CN106960192A - Based on the preceding roadmarking extraction system to camera in automatic Pilot - Google Patents
Based on the preceding roadmarking extraction system to camera in automatic Pilot Download PDFInfo
- Publication number
- CN106960192A CN106960192A CN201710180016.9A CN201710180016A CN106960192A CN 106960192 A CN106960192 A CN 106960192A CN 201710180016 A CN201710180016 A CN 201710180016A CN 106960192 A CN106960192 A CN 106960192A
- Authority
- CN
- China
- Prior art keywords
- resolution
- roadmarking
- component
- processing
- road
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention provides in automatic Pilot based on the preceding roadmarking extraction system to camera, including road image acquisition module, road image processing module and roadmarking identification module, the road image acquisition module is used to obtain vehicle front road realtime graphic；The road image processing module is used to carry out High-resolution Processing to the vehicle front road realtime graphic collected, obtains high-resolution road image；The roadmarking identification module is used to the roadmarking in high-resolution road image is identified, and makees reinforcement processing to roadmarking after identification.Vehicle running state is identified to the roadmarking extracting mode of camera before by the present invention; the transport condition of vehicle in the road can accurately be judged; aid in driver safety to drive, strengthen vehicle safety energy, protect driver's personal safety as well as the property safety.
Description
Technical field
Field is extracted the present invention relates to roadmarking, and in particular to based on the preceding roadmarking to camera in automatic Pilot
Extraction system.
Background technology
Roadmarking extraction system in correlation technique uses instant visual transmission to road image, not to vehicle front
Real-time road image make High-resolution Processing, often lead to that roadmarking can not be accurately identified, do not play auxiliary driver
The due effect of safe driving.
High-resolution image can provide abundant detailed information, but vision sensor is in environment dynamic changing process
In due to existing apart from limited, the problems such as environmental disturbances, high-resolution image is often difficult to obtain, and by technique, cost
With the restriction of the factor such as environment, high-resolution image is allowed to be more difficult to extensive acquisition.
The sophisticated signal or image being typically observed are made up of a variety of different types of basic information sources or composition, each class letter
Source or composition have different functions.In recent years, Starcket et al. is different in nature and openness according to poor morphology, it is proposed that form point
Amount analysis (Morphological Component Analysis, MCA).Because it can effectively solve have difference in complicated image
The resolution problem of morphological feature content, turns into the main stream approach of picture breakdown at present.
The content of usual natural image may be considered to be made up of heterogeneity, and per the element of the first species with unique form
Learn feature, such as common smooth component and texture component, the large-scale structure feature in smooth representation in components image, and line
Manage detailed information in representation in components image.
At present, multiresolution analysis method is the most commonly used character representation method in image super-resolution field, no
Same multiresolution analysis method is suitable for extracting different characteristic in image respectively, and Stationary Wavelet Transform (SWT) is used to represent to scheme
As point-like character, non-down sampling contourlet transform (NSCT) is used for the line and contour feature for representing image.Effectively combine steady small
Wave conversion, non-down sampling contourlet transform complementary and smooth component, the different shape feature of texture component, can rationally be designed
Go out ultra-resolution method, the definition of vision dynamic image will be greatly improved.
The content of the invention
Regarding to the issue above, the present invention is intended to provide extracting system based on the preceding roadmarking to camera in automatic Pilot
System.
The purpose of the present invention is realized using following technical scheme：
Based on the preceding roadmarking extraction system to camera, including road image acquisition module, road in automatic Pilot
Image processing module, roadmarking identification module and display and alarm module, the road image acquisition module are used to obtain car
Road ahead realtime graphic；The road image processing module is used to carry out high-resolution to the road realtime graphic collected
Rate processing, obtains high-resolution road image；The roadmarking identification module is used for the high-resolution road image to obtaining
In roadmarking be identified, and reinforcement processing is made to roadmarking after identification；The display is used for car with alarm module
Transport condition is shown, and sends alarm in automotive run-off-road graticule.Beneficial effects of the present invention are：Using it is preceding to
Vehicle running state is identified the roadmarking extracting mode of camera, can accurately judge the row of vehicle in the road
State is sailed, auxiliary driver safety drives, and strengthens vehicle safety energy, protects driver's personal safety as well as the property safety.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
The frame construction drawing of Fig. 1 present invention；
Fig. 2 is the frame construction drawing of road image processing module of the present invention.
Reference：
Road image acquisition module 1, road image processing module 2, roadmarking identification module 3, display and alarm module
4th, road image pretreatment unit 20, road image post-processing unit 21, road image synthesis unit 22.
Embodiment
With reference to following application scenarios, the invention will be further described.
Referring to Fig. 1, Fig. 2, based on the preceding roadmarking extraction system to camera in the automatic Pilot of the present embodiment, including
Road image acquisition module 1, road image processing module 2, roadmarking identification module 3, the road image acquisition module 1 are used
In acquisition vehicle front road realtime graphic；The road image processing module 2 is used for the vehicle front road to collecting
Realtime graphic carries out High-resolution Processing, obtains high-resolution road image；The roadmarking identification module 3 is used for obtaining
Road image in roadmarking be identified, and reinforcement processing is made to roadmarking after identification.
Further, display and report are also included based on the preceding roadmarking extraction system to camera in the automatic Pilot
Alert module 4, this also includes display and alarm module 4, and the display is travelled in roadmarking recognition result with alarm module 4 in vehicle
During in track, normal vehicle operation is shown by display screen, in vehicle traveling in roadmarking recognition result is non-track
When automotive run-off-road and its offset direction are shown by display screen and alarm is sent.
Preferably, the roadmarking identification module 3 handles road the high-resolution mileage chart after image module processing
Roadmarking as in carries out processing identification, and reinforcement processing, prominent roadmarking are carried out after recognition.
The above embodiment of the present invention, is carried out using the preceding roadmarking extracting mode to camera to vehicle running state
Identification, can accurately judge the transport condition of vehicle in the road, and auxiliary driver safety drives, and strengthens vehicle safety
Can, protect driver's personal safety as well as the property safety.
Preferably, the road image pretreatment unit 20, form component point is passed through to vehicle front road realtime graphic
Analysis (MCA) method is handled, and the different shape in road realtime graphic is separated, and is selected corresponding low resolution and is put down
Sliding component and low resolution texture component, set the algebraically repeatly in MCA methods as 50, iteration threshold is 10-6。
This preferred embodiment, is separated to the different shape of vehicle front road realtime graphic, is maintained and is preferably divided
Performance is measured, is that the follow-up carry out different disposal to different components lays the foundation, concurrently sets optimal algebraical sum iteration threshold repeatly,
Amount of calculation is reduced, the calculating speed of total system is lifted.
Preferably, the low resolution that the road image post-processing unit 21 is used to obtain after separating road image is smooth
Component carries out, based on Stationary Wavelet Transform (SWT) processing, non-down sampling contourlet transform being carried out to low resolution texture component
(NSCT) handle, including：
(1) Stationary Wavelet Transform (SWT) is based on, component smooth to low resolution carries out High-resolution Processing, will be low point
The smooth component of resolution is decomposed into low pass, horizontal direction, vertical direction with vehicle front road realtime graphic size formed objects
With diagonally opposed four subbands, the smooth component of low resolution is more more comprising information than low pass subband, therefore directly uses low resolution
Smooth component replaces low pass subband, then to the smooth component of low resolution, horizontal direction, vertical direction and diagonally opposed four subbands
2n times of interpolation is carried out, finally, high-resolution is obtained to four son band progress SWT inverse transformations (ISWT) reconstruct after interpolation and smoothly divided
Amount；
(2) initial high resolution texture component is obtained after the Bicubic interpolation that 2n times is carried out to low resolution texture componentThen non-down sampling contourlet transform (NSCT) is carried out, low pass for obtaining an initial high resolution texture component is decomposed
Band, corresponding low pass subband coefficient is2 are included with x different scale and each yardstickxThe band logical side of individual different directions
To subband, the band logical directional subband coefficient in corresponding x-th of yardstick, y-th of direction isCalculate under current scale and own
The weighted value of coefficient in band logical directional subbandAnd maximum
In formula, wyFor weight factor,For the band logical direction in x-th of yardstick, y-th of direction
Sub-band coefficients；To each pixel in whole band logical directional subbandsStrong side is classified as according to row (m) and row (n)
Edge or weak edge, defining strong and weak marginal classification decision criteria is：
Wherein ε is the classification control parameter of setting, and inventor is by largely testing, and it is current scale x to determine ε=0.62, β
The standard deviation of lower noise；
(3) to each pixel of strong edge in whole band logical directional subbandsCoefficient carry out enhancing processing, definition
Enhancing handles function：
To including fuzzy and deformation each pixel in weak edgeCoefficient carry out decrease processing, definition weaken
Handling function is：
In formula,For pixelCorresponding NSCT conversion coefficients,Converted for NSCT after processing
Coefficient, ε is the classification control parameter of setting, and β is the standard deviation of noise under current scale x；
Finally, whole band logical directional subband coefficients of texture component after being handledAnd low pass subband coefficientAfterwards, high-resolution texture component is obtained by NSCT inverse transformations (INSCT) reconstruct
In this preferred embodiment, road image different shape is separated, by the smooth component of low resolution after separation
Stationary Wavelet Transform processing is carried out, non-down sampling contourlet transform is carried out to low resolution texture component, and using customized
Strong and weak marginal classification decision criteria accurately distinguishes the strong and weak edge of each pixel, and carries out different processing to it, wherein fixed
Enhancing processing function needed for justice processing and weaken processing function formula, be conducive to highlighting the contour feature of strong edge, weaken
The fuzzy and metaboly at weak edge, it is to avoid the shape that the artifact or grain details that road image smooth region is produced are smoothed
Condition and occur the situation of distortion at signal breakpoint, it is more accurate to represent roadmarking image detail.
Preferably, the road image synthesis unit 22, is synthesized to road image, is specially：By being superimposed high score
The residual error of the smooth component of resolution, high-resolution texture component and vehicle front road realtime graphic obtains final high-resolution road
Reticle image, defining Superposition Formula is：
R=I-Ia-Ib
In formula,For final high resolution output image,For the smooth component of high-resolution,For high-resolution texture point
Amount, γ is customized parameter, 0<γ<2, adjustable γ strengthen or reduce texture component；
R represents the residual error portion of the road image collected, and I is vehicle front road realtime graphic, IaDifferentiated to be low
Counting smooth component, IbLow resolution texture component.
In this preferred embodiment, customized parameter is introduced in Superposition Formula, can be to the road of output according to the difference of environment
Marking lines image smoothing component and texture component, and the residual error of vehicle front road realtime graphic are adjusted, enhancing sensing
The adaptability of device, display that can be under various circumstances to different information is selected.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than to present invention guarantor
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (7)
1. based on the preceding roadmarking extraction system to camera in automatic Pilot, it is characterized in that, including road image collection mould
Block, road image processing module, roadmarking identification module, the road image acquisition module are real-time for obtaining vehicle front
Image；The road image processing module is used to carry out High-resolution Processing to the vehicle front realtime graphic collected, obtains
To high-resolution road image；The roadmarking identification module is used to carry out the roadmarking in high-resolution road image
Identification, and reinforcement processing is made to the roadmarking after identification.
2. based on the preceding roadmarking extraction system to camera in automatic Pilot according to claim 1, it is characterized in that,
Also include display and alarm module, the display is travelled when roadmarking recognition result is in track with alarm module in vehicle,
Normal vehicle operation is shown by display screen, display screen is passed through when roadmarking recognition result is in non-track in vehicle traveling
Show automotive run-off-road and its offset direction and send alarm.
3. based on the preceding roadmarking extraction system to camera in automatic Pilot according to claim 1, it is characterized in that,
The roadmarking that the roadmarking identification module is handled road in the high-resolution road image after image module processing enters
Row identification, and reinforcement processing is carried out to the roadmarking after identification.
4. based on the preceding roadmarking extraction system to camera in automatic Pilot according to claim 1, it is characterized in that,
The road image processing module includes the road image pretreatment unit, road image post-processing unit, road being sequentially connected
Image composing unit；The road image pretreatment unit is used to carry out component processing to vehicle front realtime graphic, obtains car
The smooth component of low resolution and texture component of realtime graphic in front of；The road image post-processing unit is used to differentiate to low
Counting smooth component carries out, based on Stationary Wavelet Transform (SWT) processing, high-resolution smooth component being obtained, to the line of low resolution
Manage component and carry out non-down sampling contourlet transform (NSCT) processing, obtain high-resolution texture component；The road image is closed
It is used to be overlapped synthesis to the smooth component of high-resolution and high-resolution texture component into unit.
5. based on the preceding roadmarking extraction system to camera in automatic Pilot according to claim 4, it is characterized in that,
It is described that component processing is carried out to vehicle front realtime graphic, i.e., form PCA (MCA) is passed through to vehicle front realtime graphic
Method is handled, and the different shape in road image is separated, and chooses the corresponding smooth component of low resolution and low point
Resolution texture component.
6. based on the preceding roadmarking extraction system to camera in automatic Pilot according to claim 5, it is characterized in that,
The smooth component of low resolution obtained after the vehicle front realtime graphic separation carries out being based on Stationary Wavelet Transform (SWT) place
Reason, carries out non-down sampling contourlet transform (NSCT) to low resolution texture component and handles, including：
(1) Stationary Wavelet Transform (SWT) is based on, component smooth to low resolution carries out High-resolution Processing, i.e., by low resolution
Smooth component is decomposed into the low pass with vehicle front road realtime graphic size formed objects, horizontal direction, vertical direction and right
The subband of angular direction four, directly replaces low pass subband with the smooth component of low resolution, then to smooth component, horizontal direction, vertical
Direction and diagonally opposed four sons band carry out 2n times of interpolation, finally, and SWT inverse transformations (ISWT) are carried out to four son bands after interpolation
Reconstruct obtains the smooth component of high-resolution；
(2) initial high resolution texture component is obtained after the Bicubic interpolation that 2n times is carried out to low resolution texture componentSo
Non-down sampling contourlet transform (NSCT) is carried out afterwards, decomposes the low pass subband for obtaining an initial high resolution texture component, it is right
The low pass subband coefficient answered is2 are included with x different scale and each yardstickxThe band logical director of individual different directions
Band, the band logical directional subband coefficient in corresponding x-th of yardstick, y-th of direction isCalculate all band logicals under current scale
The weighted value of coefficient in directional subbandAnd maximum
In formula, wyFor weight factor, For the band logical directional subband system in x-th of yardstick, y-th of direction
Number；To each pixel in whole band logical directional subbandsStrong edge is classified as according to row (m) and row (n) or weak
Edge, defining strong and weak marginal classification decision criteria is：
Wherein ε is the classification control parameter of setting, and β is the standard deviation of noise under current scale x；
(3) to each pixel of strong edge in whole band logical directional subbandsCoefficient carry out enhancing processing, definition enhancing
Handling function is：
To including fuzzy and deformation each pixel in weak edgeCoefficient carry out decrease processing, definition weaken processing letter
Number is：
In formula,For pixelCorresponding NSCT conversion coefficients,For NSCT conversion coefficients after processing,
ε is the classification control parameter of setting, and β is the standard deviation of noise under current scale x；
Finally, whole band logical directional subband coefficients of texture component after being handledAnd low pass subband coefficientAfterwards, high-resolution texture component is obtained by NSCT inverse transformations (INSCT) reconstruct.
7. based on the preceding roadmarking extraction system to camera in automatic Pilot according to claim 6, it is characterized in that,
It is described to vehicle front road realtime graphic be based on Stationary Wavelet Transform (SWT) handle after the smooth component of high-resolution and it is non-under
High-resolution texture component after sampled contour wave conversion (NSCT) processing is overlapped synthesis, is specially：By being superimposed high score
The smooth component of resolution, high-resolution texture component and the road image residual error collected obtain final high resolution output figure
Picture, defining Superposition Formula is：
R=I-Ia-Ib
In formula,For final high resolution output image,For the smooth component of high-resolution,For high-resolution texture component,
γ is customized parameter, 0<γ<2, adjustable γ strengthen or reduce texture component；
R represents the residual error portion of the road image collected, and I is vehicle front road realtime graphic, IaIt is smooth for low resolution
Component, IbLow resolution texture component.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710180016.9A CN106960192B (en) | 2017-03-23 | 2017-03-23 | Based on the preceding roadmarking extraction system to camera in automatic Pilot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710180016.9A CN106960192B (en) | 2017-03-23 | 2017-03-23 | Based on the preceding roadmarking extraction system to camera in automatic Pilot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106960192A true CN106960192A (en) | 2017-07-18 |
| CN106960192B CN106960192B (en) | 2018-12-18 |
Family
ID=59471215
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710180016.9A Active CN106960192B (en) | 2017-03-23 | 2017-03-23 | Based on the preceding roadmarking extraction system to camera in automatic Pilot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106960192B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110532892A (en) * | 2019-08-05 | 2019-12-03 | 西安交通大学 | A kind of unstructured road single image road vanishing Point Detection Method method |
| CN111002937A (en) * | 2019-12-19 | 2020-04-14 | 厦门理工学院 | AGV (automatic guided vehicle) with anti-collision mechanism and control method thereof |
| CN111381269A (en) * | 2018-12-28 | 2020-07-07 | 沈阳美行科技有限公司 | Vehicle positioning method and device, electronic equipment and computer readable storage medium |
| CN115082701A (en) * | 2022-08-16 | 2022-09-20 | 山东高速集团有限公司创新研究院 | Multi-water-line cross identification positioning method based on double cameras |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103295204A (en) * | 2013-06-20 | 2013-09-11 | 河海大学常州校区 | Image adaptive enhancement method based on non-subsampled contourlet transform |
| CN103991449A (en) * | 2014-06-12 | 2014-08-20 | 北京联合大学 | Vehicle travelling control method and system |
| CN105718870A (en) * | 2016-01-15 | 2016-06-29 | 武汉光庭科技有限公司 | Road marking line extracting method based on forward camera head in automatic driving |
| CN106143308A (en) * | 2016-07-18 | 2016-11-23 | 上海交通大学 | Lane Departure Warning System based on intelligent back vision mirror |
-
2017
- 2017-03-23 CN CN201710180016.9A patent/CN106960192B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103295204A (en) * | 2013-06-20 | 2013-09-11 | 河海大学常州校区 | Image adaptive enhancement method based on non-subsampled contourlet transform |
| CN103991449A (en) * | 2014-06-12 | 2014-08-20 | 北京联合大学 | Vehicle travelling control method and system |
| CN105718870A (en) * | 2016-01-15 | 2016-06-29 | 武汉光庭科技有限公司 | Road marking line extracting method based on forward camera head in automatic driving |
| CN106143308A (en) * | 2016-07-18 | 2016-11-23 | 上海交通大学 | Lane Departure Warning System based on intelligent back vision mirror |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111381269A (en) * | 2018-12-28 | 2020-07-07 | 沈阳美行科技有限公司 | Vehicle positioning method and device, electronic equipment and computer readable storage medium |
| CN111381269B (en) * | 2018-12-28 | 2023-09-05 | 沈阳美行科技股份有限公司 | Vehicle positioning method, device, electronic equipment and computer readable storage medium |
| CN110532892A (en) * | 2019-08-05 | 2019-12-03 | 西安交通大学 | A kind of unstructured road single image road vanishing Point Detection Method method |
| CN110532892B (en) * | 2019-08-05 | 2021-12-28 | 西安交通大学 | Method for detecting road vanishing point of single image of unstructured road |
| CN111002937A (en) * | 2019-12-19 | 2020-04-14 | 厦门理工学院 | AGV (automatic guided vehicle) with anti-collision mechanism and control method thereof |
| CN115082701A (en) * | 2022-08-16 | 2022-09-20 | 山东高速集团有限公司创新研究院 | Multi-water-line cross identification positioning method based on double cameras |
| CN115082701B (en) * | 2022-08-16 | 2022-11-08 | 山东高速集团有限公司创新研究院 | Multi-water-line cross identification positioning method based on double cameras |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106960192B (en) | 2018-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110033002B (en) | License plate detection method based on multitask cascade convolution neural network | |
| CN106960192A (en) | Based on the preceding roadmarking extraction system to camera in automatic Pilot | |
| CN104657727B (en) | A kind of detection method of lane line | |
| CN104463241A (en) | Vehicle type recognition method in intelligent transportation monitoring system | |
| CN104299009B (en) | License plate character recognition method based on multi-feature fusion | |
| CN106128121B (en) | Vehicle queue length fast algorithm of detecting based on Local Features Analysis | |
| CN110516633B (en) | Lane line detection method and system based on deep learning | |
| CN103577809B (en) | A kind of method that traffic above-ground mark based on intelligent driving detects in real time | |
| CN107665348B (en) | Digital identification method and device for digital instrument of transformer substation | |
| CN106778646A (en) | Model recognizing method and device based on convolutional neural networks | |
| CN103324958B (en) | Based on the license plate locating method of sciagraphy and SVM under a kind of complex background | |
| CN104580829A (en) | Terahertz image enhancing method and system | |
| CN106980837A (en) | A kind of model recognizing method analyzed based on vehicle characteristic information and system | |
| CN101246553B (en) | Cab model recognizing method based on triangulation transformation | |
| CN110321855A (en) | A kind of greasy weather detection prior-warning device | |
| CN109977862B (en) | Recognition method of parking space limiter | |
| CN107705256B (en) | Forward vehicle-mounted video image enhancement method based on contact network semantics | |
| CN106203273A (en) | The lane detection system of multiple features fusion, method and senior drive assist system | |
| CN104598906B (en) | Vehicle outline detection method and its device | |
| CN106601076B (en) | A kind of automobile self training device and method based on inertial navigation and area array cameras | |
| CN108133231A (en) | A kind of real-time vehicle detection method of dimension self-adaption | |
| CN101571594B (en) | Method for recognizing SAR target based on curvelet transform | |
| CN105844614A (en) | Vision north finding method based on correction of angle of robot | |
| CN100555329C (en) | Based on multi-scale wavelet transform video foreground moving Object Segmentation method | |
| CN106960191A (en) | A kind of fingerprint recognition system |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20181106 Address after: 221400 West Xinyi third floor, 37 West Beijing Road, Xinyi Economic Development Zone, Xuzhou, Jiangsu Applicant after: Jiangsu soul chicken soup Information Technology Co., Ltd. Address before: 518000 B1002, 2022 Kangle tower, Nanshan Road, Nanshan District, Shenzhen, Guangdong Applicant before: Shenzhen Zhida Machinery Technology Co. Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |