CN109612534A - Farming data acquisition and transmission method - Google Patents
Farming data acquisition and transmission method Download PDFInfo
- Publication number
- CN109612534A CN109612534A CN201910028179.4A CN201910028179A CN109612534A CN 109612534 A CN109612534 A CN 109612534A CN 201910028179 A CN201910028179 A CN 201910028179A CN 109612534 A CN109612534 A CN 109612534A
- Authority
- CN
- China
- Prior art keywords
- data
- primary monitoring
- monitoring data
- vector
- farming
- 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
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of farming data acquisition and transmission methods, it is related to agriculture farming data transmission and processing technical field, sensor node acquires primary monitoring data, primary monitoring data is obtained to parameter sensing vector after compressed sensing using compressive sensing theory, and be transferred to aggregation node；Aggregation node carries out dimension-reduction treatment to parameter sensing vector, obtains coding vector, and be sent to background server by wireless transmission channel；Background server is configured to carry out the primary monitoring data in the bi-orthogonal wavelet transformation matrix of rarefaction representation；Background server constructs data reconstruction model according to bi-orthogonal wavelet transformation matrix, and is reconstructed based on orthogonal matching pursuit algorithm to coding vector and obtain primary monitoring data.The present invention can well solve the problem that data volume in network is huge, wireless transmission energy consumption is big by combining compressed sensing technology to propose a kind of wireless collection transmission method of farming data.
Description
Technical field
The present invention relates to agriculture farming data transmission and processing technical fields, and in particular to one kind can reduce sensor node energy
The farming data acquisition and transmission method of consumption.
Background technique
Agricultural is as basic industry, for immediately following big data era paces, traditional mode of agriculture should be to data-driven
Wisdom production method transformation.And cloud computing, big data, internet etc. science and technology by be this change main promotion
Power.Based on this, theoretical circles largely explore agriculture big data, and Zhang Haoran etc. describes the concept of big data and using skill
Art, Wen Fujiang illustrate the strategic importance and synergistic mechanism of agriculture big data research, and Sun Zhongfu etc. discusses agriculture big data pipe
Whole life cycle is managed in the application of agriculture big data field most study or agriculture big data, Liu Pingzeng is with the Bohai Sea
Application of the agriculture big data platform in wisdom agricultural is described for silo science and technology demonstration project big data platform, WHO is situated between perhaps
The agriculture big data that continued to the significant role of agricultural product monitoring and warning, Guo Chengkun etc. with regard to developing agricultural big data main problem and
Main task inquire into as it can be seen that study today in the ascendant in agriculture big data, inquire into its obtain have with utilization it is non-
Often important meaning.
Agriculture big data application relies on various sensing nodes (ambient temperature and humidity, the soil water for being deployed in agricultural production scene
Point, carbon dioxide, image etc.) and cordless communication network, complete the number of the links such as agriculture big data acquisition, transmission, storage, processing
According to management, in conjunction with big data analysis digging technology, the final Intellisense for realizing agriculture production environment, intelligent early-warning, intelligence are determined
Plan, intellectual analysis, expert's online direction provide precision plantation, visualized management, intelligent decision making for agricultural production.In recent years
Wireless sensor network (Wireless Sensor Network, WSN) to develop is a large amount of numbers needed for crop growth environment
According to acquisition and monitoring provide new method, and the data science of acquisition can analyze, progress information early warning provides rationalization
It is recommended that bringing preferable economic benefit to improve crop quality and yield.
WSN has the characteristics that node energy is limited with network communication bandwidth, therefore how to reduce energy consumption, makes full use of
Communication bandwidth is a problem to be solved.One feasible method is to carry out compression processing to transmission data, and compression algorithm has more
Kind, such as distributed wavelet data compression algorithm, the data compression algorithm based on pipeline, pre-code data compression algorithm etc..But this
There is also defects for several traditional compress modes: after data compression transmission, receiving end restores have certain error；Compression ratio is not high-incidence
Send data volume big, energy consumption is higher when node wireless being caused to transmit data.
In recent years, the compressed sensing technology (Compressed Sensing, CS) of proposition is a kind of new compression sampling skill
Art, thought are that sampling is carried out with compression with lower rate simultaneously.CS technical application is into wireless sensor network, concrete thought
Be: sensor node acquires original signal f, the sparse domain representation x of signal is arrived using discrete Fourier transform, with random shellfish effort
Matrix observation X obtains low-dimensional observation signal.Observation signal is transmitted to after Sink node and is transmitted to data center's progress signal weight
Structure restores original signal.
Summary of the invention
The purpose of the present invention is to provide a kind of observation signal is transmitted to after Sink node be transmitted to data center progress
Signal reconstruction restores original signal, calculates simply, can reduce the farming data acquisition and transmission method of sensor node energy consumption, with solution
Technical problem present in certainly above-mentioned background technique.
To achieve the goals above, this invention takes following technical solutions:
The present invention provide it is a kind of using CS technical application into wireless sensor network, carry out the acquisition of farming data and transmission.
Specifically include the following steps:
Step S110: sensor node acquires primary monitoring data, using compressive sensing theory by the raw monitored number
According to obtaining parameter sensing vector after compressed sensing, and the parameter sensing vector is transferred to aggregation node；
Step S120: the aggregation node carries out dimension-reduction treatment to the parameter sensing vector, obtains coding vector, and will
The coding vector is sent to background server by wireless transmission channel；
Step S130: the background server receive the coding vector and be configured to the primary monitoring data into
The bi-orthogonal wavelet transformation matrix of row rarefaction representation；
Step S140: the background server constructs data reconstruction model according to the bi-orthogonal wavelet transformation matrix, and
Orthogonal matching pursuit algorithm is based on according to the data reconstruction model, and acquisition primary monitoring data is reconstructed to coding vector.
Further, the sensor node includes air temperature sensor, air humidity sensor, soil moisture sensing
Device, soil humidity sensor and P in soil H value sensor.
Further, the primary monitoring data include air themperature data, air humidity data, soil temperature data,
Soil moisture data and P in soil H Value Data.
Further, the step S120 is specifically included:
The aggregation node is observed coding to primary monitoring data f using the random gaussian matrix of M × N, so that institute
It states primary monitoring data and is converted into M dimension from N-dimensional, the M is the integer greater than 0 and less than N.
Further, the step S130 is specifically included:
The primary monitoring data f of sensor node acquisition can regard a R asNSpace N × 1 tie up column vector, with N ×
The orthonormal basis of 1 dimensionLinear expression,Constitute N × N-dimensional bi-orthogonal wavelet transformation matrix Ψ=[Φ1,
Φ2,Φ3,...Φn], then,
Or f=Ψ X；Wherein,
X is that number vector, and X=Ψ are maintained in N × 1 that f is projected under ΨTF, X and f are table of the same signal in not same area
Show, f is the time-domain coefficients of signal, and X is the Ψ domain coefficient of signal, and x indicates sparse signal.
Further, in the step S140, the data reconstruction model is
Wherein, y is the coding vector to y=Φ Ψ θ, and θ is base transformation system, x=Ψ θ.
Further, described that orthogonal matching pursuit algorithm pair is based on according to the data reconstruction model in step S140
Coding vector reconstruct obtains primary monitoring data
Wherein,To be directed to arg min based on orthogonal matching pursuit algorithm | | θ1Institute is calculated optimal to be forced
Nearly coefficient,For the primary monitoring data after reconstruct.
The invention has the advantages that: the rarefaction representation of sensing data is realized by constructing bi-orthogonal wavelet transformation matrix, completely
The signal sparsity condition of sufficient compressed sensing realizes the high-precision weight of data by using orthogonal matching pursuit (OMP) algorithm
Structure, while the acquisition dimension of raw sensory data is reduced, volume of transmitted data can be effectively reduced, transmission cost and energy are reduced
Consumption, effectively extends the life cycle of sensor network, improves radio transmission efficiency and its robust when multi-parameter monitoring
Property.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill of field, without creative efforts, it can also be obtained according to these attached drawings others
Attached drawing.
Fig. 1 is farming data acquisition and transmission method flow chart described in the embodiment of the present invention.
Specific embodiment
Embodiments of the present invention are described below in detail, the example of the embodiment is shown in the accompanying drawings, wherein from beginning
Same or similar element or module with the same or similar functions are indicated to same or similar label eventually.Below by ginseng
The embodiment for examining attached drawing description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in specification of the invention
Diction " comprising " refer to that there are the feature, integer, step, operation, element and/or modules, but it is not excluded that in the presence of or addition
Other one or more features, integer, step, operation, element, module and/or their group.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art
Language and scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Should also
Understand, those terms such as defined in the general dictionary, which should be understood that, to be had and the meaning in the context of the prior art
The consistent meaning of justice, and unless defined as here, it will not be explained in an idealized or overly formal meaning.
In order to facilitate understanding of embodiments of the present invention, further by taking specific embodiment as an example below in conjunction with attached drawing to be solved
Explanation is released, and embodiment does not constitute the restriction to the embodiment of the present invention.
Those of ordinary skill in the art are it should be understood that attached drawing is the schematic diagram of one embodiment, the portion in attached drawing
Part or device are not necessarily implemented necessary to the present invention.
Embodiment
Compressive sensing theory (CS, Compress Sensing) is used primarily for solving as a kind of new information acquisition method
The certainly acquisition and processing problem of picture signal.As long as the theory points out that signal can be in some suitable orthogonal basis sparsity table
Show, signal can be by the frequency acquisition overall situation observation far below nyquist sampling rate, can be with lower sampling frequency
Rate sampled signal, and can by restructing algorithm appropriate with high probability, original signal is reconstructed from observation in high precision.
It utilizes CS technical application into wireless sensor network as shown in Figure 1, the embodiment of the present invention is a kind of, carries out farming data
Acquisition and transmission.Specifically include the following steps:
Step S110: sensor node acquires primary monitoring data, using compressive sensing theory by the raw monitored number
According to obtaining parameter sensing vector after compressed sensing, and the parameter sensing vector is transferred to aggregation node；
Step S120: the aggregation node carries out dimension-reduction treatment to the parameter sensing vector, obtains coding vector, and will
The coding vector is sent to background server by wireless transmission channel；
Step S130: the background server receive the coding vector and be configured to the primary monitoring data into
The bi-orthogonal wavelet transformation matrix of row rarefaction representation；
Step S140: the background server constructs data reconstruction model according to the bi-orthogonal wavelet transformation matrix, and
Orthogonal matching pursuit algorithm is based on according to the data reconstruction model, and acquisition primary monitoring data is reconstructed to coding vector.
Further, the sensor node includes air temperature sensor, air humidity sensor, soil moisture sensing
Device, soil humidity sensor and P in soil H value sensor.
Further, the primary monitoring data include air themperature data, air humidity data, soil temperature data,
Soil moisture data and P in soil H Value Data.
Further, the step S120 is specifically included:
The aggregation node is observed coding to primary monitoring data f using the random gaussian matrix of M × N, so that institute
It states primary monitoring data and is converted into M dimension from N-dimensional, the M is the integer greater than 0 and less than N.
Further, the step S130 is specifically included:
The primary monitoring data f of sensor node acquisition can regard a R asNSpace N × 1 tie up column vector, with N ×
The orthonormal basis of 1 dimensionLinear expression,Constitute N × N-dimensional bi-orthogonal wavelet transformation matrix Ψ=[Φ1,
Φ2,Φ3,...Φn], then,
Or f=Ψ X；Wherein,
X is that number vector, and X=Ψ are maintained in N × 1 that f is projected under ΨTF, X and f are table of the same signal in not same area
Show, f is the time-domain coefficients of signal, and X is the Ψ domain coefficient of signal, and x indicates sparse signal.
Further, in the step S140, the data reconstruction model is
Wherein, y is the coding vector to y=Φ Ψ θ, and θ is base transformation system, x=Ψ θ.
Further, described that orthogonal matching pursuit algorithm pair is based on according to the data reconstruction model in step S140
Coding vector reconstruct obtains primary monitoring data
Wherein,To be directed to argmin based on orthogonal matching pursuit algorithm | | θ | |1Institute is calculated optimal to be forced
Nearly coefficient,For the primary monitoring data after reconstruct.
In conclusion the embodiment of the present invention is by combining compressed sensing technology to propose that a kind of wireless collection of farming data passes
Transmission method can well solve the problem that data volume in network is huge, wireless transmission energy consumption is big；By constructing biorthogonal
Wavelet transform matrix realizes the rarefaction representation of sensing data, meets the signal sparsity condition of compressed sensing, by using just
It hands over match tracing (OMP) algorithm to realize the High precision reconstruction of data, while reducing the acquisition dimension of raw sensory data, energy
Enough effective reduction volumes of transmitted data, reduce transmission cost and energy consumption, effectively extend the life cycle of sensor network, mention
Radio transmission efficiency and its robustness when high multi-parameter monitoring.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims
Subject to.
Claims (7)
1. a kind of farming data acquisition and transmission method, which is characterized in that including following process step:
Step S110: sensor node acquires primary monitoring data, is passed through the primary monitoring data using compressive sensing theory
Parameter sensing vector is obtained after overcompression perception, and the parameter sensing vector is transferred to aggregation node；
Step S120: the aggregation node carries out dimension-reduction treatment to the parameter sensing vector, obtains coding vector, and will be described
Coding vector is sent to background server by wireless transmission channel；
Step S130: the background server receives the coding vector and is configured to carry out the primary monitoring data dilute
Dredge the bi-orthogonal wavelet transformation matrix indicated；
Step S140: the background server according to the bi-orthogonal wavelet transformation matrix construct data reconstruction model, and according to
The data reconstruction model is based on orthogonal matching pursuit algorithm and reconstructs acquisition primary monitoring data to coding vector.
2. farming data acquisition and transmission method according to claim 1, it is characterised in that: the sensor node includes sky
Gas temperature sensor, air humidity sensor, soil temperature sensor, soil humidity sensor and P in soil H value sensor.
3. farming data acquisition and transmission method according to claim 2, it is characterised in that: the primary monitoring data includes
Air themperature data, air humidity data, soil temperature data, soil moisture data and P in soil H Value Data.
4. farming data acquisition and transmission method according to claim 1-3, which is characterized in that the step S120
It specifically includes:
The aggregation node is observed coding to primary monitoring data f using the random gaussian matrix of M × N, so that the original
Beginning monitoring data are converted into M dimension from N-dimensional, and the M is the integer greater than 0 and less than N.
5. farming data acquisition and transmission method according to claim 4, which is characterized in that the step S130 is specifically wrapped
It includes:
The primary monitoring data f of sensor node acquisition can regard a R asNThe column vector that space N × 1 is tieed up is tieed up with N × 1
Orthonormal basisLinear expression,Constitute N × N-dimensional bi-orthogonal wavelet transformation matrix Ψ=[Φ1,Φ2,
Φ3,...Φn], then,
Or f=Ψ X；Wherein,
X is that number vector, and X=Ψ are maintained in N × 1 that f is projected under ΨTF, X and f are expression of the same signal in not same area, f
It is the time-domain coefficients of signal, X is the Ψ domain coefficient of signal, and x indicates sparse signal.
6. farming data acquisition and transmission method according to claim 5, it is characterised in that: described in the step S140
Data reconstruction model is
Wherein, y is the coding vector to y=Φ Ψ θ, and θ is base transformation system, x=Ψ θ.
7. farming data acquisition and transmission method according to claim 6, it is characterised in that: in step S140, described
Be based on orthogonal matching pursuit algorithm according to the data reconstruction model includes: to coding vector reconstruct acquisition primary monitoring data
Wherein,To be directed to arg min based on orthogonal matching pursuit algorithm | | θ | |1The calculated best approximation of institute
Coefficient,For the primary monitoring data after reconstruct.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910028179.4A CN109612534A (en) | 2019-01-11 | 2019-01-11 | Farming data acquisition and transmission method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910028179.4A CN109612534A (en) | 2019-01-11 | 2019-01-11 | Farming data acquisition and transmission method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109612534A true CN109612534A (en) | 2019-04-12 |
Family
ID=66015724
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910028179.4A Pending CN109612534A (en) | 2019-01-11 | 2019-01-11 | Farming data acquisition and transmission method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109612534A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110413581A (en) * | 2019-08-07 | 2019-11-05 | 江苏康尚生物医疗科技有限公司 | A kind of medical data processing method and system based on Internet of Things |
| CN110944373A (en) * | 2019-09-27 | 2020-03-31 | 国家电网有限公司 | Wireless sensor network system, data transmission method, storage medium and terminal |
| CN112953551A (en) * | 2021-04-15 | 2021-06-11 | 中国建筑股份有限公司 | Compressed sensing monitoring method and system suitable for complex engineering environment multi-scene |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011011811A1 (en) * | 2009-07-29 | 2011-02-03 | Commonwealth Scientific And Industrial Research Organisation | Energy-aware compressive sensing |
| CN103280084A (en) * | 2013-04-24 | 2013-09-04 | 中国农业大学 | Data acquisition method for multi-parameter real-time monitoring |
| CN103823133A (en) * | 2013-12-07 | 2014-05-28 | 西南交通大学 | On-line power quality monitoring system based on compression sensing |
| CN105791190A (en) * | 2016-02-29 | 2016-07-20 | 中国农业大学 | Multi-parameter real-time monitoring type data transmission method and system |
-
2019
- 2019-01-11 CN CN201910028179.4A patent/CN109612534A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011011811A1 (en) * | 2009-07-29 | 2011-02-03 | Commonwealth Scientific And Industrial Research Organisation | Energy-aware compressive sensing |
| CN103280084A (en) * | 2013-04-24 | 2013-09-04 | 中国农业大学 | Data acquisition method for multi-parameter real-time monitoring |
| CN103823133A (en) * | 2013-12-07 | 2014-05-28 | 西南交通大学 | On-line power quality monitoring system based on compression sensing |
| CN105791190A (en) * | 2016-02-29 | 2016-07-20 | 中国农业大学 | Multi-parameter real-time monitoring type data transmission method and system |
Non-Patent Citations (1)
| Title |
|---|
| 龚静: "无线传感器网络中基于压缩感知技术的数据压缩方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑（月刊）自动化技术》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110413581A (en) * | 2019-08-07 | 2019-11-05 | 江苏康尚生物医疗科技有限公司 | A kind of medical data processing method and system based on Internet of Things |
| CN110944373A (en) * | 2019-09-27 | 2020-03-31 | 国家电网有限公司 | Wireless sensor network system, data transmission method, storage medium and terminal |
| CN110944373B (en) * | 2019-09-27 | 2023-09-26 | 国家电网有限公司 | Wireless sensor network system, data transmission method, storage medium and terminal |
| CN112953551A (en) * | 2021-04-15 | 2021-06-11 | 中国建筑股份有限公司 | Compressed sensing monitoring method and system suitable for complex engineering environment multi-scene |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109612534A (en) | Farming data acquisition and transmission method | |
| CN105163121B (en) | Big compression ratio satellite remote sensing images compression method based on depth autoencoder network | |
| WO2021203242A1 (en) | Deep learning-based mimo multi-antenna signal transmission and detection technologies | |
| ZainEldin et al. | Image compression algorithms in wireless multimedia sensor networks: A survey | |
| Li et al. | An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks | |
| CN103280084B (en) | A kind of collecting method of multi-parameters real-time monitoring | |
| CN106341842B (en) | Method and device for transmitting wireless sensor network data | |
| CN109672464A (en) | Extensive mimo channel state information feedback method based on FCFNN | |
| CN108924148B (en) | Multi-source signal collaborative compressed sensing data recovery method | |
| CN116054887A (en) | Antenna signal modulation method based on neural network model | |
| CN105811993B (en) | Data collection method based on compression dictionary learning in wireless sensor network | |
| Al-Marridi et al. | Convolutional autoencoder approach for EEG compression and reconstruction in m-health systems | |
| CN104935349A (en) | Vibration signal compressing and sampling method | |
| CN106304191A (en) | A kind of data receiver method based on cluster structured radio sensor network and device | |
| CN103368578A (en) | Compressed-sensing-based signal sampling method for distributed wireless sensor network nodes | |
| CN113258934A (en) | Data compression method, system and equipment | |
| Sekar et al. | Compressed tensor completion: A robust technique for fast and efficient data reconstruction in wireless sensor networks | |
| CN113595993A (en) | Vehicle-mounted sensing equipment joint learning method for model structure optimization under edge calculation | |
| CN104270829A (en) | Underground data acquiring and processing method based on compressed sensing | |
| CN105354867A (en) | Hyperspectral image compression algorithm research of adaptive redundant dictionary compressed sensing | |
| CN105100810B (en) | Compression of images decompressing method and system in a kind of imaging sonar real time processing system | |
| Khosravi et al. | Modified data aggregation for aerial ViSAR sensor networks in transform domain | |
| CN105791190A (en) | Multi-parameter real-time monitoring type data transmission method and system | |
| CN102630092A (en) | Compression method of agricultural wireless sensing data flow integrated with wavelet transformation and principal component | |
| CN104579361A (en) | Low-power-consumption multi-source physiological signal mixed compression method for wireless body area network |
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: 20190412 |