CN114841843A - Method and system for analyzing suspicious green traffic - Google Patents
Method and system for analyzing suspicious green traffic Download PDFInfo
- Publication number
- CN114841843A CN114841843A CN202210397481.9A CN202210397481A CN114841843A CN 114841843 A CN114841843 A CN 114841843A CN 202210397481 A CN202210397481 A CN 202210397481A CN 114841843 A CN114841843 A CN 114841843A
- Authority
- CN
- China
- Prior art keywords
- data
- green traffic
- real
- vehicle
- index
- 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
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000010801 machine learning Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims abstract description 8
- 238000003032 molecular docking Methods 0.000 claims abstract description 5
- 230000006399 behavior Effects 0.000 claims description 13
- 238000013500 data storage Methods 0.000 claims description 5
- 210000001503 joint Anatomy 0.000 claims description 5
- 238000012795 verification Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 description 5
- 101001072091 Homo sapiens ProSAAS Proteins 0.000 description 3
- 102100036366 ProSAAS Human genes 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24552—Database cache management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a system for analyzing suspicious green traffic, wherein the method comprises the following steps: s1, docking the high-speed data centers of all provinces through a big data platform, acquiring historical passing data of the vehicles, then performing data standardization, and generating a data base table; s2, pushing the processed data to a streaming calculation engine to calculate script indexes and generate index data; s3, acquiring real-time data, generating index data through a stream computing engine, and storing the index data in a nosql cache database; s4, merging the real-time data after data standardization into a big data environment table, and processing to generate a green channel intermediate table; s5, performing machine learning training on the data to generate a green traffic vehicle model; s6, generating green traffic portraits by using the real-time data and the green traffic vehicle models. The vehicle is judged through quick query feedback, accurate manual verification is carried out when the vehicle has risks, various suggestions and references are provided through vehicle images, support is provided for verification personnel in a multi-aspect mode, and the purpose of intercepting the risks in advance is achieved.
Description
Technical Field
The invention belongs to the field of methods and systems for analyzing suspicious green traffic vehicles.
Background
After the highway is networked nationwide in 2020, the carrying conditions of green traffic vehicles at the exits of the toll stations change more, the operation of free-of-traffic toll fee by counterfeiting the green traffic vehicles is simpler, and the profit is larger. In the case where drivers in different regions are connected to each other through communication means such as a WeChat group and a QQ group, if a toll station is loosened, the driver may be a serious area for fee evasion. The data sharing of different road section management companies is mainly to be synchronized to provincial and ministry centers, and for a first-line toll station, the data is complicated, the query speed is limited, and the historical situation is difficult to trace.
The green traffic conditions are various, and the green traffic cannot be free according to different conditions. The traditional checking method has manual and equipment checking modes, pure manual work is continuously updated along with the upgrade of a fee evasion mode committing technique, the checking skill and the proficiency of personnel are greatly required, the personnel are easily multiplied by lawbreakers, and congestion is easily caused due to low efficiency; the equipment inspection investment cost is high, a large amount of hardware basic equipment is needed, and after a long time, a cracking scheme is easy to find for avoiding and continuing fee evasion, so that the equipment upgrading cost is high. Due to the continuous upgrading of policies and fee evasion means, the application range of the common Lvtong software is smaller and smaller, and the indirect cost is higher and higher due to the continuous improvement. The longer the time, the more and more data stock, the query efficiency will also decrease.
Therefore, the invention mainly aims to analyze the vehicle historical behaviors, quickly establish a batch fluid system decision system, analyze the behavior condition of the current green traffic in real time, generate vehicle images in second level and provide powerful support for green traffic analysis for field processing personnel.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method and a system for analyzing suspicious green traffic vehicles.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention provides a method for analyzing suspicious green traffic, which comprises the following steps:
s1, docking the high-speed data centers of each province through a big data platform, acquiring historical traffic data of the vehicles, and then performing data standardization to generate a data base table;
s2, pushing the data after data standardization to a streaming calculation engine to calculate script indexes, generating index data, and storing the index data and the data after data standardization to a nosql cache database;
s3, acquiring real-time data, generating index data through a stream computing engine, and storing the index data in a nosql cache database;
s4, merging the real-time data after data standardization into a big data environment table to generate a green channel intermediate table;
s5, performing machine learning training on the data obtained in the step S4 to generate a green traffic vehicle model;
s6, generating green traffic portraits by using the real-time data obtained in the step S3 and the green traffic vehicle model obtained in the step S5.
Further, the historical traffic data of the vehicle in the step S1 includes entrance information, exit information, green traffic related data, and portal data.
Further, the data interfacing method of the green channel system of each province in step S1 is text file, database, KAFKA middleware.
Further, the real-time data in step S3 includes vehicle history basic information and vehicle behavior information.
Furthermore, the invention also provides a system for analyzing the suspicious green traffic, which comprises a big data platform, a streaming calculation engine, a machine learning platform, a decision engine, a relational database and a nosql cache database;
the big data platform is in butt joint with the green traffic system of each province, obtains vehicle historical traffic data of each province and sends the vehicle historical traffic data to a distributed file storage medium;
after the big data platform standardizes a data structure, the spark middleware reads data to realize data pushing and data storage, pushes the data to a streaming calculation engine to calculate script indexes to generate index data, and then stores the index data and the data after data standardization to a nosql cache database;
the big data platform is in butt joint with a real-time interface to obtain real-time data, a push streaming type calculation engine calculates a real-time index, and the push data and the index data are landed in the big data platform;
performing machine learning training on green traffic intermediate table data of the large landing data platform to generate a green traffic vehicle model;
and pushing the real-time data to a decision engine to analyze and predict the probability through a green traffic vehicle model, and depicting the green traffic vehicle portrait together with the result and the real-time behavior index.
Compared with the prior art, the invention has the following beneficial effects:
the invention combines stream and batch, saves intermediate variable of data, does not need to repeatedly calculate stock data every time, and greatly improves sustainable service life and time. The vehicle is judged through quick query feedback, accurate manual verification is carried out when the vehicle has risks, and support is provided for verification personnel in multiple directions through different vehicle images.
Drawings
FIG. 1 is a flow chart of the operation of a method of analyzing a suspicious green traffic vehicle according to the present invention;
fig. 2 is a block diagram of a system for analyzing suspicious green traffic according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1
As shown in fig. 1, the present embodiment provides a method for analyzing suspicious green traffic, which includes the following steps:
s1, docking the high-speed data centers of all provinces through a big data platform, acquiring historical passing data of the vehicles, then performing data standardization, and generating a data base table; the historical traffic data of the vehicle comprises entrance information, exit information, green traffic related data and portal data; the data docking mode of the green communication system of each province is a text file, a database and KAFKA middleware;
a large amount of hardware resources related in the operation are shared uniformly through a cloud SAAS mode, low-cost service guarantee output is provided for cooperation owners needing service, data safety only provides relevant data to output through protocol and software constraint, the obtained data are stored in a standardized mode, data results are unified, and different provincial output difference information is still stored in data storage;
s2, pushing the data after data standardization to a streaming calculation engine to calculate script indexes, generating index data, and storing the index data and the data after data standardization to a nosql cache database; the step is used for improving the performance, and the data storage interior mainly comprises some statistical intermediate variables related to the historical travel of the vehicle;
s3, acquiring real-time data, generating index data through a stream computing engine, and storing the index data in a nosql cache database; the real-time data comprises vehicle historical basic information and vehicle behavior information; thereby guaranteeing the timeliness of the behavior data and ensuring that the vehicle information is directly acquired and updated in a short time;
s4, merging the data after data standardization into a big data environment table in real time to generate a green-passing intermediate table, and providing more and more complete data support for machine learning modeling;
s5, performing machine learning training on the data obtained in the step S4 to generate a green traffic vehicle model; modeling is mainly focused on a supervised algorithm, such as GBDT and the like, and data accuracy is mainly guaranteed and accuracy is provided in historical analysis; under the condition of time keeping, the depth is reduced as much as possible; the model training is not limited to a single model, an algorithm model with practical support is used, and an analysis probability support is mainly made for a finally obtained vehicle image;
s6, generating green traffic portraits by using the real-time data obtained in the step S3 and the green traffic vehicle model obtained in the step S5.
As shown in fig. 2, the embodiment further provides a system for analyzing suspicious green traffic, which includes a big data platform, a streaming computation engine, a machine learning platform, a decision engine, a relational database, and a nosql cache database; the relational database comprises HDFS or HBASE,
the big data platform is in butt joint with the green traffic system of each province, obtains vehicle historical traffic data of each province and sends the vehicle historical traffic data to a distributed file storage medium;
after the big data platform standardizes a data structure, the spark middleware reads data to realize data pushing and data storage, pushes the data to a streaming computation engine to compute script indexes to generate index data, and then stores the index data and the basically processed data in a nosql cache database;
the big data platform is connected with a real-time interface to obtain real-time data, a push streaming computation engine computes a real-time index, and the push data and the index data are landed in the big data platform;
performing machine learning training on green traffic intermediate table data of the large landing data platform to generate a green traffic vehicle model;
and pushing the real-time data to a decision engine to analyze and predict the probability through a green traffic vehicle model, and depicting the green traffic vehicle portrait together with the result and the real-time behavior index.
The invention mainly provides a simple and clear SAAS service for owner units, other components are maintained and updated uniformly, the operation is stable and safe in a cloud mode, and the service is provided for each business unit rapidly in a low-cost mode.
The SAAS service mainly provides a direction as a vehicle portrait, mainly aims at historical vehicle behaviors, historical green traffic transportation behaviors, vehicle basic information, system measurement values and the like, checks the output probability values of various machine learning models by a high-level option, and suggests whether to check the vehicle by a level program.
And the machine learning platform develops model feature engineering, index characterization, machine learning model output and model probability value by taking historical green-channel unqualified and green-channel inspection and pursuit payment as black samples. The learning samples are adjusted according to different province data, the generated data are integrated into images to be divided into basic images of the vehicle, behavior analysis of the vehicle and suspicious probability, and the front end queries corresponding data in a second level. And after confirming whether the vehicle has a problem on site, acquiring corresponding data and updating and optimizing the data in a model iteration period.
The data is standardized, summarized and analyzed in a batch flow combined scene, a green traffic vehicle portrait suitable for clear analysis of front-line service personnel is finally formed, the batch flow is used for data updating and machine learning modeling, currently, models are mainly used for carrying out GBDT and other supervised models according to the data, and a fast and efficient model file is obtained and used for measuring and calculating the model probability; and in a streaming scene, the data real-time calculation intermediate result falls into a nosql database, and the vehicle behavior information is quickly returned in millisecond level. The two are combined to aggregate the data to generate a vehicle representation.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method for analyzing suspicious green traffic, which is characterized by comprising the following steps:
s1, docking the high-speed data centers of all provinces through a big data platform, acquiring historical passing data of the vehicles, then performing data standardization, and generating a data base table;
s2, pushing the data after data standardization to a streaming calculation engine to calculate script indexes, generating index data, and storing the index data and the data after data standardization to a nosql cache database;
s3, acquiring real-time data, generating index data through a stream type calculation engine, and storing the index data in a nosql cache database;
s4, merging the real-time data after data standardization into a big data environment table to generate a green channel intermediate table;
s5, performing machine learning training on the data obtained in the step S4 to generate a green traffic vehicle model;
s6, generating green traffic portraits by using the real-time data obtained in the step S3 and the green traffic vehicle model obtained in the step S5.
2. The method for analyzing suspicious green traffic according to claim 1, wherein the historical traffic data of vehicles in step S1 comprises entrance information, exit information, green traffic related data and portal data.
3. The method of claim 1, wherein the data interfacing manner of the high speed data center of each province in the step S1 is text file, relational database, KAFKA middleware.
4. The method for analyzing suspicious green traffic vehicles according to claim 1, wherein the real-time data in step S3 includes basic vehicle history information and vehicle behavior information.
5. A system for analyzing suspicious green traffic is characterized by comprising a big data platform, a streaming calculation engine, a machine learning platform, a decision engine, a relational database and a nosql cache database;
the big data platform is in butt joint with a high-speed data center of each province, obtains vehicle historical traffic data of each province and sends the vehicle historical traffic data to a distributed file storage medium;
after the big data platform standardizes a data structure, the spark middleware reads data to realize data pushing and data storage, pushes the data to a streaming computation engine to compute script indexes to generate index data, and then stores the index data and the basically processed data in a nosql cache database;
the big data platform is in butt joint with a real-time interface to obtain real-time data, a push streaming type calculation engine calculates a real-time index, and the push data and the index data are landed in the big data platform;
performing machine learning training on green traffic intermediate table data of the large landing data platform to generate a green traffic vehicle model;
and pushing the real-time data to a decision engine to analyze and predict the probability through a green traffic vehicle model, and depicting the green traffic vehicle portrait together with the result and the real-time behavior index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210397481.9A CN114841843A (en) | 2022-04-15 | 2022-04-15 | Method and system for analyzing suspicious green traffic |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210397481.9A CN114841843A (en) | 2022-04-15 | 2022-04-15 | Method and system for analyzing suspicious green traffic |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114841843A true CN114841843A (en) | 2022-08-02 |
Family
ID=82565606
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210397481.9A Pending CN114841843A (en) | 2022-04-15 | 2022-04-15 | Method and system for analyzing suspicious green traffic |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114841843A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115481812A (en) * | 2022-09-30 | 2022-12-16 | 广州天长信息技术有限公司 | Green traffic mixed loading prediction method based on hidden Markov model |
| CN117373143A (en) * | 2023-12-06 | 2024-01-09 | 山东高速股份有限公司 | Reserved traffic method, reserved traffic device, reserved traffic equipment and storage medium for road vehicles |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108806003A (en) * | 2018-05-25 | 2018-11-13 | 西南交通大学 | A kind of green detection method that is open to traffic of the turn pike based on machine learning |
| CN110288035A (en) * | 2019-06-28 | 2019-09-27 | 海南树印网络科技有限公司 | A kind of online autonomous learning method and system of intelligent garbage bin |
| CN111638988A (en) * | 2019-04-28 | 2020-09-08 | 上海伽易信息技术有限公司 | Cloud host fault intelligent prediction method based on deep learning |
| US20210089938A1 (en) * | 2019-09-24 | 2021-03-25 | Ford Global Technologies, Llc | Vehicle-to-everything (v2x)-based real-time vehicular incident risk prediction |
| CN112766546A (en) * | 2021-01-05 | 2021-05-07 | 上海大学 | Machine learning-based football match win and loss real-time prediction method |
| CN113487103A (en) * | 2021-07-26 | 2021-10-08 | 平安银行股份有限公司 | Model updating method, device, equipment and storage medium |
-
2022
- 2022-04-15 CN CN202210397481.9A patent/CN114841843A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108806003A (en) * | 2018-05-25 | 2018-11-13 | 西南交通大学 | A kind of green detection method that is open to traffic of the turn pike based on machine learning |
| CN111638988A (en) * | 2019-04-28 | 2020-09-08 | 上海伽易信息技术有限公司 | Cloud host fault intelligent prediction method based on deep learning |
| CN110288035A (en) * | 2019-06-28 | 2019-09-27 | 海南树印网络科技有限公司 | A kind of online autonomous learning method and system of intelligent garbage bin |
| US20210089938A1 (en) * | 2019-09-24 | 2021-03-25 | Ford Global Technologies, Llc | Vehicle-to-everything (v2x)-based real-time vehicular incident risk prediction |
| CN112766546A (en) * | 2021-01-05 | 2021-05-07 | 上海大学 | Machine learning-based football match win and loss real-time prediction method |
| CN113487103A (en) * | 2021-07-26 | 2021-10-08 | 平安银行股份有限公司 | Model updating method, device, equipment and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115481812A (en) * | 2022-09-30 | 2022-12-16 | 广州天长信息技术有限公司 | Green traffic mixed loading prediction method based on hidden Markov model |
| CN115481812B (en) * | 2022-09-30 | 2023-09-08 | 广州天长信息技术有限公司 | Green traffic mixed loading prediction method based on hidden Markov model |
| CN117373143A (en) * | 2023-12-06 | 2024-01-09 | 山东高速股份有限公司 | Reserved traffic method, reserved traffic device, reserved traffic equipment and storage medium for road vehicles |
| CN117373143B (en) * | 2023-12-06 | 2024-05-31 | 山东高速股份有限公司 | Reserved traffic method, reserved traffic device, reserved traffic equipment and storage medium for road vehicles |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114841843A (en) | Method and system for analyzing suspicious green traffic | |
| CN101930668B (en) | Processing method of road traffic OD (Optical Density) information collection system for license plate recognition | |
| DE112020003380B4 (en) | DETERMINING THE STATUS OF THE INFRASTRUCTURE IN AN AREA OF INTEREST | |
| US11842549B2 (en) | Method and system for muck truck management in smart city based on internet of things | |
| US20160370333A1 (en) | Generating fine resolution air pollution estimates | |
| CN114354219A (en) | Test method and device for automatic driving vehicle | |
| CN109859505B (en) | Early warning processing method, device, server and medium for high-speed station | |
| CN115291515A (en) | Automatic driving simulation test system and evaluation method based on digital twinning | |
| CN114707035A (en) | Visual traffic initial point analysis system | |
| CN116665441A (en) | Intelligent traffic monitoring analysis system based on big data technology | |
| CN114005297B (en) | Vehicle team coordinated driving method based on Internet of vehicles | |
| CN116824868B (en) | Method, device, equipment and medium for identifying illegal parking points and predicting congestion of vehicles | |
| CN105489010A (en) | System and method for monitoring and analyzing fast road travel time reliability | |
| CN100395759C (en) | Computer assistant checking system based on CAD platform and method thereof | |
| CN114312930B (en) | Train operation abnormality diagnosis method and device based on log data | |
| CN111739176B (en) | ETC portal vehicle passing data processing method | |
| CN114398251A (en) | Automatic driving simulation test method and system based on scene database | |
| CN113807704A (en) | Intelligent algorithm platform construction method for urban rail transit data | |
| CN112883013A (en) | Consistency auditing system for urban atmospheric pollution source emission list | |
| CN113064940A (en) | Highway intelligence real-time charging analytic system based on big data | |
| Zoev et al. | The monitoring system of hazardous technological objects based on unmanned aerial vehicles | |
| Şerban et al. | Statistical analysis using machine learning algorithms in traffic control | |
| Bhatnagar et al. | On the challenges of on-the-fly knowledge acquisition for automated planning applications | |
| CN116866631B (en) | Integrated traffic video management method and system | |
| CN113360306B (en) | Real-time fault detection method for airborne information 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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220802 |