CN112586548B - Blade parameter selection system utilizing cloud storage - Google Patents
Blade parameter selection system utilizing cloud storage Download PDFInfo
- Publication number
- CN112586548B CN112586548B CN202011228531.8A CN202011228531A CN112586548B CN 112586548 B CN112586548 B CN 112586548B CN 202011228531 A CN202011228531 A CN 202011228531A CN 112586548 B CN112586548 B CN 112586548B
- Authority
- CN
- China
- Prior art keywords
- fish body
- blade
- type
- thickness
- storage node
- 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.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A22—BUTCHERING; MEAT TREATMENT; PROCESSING POULTRY OR FISH
- A22C—PROCESSING MEAT, POULTRY, OR FISH
- A22C25/00—Processing fish ; Curing of fish; Stunning of fish by electric current; Investigating fish by optical means
- A22C25/02—Washing or descaling fish
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Food Science & Technology (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Image Processing (AREA)
Abstract
The invention relates to a blade parameter selection system utilizing cloud storage, which comprises: the blade array is arranged in the automatic descaler and is used for placing each blade structure with different thicknesses in pairs side by side; the first storage node is used for storing a type thickness comparison table, the type thickness comparison table takes the fish body type as an index to store the blade thickness corresponding to each fish body type, and a cutter body with the blade thickness is suitable for descaling the corresponding fish body type; and a second storage node for storing respective standard outline patterns of various types of fish bodies, each standard outline pattern being an image including only a fish body obtained by photographing a fish body of a corresponding type at a preset weight. The blade parameter selection system utilizing the cloud storage is intelligent in control and convenient to manage. The blade structure with the corresponding thickness suitable for the current fish body type can be automatically selected for the automatic descaler, so that the effectiveness of the subsequent fish body descaling operation is guaranteed.
Description
Technical Field
The invention relates to the field of cloud service, in particular to a blade parameter selection system utilizing cloud storage.
Background
Cloud storage is a new concept that has been extended and evolved over the concept of cloud computing (cloud computing). Cloud Computing is a development of Distributed processing (Distributed Computing), Parallel processing (Parallel Computing) and Grid Computing (Grid Computing), and is a method of automatically splitting a huge Computing processing program into numerous smaller sub-programs through a network, and then returning a processing result to a user after Computing and analyzing the huge system composed of a plurality of servers. Through the cloud computing technology, a network service provider can process tens of millions or even hundreds of millions of information within seconds to achieve the same powerful network service as a super computer. The concept of cloud storage is similar to that of cloud computing, and refers to a system which integrates a large number of storage devices of different types in a network through application software to cooperatively work through functions such as cluster application, a grid technology or a distributed file system, and provides data storage and service access functions to the outside, so that the safety of data is ensured, and the storage space is saved. Briefly, cloud storage is an emerging solution for putting storage resources on the cloud for human access. The user can conveniently access data at any time and any place through connecting to the cloud through any internet-connected device. If this interpretation is still difficult to understand, we can borrow the structure of the wide area network and the internet to interpret the cloud storage.
In the prior art, the automatic descaling machine can automatically finish descaling operation on placed fish bodies, even fresh and live fish bodies, so that operators are prevented from being involved in a complicated and messy descaling procedure. However, as fish body types vary widely, different fish body sizes and different skin smoothness of fish bodies place different demands on the scaling strategy of an automatic descaler.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a blade parameter selection system utilizing cloud storage, which can automatically select a blade structure with a corresponding thickness suitable for the current fish body type for an automatic descaler, thereby providing guarantee for the effectiveness of the subsequent fish body descaling operation.
For this reason, the present invention needs to have at least the following important points:
(1) on the basis of cloud service, the fish body type to be descaled in the automatic descaler is detected, and then blades with corresponding thicknesses are selected in a self-adaptive mode on the basis of the detected fish body type to perform the fish body descaler, so that the descaler effect on various fish body types is guaranteed;
(2) in the type thickness comparison table, the smoother the skin of the fish body corresponding to the fish body type is, the thinner the blade corresponding to the fish body type is, and the larger the average volume of the fish body corresponding to the fish body type is, the thicker the blade corresponding to the fish body type is.
According to an aspect of the present invention, there is provided a blade parameter selection system using cloud storage, the system including:
and the blade array is arranged in the automatic descaler and is used for placing each blade structure with different thickness in pairs side by side.
More specifically, in the blade parameter selection system using cloud storage according to the present invention, the system further includes:
the first storage node is used for storing a type thickness comparison table, and the type thickness comparison table takes the fish body type as an index to store the blade thickness corresponding to each fish body type.
More specifically, in the blade parameter selection system using cloud storage according to the present invention:
in the first storage node, the blade body of the blade thickness is adapted to descale the corresponding fish body type.
More specifically, in the blade parameter selection system using cloud storage according to the present invention, the system further includes:
the second storage node is used for storing various standard outline patterns of various types of fish bodies, and each standard outline pattern is an image which is obtained by shooting a corresponding type of fish body with preset weight and only comprises the fish body;
the first storage node and the second storage node are cloud storage nodes and are arranged in the same cloud server;
the content acquisition mechanism is arranged in the automatic descaling machine and is used for carrying out image content acquisition processing on the current fish body which is not descaled at the front end of the descaling station so as to obtain a corresponding descaled front end image;
the sequencing filtering equipment is arranged in the automatic descaler, is connected with the content acquisition mechanism and is used for performing statistical sequencing filtering processing on the received descaled front-end image so as to obtain and output a corresponding sequencing filtering image;
the gray level correction device is connected with the sequencing filtering device and is used for executing gray level non-uniform correction processing on the received sequencing filtering setting image so as to obtain and output a corresponding gray level correction image;
the data comparison mechanism is respectively connected with the second storage node and the gray correction equipment and is used for matching each standard outline pattern in the second storage node with the gray correction image and outputting the fish body type corresponding to the standard outline pattern with the highest matching degree as a field analysis type;
and the thickness analyzing equipment is respectively connected with the first storage node and the data comparison mechanism and is used for searching the blade thickness corresponding to the received field analysis type in the type thickness comparison table.
The blade parameter selection system utilizing the cloud storage is intelligent in control and convenient to manage. The blade structure with the corresponding thickness suitable for the current fish body type can be automatically selected for the automatic descaler, so that the effectiveness of the subsequent fish body descaling operation is guaranteed.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a schematic structural diagram illustrating an automatic descaler to which a blade parameter selection system using cloud storage is applied according to an embodiment of the present invention.
11, a control panel; 12: a fish inlet; 13: a water inlet; 14: a fish outlet; 15: and a universal roller.
Detailed Description
Embodiments of a blade parameter selection system using cloud storage according to the present invention will be described in detail below with reference to the accompanying drawings.
The research of the automatic control technology is beneficial to freeing human from complex, dangerous and fussy working environment and greatly improving the control efficiency. Automatic control is a branch of engineering science. It involves automatic influence on the dynamic system using feedback principles to bring the output value close to what we want. From a process point of view, it is based on mathematical system theory. What people today call automatic control is a branch of the theory of control that arose in the middle of the twentieth century. The underlying conclusions are presented by norbert wiener, ludaff kalman. The regulation of the room temperature is a straightforward example. The purpose is to maintain the indoor temperature at a constant value theta despite the fact that factors such as windowing cause indoor heat to be emitted outdoors (disturbance d). To achieve this, the heating must be suitably influenced. The temperature is kept constant by adjusting the valve. In addition, the temperature of the hot water of the warmer is disturbed by the outside temperature until the user feels it. Other examples are three-oil drum systems.
In the prior art, the automatic descaling machine can automatically finish descaling operation on placed fish bodies, even fresh and live fish bodies, so that operators are prevented from being involved in a complicated and messy descaling procedure. However, as fish body types vary widely, different fish body sizes and different skin smoothness of fish bodies place different demands on the scaling strategy of an automatic descaler.
In order to overcome the defects, the invention builds a blade parameter selection system utilizing cloud storage, and can effectively solve the corresponding technical problem.
The blade parameter selection system utilizing cloud storage shown according to the embodiment of the invention comprises:
the blade array is arranged in the automatic descaler, and each blade structure with different thicknesses is placed side by side, wherein the structure of the automatic descaler is shown in figure 1;
the first storage node is used for storing a type thickness comparison table, the type thickness comparison table takes the fish body type as an index to store the blade thickness corresponding to each fish body type, and a cutter body with the blade thickness is suitable for descaling the corresponding fish body type;
the second storage node is used for storing various standard outline patterns of various types of fish bodies, and each standard outline pattern is an image which is obtained by shooting a corresponding type of fish body with preset weight and only comprises the fish body;
the first storage node and the second storage node are cloud storage nodes and are arranged in the same cloud server;
the content acquisition mechanism is arranged in the automatic descaling machine and is used for carrying out image content acquisition processing on the current fish body which is not descaled at the front end of the descaling station so as to obtain a corresponding descaled front end image;
the sequencing filtering equipment is arranged in the automatic descaler, is connected with the content acquisition mechanism and is used for performing statistical sequencing filtering processing on the received descaled front-end image so as to obtain and output a corresponding sequencing filtering image;
the gray level correction device is connected with the sequencing filtering device and is used for executing gray level non-uniform correction processing on the received sequencing filtering setting image so as to obtain and output a corresponding gray level correction image;
the data comparison mechanism is respectively connected with the second storage node and the gray correction equipment and is used for matching each standard outline pattern in the second storage node with the gray correction image and outputting the fish body type corresponding to the standard outline pattern with the highest matching degree as a field analysis type;
and the thickness analyzing equipment is respectively connected with the first storage node and the data comparison mechanism and is used for searching the blade thickness corresponding to the received field analysis type in the type thickness comparison table.
Next, a detailed structure of the blade parameter selection system using cloud storage according to the present invention will be further described.
In the blade parameter selection system using cloud storage:
in the type thickness comparison table, the smoother the skin of the fish body corresponding to the fish body type is, and the thinner the blade corresponding to the fish body type is.
In the blade parameter selection system using cloud storage:
in the type thickness comparison table, the larger the average volume of the fish body corresponding to the fish body type is, the thicker the blade corresponding to the fish body type is.
The blade parameter selection system using cloud storage may further include:
and the real-time pushing mechanism is respectively connected with the thickness analyzing equipment and the blade array and is used for pushing the blade structure matched with the received blade thickness in the blade array to the position right above the descaling station.
In the blade parameter selection system using cloud storage:
the data comparison mechanism is connected with the second storage node through a wireless network, and the thickness analysis equipment is connected with the first storage node through a wireless network.
The blade parameter selection system using cloud storage may further include:
and the fish body conveying mechanism is arranged in the automatic descaling machine, is connected with the real-time pushing mechanism and is used for conveying the current fish bodies which are not scaled at the front end of the scaling station to the scaling station after the real-time pushing mechanism finishes the pushing action of the blade structure.
In the blade parameter selection system using cloud storage:
matching each standard outline pattern in the second storage node with the gray-scale correction image, and outputting the fish body type corresponding to the standard outline pattern with the highest matching degree as a field analysis type, wherein the step of outputting comprises the following steps: and sorting the matching degrees respectively corresponding to the standard outline patterns in the second storage node from small to large, and outputting the fish body type corresponding to the standard outline pattern corresponding to the matching degree with the largest sequence number as a field analysis type.
The blade parameter selection system using cloud storage may further include:
the direct-current power supply is arranged in the automatic descaler and is respectively connected with the fish body conveying mechanism and the real-time pushing mechanism;
the direct-current power supply respectively provides power required by the fish body conveying mechanism and the real-time pushing mechanism.
In the blade parameter selection system using cloud storage:
the gray scale correction device, the sorting filter device and the data comparison mechanism share the same clock generation mechanism, and the clock generation mechanism respectively provides required working clock signals for the gray scale correction device, the sorting filter device and the data comparison mechanism.
In addition, the gradation correction device, the sorting filter device, and the data alignment mechanism may be integrated into the same ASIC chip. A fully-customized ASIC is a design method that uses the most basic design method of an integrated circuit (without using existing library cells) to perform fine-grained operations on all components in the integrated circuit. The full-custom design can achieve the minimum area, the optimal wiring layout, the optimal power consumption speed product, and the best electrical characteristics. The method is particularly suitable for analog circuits, digital-analog mixed circuits and occasions with special requirements on speed, power consumption, tube core area and other device characteristics (such as linearity, symmetry, current capacity, voltage resistance and the like); or where there is no off-the-shelf component library. The method is characterized in that: fine work, high design requirement, long period and high design cost. As cell libraries and functional module circuits become more mature, the fully-customized design approach is gradually replaced by the semi-customized approach. In the current IC design, the phenomenon that the whole circuit adopts the full-custom design is less and less. Full customization design requirements: the full-custom design needs to consider process conditions, and factors such as device process types, wiring layer numbers, material parameters, process methods, limit parameters, yield and the like are determined according to the complexity and difficulty of a circuit. The method needs experience and skill, masters various design rules and methods, and is generally completed by professional microelectronic IC designers; the conventional design can be used for reference, and part of devices need to be designed independently according to electrical characteristics; layout, wiring, typesetting combination and the like all need to be repeatedly adjusted, and the layout is designed according to the design principles of optimal size, optimal layout, shortest connecting line, most convenient pins and the like. The layout design is related to the process, the process specification is fully known, and the layout and the process are reasonably designed according to the process parameters and the process requirements.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A blade parameter selection system utilizing cloud storage, comprising:
the blade array is arranged in the automatic descaler and is used for placing each blade structure with different thicknesses in pairs side by side;
the first storage node is used for storing a type thickness comparison table, and the type thickness comparison table takes the fish body type as an index to store the blade thickness corresponding to each fish body type;
in the first storage node, the blade body of the blade thickness is adapted to descale the corresponding fish body type;
the second storage node is used for storing various standard outline patterns of various types of fish bodies, and each standard outline pattern is an image which is obtained by shooting a corresponding type of fish body with preset weight and only comprises the fish body;
the first storage node and the second storage node are cloud storage nodes and are arranged in the same cloud server;
the content acquisition mechanism is arranged in the automatic descaling machine and is used for carrying out image content acquisition processing on the current fish body which is not descaled at the front end of the descaling station so as to obtain a corresponding descaled front end image;
the sequencing filtering equipment is arranged in the automatic descaler, is connected with the content acquisition mechanism and is used for performing statistical sequencing filtering processing on the received descaled front-end image so as to obtain and output a corresponding sequencing filtering image;
the gray level correction device is connected with the sequencing filtering device and is used for executing gray level non-uniform correction processing on the received sequencing filtering setting image so as to obtain and output a corresponding gray level correction image;
the data comparison mechanism is respectively connected with the second storage node and the gray correction equipment and is used for matching each standard outline pattern in the second storage node with the gray correction image and outputting the fish body type corresponding to the standard outline pattern with the highest matching degree as a field analysis type;
the thickness analyzing equipment is respectively connected with the first storage node and the data comparison mechanism and is used for searching the blade thickness corresponding to the received field analysis type in the type thickness comparison table;
in the type thickness comparison table, the smoother the skin of the fish body corresponding to the fish body type is, and the thinner the blade corresponding to the fish body type is;
in the type thickness comparison table, the larger the average volume of the fish body corresponding to the fish body type is, the thicker the blade corresponding to the fish body type is;
the real-time pushing mechanism is respectively connected with the thickness analyzing equipment and the blade array and used for pushing a blade structure matched with the received blade thickness in the blade array to the position right above the descaling station.
2. The blade parameter selection system utilizing cloud storage of claim 1, wherein:
the data comparison mechanism is connected with the second storage node through a wireless network, and the thickness analysis equipment is connected with the first storage node through a wireless network.
3. The blade parameter selection system utilizing cloud storage of claim 2, said system further comprising:
and the fish body conveying mechanism is arranged in the automatic descaling machine, is connected with the real-time pushing mechanism and is used for conveying the current fish bodies which are not scaled at the front end of the scaling station to the scaling station after the real-time pushing mechanism finishes the pushing action of the blade structure.
4. The blade parameter selection system using cloud storage of claim 3, wherein:
matching each standard outline pattern in the second storage node with the gray-scale correction image, and outputting the fish body type corresponding to the standard outline pattern with the highest matching degree as a field analysis type, wherein the step of outputting comprises the following steps: and sorting the matching degrees respectively corresponding to the standard outline patterns in the second storage node from small to large, and outputting the fish body type corresponding to the standard outline pattern corresponding to the matching degree with the largest sequence number as a field analysis type.
5. The blade parameter selection system utilizing cloud storage of claim 4, said system further comprising:
the direct-current power supply is arranged in the automatic descaler and is respectively connected with the fish body conveying mechanism and the real-time pushing mechanism;
the direct-current power supply respectively provides power required by the fish body conveying mechanism and the real-time pushing mechanism.
6. The blade parameter selection system utilizing cloud storage of claim 5, wherein:
the gray scale correction device, the sorting filter device and the data comparison mechanism share the same clock generation mechanism, and the clock generation mechanism respectively provides required working clock signals for the gray scale correction device, the sorting filter device and the data comparison mechanism.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011228531.8A CN112586548B (en) | 2020-11-06 | 2020-11-06 | Blade parameter selection system utilizing cloud storage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011228531.8A CN112586548B (en) | 2020-11-06 | 2020-11-06 | Blade parameter selection system utilizing cloud storage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112586548A CN112586548A (en) | 2021-04-02 |
| CN112586548B true CN112586548B (en) | 2021-12-10 |
Family
ID=75182819
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011228531.8A Active CN112586548B (en) | 2020-11-06 | 2020-11-06 | Blade parameter selection system utilizing cloud storage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112586548B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201640297U (en) * | 2010-01-28 | 2010-11-24 | 金振家 | Fish scale remover |
| CN103098858A (en) * | 2012-12-08 | 2013-05-15 | 宁波市鄞州云帆工程咨询有限公司 | Electric flexible adjustable scaler |
| CN104095018A (en) * | 2014-07-25 | 2014-10-15 | 马润海 | Fish scale removing machine |
| CN104636737A (en) * | 2015-03-10 | 2015-05-20 | 无锡桑尼安科技有限公司 | Fish body fresh degree recognition system |
| CN105046228A (en) * | 2015-07-25 | 2015-11-11 | 张丽 | Data communication based fish body identification method |
| CN111639512A (en) * | 2019-12-05 | 2020-09-08 | 王体 | On-site data distributed cloud processing platform |
-
2020
- 2020-11-06 CN CN202011228531.8A patent/CN112586548B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201640297U (en) * | 2010-01-28 | 2010-11-24 | 金振家 | Fish scale remover |
| CN103098858A (en) * | 2012-12-08 | 2013-05-15 | 宁波市鄞州云帆工程咨询有限公司 | Electric flexible adjustable scaler |
| CN104095018A (en) * | 2014-07-25 | 2014-10-15 | 马润海 | Fish scale removing machine |
| CN104636737A (en) * | 2015-03-10 | 2015-05-20 | 无锡桑尼安科技有限公司 | Fish body fresh degree recognition system |
| CN105046228A (en) * | 2015-07-25 | 2015-11-11 | 张丽 | Data communication based fish body identification method |
| CN111639512A (en) * | 2019-12-05 | 2020-09-08 | 王体 | On-site data distributed cloud processing platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112586548A (en) | 2021-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109376869A (en) | A kind of super ginseng optimization system of machine learning based on asynchronous Bayes optimization and method | |
| CN109726822B (en) | Operation method, device and related product | |
| CN108776717A (en) | Kernel and data predication method, device, equipment and storage medium | |
| CN112217207A (en) | Transient voltage stability margin prediction method and device | |
| CN113010312B (en) | Super-parameter tuning method, device and storage medium | |
| CN104054254A (en) | Multicore implemented weight - function based predictive control of an electric converter | |
| CN112586548B (en) | Blade parameter selection system utilizing cloud storage | |
| CN111723907B (en) | Model training device, method, system and computer readable storage medium | |
| CN111695689B (en) | Natural language processing method, device, equipment and readable storage medium | |
| CN112288046A (en) | Mixed granularity-based joint sparse method for neural network | |
| CN109344875B (en) | Method and device for generating solar wind power output time sequence based on cluster analysis | |
| CN116128019A (en) | Parallel training method and device for transducer model | |
| CN113112009B (en) | Method, apparatus and computer-readable storage medium for neural network data quantization | |
| CN110991083B (en) | Photovoltaic power station model determining method, device, equipment and storage medium | |
| CN113434034A (en) | Large-scale cluster energy-saving method for adjusting CPU frequency of calculation task by utilizing deep learning | |
| CN107784363A (en) | Data processing method, apparatus and system | |
| CN112415306A (en) | Intelligent detection device for distribution automation equipment module | |
| CN114444681A (en) | Neural network sparsing device, method and corresponding product | |
| CN116050433B (en) | Scene adaptation method, device, equipment and medium of natural language processing model | |
| Han et al. | Nodal load forecast model considering network topology constraints | |
| CN117350549B (en) | Distribution network voltage risk identification method, device and equipment considering output correlation | |
| CN116528270B (en) | Base station energy saving potential evaluation method, device, equipment and storage medium | |
| CN108399369A (en) | Electrocardio beat sorting technique based on Distributed Calculation and deep learning | |
| CN112531741B (en) | Decision method and device for flexible load to participate in frequency modulation | |
| CN114691589A (en) | Processing device and related product |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211119 Address after: 230000 Room 403, building 2, Laojie library, Dianbu Town, Feidong County, Hefei City, Anhui Province Applicant after: Hefei guiqian Information Technology Co.,Ltd. Address before: 225300 609, 6th floor, building a, No. 26, Chengzhong Qingnian Road, Hailing District, Taizhou City, Jiangsu Province Applicant before: Taizhou REBIT Intelligent Technology Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |