CN109034243A - Bottom wheel conversion method based on big data analysis - Google Patents
Bottom wheel conversion method based on big data analysis Download PDFInfo
- Publication number
- CN109034243A CN109034243A CN201810842308.9A CN201810842308A CN109034243A CN 109034243 A CN109034243 A CN 109034243A CN 201810842308 A CN201810842308 A CN 201810842308A CN 109034243 A CN109034243 A CN 109034243A
- Authority
- CN
- China
- Prior art keywords
- equipment
- wheel
- image
- target
- region
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
- G06F18/2193—Validation; Performance evaluation; Active pattern learning techniques based on specific statistical tests
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/2163—Partitioning the feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
Abstract
The bottom wheel conversion method based on big data analysis that the present invention relates to a kind of, this method includes the bottom wheel converting system using a kind of based on big data analysis come cross box bottom wheel, the bottom wheel converting system based on big data analysis includes: wheel switching equipment, for when receiving anti-skidding switching command, trolley case to be worked as front vehicle wheel by overcoming resistance type wheel to replace with Anti-slip type wheel；The wheel switching equipment is also used to receive when overcoming resistance switching command, and trolley case is overcome resistance type wheel when front vehicle wheel is replaced with by Anti-slip type wheel；The bottom of trolley case is arranged in accommodating space, for accommodating the resistance type wheel or the Anti-slip type wheel；Shape sharpens equipment, for executing following shape sharpening movement based on big data analysis；DSP handles chip, for issuing and overcoming resistance switching command, be also used to issue anti-skidding switching command when the fatigue strength grade does not transfinite when the fatigue strength grade transfinites.
Description
Technical field
The present invention relates to big data field more particularly to a kind of bottom wheel conversion methods based on big data analysis.
Background technique
One of the application field that big data analysis is finally wanted is exactly predictive analysis, and feature is excavated from big data, is led to
That crosses science establishes model, can bring new data by model later, to predict following data.
The diversification of unstructured data brings new challenge to data analysis, it would be desirable to which kit system is gone point
Data are refined in analysis.Semantic engine needs have been designed into enough artificial intelligence to be enough initiatively to extract information from data.
The quality of data and data management.Big data analysis be unable to do without the quality of data and data management, the data of high quality and
Effective data management, either in academic research still in commercial application field, can guarantee to analyze the true of result and
It is valuable.
Summary of the invention
In order to solve the technical issues of trolley case shortage effectively pushes self-adaptive controlled making mechanism, the present invention provides one kind
Bottom wheel conversion method based on big data analysis, holds trolley case using the image recognition mechanism based on big data analysis
The fatigue strength grade of people carries out on-site test, when excessively tired, the front vehicle wheel of working as of trolley case to be replaced by Anti-slip type wheel
To overcome resistance type wheel, to improve the pushing speed of trolley case；Wherein, the fitting knot also based on target shape each in image
Fruit obtains the module size divided to image, and carries out the different filters based on signal-to-noise ratio to each image-region after dividing
The processing of wave mode, wherein be utilized the processing of first identifier processing equipment image be apparent and second identifier processing equipment at
The more superior characteristic of image ring effect is managed, and when selecting first identifier processing equipment, so that the image-region handled it
The signal-to-noise ratio for the image-region that the filter order of the Butterworth filter action of execution is handled with it is inversely proportional, and improves image filter
The validity of wave operand.
According to an aspect of the present invention, a kind of bottom wheel conversion method based on big data analysis, this method packet are provided
The bottom wheel converting system using a kind of based on big data analysis is included come cross box bottom wheel, it is described based on big data analysis
Bottom wheel converting system includes:
Wheel switching equipment, for when receiving anti-skidding switching command, trolley case to be worked as front vehicle wheel by overcoming resistance
Formula wheel replaces with Anti-slip type wheel；The wheel switching equipment is also used to receive when overcoming resistance switching command, by pull rod
Case overcomes resistance type wheel when front vehicle wheel is replaced with by Anti-slip type wheel；Accommodating space is arranged in the bottom of trolley case, is used for
Accommodate the resistance type wheel or the Anti-slip type wheel；With reference to picture pick-up device, the user oriented side of trolley case is set,
It is shot for the user to trolley case, refers to user images to obtain, and exported described with reference to user images；Edge extension
Equipment connect with described with reference to picture pick-up device, described with reference to user images for receiving, to it is described refer in user images with figure
It is described with reference to the corresponding edge expander graphs of user images to obtain as adjacent each target progress target edge extension in edge
Picture；Shape sharpens equipment, connect with the edge expansion equipment, for executing following movement based on big data analysis: obtaining institute
Each of edge expanded images target shape is stated, in the edge expanded images, in the edge expanded images
Each target shape is sharpened processing, to obtain and export shape sharpening image corresponding with the edge expanded images；
In the edge expansion equipment, target side is carried out with reference to each target adjacent with image edge in user images to described
Along extension include: using it is described with reference in user images with each adjacent target of image edge as corresponding imperfect mesh
Mark refers to user images described to the imperfect target based on the shape with reference to the imperfect target in user images
Except part carry out predictive extension；Reference data analyzing device sharpens equipment with the shape and connect, described for receiving
Shape sharpening image carries out configuration identification to each target in the shape sharpening image, to obtain the shape sharpening figure
The target segment where each target difference as in, carries out contour mimicry to each target segment, to obtain and the mesh
The immediate square of area of piecemeal is marked, and obtains the side length of the corresponding square of each target segment, to each target
Each side length of the corresponding square of piecemeal carries out mean value computation, to obtain the square with the side length equal with mean value
As reference square；Data splitting equipment is connect with the reference data analyzing device, for based on the reference square
Size uniform formula subarea processing is carried out to the shape sharpening image, to obtain each image of the shape sharpening image
Region；Analysis signal-to-noise ratio (SNR) equipment is connect with the data splitting equipment, for receiving each image-region, to each
Image-region carries out signal-to-noise ratio detection, and when signal-to-noise ratio is less than limitation, and Butterworth is arranged for described image region and filters
Mark, and when signal-to-noise ratio is more than limitation, Gassian low-pass filter is set for described image region and is identified；First identifier processing is set
It is standby, it is connect with the Analysis signal-to-noise ratio (SNR) equipment, it is fertile for executing Bart to the image-region for being provided with Butterworth filtering mark
This filter action, to obtain the corresponding filter field in described image region；Second identifier processing equipment, with the Analysis signal-to-noise ratio (SNR)
Equipment connection, for executing Gassian low-pass filter movement to the image-region for being provided with Gassian low-pass filter mark, to obtain
State the corresponding filter field of image-region；Regional Integration equipment is marked with the first identifier processing equipment and described second respectively
Know processing equipment connection, for receiving each filter field of first identifier processing equipment output, and receives described the
Two mark processing equipments output each filter field, and to the first identifier processing equipment output each filter field and
Each filter field of the second identifier processing equipment output merges, to obtain the corresponding area of the shape sharpening image
Domain integral image；Reference data capture apparatus is connect with the Regional Integration equipment, for receiving the Regional Integration image,
User's face pattern is searched out from the Regional Integration image based on reference user face contour, to the user's face pattern
The fatigue strength detection based on image characteristic analysis is executed, to obtain and export corresponding fatigue strength grade；DSP handles chip, point
It is not connect with the reference data capture apparatus and the wheel switching equipment, for sending out when the fatigue strength grade transfinites
Resistance switching command is overcome out, is also used to issue anti-skidding switching command when the fatigue strength grade does not transfinite；Wherein, currently
Wheel is replaced with by Anti-slip type wheel overcomes resistance type wheel, accommodates four in the accommodating space and overcomes resistance type wheel.
More specifically, in the bottom wheel converting system based on big data analysis: when front vehicle wheel is by overcoming resistance
Formula wheel replaces with Anti-slip type wheel, accommodates four Anti-slip type wheels in the accommodating space.
More specifically, in the bottom wheel converting system based on big data analysis: the first identifier processing is set
The SOC chip of different model is respectively adopted to realize in the standby and described second identifier processing equipment.
More specifically, in the bottom wheel converting system based on big data analysis: the Analysis signal-to-noise ratio (SNR) equipment
Sub- equipment is received including region, signal-to-noise ratio detects sub- equipment and sub- equipment is arranged in mark.
More specifically, in the bottom wheel converting system based on big data analysis: in first identifier processing
In equipment, the filter order for the Butterworth filter action that the image-region that the first identifier processing equipment handles it executes
The signal-to-noise ratio of the image-region handled with it is inversely proportional.
More specifically, in the bottom wheel converting system based on big data analysis: being set in the Analysis signal-to-noise ratio (SNR)
In standby, the signal-to-noise ratio detects sub- equipment and connect respectively with the region sub- equipment of reception and the sub- equipment of mark setting.
More specifically, in the bottom wheel converting system based on big data analysis: in the DSP processing chip
ROM cell is set, for the corresponding limit value of the fatigue strength grade to be stored in advance.
Detailed description of the invention
Embodiment of the present invention is described below with reference to attached drawing, in which:
Fig. 1 is according to pull rod where the bottom wheel converting system based on big data analysis shown in embodiment of the present invention
The side schematic view of case.
Specific embodiment
Embodiment of the present invention is described in detail below with reference to accompanying drawings.
The user of big data analysis has a big data analysis expert, while there are also ordinary users, but both they for
The most basic requirement of big data analysis is exactly visual analyzing, because big data feature can be intuitively presented in visual analyzing,
It can be very easy to be received by reader simultaneously, it is equally simple and clear just as picture talk.
The theoretical core of big data analysis is exactly data mining algorithm, and the algorithm of various data minings is based on different data
Type and format could be more scientific show the characteristics of data itself have, exactly because also these are by whole world statistics
Various statistical methods (can be referred to as truth) recognized by family could be goed deep into inside data, and generally acknowledged value is excavated.
Also in that big data could faster be handled by having the algorithm of these data minings in terms of another, if one
A algorithm must take several years and can just draw a conclusion, and the value of that big data is not just known where to begin yet.
In order to overcome above-mentioned deficiency, the present invention builds a kind of bottom wheel conversion method based on big data analysis, the party
Method includes the bottom wheel converting system using a kind of based on big data analysis come cross box bottom wheel.It is described to be divided based on big data
The bottom wheel converting system of analysis can effectively solve the problem that corresponding technical problem.
Fig. 1 is according to pull rod where the bottom wheel converting system based on big data analysis shown in embodiment of the present invention
The side schematic view of case.Wherein, 1 is, with reference to picture pick-up device, 2 is fixed plate, and 3 be support plate, and 4 be bottom horizontal plate.
The bottom wheel converting system based on big data analysis shown according to an embodiment of the present invention includes:
Wheel switching equipment, for when receiving anti-skidding switching command, trolley case to be worked as front vehicle wheel by overcoming resistance
Formula wheel replaces with Anti-slip type wheel；The wheel switching equipment is also used to receive when overcoming resistance switching command, by pull rod
Case overcomes resistance type wheel when front vehicle wheel is replaced with by Anti-slip type wheel；
The bottom of trolley case is arranged in accommodating space, for accommodating the resistance type wheel or the Anti-slip type wheel；
With reference to picture pick-up device, the user oriented side of trolley case is set, is shot for the user to trolley case,
User images are referred to obtain, and are exported described with reference to user images；
Edge expansion equipment is connect with described with reference to picture pick-up device, described with reference to user images for receiving, to the ginseng
It examines each target adjacent with image edge in user images and carries out the extension of target edge, it is described with reference to user images to obtain
Corresponding edge expanded images；
Shape sharpens equipment, connect with the edge expansion equipment, for executing following movement based on big data analysis: obtaining
Each of edge expanded images target shape is taken, in the edge expanded images, to the edge expanded images
Each of target shape be sharpened processing, to obtain and export shape sharpening figure corresponding with the edge expanded images
Picture；In the edge expansion equipment, mesh is carried out with reference to each target adjacent with image edge in user images to described
Marking edge extension includes: with reference to each target adjacent with image edge in user images using described as corresponding imperfect
Target schemes the imperfect target described based on the shape with reference to the imperfect target in user images with reference to user
Part as except carries out predictive extension；
Reference data analyzing device sharpens equipment with the shape and connect, for receiving the shape sharpening image, to institute
The each target stated in shape sharpening image carries out configuration identification, to obtain the difference of each target in the shape sharpening image
The target segment at place carries out contour mimicry to each target segment, closest with the area of the target segment to obtain
Square, and obtain the side length of the corresponding square of each target segment, pros corresponding to each target segment
Each side length of shape carries out mean value computation, has the square of the side length equal with mean value as with reference to square to obtain；
Data splitting equipment is connect with the reference data analyzing device, for based on the size with reference to square
Uniform formula subarea processing is carried out to the shape sharpening image, to obtain each image-region of the shape sharpening image；
Analysis signal-to-noise ratio (SNR) equipment is connect with the data splitting equipment, for receiving each image-region, to each
A image-region carries out signal-to-noise ratio detection, and when signal-to-noise ratio is less than limitation, and Butterworth is arranged for described image region and filters
Wave mark, and when signal-to-noise ratio is more than limitation, Gassian low-pass filter is set for described image region and is identified；
First identifier processing equipment is connect with the Analysis signal-to-noise ratio (SNR) equipment, for be provided with Butterworth filtering mark
The image-region of knowledge executes Butterworth filter action, to obtain the corresponding filter field in described image region；
Second identifier processing equipment is connect with the Analysis signal-to-noise ratio (SNR) equipment, for being provided with Gassian low-pass filter mark
The image-region of knowledge executes Gassian low-pass filter movement, to obtain the corresponding filter field in described image region；
Regional Integration equipment is connect with the first identifier processing equipment and the second identifier processing equipment respectively, is used
In each filter field for receiving the first identifier processing equipment output, and the reception second identifier processing equipment output
Each filter field, and to the first identifier processing equipment output each filter field and the second identifier processing set
Each filter field of standby output merges, to obtain the corresponding Regional Integration image of the shape sharpening image；
Reference data capture apparatus is connect with the Regional Integration equipment, for receiving the Regional Integration image, is based on
Reference user face contour searches out user's face pattern from the Regional Integration image, executes to the user's face pattern
Fatigue strength detection based on image characteristic analysis, to obtain and export corresponding fatigue strength grade；
DSP handles chip, connect respectively with the reference data capture apparatus and the wheel switching equipment, in institute
When stating fatigue strength grade transfinites, sending overcomes resistance switching command, is also used to when the fatigue strength grade does not transfinite, issues anti-
Sliding switching command；
Wherein, resistance type wheel is overcome when front vehicle wheel is replaced with by Anti-slip type wheel, four are accommodated in the accommodating space
Overcome resistance type wheel.
Then, the specific structure for continuing the bottom wheel converting system to of the invention based on big data analysis is carried out into one
The explanation of step.
In the bottom wheel converting system based on big data analysis: when front vehicle wheel is by overcoming resistance type wheel to replace
For Anti-slip type wheel, the accommodating space is interior to accommodate four Anti-slip type wheels.
In the bottom wheel converting system based on big data analysis: the first identifier processing equipment and described
The SOC chip of different model is respectively adopted to realize in two mark processing equipments.
In the bottom wheel converting system based on big data analysis: the Analysis signal-to-noise ratio (SNR) equipment includes that region connects
Receive sub- equipment, signal-to-noise ratio detects sub- equipment and sub- equipment is arranged in mark.
In the bottom wheel converting system based on big data analysis: in the first identifier processing equipment, institute
The filter order for stating the Butterworth filter action that the image-region that first identifier processing equipment handles it executes is handled with it
The signal-to-noise ratio of image-region be inversely proportional.
In the bottom wheel converting system based on big data analysis: described in the Analysis signal-to-noise ratio (SNR) equipment
Signal-to-noise ratio detects sub- equipment and connect respectively with the region sub- equipment of reception and the sub- equipment of mark setting.
In the bottom wheel converting system based on big data analysis: built-in ROM is mono- in the DSP processing chip
Member, for the corresponding limit value of the fatigue strength grade to be stored in advance.
In addition, the Harvard structure that the inside of the DSP processing chip is separated using program and data, has special hardware
Pile line operation is widely used in multiplier, provides special DSP instruction, can be used to quickly realize at various digital signals
Adjustment method.
According to the requirement of Digital Signal Processing, DSP processing chip generally has following some main features: (1) one
An achievable multiplication and a sub-addition in a instruction cycle.(2) program and data space is separated, can simultaneously access instruction and
Data.(3) there is quick RAM in piece, can usually be accessed simultaneously in two pieces by independent data/address bus.(4) there is low open
It sells or without overhead loop and the hardware supported jumped.(5) quickly interrupt processing and Hardware I/O are supported.(6) have in the monocycle
Multiple hardware address generators of interior operation.(7) multiple operations can be executed parallel.(8) support pile line operation, make fetching,
The operations such as decoding and execution can be with Overlapped Execution.
The data format of chip operation is handled according to DSP to classify.Data handle chip with the DSP that fixed point format works
Referred to as fixed DSP handles chip, such as TMS320C1X/C2X, TMS320C2XX/C5X, TMS320C54X/C62XX system of TI company
Column, the ADSP21XX series of AD company, the DSP16/16A of AT & T Corp., the MC56000 etc. of Motolora company.With floating-point lattice
The Floating-point DSP that is known as of formula work handles chip, such as the TMS320C3X/C4X/C8X of TI company, the ADSP21XXX system of AD company
Column, the DSP32/32C of AT & T Corp., the MC96002 etc. of Motolora company.
Not exclusively, some DSP processing chips use to be made by oneself floating-point format used by different Floating-point DSP processing chips
The floating-point format of justice, such as TMS320C3X, and some DSP processing chip then uses the standard floating-point format of IEEE, such as Motorola
The ZR35325 etc. of the MB86232 and ZORAN company of MC96002, FUJITSU company of company.
Using the bottom wheel converting system of the invention based on big data analysis, for push rod boxcar wheel in the prior art
Excessively unification the technical issues of, by using the image recognition mechanism based on big data analysis to the fatigue of trolley case holder
It spends grade and carries out on-site test, when excessively tired, trolley case is overcome resistance when front vehicle wheel is replaced with by Anti-slip type wheel
Power formula wheel, to improve the pushing speed of trolley case；Wherein, the fitting result also based on target shape each in image obtains
The different filter patterns based on signal-to-noise ratio are carried out to the module size that image divides, and to each image-region after dividing
Processing, wherein the processing of first identifier processing equipment is utilized, and image is apparent and the processing image vibration of second identifier processing equipment
The more superior characteristic of bell effect, and when selecting first identifier processing equipment, so that bar that the image-region handled it executes
The signal-to-noise ratio for the image-region that the filter order of special Butterworth filter action is handled with it is inversely proportional, and improves image filtering operand
Validity, to solve above-mentioned technical problem.
It is understood that although the present invention has been disclosed in the preferred embodiments as above, above-described embodiment not to
Limit the present invention.For any person skilled in the art, without departing from the scope of the technical proposal of the invention,
Many possible changes and modifications all are made to technical solution of the present invention using the technology contents of the disclosure above, or are revised as
With the equivalent embodiment of variation.Therefore, anything that does not depart from the technical scheme of the invention are right according to the technical essence of the invention
Any simple modifications, equivalents, and modifications made for any of the above embodiments still fall within the range of technical solution of the present invention protection
It is interior.
Claims (7)
1. a kind of bottom wheel conversion method based on big data analysis, this method includes using a kind of based on big data analysis
Bottom wheel converting system carrys out cross box bottom wheel, which is characterized in that the bottom wheel based on big data analysis converts system
System includes:
Wheel switching equipment, for when receiving anti-skidding switching command, trolley case to be worked as front vehicle wheel by overcoming resistance type vehicle
Wheel replaces with Anti-slip type wheel；The wheel switching equipment is also used to receive when overcoming resistance switching command, by trolley case
Resistance type wheel is overcome when front vehicle wheel is replaced with by Anti-slip type wheel；
The bottom of trolley case is arranged in accommodating space, for accommodating the resistance type wheel or the Anti-slip type wheel；
With reference to picture pick-up device, the user oriented side of trolley case is set, is shot for the user to trolley case, to obtain
It obtains and refers to user images, and export described with reference to user images；
Edge expansion equipment is connect with described with reference to picture pick-up device, described with reference to user images for receiving, to described with reference to use
Each target adjacent with image edge carries out the extension of target edge in the image of family, described corresponding with reference to user images to obtain
Edge expanded images；
Shape sharpens equipment, connect with the edge expansion equipment, for executing following movement based on big data analysis: obtaining institute
Each of edge expanded images target shape is stated, in the edge expanded images, in the edge expanded images
Each target shape is sharpened processing, to obtain and export shape sharpening image corresponding with the edge expanded images；
In the edge expansion equipment, target side is carried out with reference to each target adjacent with image edge in user images to described
Along extension include: using it is described with reference in user images with each adjacent target of image edge as corresponding imperfect mesh
Mark refers to user images described to the imperfect target based on the shape with reference to the imperfect target in user images
Except part carry out predictive extension；
Reference data analyzing device sharpens equipment with the shape and connect, for receiving the shape sharpening image, to described outer
Each target in shape sharpening image carries out configuration identification, where obtaining each target in the shape sharpening image respectively
Target segment, contour mimicry is carried out to each target segment, it is immediate just with the area of the target segment to obtain
It is rectangular, and the side length of the corresponding square of each target segment is obtained, square corresponding to each target segment
Each side length carries out mean value computation, has the square of the side length equal with mean value as with reference to square to obtain；
Data splitting equipment is connect with the reference data analyzing device, for the size based on the reference square to institute
It states shape sharpening image and carries out uniform formula subarea processing, to obtain each image-region of the shape sharpening image；
Analysis signal-to-noise ratio (SNR) equipment is connect with the data splitting equipment, for receiving each image-region, to each figure
As region progress signal-to-noise ratio detection, and when signal-to-noise ratio is less than limitation, Butterworth is set for described image region and filters mark
Know, and when signal-to-noise ratio is more than limitation, Gassian low-pass filter is set for described image region and is identified；
First identifier processing equipment is connect with the Analysis signal-to-noise ratio (SNR) equipment, for be provided with Butterworth filtering mark
Image-region executes Butterworth filter action, to obtain the corresponding filter field in described image region；
Second identifier processing equipment is connect with the Analysis signal-to-noise ratio (SNR) equipment, for be provided with Gassian low-pass filter mark
Image-region executes Gassian low-pass filter movement, to obtain the corresponding filter field in described image region；
Regional Integration equipment is connect, for connecing respectively with the first identifier processing equipment and the second identifier processing equipment
Each filter field of the first identifier processing equipment output is received, and receives each of the second identifier processing equipment output
A filter field, and it is defeated to each filter field and the second identifier processing equipment of first identifier processing equipment output
Each filter field out merges, to obtain the corresponding Regional Integration image of the shape sharpening image；
Reference data capture apparatus is connect with the Regional Integration equipment, for receiving the Regional Integration image, is based on benchmark
User's face profile searches out user's face pattern from the Regional Integration image, is based on to user's face pattern execution
The fatigue strength of image characteristic analysis detects, to obtain and export corresponding fatigue strength grade；
DSP handles chip, connect respectively with the reference data capture apparatus and the wheel switching equipment, for described tired
When labor degree grade transfinites, sending overcomes resistance switching command, is also used to issue anti-sliding cutting when the fatigue strength grade does not transfinite
Change order；
Wherein, resistance type wheel is overcome when front vehicle wheel is replaced with by Anti-slip type wheel, accommodate four in the accommodating space and overcome
Resistance type wheel.
2. the method as described in claim 1, it is characterised in that:
When front vehicle wheel is by overcoming resistance type wheel to replace with Anti-slip type wheel, the accommodating space is interior to accommodate four Anti-slip type vehicles
Wheel.
3. method according to claim 2, it is characterised in that:
The SOC chip that different model is respectively adopted in the first identifier processing equipment and the second identifier processing equipment is come real
It is existing.
4. method as claimed in claim 3, it is characterised in that:
The Analysis signal-to-noise ratio (SNR) equipment includes that region receives sub- equipment, signal-to-noise ratio detects sub- equipment and sub- equipment is arranged in mark.
5. method as claimed in claim 4, it is characterised in that:
In the first identifier processing equipment, the Bart for the image-region execution that the first identifier processing equipment handles it
The signal-to-noise ratio for the image-region that the filter order of Butterworth filter action is handled with it is inversely proportional.
6. method as claimed in claim 5, it is characterised in that:
In the Analysis signal-to-noise ratio (SNR) equipment, the signal-to-noise ratio detects sub- equipment and receives sub- equipment and described with the region respectively
Sub- equipment connection is arranged in mark.
7. the method as described in claim 1-6 is any, it is characterised in that:
Built-in ROM cell in the DSP processing chip, for the corresponding limit value of the fatigue strength grade to be stored in advance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810842308.9A CN109034243A (en) | 2018-07-27 | 2018-07-27 | Bottom wheel conversion method based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810842308.9A CN109034243A (en) | 2018-07-27 | 2018-07-27 | Bottom wheel conversion method based on big data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109034243A true CN109034243A (en) | 2018-12-18 |
Family
ID=64646084
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810842308.9A Pending CN109034243A (en) | 2018-07-27 | 2018-07-27 | Bottom wheel conversion method based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109034243A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109806089A (en) * | 2019-03-28 | 2019-05-28 | 夏梅 | Treat parameter extraction system |
| CN110262301A (en) * | 2019-04-02 | 2019-09-20 | 泰州腾翔信息科技有限公司 | Big data analysis control platform |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8282113B2 (en) * | 2009-06-01 | 2012-10-09 | Veal James W | Rideable luggage |
| CN104799538A (en) * | 2015-05-25 | 2015-07-29 | 管存忠 | Manual-electric trolley case |
| CN104883589A (en) * | 2015-06-06 | 2015-09-02 | 岳文伟 | Electronic detection based video file playing platform |
| CN105678961A (en) * | 2016-03-22 | 2016-06-15 | 重庆长安汽车股份有限公司 | Anti-fatigue driving automobile safety warning system and warning method |
| CN105831928A (en) * | 2015-10-21 | 2016-08-10 | 上海酷哇机器人有限公司 | Automatic lifting wheel mechanism and power wheel |
| CN106127117A (en) * | 2016-06-16 | 2016-11-16 | 哈尔滨工程大学 | Based on binocular vision quick high robust identification, location automatically follow luggage case |
| CN107440283A (en) * | 2017-08-09 | 2017-12-08 | 苏州奇控机器人科技有限公司 | A kind of intelligence follows draw-bar box |
| CN107831898A (en) * | 2017-11-20 | 2018-03-23 | 杨文娟 | Notebook application program controlling platform |
-
2018
- 2018-07-27 CN CN201810842308.9A patent/CN109034243A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8282113B2 (en) * | 2009-06-01 | 2012-10-09 | Veal James W | Rideable luggage |
| CN104799538A (en) * | 2015-05-25 | 2015-07-29 | 管存忠 | Manual-electric trolley case |
| CN104883589A (en) * | 2015-06-06 | 2015-09-02 | 岳文伟 | Electronic detection based video file playing platform |
| CN105831928A (en) * | 2015-10-21 | 2016-08-10 | 上海酷哇机器人有限公司 | Automatic lifting wheel mechanism and power wheel |
| CN105678961A (en) * | 2016-03-22 | 2016-06-15 | 重庆长安汽车股份有限公司 | Anti-fatigue driving automobile safety warning system and warning method |
| CN106127117A (en) * | 2016-06-16 | 2016-11-16 | 哈尔滨工程大学 | Based on binocular vision quick high robust identification, location automatically follow luggage case |
| CN107440283A (en) * | 2017-08-09 | 2017-12-08 | 苏州奇控机器人科技有限公司 | A kind of intelligence follows draw-bar box |
| CN107831898A (en) * | 2017-11-20 | 2018-03-23 | 杨文娟 | Notebook application program controlling platform |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109806089A (en) * | 2019-03-28 | 2019-05-28 | 夏梅 | Treat parameter extraction system |
| CN110262301A (en) * | 2019-04-02 | 2019-09-20 | 泰州腾翔信息科技有限公司 | Big data analysis control platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106874840B (en) | Vehicle information recognition method and device | |
| CN110674772A (en) | Intelligent safety control auxiliary system and method for electric power operation site | |
| CN107391598B (en) | Automatic threat information generation method and system | |
| CN100507971C (en) | Independent component analysis based automobile sound identification method | |
| CN108037770A (en) | Unmanned plane power transmission line polling system and method based on artificial intelligence | |
| CN108984799A (en) | A kind of video data handling procedure and device | |
| CN109034243A (en) | Bottom wheel conversion method based on big data analysis | |
| CN105786189B (en) | A kind of self contained function recognition methods of finger portion and system that signal is moved based on flesh | |
| CN110197127A (en) | Wireless signal detection and electromagnetic interference categorizing system and method based on deep learning | |
| CN108205676B (en) | The method and apparatus for extracting pictograph region | |
| CN107582061B (en) | Method for identifying human motion state and intelligent mobile device | |
| CN105956016A (en) | Associated information visualization processing system | |
| CN109002810A (en) | Model evaluation method, Radar Signal Recognition method and corresponding intrument | |
| CN109389031A (en) | Performance personnel's automatic positioning mechanism | |
| CN110138468B (en) | Lora signal monitoring system and method of multi-node software radio platform | |
| CN105162531B (en) | A kind of ultrashort wave broadband monitoring system and method | |
| CN102984131A (en) | Information recognition method and device | |
| CN110888850B (en) | Data quality detection method based on electric power Internet of things platform | |
| CN110933631B (en) | Indoor positioning method based on WIFI position fingerprint | |
| CN103150576A (en) | Monitoring method of cyanobacterial bloom condition | |
| CN113221863B (en) | Encryption protocol identification method, system and medium based on electromagnetic space period statistics | |
| CN108235002A (en) | Swing degree identifying platform based on image detection | |
| WO2020202788A1 (en) | Operation analysis method, operation analysis device, and operation analysis program | |
| CN114254672A (en) | Communication radiation source individual identification method based on double-path attention convolutional neural network | |
| Astapov et al. | A method of real-time mobile vehicle identification by means of acoustic noise analysis implemented on an embedded device |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181218 |