted value of G1 when the night vision switching criterion corresponding to the reference node is W2 in the case where the night vision switching criterion corresponding to the reference node is the adaptive criterion, G1 is equal to the minimum value of G and β, G3 represents an average value of the respective determination thresholds corresponding to the respective data reference sets of VH in the history data in the case where the night vision switching criterion is the adaptive criterion and the night vision mode is W2;
If the night vision mode corresponding to H is W1,
if G12 (VH). Gtoreq.tm+G2, the equation G12 (VH)/[ G12 (VH) -G2] = [ LH- β ]/[ G2- β ], g2= [ (β -LH). Times.G2+LHXG12 (VH) ]/G12 (VH), g1=max { G2, G4, β },
if G12 (VH) < tm+G2, then the equation G12 (VH)/[ G12 (VH) -tm ] = [ LH- β ]/[ G2- β ], g2= [ (β -LH). Times.tm+LH×G12 (VH) ]/G12 (VH), g1=min { G2, G4, β }; g12 (VH) represents a value corresponding to the adaptive time period when the vehicle speed is VH in G12, G12 (VH) > tm, tm > 0, and β < G2, G2 represents a predicted value of G1 when the night vision switching criterion corresponding to the reference node is the adaptive criterion and the night vision mode corresponding to H is W1; and g4, in the historical data, when the night vision switching judgment standard is the adaptive judgment standard and the night vision mode is W1, the average value of the judgment thresholds corresponding to the data reference sets of VH corresponding to the vehicle speed with the minimum time point of the abnormal behavior characteristics is shown.
S4, acquiring a judging threshold value of the camera in the adjusted low-light camera component for switching the night vision mode of the vehicle, comparing the obtained threshold value judging section with vehicle information corresponding to a reference node, and controlling the night vision mode of the camera in the low-light camera component on the vehicle;
When the night vision mode of the camera in the low-light camera component on the vehicle is controlled in the S4, the judgment threshold g1 for switching the night vision mode of the camera in the low-light camera component on the vehicle is obtained,
acquiring the brightness value of the surrounding environment of the vehicle in the vehicle information after the reference node in real time, recording the acquired brightness value as Lf,
if the night vision mode corresponding to H is W2,
if Lf is more than or equal to g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be changed into W2-W1; if Lf is less than g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be unchanged, wherein the night vision mode is still W2;
if the night vision mode corresponding to H is W1,
if Lf is less than or equal to g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be changed into W1-W2; if Lf is more than g1, controlling the night vision mode of the camera in the low-light camera component on the vehicle to be unchanged, and still being W1.
As shown in fig. 2, a data analysis processing system based on intelligent photoelectric sensing technology, the system comprises the following modules: an information acquisition module, a data reference set construction module, a night vision mode change characteristic analysis module, a judgment threshold adjustment module and a mode management module,
the information acquisition module acquires vehicle information corresponding to different time points of a vehicle carrying the low-light-level camera assembly in the historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light-level camera assembly;
The data reference set construction module selects a change time node of each night vision mode change of the vehicle in the historical data, vehicle information between the corresponding change time node and the previous change time node and a driving behavior characteristic array of a corresponding driver, and constructs a data reference set corresponding to the corresponding change time node;
the night vision mode change feature analysis module divides types corresponding to data reference sets according to vehicle information in the data reference sets, each data reference set type corresponds to a night vision switching judgment standard, and analyzes data reference sets respectively corresponding to the same vehicle at each change time node in historical data to obtain night vision mode change features of the low-light camera assembly corresponding to the corresponding vehicle under different night vision switching judgment standards;
the judging threshold value adjusting module takes the current time as a reference node, acquires vehicle information between the vehicle reference node and a previous change time node and a driving behavior characteristic array of a corresponding driver, and acquires a judging threshold value of a camera in a low-light camera component of the vehicle for switching a night vision mode by combining a night vision switching judging standard of the vehicle when the reference node is used;
the mode management module acquires a judging threshold value of the camera in the adjusted low-light camera component for switching the night vision mode of the vehicle, compares the obtained threshold value judging section with vehicle information corresponding to a reference node, and controls the night vision mode of the camera in the low-light camera component on the vehicle.
In the historical data in the information acquisition module, vehicle information corresponding to different time points of a vehicle carrying the low-light camera assembly in a running process, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light camera assembly are acquired through a sensor, the sensor acquires the vehicle information once every first threshold time t0, and the first threshold time t0 is a preset constant in a database; the vehicle information comprises the vehicle speed and the brightness value of the surrounding environment of the vehicle monitored by a brightness sensor carried by the vehicle; the driving behavior characteristics of the driver comprise a brake state coefficient and an accelerator state coefficient, the corresponding ranges of the brake state coefficient and the accelerator state coefficient are 0 and 1, each brake state coefficient corresponds to a unique brake stepping degree, each accelerator state coefficient corresponds to a unique accelerator stepping degree, and the brake state coefficient/the accelerator state coefficient is obtained through corresponding values corresponding to the brake stepping degree/the accelerator stepping degree in a database.
The low-light camera component in the embodiment comprises a main low-light camera, a wide-angle low-light camera, an auxiliary low-light camera 1, an auxiliary low-light camera 2, an image processing module, a power module, a gyro component, a shell, a control module, a display terminal, a data cable and a power supply cable, wherein the shell is used for enveloping the main low-light camera, the wide-angle low-light camera, the auxiliary low-light camera 1, the auxiliary low-light camera 2, the image processing module and the gyro component; the image processing module is respectively and electrically connected with the main low-light camera, the wide-angle low-light camera, the auxiliary low-light camera 1, the auxiliary low-light camera 2 and the gyro component; the effective focal length of the main micro-light camera is 14mm, the resolution is 800 multiplied by 600, the horizontal view field is 56 degrees, the vertical view field is 43 degrees, and the maximum optical distortion is less than 5 percent; the effective focal length of the auxiliary glimmer camera 1 and the auxiliary glimmer camera 2 is 6.25mm, the resolution is 800 multiplied by 600, the horizontal view field is 54 degrees, the vertical view field is 42 degrees, and the maximum optical distortion is less than 5 percent; the effective focal length of the wide-angle low-light camera is 3.2mm, the resolution is 800 multiplied by 600, the horizontal view field is 106 degrees, and the vertical view field is 80 degrees; the auxiliary low-light level cameras 1 and 2 are symmetrically distributed on the left side and the right side of the main low-light level camera, the wide-angle low-light level camera is located right above the main low-light level camera, and the main low-light level camera is in the middle. Each low-light level camera is arranged in the following position, the auxiliary low-light level cameras 1 and 2 are symmetrically distributed on the left side and the right side of the main low-light level camera, the wide-angle low-light level camera is positioned right above the main low-light level camera, and the main low-light level camera is positioned in the center. The shape of the shell is shown in fig. 3, and a 2-wide-angle low-light camera protection window, a 3-auxiliary low-light camera 1 protection window, a 4-main low-light camera protection window and a 5-auxiliary low-light camera 2 protection window are arranged on the shell 1.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The data analysis processing method based on the intelligent photoelectric sensing technology is characterized by comprising the following steps of:
s1, acquiring vehicle information corresponding to different time points of a vehicle carrying a low-light-level camera assembly in historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light-level camera assembly; selecting a change time node of each night vision mode change of the vehicle in the historical data, vehicle information between the corresponding change time node and a previous change time node and a driving behavior characteristic array of a corresponding driver, and constructing a data reference set corresponding to the corresponding change time node;
s2, dividing types corresponding to the data reference sets according to vehicle information in the data reference sets, wherein each data reference set type corresponds to a night vision switching judgment standard, and analyzing the data reference sets respectively corresponding to the same vehicle in each change time node in the historical data to obtain night vision mode change characteristics of the corresponding low-light camera assembly of the corresponding vehicle under the condition of different night vision switching judgment standards;
s3, taking the current time as a reference node, acquiring vehicle information between the reference node and a previous change time node of the vehicle and a driving behavior feature array of a corresponding driver, and acquiring a judgment threshold value for switching a night vision mode of a camera in a low-light camera component of the vehicle by combining a night vision switching judgment standard of the vehicle when the reference node is used;
S4, acquiring a judging threshold value of the camera in the adjusted low-light camera component for switching the night vision mode, comparing the obtained threshold value judging section with vehicle information corresponding to a reference node, and controlling the night vision mode of the camera in the low-light camera component on the vehicle.
2. The data analysis processing method based on the intelligent photoelectric sensing technology according to claim 1, wherein the method comprises the following steps: the vehicle information corresponding to different time points of the vehicle carrying the low-light camera assembly in the historical data, the driving behavior characteristics of the corresponding driver and the night vision mode of the low-light camera assembly are obtained through the sensor, and the sensor obtains once every a first threshold time t 0;
the vehicle information comprises the vehicle speed and the brightness value of the surrounding environment of the vehicle monitored by a brightness sensor carried by the vehicle;
the driving behavior characteristics of the driver comprise a brake state coefficient and an accelerator state coefficient, wherein the corresponding ranges of the brake state coefficient and the accelerator state coefficient are 0 and 1, each brake state coefficient corresponds to a unique brake stepping degree, each accelerator state coefficient corresponds to a unique accelerator stepping degree, and the brake state coefficient/the accelerator state coefficient is obtained through corresponding values corresponding to the brake stepping degree/the accelerator stepping degree in a database;
The night vision mode of the low-light camera component comprises a bright light state W1 and a dark light state W2, the change of the night vision mode is a process of switching the bright light state W1 and the dark light state W2, the night vision mode comprises a process of W1-W2 and W2-W1, the camera component running in the bright light state of the low-light camera component is different from the camera component running in the dark light state of the low-light camera component,
the data reference set type comprises a first data reference set type and a second data reference set type, the corresponding night vision switching judgment standards are a constant value judgment standard and an adaptive judgment standard respectively, and the constant value judgment standard represents the preset beta of the environment light brightness value in the database when the brightness state and the darkness state of the micro-light camera component are switched; the self-adaptive judging standard indicates that the corresponding ambient light brightness value is a value obtained after self-adaptive adjustment of beta when the brightness state and the darkness state of the glimmer camera component are switched;
recording a change time node of a vehicle with a night vision mode change at the ith time in the historical data as Ti, constructing a data reference set corresponding to the change time node Ti, wherein when the data reference set corresponding to the change time node Ti is constructed, when Ti-T (i-1) > T1, a time interval corresponding to an element in the data reference set is [ Ti-T1, ti ], and when Ti-T (i-1) < T1, a time interval corresponding to the element in the data reference set is [ T (i-1), ti ], wherein T1 represents a preset constant in a database;
The first element in the data reference set is a vehicle information interval array, each element in the vehicle information interval array corresponds to vehicle information acquired by a time point sensor, the second element in the data reference set is a driving behavior feature array, and each element in the driving behavior feature array corresponds to driving behavior features acquired by a time point sensor;
the night vision modes corresponding to the glimmer camera assemblies of the corresponding vehicles in the same data reference set at different time points are the same.
3. The data analysis processing method based on the intelligent photoelectric sensing technology according to claim 2, wherein the data analysis processing method is characterized in that: when night vision mode changing characteristics of the low-light camera assemblies corresponding to the corresponding vehicles under the condition of different night vision switching judging standards are obtained in the S2, data reference sets corresponding to the same vehicle at each changing time node in the historical data are obtained, and the data reference set corresponding to the j-th changing time node of the same vehicle is marked as Cj;
the night vision mode change characteristic of the low-light camera component of the vehicle is recorded as { G11, G12, G2}, and G1 represents a bright adaptation behavior response characteristic function of a driver of the vehicle under the condition that a night vision switching judgment standard is a fixed value judgment standard; g12 represents a dark adaptation behavior reaction characteristic function of a vehicle driver under the condition that the night vision switching judgment standard is a fixed value judgment standard; and G2 represents the maximum time length of the mutual switching between W1 and W2 in the night vision mode of the low-light camera component in the historical data.
4. A data analysis processing method based on intelligent photoelectric sensing technology according to claim 3, wherein: calculating an adaptive time length corresponding to the data reference set Cj, which is denoted as Dtj, wherein Dtj =t1 Cj TCj in which TCj represents the point in time of minimum abnormal behavior feature in the data reference set Cj, T1 Cj Representing the maximum time point of the time interval corresponding to the element in the data reference set Cj;
the method of acquiring TCj includes the steps of:
s21, acquiring each abnormal behavior feature in the data reference set Cj, and obtaining a time point corresponding to each abnormal behavior feature to T1 Cj In the formed abnormal time interval, the vehicle speeds corresponding to different time points monotonically decrease, the comprehensive evaluation value of the driving behavior characteristics from the time point corresponding to the abnormal behavior characteristics to any point in the corresponding abnormal time interval is less than 0,
the comprehensive evaluation value of the driving behavior characteristic between the time point a1 corresponding to the abnormal behavior characteristic and the time point a2 in the corresponding abnormal time interval is marked as F (a1，a2) ，F (a1，a2) ＝∑ x＝a1/t0 x1 (r1×P1x-r2×P2x)×t0，
Wherein, P1x represents a brake state coefficient, P2x represents an accelerator state coefficient, x1= [ a2/t0] represents a value of an integer part corresponding to a2/t0, r1 is a first coefficient preset in a database, and r2 is a second coefficient preset in the database;
S22, taking the abnormal behavior characteristic time point with the minimum corresponding time point as TCj;
the adaptive duration Dtj corresponding to the data reference set Cj is acquired,
when the data reference set type of Cj is the first data reference set type,
if the night vision mode corresponding to the data reference set Cj is W1, a first type data pair (VCj, dtj) is constructed, and if the night vision mode corresponding to the data reference set Cj is W2, a second type data pair (VCj, dtj) is constructed, wherein VCj represents the vehicle speed corresponding to TCj in the data reference set Cj;
when the data reference set type of Cj is the second data reference set type, the data pair is not constructed;
recording a plurality of first type data pairs with the same vehicle speed value and the minimum self-adaptive time length as reference data pairs under the corresponding vehicle speed, and determining each coordinate point corresponding to each reference data pair in a first plane rectangular coordinate system, wherein the first plane rectangular coordinate system is a coordinate system of the vehicle speed and the self-adaptive time length; according to a broken line formed by sequentially connecting the coordinate points in the first plane rectangular coordinate system, if the function corresponding to the obtained broken line is that the night vision switching judgment standard is a fixed value judgment standard, the explicit adaptation behavior of the vehicle driver reacts to the characteristic function G11;
Recording a plurality of second type data pairs with the same vehicle speed value and the minimum self-adaptive time length as reference data pairs under the corresponding vehicle speed, and determining each coordinate point corresponding to each reference data pair in a second plane rectangular coordinate system, wherein the second plane rectangular coordinate system is a coordinate system of the vehicle speed and the self-adaptive time length; and according to a broken line formed by sequentially connecting the coordinate points in the second plane rectangular coordinate system, when the function corresponding to the obtained broken line is that the night vision switching judgment standard is a fixed value judgment standard, the dark adaptation behavior of the vehicle driver reacts to the characteristic function G12.
5. The data analysis processing method based on the intelligent photoelectric sensing technology according to claim 4, wherein the data analysis processing method is characterized in that: the S3 obtains vehicle information between a vehicle reference node and a previous change time node and a corresponding minimum time point of abnormal behavior characteristics in a driving behavior characteristic array of a corresponding driver, and marks the minimum time point as H;
the method for adjusting the judging threshold value of the camera switching night vision mode in the low-light camera component of the vehicle in the S3 comprises the following steps:
s31, acquiring vehicle information between a vehicle reference node and a previous change time node and brightness values LH of surrounding environments of the vehicle corresponding to the vehicle speeds VH and H in a driving behavior feature array of a corresponding driver, night vision modes and time periods tm corresponding to the current time from H,
S32, obtaining a judging threshold value of the camera switching night vision mode in the low-light camera component of the vehicle, which is marked as g1,
when the night vision switching judgment standard corresponding to the reference node is a fixed value judgment standard, g1=beta;
when the night vision handover decision criterion corresponding to the reference node is an adaptive decision criterion,
if the night vision mode corresponding to H is W2,
if G11 (VH). Gtoreq.tm+G2, g= [ (. Beta. -LH). Times.G2+LHXG11 (VH) ]/G11 (VH), g1=min { G, G3, beta },
if G11 (VH) < tm+G2, g= [ (. Beta. -LH). Times.tm+LH.times.G11 (VH) ]/G11 (VH), g1=min { G, G3, beta }; g11 (VH) represents a value corresponding to an adaptive time period when the vehicle speed is VH in G11, G11 (VH) > tm, tm > 0, and β > G, G represents a predicted value of G1 when the night vision switching criterion corresponding to the reference node is W2 in the case where the night vision switching criterion corresponding to the reference node is the adaptive criterion, G1 is equal to the minimum value of G and β, G3 represents an average value of the respective determination thresholds corresponding to the respective data reference sets of VH in the history data in the case where the night vision switching criterion is the adaptive criterion and the night vision mode is W2;
if the night vision mode corresponding to H is W1,
if G12 (VH). Gtoreq.tm+G2, g2= [ (. Beta. -LH). Times.G2+LHXG12 (VH) ]/G12 (VH), g1=max { G2, G4, beta },
If G12 (VH) < tm+g2, g2= [ (. Beta. -LH) ×tm+lh×g12 (VH) ]/G12 (VH), g1=min { G2, G4, β }; g12 (VH) represents a value corresponding to the adaptive time period when the vehicle speed is VH in G12, G12 (VH) > tm, tm > 0, and β < G2, G2 represents a predicted value of G1 when the night vision switching criterion corresponding to the reference node is the adaptive criterion and the night vision mode corresponding to H is W1; and g4, in the historical data, when the night vision switching judgment standard is the adaptive judgment standard and the night vision mode is W1, the average value of the judgment thresholds corresponding to the data reference sets of VH corresponding to the vehicle speed with the minimum time point of the abnormal behavior characteristics is shown.
6. The data analysis processing method based on the intelligent photoelectric sensing technology according to claim 5, wherein the data analysis processing method is characterized in that: when the night vision mode of the camera in the low-light camera component on the vehicle is controlled in the S4, the judgment threshold g1 for switching the night vision mode of the camera in the low-light camera component on the vehicle is obtained,
acquiring the brightness value of the surrounding environment of the vehicle in the vehicle information after the reference node in real time, recording the acquired brightness value as Lf,
if the night vision mode corresponding to H is W2,
if Lf is more than or equal to g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be changed into W2-W1; if Lf is less than g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be unchanged, wherein the night vision mode is still W2;
If the night vision mode corresponding to H is W1,
if Lf is less than or equal to g1, controlling a night vision mode of a camera in the low-light camera assembly on the vehicle to be changed into W1-W2; if Lf is more than g1, controlling the night vision mode of the camera in the low-light camera component on the vehicle to be unchanged, and still being W1.
7. A data analysis processing system based on intelligent photoelectric sensing technology, which is characterized by comprising the following modules: an information acquisition module, a data reference set construction module, a night vision mode change characteristic analysis module, a judgment threshold adjustment module and a mode management module,
the information acquisition module acquires vehicle information corresponding to different time points of a vehicle carrying the low-light-level camera assembly in the historical data, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light-level camera assembly;
the data reference set construction module selects a change time node of each night vision mode change of the vehicle in the historical data, vehicle information between the corresponding change time node and the previous change time node and a driving behavior characteristic array of a corresponding driver, and constructs a data reference set corresponding to the corresponding change time node;
the night vision mode change feature analysis module divides types corresponding to data reference sets according to vehicle information in the data reference sets, each data reference set type corresponds to a night vision switching judgment standard, and analyzes data reference sets respectively corresponding to the same vehicle at each change time node in historical data to obtain night vision mode change features of the low-light camera assembly corresponding to the corresponding vehicle under different night vision switching judgment standards;
The judging threshold value adjusting module takes the current time as a reference node, acquires vehicle information between the vehicle reference node and a previous change time node and a driving behavior characteristic array of a corresponding driver, and acquires a judging threshold value of a camera in a low-light camera component of the vehicle for switching a night vision mode by combining a night vision switching judging standard of the vehicle when the reference node is used;
the mode management module acquires a judging threshold value of the camera in the adjusted low-light camera component for switching the night vision mode of the vehicle, compares the obtained threshold value judging section with vehicle information corresponding to a reference node, and controls the night vision mode of the camera in the low-light camera component on the vehicle.
8. The data analysis processing system based on the intelligent photoelectric sensing technology according to claim 7, wherein: in the historical data in the information acquisition module, vehicle information corresponding to different time points of a vehicle carrying the low-light camera assembly in the running process, driving behavior characteristics of a corresponding driver and a night vision mode of the low-light camera assembly are acquired through a sensor, and the sensor acquires the vehicle information at intervals of a first threshold time t 0; the vehicle information comprises the vehicle speed and the brightness value of the surrounding environment of the vehicle monitored by a brightness sensor carried by the vehicle; the driving behavior characteristics of the driver comprise a brake state coefficient and an accelerator state coefficient, the corresponding ranges of the brake state coefficient and the accelerator state coefficient are 0 and 1, each brake state coefficient corresponds to a unique brake stepping degree, each accelerator state coefficient corresponds to a unique accelerator stepping degree, and the brake state coefficient/the accelerator state coefficient is obtained through corresponding values corresponding to the brake stepping degree/the accelerator stepping degree in a database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310395508.5A CN116409331B (en) | 2023-04-14 | 2023-04-14 | Data analysis processing system and method based on intelligent photoelectric sensing technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310395508.5A CN116409331B (en) | 2023-04-14 | 2023-04-14 | Data analysis processing system and method based on intelligent photoelectric sensing technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116409331A CN116409331A (en) | 2023-07-11 |
| CN116409331B true CN116409331B (en) | 2023-09-19 |
Family
ID=87059234
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310395508.5A Active CN116409331B (en) | 2023-04-14 | 2023-04-14 | Data analysis processing system and method based on intelligent photoelectric sensing technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116409331B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR19990032879A (en) * | 1997-10-21 | 1999-05-15 | 한승준 | How to control lighting and CD camera at night driving |
| JP2010123046A (en) * | 2008-11-21 | 2010-06-03 | Toyota Motor Corp | Visual recognition support device |
| JP2019189081A (en) * | 2018-04-26 | 2019-10-31 | トヨタ自動車株式会社 | Image display device |
| CN113954844A (en) * | 2021-10-15 | 2022-01-21 | 南通漫行信息科技有限公司 | Intelligent automobile man-machine driving mode switching system |
| CN114228491A (en) * | 2021-12-29 | 2022-03-25 | 重庆长安汽车股份有限公司 | Head-up display system and method with night vision enhanced virtual reality |
| WO2022142354A1 (en) * | 2020-12-30 | 2022-07-07 | 常州星宇车灯股份有限公司 | Vehicle, and vehicle headlamp adjustment device and method |
-
2023
- 2023-04-14 CN CN202310395508.5A patent/CN116409331B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR19990032879A (en) * | 1997-10-21 | 1999-05-15 | 한승준 | How to control lighting and CD camera at night driving |
| JP2010123046A (en) * | 2008-11-21 | 2010-06-03 | Toyota Motor Corp | Visual recognition support device |
| JP2019189081A (en) * | 2018-04-26 | 2019-10-31 | トヨタ自動車株式会社 | Image display device |
| WO2022142354A1 (en) * | 2020-12-30 | 2022-07-07 | 常州星宇车灯股份有限公司 | Vehicle, and vehicle headlamp adjustment device and method |
| CN113954844A (en) * | 2021-10-15 | 2022-01-21 | 南通漫行信息科技有限公司 | Intelligent automobile man-machine driving mode switching system |
| CN114228491A (en) * | 2021-12-29 | 2022-03-25 | 重庆长安汽车股份有限公司 | Head-up display system and method with night vision enhanced virtual reality |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116409331A (en) | 2023-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP4931831B2 (en) | Infrared camera system and method | |
| KR101789984B1 (en) | Side Mirror Camera System For Vehicle | |
| JP4910802B2 (en) | Monitoring device and method, recording medium, and program | |
| KR20010033768A (en) | Vehicle vision system | |
| CN208479822U (en) | A kind of automobile-used panoramic looking-around system | |
| CN108650495A (en) | A kind of automobile-used panoramic looking-around system and its adaptive light compensation method | |
| CN109873981A (en) | Vehicle-mounted 360 viewing system, four tunnel intelligence exposure strategies | |
| CN112105526A (en) | Method and system for setting the lighting conditions of a vehicle and vehicle | |
| JP2019189081A (en) | Image display device | |
| KR101601324B1 (en) | Image acquiring method of vehicle camera system | |
| KR102106180B1 (en) | Method and device for daytime motor vehicle driving assistance | |
| KR102284128B1 (en) | Camera for vehicle | |
| CN116409331B (en) | Data analysis processing system and method based on intelligent photoelectric sensing technology | |
| CN118430476B (en) | CMS product liquid crystal display screen brightness adjusting method and system | |
| JP2008170785A (en) | In-car display device | |
| JP3484899B2 (en) | In-vehicle image display device | |
| US20140049645A1 (en) | Method and system for imaging an external scene by employing a custom image sensor | |
| EP3930307B1 (en) | A method for enhancing the performance of a video camera | |
| CN104508595B (en) | The multistage vehicle imaging systems indicated quasi-stability are provided | |
| CN107512222B (en) | vehicle rearview auxiliary system and control method thereof | |
| CN112009216A (en) | Active illumination control system in vehicle | |
| JP2002099999A (en) | Vehicular traffice lane detecting device | |
| US11790823B2 (en) | Image display device | |
| CN210606586U (en) | Automobile data recorder display screen brightness control device | |
| CN112399089B (en) | Method for improving visual effect of high dynamic range image and related equipment thereof |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |