CN108229541A - Bar stress data classification method in a kind of gantry crane based on K nearest neighbor algorithms - Google Patents
Bar stress data classification method in a kind of gantry crane based on K nearest neighbor algorithms Download PDFInfo
- Publication number
- CN108229541A CN108229541A CN201711311202.8A CN201711311202A CN108229541A CN 108229541 A CN108229541 A CN 108229541A CN 201711311202 A CN201711311202 A CN 201711311202A CN 108229541 A CN108229541 A CN 108229541A
- Authority
- CN
- China
- Prior art keywords
- sample
- gantry crane
- data
- classification
- stress
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
Bar stress data classification method in a kind of gantry crane based on K nearest neighbor algorithms, stress Value Data is acquired from the strain transducer on 2 meter Chu sections of hinge by pull rod in gantry crane, recycle K nearest neighbor algorithms that data are classified, by to pull rod in gantry crane from hinge 2 meter Chu sections on loaded-up condition gantry crane different work for statistical analysis under pull rod metal structure stressing conditions, so as to draw a conclusion.The working condition of gantry crane is obtained from the engineering actual stress Value Data of magnanimity, the situation for being conducive to generate in the actual use to gantry crane is analyzed and controlled, convenient for extending the service life of gantry crane.The present invention is that the method based on K nearest neighbor algorithms its feature is simply handy, it is readily appreciated that, precision is high, theoretical ripe, for gantry crane Data Classifying Quality more preferably.
Description
Technical field
The invention belongs to gantry crane data classification field, specifically, being pull rod in a kind of gantry crane based on K nearest neighbor algorithms
Stress data sorting technique.
Background technology
It is commonly used in container hargour harbour as important handling facilities --- the gantry crane of container hargour.Gantry crane energy
No normal operation directly affects the production efficiency and economic benefit at harbour.Therefore gantry crane mechanical features information data mining and
State recognition becomes mainstream research direction, is used to acquire a large amount of data by installing sensor in the key position of gantry crane
Information, these data are acquired in gantry crane operation, have reacted mechanical features of the gantry crane under various operating modes, due to gantry crane gold
Belong to the generation of structure stress moment, need to constantly acquire its stress data and record, but since the amount of data is very huge, feature
Very unobvious then can not timely and effectively handle these data only with manual method, be had in terms of efficiency very very much not
Foot.Therefore break through traditional knowledge obtaining means is preferably particularly important using the data in database, and K nearest neighbor algorithms
It is one of most common method in Data Mining Classification technology.
K nearest neighbor algorithms are one of simplest machine learning algorithms as a kind of Supervised classification algorithm, algorithm master
Body thought is exactly according to closely located neighbours' classification, to judge the generic of oneself.Belonging to same category of sample has
Similar features, the distribution in feature space have uniformity.Since K nearest neighbor algorithms are neighbouring mainly by limited of surrounding
Sample rather than generic is judged, therefore K nearest neighbor algorithms are very suitable for the number of gantry crane by judging category regions mode
According to classification.
The artificial detection method used at present needs have special experienced work without a large amount of manpower and materials of cost
Cheng Shi is worked however actual result is not well positioned to meet Practical Project demand still.
Invention content
The present invention is directed to existing for the above-mentioned prior art deficiencies and defect, provide a kind of based on K nearest neighbor algorithms
Gantry crane in bar stress data classification method, reach the accurate purpose of classification.
The present invention is realized by using following technical proposals：
Step 1：Obtain data step
, from the strain transducer on 2 meter Chu sections of hinge, it is real to be spaced extraction in 10-20 seconds or so by being mounted on pull rod in gantry crane
Shi Yingli Value Datas, and store in the database, stress Value Data is used to assess working condition during gantry crane stress；
Step 2：Data classification is carried out based on K nearest neighbor algorithms
Step 2.1 initializes training set and classification
Input:Training dataset traindataN={ (XNi,YNi), 1≤i≤n }, N ∈ Z, wherein XNiIt is i-th training
The conditional attribute of sample, i.e., the testing time of i-th training sample, YNiThe decision attribute of i-th of sample, i.e. i-th of sample
Stress value, N is class categories label, and it be to be lightly loaded that choose N values, which be 1,2,3 correspondence classifications, and middle load is heavily loaded, and test sample is
Testdata={ (Xa,Ya), 1≤a≤m }, XaThe conditional attribute of a-th of training sample, i.e., the test of a-th training sample
Time, YaIt is the decision attribute of a-th of sample, i.e., the stress value of a-th sample, distance function d, test sample is continuous
Bar stress value in the gantry crane of 28 days, the data acquired daily are in 7000-8000 or so, and training set sample is to be lightly loaded, middle load,
Several data chosen in the stress value section of middle load
Step 2.2 calculates test sample and the Euclidean distance d (Y of training set samplea,YNi), due to sample point and training set
In sample point belong to point on two dimensional surface, therefore used Euclidean distance formula is：
Step 2.3 carries out ascending sort according to Euclidean distance size to training set sample
Obtain d (Ya, YN1)≤d(Ya, YN2)≤……≤d(Ya,YNn)；Clooating sequence changes with the change of parameter
Step 2.4 chooses the preceding K training sample of Euclidean distance minimum, (K<N), this preceding K training sample is counted each
Frequency S={ (X in classificationNi1,YNi1), (XNi2,YNi2) ... ..., (XNiK,YNiK)}；
The classification of step 2.5 return frequency maximum, i.e. test set sample belong to the category
Output：The class categories label N of test set sample
The working condition of step 3 statistical analysis gantry crane
The stress Value Data that gantry crane has been obtained by step 2 is classified, and the number at each classification midpoint is counted, so as to obtain it
The ratio of shared one day is to get going out the ratio that gantry crane is worked in this state in one day.
What the present invention was reached has the beneficial effect that：
Screening gantry crane stress ratio under classification within the specific time from a large amount of engineering real data；It may determine that
The variation tendency of current gantry crane stress, you can the stress situation of change of the prediction lower subjob of gantry crane, this programme can pass through：According to
Stress degree divides classification section, can be divided by decile, increases or decreases sample in training set traindata
Number changes the setting of the stress value size and K values of training sample, to realize the gantry crane metal structure of different data gauge mould
Stress Value Data classification and assessment gantry crane stress when working condition；Meanwhile K nearest neighbor algorithms possess simple handy, appearance
The advantages of readily understood realization, precision is high, theoretical maturation.
Description of the drawings
Below in conjunction with specification drawings and specific embodiments, the present invention is described in further detail, wherein：
Fig. 1 is overall flow figure of the present invention；
Fig. 2 is strain transducer schematic view of the mounting position.
Specific embodiment
The attached drawing in the embodiment of the present invention will be combined first below, the technical solution in the embodiment of the present invention is carried out clear
Chu is fully described by；Then, technical scheme of the present invention is introduced by a specific case history.Obviously, described reality
It is only part of the embodiment of the present invention to apply example, and instead of all the embodiments, based on the embodiments of the present invention, this field is general
Logical technical staff all other embodiments obtained without making creative work belong to what the present invention protected
Range.
The specific implementation of the present invention be by being mounted on pull rod in gantry crane from the strain transducer on 2 meter Chu sections of hinge,
Every 10-20 seconds or so the real-time stress Value Datas of extraction.It carries out writing based on K nearest neighbor algorithms on Matlab softwares and visually
Change is handled, and sets three training set traindata1, traindata2, traindata3 classification, class categories be respectively be lightly loaded,
Middle load, heavily loaded three kinds of working conditions, import the setting of test set testData and K value.Calculate test set sample and training set sample
Between Euclidean distance, ascending sort is carried out to training set according to apart from size, minimum preceding K training set sample is chosen, unites
Its frequency in its is of all categories is counted, is exported as frequency maximum classification, i.e. test set sample generic.
Fig. 1 is the overall flow figure of the present invention.
1st, bar stress data classification method in the gantry crane based on K nearest neighbor algorithms, includes the following steps：
Step 1：Obtain data step
, from the strain transducer on 2 meter Chu sections of hinge, it is real to be spaced extraction in 10-20 seconds or so by being mounted on pull rod in gantry crane
Shi Yingli Value Datas, and store in the database；Stress Value Data is used to assess working condition during gantry crane stress；
Such as Fig. 2, strain transducer is installed in gantry crane at A of the pull rod from 2 meter Chu positions of hinge, for measuring pull rod in crossbeam
Metal structure stress.
Step 2：Data classification is carried out based on K nearest neighbor algorithms
Step 2.1 initializes training set and classification
Input:Training dataset traindataN={ (XNi,YNi), 1≤i≤n }, N ∈ Z, wherein XNiIt is i-th training
The conditional attribute of sample, i.e., the testing time of i-th training sample, YNiThe decision attribute of i-th of sample, i.e. i-th of sample
Stress value, N is class categories label, and it be to be lightly loaded that choose N values, which be 1,2,3 correspondence classifications, and middle load is heavily loaded, and test sample is
Testdata={ (Xa,Ya), 1≤a≤m }, XaThe conditional attribute of a-th of training sample, i.e., the test of a-th training sample
Time, YaIt is the decision attribute of a-th of sample, i.e., the stress value of a-th sample, distance function d, test sample is continuous
Bar stress value in the gantry crane of 28 days, the data acquired daily are in 7000-8000 or so, and training set sample is to be lightly loaded, middle load,
Several data chosen in the stress value section of middle load
Step 2.2 calculates test sample and the Euclidean distance d (Y of training set samplea,YNi), due to sample point and training set
In sample point belong to point on two dimensional surface, therefore used Euclidean distance formula is：
Step 2.3 carries out ascending sort according to Euclidean distance size to training set sample
Obtain d (Ya, YN1)≤d(Ya, YN2)≤……≤d(Ya,YNn)；Clooating sequence changes with the change of parameter
Step 2.4 chooses the preceding K training sample of Euclidean distance minimum, (K<N), this preceding K training sample is counted each
Frequency S={ (X in classificationNi1,YNi1), (XNi2,YNi2) ... ..., (XNiK,YNiK)}；
The classification of step 2.5 return frequency maximum, i.e. test set sample belong to the category
Output：The class categories label N of test set sample
The working condition of step 3 statistical analysis gantry crane
The stress Value Data that gantry crane has been obtained by step 2 is classified, and the number at each classification midpoint is counted, so as to obtain it
The ratio of shared one day is to get going out the ratio that gantry crane is worked in this state in one day.
Further, classification section is divided according to stress degree, is divided by decile, increase or decrease training set
The number of sample in traindata changes the stress value of training sample and the setting of K values, to realize different data gauge
The working condition when classification of the stress Value Data of the gantry crane metal structure of mould and assessment gantry crane stress.
Case history：
Pull rod in gantry crane has been selected herein from being measured at 2 meter Chu sections of hinge at 2009 end of the years to 2010 beginning of the years
In 4 all strain datas classify as sample.One data of acquisition in every 10 seconds or so, can acquire 7000- in one day
8000 or so data.Classified with MATLAB software programming K nearest neighbor algorithms for data and carry out analysis mapping, the three of setting
A training set traindata1, traindata2, traindata3, class categories are respectively three kinds of underloading, middle load, heavy duty work
State, working condition lower stress degree see the table below one.Sample in training set corresponds to several random points in classification respectively, in order to
The accuracy of algorithm application chooses K=1, calculates the Euclidean distance of test sample and training sample, and test data belongs to nearest instruction
Practice a classification most in sample, complete to classify with this.
Stress value section under one working condition of table
| Classification | Underloading | Middle load | Heavy duty |
| Stress value section | (-50,0) | (-150,-50) | (-250,150) |
This three kinds of loaded-up conditions are three kinds of typical working conditions of gantry crane work, and the work period is 24 hours.
(1) it is lightly loaded：Minimal amount of raising rated load generally carries slight stress degree.
(2) it is carried in：Raising rated load, often with medium stress levels.
(3) it is heavily loaded：Frequent raising rated load, with fairly obvious strong stress levels.
Classified with MATLAB software programming K nearest neighbor algorithms programs for sample data and calculate its accounting.It obtains
Such as following table two：
The classification of two sample of table and accounting situation
Accounting is sorted in it is found that underloading accounting is substantially in 0.1%-5% by sample in table, middle load accounting exists substantially
70%-99.99%, for heavily loaded accounting substantially in 0-30%, there is error once in a while in each item data, but conceptual data is influenced less,
Underloading and heavily loaded institute accounting number are carried in being far smaller than than number, illustrate to focus primarily upon middle load part, class normal state is presented in overall data
The situation of distribution.By practical engineering experience it is found that long-term heavy duty or large impact can cause gantry crane strain, therefore, pass through this
Invent propose sorting technique, pull rod metal stress degree of strain in gantry crane crossbeam is classified, calculate its underloading, it is middle carry,
The accounting of heavy duty can effectively evaluate the accuracy of the service life of gantry crane.
Claims (2)
1. bar stress data classification method in the gantry crane based on K nearest neighbor algorithms, it is characterised in that include the following steps：
Step 1：Obtain data
It, from the strain transducer on 2 meter Chu sections of hinge, is spaced extraction in 10-20 seconds or so by being mounted on pull rod in gantry crane and in real time should
Force data, and store in the database, stress data is used to assess working condition during gantry crane stress, makes for assessment gantry crane
Use the service life；
Step 2：Data classification is carried out based on K nearest neighbor algorithms
Step 2.1 initializes training set and classification
Input:Training dataset traindataN={ (XNi,YNi), 1≤i≤n }, N ∈ Z, wherein XNiIt is i-th of training sample
Conditional attribute, i.e., the testing time of i-th training sample, YNiThe decision attribute of i-th of sample, i.e. i-th sample should
Force value, N are class categories label, and selection N values are that 1,2,3 correspondence classifications are underloading, and middle load is heavily loaded, and test sample is
Testdata={ (Xa,Ya), 1≤a≤m }, XaThe conditional attribute of a-th of training sample, i.e., the test of a-th training sample
Time, YaIt is the decision attribute of a-th of sample, i.e., the stress value of a-th sample, distance function d, test sample is continuous 28
Bar stress value in it gantry crane, the data acquired daily are in 7000-8000 or so, and training set sample is to be lightly loaded, middle load, in
Several data chosen in the stress value section of load；
Step 2.2 calculates test sample and the Euclidean distance d (Y of training set samplea,YNi), due in sample point and training set
Sample point belongs to the point on two dimensional surface, therefore used Euclidean distance formula is：
Step 2.3 carries out ascending sort to training set sample according to Euclidean distance size and obtains d (Ya, YN1)≤d(Ya, YN2)
≤……≤d(Ya,YNn)；Clooating sequence changes with the change of parameter；
Step 2.4 chooses the preceding K training sample of Euclidean distance minimum, (K<N), this preceding K training sample is counted of all categories
In frequency S={ (XNi1,YNi1), (XNi2,YNi2) ... ..., (XNiK,YNiK)}；
The classification of step 2.5 return frequency maximum, i.e. test set sample belong to the category
Output：The class categories label N of test set sample；
The working condition of step 3 statistical analysis gantry crane
The sample stress Value Data that gantry crane has been obtained by step 2 is classified, and counts the number at each sample classification midpoint, so as to
Go out the ratio of one day shared by each sample classification to get the ratio that gantry crane is worked under each sample classification state in one day is gone out
Example.
2. according to bar stress data classification method in the gantry crane described in claim 1 based on K nearest neighbor algorithms, feature exists
In：Classification section is divided according to stress degree, is divided by decile, is increased or decreased in training set traindata
The number of sample changes the stress value of training sample and the setting of K values, to realize the gantry crane metal knot of different data gauge mould
The working condition when classification of the stress Value Data of structure and assessment gantry crane stress.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711311202.8A CN108229541B (en) | 2017-12-11 | 2017-12-11 | Shore bridge middle pull rod stress data classification method based on K nearest neighbor algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711311202.8A CN108229541B (en) | 2017-12-11 | 2017-12-11 | Shore bridge middle pull rod stress data classification method based on K nearest neighbor algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108229541A true CN108229541A (en) | 2018-06-29 |
| CN108229541B CN108229541B (en) | 2021-09-28 |
Family
ID=62651953
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711311202.8A Active CN108229541B (en) | 2017-12-11 | 2017-12-11 | Shore bridge middle pull rod stress data classification method based on K nearest neighbor algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108229541B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021093096A1 (en) * | 2019-11-11 | 2021-05-20 | 深圳市商汤科技有限公司 | Image classification model training method, image processing method and device |
| CN117251830A (en) * | 2023-11-14 | 2023-12-19 | 成都态坦测试科技有限公司 | Flash memory life prediction method and device, readable storage medium and electronic equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080059081A1 (en) * | 2006-08-31 | 2008-03-06 | Gualandri J Joseph | Systems and methods for monitoring a machine |
| CN101712438A (en) * | 2009-12-15 | 2010-05-26 | 太原科技大学 | Method for acquiring evaluation point stress time domain value by utilizing tower crane operating data |
| US20120010488A1 (en) * | 2010-07-01 | 2012-01-12 | Henry Barry J | Method and apparatus for improving personnel safety and performance using logged and real-time vital sign monitoring |
| CN103959190A (en) * | 2011-12-23 | 2014-07-30 | 惠普发展公司，有限责任合伙企业 | Managing a facility |
| CN104285138A (en) * | 2012-04-24 | 2015-01-14 | Skf公司 | Bearing monitoring method and system |
| CN104809317A (en) * | 2015-05-19 | 2015-07-29 | 武汉理工大学 | Entire load spectrum compiling method for bridge type crane based on clustering analysis |
| CN106698197A (en) * | 2016-12-01 | 2017-05-24 | 上海振华重工电气有限公司 | System for online diagnosis and preventive maintenance of container crane based on big data |
| CN107451720A (en) * | 2017-07-06 | 2017-12-08 | 上海振华重工电气有限公司 | Towards the number indicating a ship's order of departure production plan evaluation method of automated container terminal |
-
2017
- 2017-12-11 CN CN201711311202.8A patent/CN108229541B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080059081A1 (en) * | 2006-08-31 | 2008-03-06 | Gualandri J Joseph | Systems and methods for monitoring a machine |
| CN101712438A (en) * | 2009-12-15 | 2010-05-26 | 太原科技大学 | Method for acquiring evaluation point stress time domain value by utilizing tower crane operating data |
| US20120010488A1 (en) * | 2010-07-01 | 2012-01-12 | Henry Barry J | Method and apparatus for improving personnel safety and performance using logged and real-time vital sign monitoring |
| CN103959190A (en) * | 2011-12-23 | 2014-07-30 | 惠普发展公司，有限责任合伙企业 | Managing a facility |
| CN104285138A (en) * | 2012-04-24 | 2015-01-14 | Skf公司 | Bearing monitoring method and system |
| CN104809317A (en) * | 2015-05-19 | 2015-07-29 | 武汉理工大学 | Entire load spectrum compiling method for bridge type crane based on clustering analysis |
| CN106698197A (en) * | 2016-12-01 | 2017-05-24 | 上海振华重工电气有限公司 | System for online diagnosis and preventive maintenance of container crane based on big data |
| CN107451720A (en) * | 2017-07-06 | 2017-12-08 | 上海振华重工电气有限公司 | Towards the number indicating a ship's order of departure production plan evaluation method of automated container terminal |
Non-Patent Citations (2)
| Title |
|---|
| 周顺辉: "《基于模糊神经网络的STS起重机工况识别技术及其应用研究》", 《中国优秀博硕士学位论文全文数据库 (硕士) 工程科技Ⅱ辑》 * |
| 尹怡楠 等: "《基于SVR的桥吊实时应力预测模型及其应用》", 《中国航海》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021093096A1 (en) * | 2019-11-11 | 2021-05-20 | 深圳市商汤科技有限公司 | Image classification model training method, image processing method and device |
| CN117251830A (en) * | 2023-11-14 | 2023-12-19 | 成都态坦测试科技有限公司 | Flash memory life prediction method and device, readable storage medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108229541B (en) | 2021-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110852395B (en) | Ore granularity detection method and system based on autonomous learning and deep learning | |
| CN106371427A (en) | Industrial process fault classification method based on analytic hierarchy process and fuzzy fusion | |
| CN109740661A (en) | A kind of well fluid level adaptive forecasting method based on fuzzy evaluation | |
| CN104820724B (en) | Text class educational resource knowledge point forecast model preparation method and application method | |
| CN108460213A (en) | Based on the T-S models of multi-cluster prototype to the prediction technique and program of gas flowrate in bosh | |
| CN111784093B (en) | Enterprise reworking auxiliary judging method based on power big data analysis | |
| CN110175416A (en) | Three Gorges Reservoir water temperature prediction method based on principal component analysis and neural network | |
| CN112214933B (en) | Fatigue performance prediction method based on machine learning | |
| CN108241925A (en) | A kind of discrete manufacture mechanical product quality source tracing method based on outlier detection | |
| CN108229541A (en) | Bar stress data classification method in a kind of gantry crane based on K nearest neighbor algorithms | |
| CN111949939A (en) | Intelligent electric meter running state evaluation method based on improved TOPSIS and cluster analysis | |
| CN106777652B (en) | method for predicting air permeability of blast furnace | |
| CN106446478A (en) | System and method for optimizing cutting process | |
| CN108491991A (en) | Constraints analysis system based on the industrial big data product duration and method | |
| CN112287980B (en) | Power battery screening method based on typical feature vector | |
| CN112749840A (en) | Method for acquiring reference value of energy efficiency characteristic index of thermal power generating unit | |
| CN108733003A (en) | Slewing parts process working hour prediction technique based on kmeans clustering algorithms and system | |
| CN116520772A (en) | Sheet part numerical control machining quality prediction method based on multitasking transfer learning | |
| CN113240527A (en) | Bond market default risk early warning method based on interpretable machine learning | |
| CN107330454B (en) | Nonlinear massive high-dimensional sequence data classification characteristic visualization and quantitative analysis method | |
| CN109447026B (en) | Automatic pile body integrity category identification method based on support vector machine | |
| CN102621953A (en) | Automatic online quality monitoring and prediction model updating method for rubber hardness | |
| CN1598534A (en) | Soft investigating method for overflow grain index of ore grinding system based on case inference | |
| CN105740617B (en) | The balancing method of integrated antenna-feeder system quality degree | |
| CN109358185A (en) | Rubber compounding performance prediction model and prediction technique based on extreme learning machine |
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 |