CN114692777A - Intelligent agricultural management method based on multi-sensor and micro machine learning - Google Patents
Intelligent agricultural management method based on multi-sensor and micro machine learning Download PDFInfo
- Publication number
- CN114692777A CN114692777A CN202210384511.2A CN202210384511A CN114692777A CN 114692777 A CN114692777 A CN 114692777A CN 202210384511 A CN202210384511 A CN 202210384511A CN 114692777 A CN114692777 A CN 114692777A
- Authority
- CN
- China
- Prior art keywords
- sensor
- machine learning
- micro
- data
- sensors
- 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.)
- Withdrawn
Links
- 238000010801 machine learning Methods 0.000 title claims abstract description 70
- 238000007726 management method Methods 0.000 title claims abstract description 33
- 238000003973 irrigation Methods 0.000 claims abstract description 29
- 230000002262 irrigation Effects 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims description 20
- 238000005286 illumination Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 9
- 230000007613 environmental effect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 235000013619 trace mineral Nutrition 0.000 description 1
- 239000011573 trace mineral Substances 0.000 description 1
Images
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
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
- A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Soil Sciences (AREA)
- Water Supply & Treatment (AREA)
- Environmental Sciences (AREA)
- Medical Informatics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent agricultural management method based on multi-sensor and micro machine learning, and relates to the technical field of micro machine learning application; dividing deployment units according to the coverage area of each irrigation device, deploying various sensors according to the deployment units, establishing connection between each sensor and a microprocessor, enabling the microprocessor to acquire data of each sensor, reading the data of the sensors in the microprocessor, collecting growth condition data of crops in the deployment units at the same time, taking the data of each sensor as input, training the data of the sensors by taking a time sequence as a sequence and taking the logic of whether the data of each sensor is suitable for the growth of the crops under different numerical conditions or not to obtain a micro machine learning model, and performing inference on the microcontroller by utilizing the micro machine learning model to control the irrigation of the crops.
Description
Technical Field
The invention discloses a method, relates to the technical field of micro machine learning application, and particularly relates to an intelligent agricultural management method based on multi-sensor and micro machine learning.
Background
Agriculture is the oldest industry in the world and can be said to be the most important industry. It occupies about one quarter of the world's population, and lives billions of people. The agricultural field has almost occupied the world's largest use of fresh water. But the service efficiency is extremely low, so that serious water resource waste exists and the underground water level is influenced.
There are implementations for smart agricultural projects, but it is not always feasible to connect all devices to the network and transmit data to the cloud, especially in cases where a large number of data sources are involved, because monitoring of various environmental variables is performed on thousands of devices, the amount of data generated is huge, and there are also problems of security and timeliness.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent agricultural management method based on multi-sensor and micro machine learning, and agricultural production management is improved through micro machine learning (TinyML) and multi-sensor fusion.
The specific scheme provided by the invention is as follows:
the invention provides an intelligent agricultural management method based on multi-sensor and micro-machine learning, which comprises dividing deployment units according to the coverage area of each irrigation device, deploying various sensors according to the deployment units, establishing the connection between each sensor and a microprocessor, enabling the microprocessor to acquire the data of each sensor,
reading data of sensors in a microprocessor, collecting growth condition data of crops in a deployment unit, taking the data of each sensor as input, taking a time sequence as a sequence, taking the logic of whether the data of each sensor is suitable for the growth of the crops under different numerical conditions as logic to train the data of the sensors, obtaining a micro machine learning model,
and reasoning is carried out on the microcontroller by utilizing the micro machine learning model to control the irrigation of crops.
Further, in the method for intelligent agriculture management based on multi-sensor and micro-machine learning, the deploying of the plurality of sensors according to the deployment unit includes:
the method comprises the step of deploying sensors influencing crop irrigation, wherein the sensors comprise a temperature and humidity sensor, an illumination sensor and a wind speed sensor.
Further, in the method for intelligent agriculture management based on multi-sensor and micro machine learning, before performing inference on a microcontroller by using the micro machine learning model, the method includes:
and converting the micro machine learning model into a C byte array and embedding the C byte array into an inference application program.
Further, in the method for intelligent agricultural management based on multi-sensor and micro-machine learning, the control of irrigation to crops includes:
and judging whether the current environment is suitable for crop growth or not according to data input of various sensors by utilizing the micro machine learning model, if so, not irrigating, and otherwise, irrigating.
Furthermore, in the intelligent agricultural management method based on the multi-sensor and the micro machine learning, different micro machine learning models are deployed according to different microprocessor nodes in a deployment unit to carry out operation reasoning.
The invention also provides an intelligent agricultural management device based on multi-sensor and micro machine learning, which comprises a deployment module, an acquisition module, a model training module and an operation module,
the deployment module divides deployment units according to the coverage area of each irrigation device, deploys a plurality of sensors according to the deployment units, establishes the connection between each sensor and the microprocessor, enables the microprocessor to acquire the data of each sensor,
the acquisition module reads data of sensors in the microprocessor, collects growth condition data of crops in a deployment unit, the model training module takes the data of each sensor as input, takes time sequence as sequence and takes the logic of whether the data of each sensor is suitable for the growth of crops under different numerical conditions as logic to train the data of the sensors to obtain a micro machine learning model,
and the operation module utilizes the micro machine learning model to carry out operation reasoning on the microcontroller so as to control the irrigation of crops.
Further, the intelligent agriculture management device based on multi-sensor and micro machine learning, wherein the deployment module deploys a plurality of sensors according to deployment units, includes:
the method comprises the step of deploying sensors influencing crop irrigation, wherein the sensors comprise a temperature and humidity sensor, an illumination sensor and a wind speed sensor.
Furthermore, in the intelligent agriculture management device based on multiple sensors and micro machine learning, the operation module converts the micro machine learning model into a C byte array and embeds the C byte array into an inference application program before the micro machine learning model is used for carrying out operation inference on the microcontroller.
Further, in an intelligent agricultural management device based on multiple sensors and micro machine learning, the operation module controls irrigation of crops, and the intelligent agricultural management device comprises:
and judging whether the current environment is suitable for crop growth or not according to data input of various sensors by utilizing the micro machine learning model, if so, not irrigating, and otherwise, irrigating.
Furthermore, the deployment module in the intelligent agricultural management device based on the multiple sensors and the micro machine learning deploys different micro machine learning models according to different microprocessor nodes in deployment units, so that the operation module performs inference on the microcontroller by using the micro machine learning models.
The invention has the advantages that:
the invention provides an intelligent agricultural management method based on multi-sensor and micro machine learning, which uses a TinyML technology, can consider various environment variables in a region by implementing a machine learning algorithm on an embedded micro controller, and dynamically adjusts water supply according to plant species and environmental conditions, thereby improving the utilization rate and efficiency of water.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The invention provides an intelligent agricultural management method based on multi-sensor and micro-machine learning, which comprises dividing deployment units according to the coverage area of each irrigation device, deploying various sensors according to the deployment units, establishing the connection between each sensor and a microprocessor, enabling the microprocessor to acquire the data of each sensor,
reading data of sensors in a microprocessor, collecting growth condition data of crops in a deployment unit, taking the data of each sensor as input, taking a time sequence as a sequence, taking the logic of whether the data of each sensor is suitable for the growth of the crops under different numerical conditions as logic to train the data of the sensors, obtaining a micro machine learning model,
and reasoning is carried out on the microcontroller by utilizing the micro machine learning model to control the irrigation of crops.
The method mainly researches the integration of a micro machine learning model (TinyML) and multiple sensors to improve the management and application of the agricultural field. Thereby reducing waste of resources and effective utilization. And the intelligent agriculture is accurately managed by utilizing multi-sensor fusion and a TinyML technology in digital agriculture. Training of lightweight artificial intelligence models is conducted by collecting and utilizing real-time data, and then various automated agricultural systems such as irrigation systems, soil trace element management systems and the like can be controlled by utilizing multi-sensor fused edge equipment and data transmission, so that consumption of other resources such as water, energy and the like is reduced, and operation cost is saved.
In particular applications, in some embodiments of the method of the present invention, a deployment unit is partitioned according to the coverage area of each irrigation device, a plurality of sensors are deployed according to the deployment unit,
further, the deploying a plurality of sensors according to a deployment unit includes:
deploying sensors influencing crop irrigation, wherein the sensors comprise sensors influencing factor types related to crops and water, such as a temperature and humidity sensor, an illumination sensor, a wind speed sensor and the like;
establishing connection between each sensor and the microprocessor to make the microprocessor obtain data of each sensor,
collecting growth condition data of crops according to the data of deployed sensors, taking the data of each sensor as input, taking a time sequence as a sequence, taking whether the data of each sensor is suitable for the growth of the crops under different numerical conditions as logic to train the data of the sensors, and obtaining a micro machine learning model;
and reasoning is carried out on the microcontroller by utilizing the micro machine learning model to control the irrigation of crops.
Further, when the inference engine learning model is run on a microcontroller, it may be deployed, for example, through an Arduino IDE. And converting the micro machine learning model into a C byte array, embedding the C byte array into an inference application program, judging whether the environmental data is suitable for crop growth according to the input of the multi-sensor data, and if so, not irrigating or not.
Furthermore, different micro machine learning models can be deployed at different microprocessor nodes to realize regional irrigation. The waste of water resources is avoided and the resources are effectively utilized.
In the process, the method provided by the invention utilizes a multi-sensor and a lightweight model of TinyML to be deployed on a microcontroller with low price and low power consumption, and can realize the partitioned irrigation management of different nodes. The method comprises the steps of deploying microcontroller nodes according to deployment units, then performing data interaction with the microcontroller nodes by using multiple sensors, and judging according to TinyML inference models of different nodes, so that the regional irrigation-only system is realized. And finally, the effective utilization of resources is achieved.
The invention also provides an intelligent agricultural management device based on multi-sensor and micro machine learning, which comprises a deployment module, an acquisition module, a model training module and an operation module,
the deployment module divides deployment units according to the coverage area of each irrigation device, deploys a plurality of sensors according to the deployment units, establishes the connection between each sensor and the microprocessor, enables the microprocessor to acquire the data of each sensor,
the acquisition module reads data of sensors in the microprocessor, collects growth condition data of crops in a deployment unit, the model training module takes the data of each sensor as input, takes time sequence as sequence and takes the logic of whether the data of each sensor is suitable for the growth of crops under different numerical conditions as logic to train the data of the sensors to obtain a micro machine learning model,
and the operation module utilizes the micro machine learning model to carry out operation reasoning on the microcontroller so as to control the irrigation of crops.
Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
Likewise, the apparatus of the present invention can use TinyML technology to improve water usage and efficiency by implementing a machine learning algorithm on an embedded microcontroller that can take into account various environmental variables in the area and dynamically adjust the water supply according to plant species and environmental conditions.
It should be noted that not all steps and modules in the above flows and device structures are necessary, and some steps or modules may be omitted according to actual needs. The execution order of the steps is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by a plurality of physical entities, or some components in a plurality of independent devices may be implemented together.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. An intelligent agricultural management method based on multiple sensors and micro-machine learning is characterized in that deployment units are divided according to the coverage area of each irrigation device, multiple sensors are deployed according to the deployment units, the connection between each sensor and a microprocessor is established, so that the microprocessor can obtain the data of each sensor,
reading data of sensors in a microprocessor, collecting growth condition data of crops in a deployment unit, taking the data of each sensor as input, taking a time sequence as a sequence, taking the logic of whether the data of each sensor is suitable for the growth of the crops under different numerical conditions as logic to train the data of the sensors, obtaining a micro machine learning model,
and reasoning is carried out on the microcontroller by utilizing the micro machine learning model to control the irrigation of crops.
2. The intelligent agricultural management method based on multi-sensor and micro-machine learning of claim 1, wherein the deploying of multiple sensors according to deployment units comprises:
the method comprises the step of deploying sensors influencing crop irrigation, wherein the sensors comprise a temperature and humidity sensor, an illumination sensor and a wind speed sensor.
3. The method for intelligent agricultural management based on multi-sensor and micro-machine learning as claimed in claim 1 or 2, wherein before the inference is performed on the microcontroller by using the micro-machine learning model, the method comprises:
and converting the micro machine learning model into a C byte array and embedding the C byte array into an inference application program.
4. The intelligent agricultural management method based on multi-sensor and micro-machine learning of claim 1, wherein the controlling of irrigation to crops comprises:
and judging whether the current environment is suitable for crop growth or not according to data input of various sensors by utilizing the micro machine learning model, if so, not irrigating, and otherwise, irrigating.
5. The intelligent agricultural management method based on multi-sensor and micro-machine learning of claim 1, wherein different micro-machine learning models are deployed according to different microprocessor nodes in a deployment unit for operation inference.
6. An intelligent agricultural management device based on multi-sensor and micro-machine learning is characterized by comprising a deployment module, an acquisition module, a model training module and an operation module,
the deployment module divides deployment units according to the coverage area of each irrigation device, deploys a plurality of sensors according to the deployment units, establishes the connection between each sensor and the microprocessor, enables the microprocessor to acquire the data of each sensor,
the acquisition module reads data of sensors in the microprocessor, collects growth condition data of crops in a deployment unit, the model training module takes the data of each sensor as input, takes time sequence as sequence and takes the logic of whether the data of each sensor is suitable for the growth of crops under different numerical conditions as logic to train the data of the sensors to obtain a micro machine learning model,
and the operation module utilizes the micro machine learning model to carry out operation reasoning on the microcontroller to control the irrigation of crops.
7. The intelligent agriculture management device based on multi-sensor and micro-machine learning of claim 6, wherein said deployment module deploys multiple sensors according to deployment unit, comprising:
the method comprises the step of deploying sensors influencing crop irrigation, wherein the sensors comprise a temperature and humidity sensor, an illumination sensor and a wind speed sensor.
8. The intelligent agriculture management device based on multi-sensor and micro-machine learning of claim 6 or 7, wherein the operation module converts the micro-machine learning model into C byte array and embeds the C byte array into the inference application program before performing inference on the microcontroller by using the micro-machine learning model.
9. The intelligent agricultural management device based on multi-sensor and micro-machine learning of claim 6, wherein the operation module controls irrigation of crops, comprising:
and judging whether the current environment is suitable for crop growth or not according to data input of various sensors by utilizing the micro machine learning model, if so, not irrigating, and otherwise, irrigating.
10. The intelligent agricultural management device based on multi-sensor and micro-machine learning of claim 6, wherein the deployment module further deploys different micro-machine learning models according to different microprocessor nodes in a deployment unit, so that the operation module utilizes the micro-machine learning models to perform inference on the microcontroller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210384511.2A CN114692777A (en) | 2022-04-13 | 2022-04-13 | Intelligent agricultural management method based on multi-sensor and micro machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210384511.2A CN114692777A (en) | 2022-04-13 | 2022-04-13 | Intelligent agricultural management method based on multi-sensor and micro machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114692777A true CN114692777A (en) | 2022-07-01 |
Family
ID=82142249
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210384511.2A Withdrawn CN114692777A (en) | 2022-04-13 | 2022-04-13 | Intelligent agricultural management method based on multi-sensor and micro machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114692777A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115186753A (en) * | 2022-07-18 | 2022-10-14 | 山东浪潮科学研究院有限公司 | Multi-scene intelligent monitoring protection system based on microcontroller, sensor and TinyML |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110389971A (en) * | 2019-06-28 | 2019-10-29 | 长春工程学院 | A kind of multi-Sensor Information Fusion Approach based on cloud computing |
| CN112949472A (en) * | 2021-02-28 | 2021-06-11 | 杭州翔毅科技有限公司 | Cooperative sensing method based on multi-sensor information fusion |
| CN113408744A (en) * | 2021-07-08 | 2021-09-17 | 山东浪潮科学研究院有限公司 | Multi-sensor multi-environment monitoring method based on AIot and TinyML technology |
| CN113841593A (en) * | 2021-10-29 | 2021-12-28 | 山东润浩水利科技有限公司 | Intelligent farmland irrigation system and irrigation method based on Internet of things |
-
2022
- 2022-04-13 CN CN202210384511.2A patent/CN114692777A/en not_active Withdrawn
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110389971A (en) * | 2019-06-28 | 2019-10-29 | 长春工程学院 | A kind of multi-Sensor Information Fusion Approach based on cloud computing |
| CN112949472A (en) * | 2021-02-28 | 2021-06-11 | 杭州翔毅科技有限公司 | Cooperative sensing method based on multi-sensor information fusion |
| CN113408744A (en) * | 2021-07-08 | 2021-09-17 | 山东浪潮科学研究院有限公司 | Multi-sensor multi-environment monitoring method based on AIot and TinyML technology |
| CN113841593A (en) * | 2021-10-29 | 2021-12-28 | 山东润浩水利科技有限公司 | Intelligent farmland irrigation system and irrigation method based on Internet of things |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115186753A (en) * | 2022-07-18 | 2022-10-14 | 山东浪潮科学研究院有限公司 | Multi-scene intelligent monitoring protection system based on microcontroller, sensor and TinyML |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN203597190U (en) | Intelligent water-saving field irrigation system of irrigation district | |
| CN107168417A (en) | A kind of smart greenhouse remote management system | |
| Aggarwal et al. | A smart irrigation system to automate irrigation process using IOT and artificial neural network | |
| CN110579961B (en) | Three-dimensional planting-oriented garden intelligent water supply method and system | |
| CN108961712A (en) | A kind of system for realizing wisdom agricultural | |
| Abdelkerim et al. | Development of solar powered irrigation system | |
| CN114692777A (en) | Intelligent agricultural management method based on multi-sensor and micro machine learning | |
| CN116138145A (en) | Intelligent agricultural management system | |
| CN110446175A (en) | A kind of Wetland Environment monitoring system based on Internet of Things | |
| Xiang | Design of fuzzy drip irrigation control system based on zigbee wireless sensor network | |
| CN117911187A (en) | Intelligent supervision system based on agricultural Internet of things | |
| CN111897267A (en) | Interactive intelligent pasture data acquisition and management system | |
| CN203324260U (en) | Internet-of-things-based on-line litopenaeus vannamei aquaculture water quality monitoring system | |
| CN113625639A (en) | Agricultural intelligent monitoring system and monitoring method thereof | |
| Ferre et al. | A wireless sensor network for greenhouse climate monitoring | |
| CN113890770A (en) | Low-power-consumption operation method and system for node equipment of Internet of things | |
| González-Briones et al. | Reuse of wasted thermal energy in power plants for agricultural crops by means of multi-agent approach | |
| CN102156408B (en) | System and method for tracking and controlling maximum power point in dynamically self-adaptive evolvement process | |
| Touhami et al. | Design of smart irrigation system in the greenhouse using WSN and renewable energies | |
| CN102736568A (en) | Configuration method of multi-span greenhouse measurement and control facility | |
| CN205450843U (en) | Intelligent agriculture remote monitoring device | |
| Lad et al. | A literature survey on smart agriculture monitoring and control system using IoT | |
| CN105354783A (en) | Residential landscaping management system | |
| Sharma | Intelligent IoT for Precision Agriculture | |
| CN205943286U (en) | Test model that offshore platform process control system used |
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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220701 |