CN114491819B - Rocket carrying capacity intelligent solving method based on speed loss calculation - Google Patents
Rocket carrying capacity intelligent solving method based on speed loss calculation Download PDFInfo
- Publication number
- CN114491819B CN114491819B CN202210142466.XA CN202210142466A CN114491819B CN 114491819 B CN114491819 B CN 114491819B CN 202210142466 A CN202210142466 A CN 202210142466A CN 114491819 B CN114491819 B CN 114491819B
- Authority
- CN
- China
- Prior art keywords
- rocket
- loss
- speed
- calculating
- neural network
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
An intelligent solution method for rocket carrying capacity based on speed loss calculation belongs to the technical field of rocket carrying. The method comprises the following steps: obtaining an intelligent relationship mapping between the flight state and the gravity velocity loss; calculating the speed increment required by the task; calculating the ideal speed increment of the rocket; calculating each speed loss item of the carrier rocket; judging whether the calculation process is ended or not; if not, adding a small increment to the initial value of the effective load to define the current effective load; under the condition of the current effective load, calculating each speed loss item of the carrier rocket again, calculating the actual speed increment of the rocket, and recovering the initial value of the effective load at the same time; and calculating a new payload initial value, and calculating each speed loss item of the carrier rocket again. The invention adopts the intelligent neural network to represent the difference and the rule of the multi-configuration and multi-task parameters, meets the intelligent calculation of the rocket carrying capacity of the multi-configuration and multi-task requirements, and can utilize the neural network to complete the quick and accurate calculation of the carrying capacity.
Description
Technical Field
The invention relates to a rocket carrying capacity intelligent solving method based on speed loss calculation, and belongs to the technical field of rocket carrying.
Background
With the increase of space detection requirements and detection technical means, researchers face increasingly heavy work on configuration demonstration and mission planning of carrier rockets.
The traditional rocket carrying capacity calculation method needs to perform detailed overall design and guidance task planning system design for each configuration or each flight task, so that the workload is large, and a series of problems such as convergence, adaptability, cost performance and the like are faced. Therefore, a method for rapidly calculating the rocket carrying capacity in a multi-configuration and multi-task mode is needed.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a rocket carrying capacity intelligent solving method based on speed loss calculation.
The invention adopts the following technical scheme: a rocket carrying capacity intelligent solving method based on speed loss calculation comprises the following steps:
s1: establishing a multi-configuration and multi-task carrier rocket model flight sample library, and obtaining the intelligent relationship mapping between the flight state and the gravity velocity loss through radial basis function neural network learning as follows:
ΔV g ＝Net(X) (1)
in formula (1):
ΔV g is the loss of gravitational velocity;
net (-) is a neural network model of learning completion;
x is the rocket flight state quantity;
s2: calculating the speed increment v required by the mission according to the flight mission parameters need ；
S3: calculating the ideal speed increment delta V of the rocket according to the basic overall parameters of the rocket;
s4: calculating each speed loss term v of the carrier rocket c0 The method comprises the following steps:
at payload initial value m 0 Under the condition, calculating aerodynamic drag speed loss and nozzle pressure speed loss according to an empirical formula, and inputting flight state quantity required by a task terminal into a radial basis function neural network to calculate gravity speed loss;
s5: according to | v c0 -v need If | is less than the threshold ε, determine if it is finishedCalculating a flow;
if | v c0 -v need If | < ε, then the payload initial value m 0 Namely the maximum carrying capacity of the rocket, and the process is finished;
if | v c0 -v need If | ≧ epsilon, give the initial value m of the payload 0 Plus a small increment delta, defined as the current payload m 1 I.e. m 1 ＝m 0 +δ；
S6: at the current payload m 1 Under the condition, calculating aerodynamic drag speed loss and nozzle pressure speed loss according to an empirical formula, and inputting flight state quantity required by a task terminal into a radial basis function neural network to calculate gravity speed loss; and calculating the actual speed increment v of the rocket c1 And simultaneously recovering the initial value of the payload, i.e. m 0 ＝m 1 -δ；
S7: calculating a new payload initial value m 'according to a gradient updating method' 0 ＝m 0 -δ(v c0 -v need )/(v c1 -v c0 ) And proceeds to S4 again.
Compared with the prior art, the invention has the beneficial effects that:
the method adopts the intelligent neural network to represent the difference and the rule of the multi-configuration and multi-task parameters, is a universal rocket carrying capacity rapid calculation method, meets the intelligent calculation of the rocket carrying capacity required by the multi-configuration and multi-task, and can utilize the neural network to complete the rapid and accurate calculation of the carrying capacity.
Drawings
FIG. 1 is an overall frame diagram of the present invention;
FIG. 2 is a diagram of a radial basis function neural network of the present invention;
FIG. 3 is a flow chart of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art without any creative work based on the embodiments of the present invention belong to the protection scope of the present invention.
A rocket carrying capacity intelligent solving method based on speed loss calculation is disclosed, wherein:
the velocity loss is the difference between the actual velocity increment that the rocket can achieve and the ideal velocity increment relative to the ideal velocity increment given by the ziz equation.
Launch capability refers to the ability of a launch vehicle to launch the maximum payload.
The method comprises the following steps:
s1: establishing a multi-configuration and multi-task carrier rocket model flight sample library, and obtaining the intelligent relationship mapping between the flight state and the gravity velocity loss through radial basis function neural network learning as follows:
ΔV g ＝Net(X) (1)
in formula (1):
ΔV g is the loss of gravitational velocity;
net (-) is a neural network model of learning completion;
x is the rocket flight state quantity;
s2: calculating the speed increment v required by the mission according to the flight mission parameters need ；
The calculation formula of the speed increment required by the task is as follows:
in formula (2):
v need a speed increment required for the task;
V t an entry speed determined for the track mission;
ω e is the earth rotation angular rate;
R 0 the radius of the earth at the emission point;
i f the inclination angle of the task track is;
wherein: the determined track entry speed of the track task is determined according to the following formula:
in formula (3):
mu is an earth gravity constant;
r f the height of the ground center of the track entering point is set;
a f is a semi-major axis of the mission orbit.
S3: according to basic overall rocket parameters (including payload initial value m) 0 Fuel specific impulse, takeoff mass, fuel mass and the like) to calculate the ideal speed increment delta V of the rocket;
s3 the calculation formula of the rocket ideal speed increment is as follows:
calculating by adopting a Zi's formula:
in formula (4):
delta V is the rocket ideal speed increment;
I spv vacuum specific impulse for rocket;
μ K is the ratio of the mass of the rocket at the shutdown point to the mass of the takeoff.
S4: calculating each speed loss term v of the carrier rocket c0 The method comprises the following steps:
at payload initial value m 0 Under the condition, calculating aerodynamic drag speed loss and nozzle pressure speed loss according to an empirical formula, and inputting flight state quantity required by a task terminal into a radial basis function neural network to calculate gravity speed loss;
the formula of the aerodynamic drag speed loss and the nozzle pressure speed loss is as follows:
in formula (5):
ΔV D is aerodynamic drag speed loss;
ΔV T is the spout pressure velocity loss;
K D is the drag loss coefficient;
C XPD is a coefficient of resistance;
S M is a pneumatic reference area;
m 0 the first-level takeoff mass;
K T is the pressure loss coefficient;
μ K the ratio of the mass of the shutdown point of the rocket to the mass of the takeoff;
μ I is the specific impulse ratio.
The calculation of the gravitational velocity loss comprises the following steps:
loss of gravitational velocity Δ V g Fitting learning is carried out by adopting a radial basis function neural network;
the radial basis function neural network is a three-layer neural network comprising an input layer, a single hidden layer and an output layer;
the input state quantity of the radial basis function neural network is as follows: the height, the speed of a launching system and the local trajectory inclination angle, and the input dimension is 3;
the output of the radial basis function neural network is a gravity velocity loss value, and the output dimension is 1;
the number, the center distance and the weight coefficient from the single hidden layer to the output layer are obtained through model learning, and the activation function of the single hidden layer node adopts a Gaussian kernel function (European radial basis function):
in formula (6):
φ jk is an activation function;
x j is the jth input quantity;
c k is the center of the kth neuron kernel function in the single hidden layer;
σ is a width parameter of the function;
the output of the radial basis function neural network is:
in formula (7):
y is the output of the radial basis function neural network;
h is the number of neurons in a single hidden layer;
ω k is the kth weight coefficient from the hidden layer to the output layer.
S5: according to | v c0 -v need If | is smaller than a threshold epsilon, judging whether the calculation process is finished;
if | v c0 -v need If | < ε, then the payload initial value m 0 Namely the maximum carrying capacity of the rocket, and the process is finished;
if | v c0 -v need If | ≧ epsilon, give the initial value m of the payload 0 Plus a small increment delta, defined as the current payload m 1 I.e. m 1 ＝m 0 +δ；
S6: at the current payload m 1 Under the condition, calculating aerodynamic drag speed loss and nozzle pressure speed loss according to an empirical formula, and inputting flight state quantity required by a task terminal into a radial basis function neural network to calculate gravity speed loss; and calculating the actual speed increment v of the rocket c1 (actual velocity increase-ideal velocity increase-aerodynamic drag velocity loss-jet pressure velocity loss-gravitational velocity loss) and simultaneously restoring the payload initial value, i.e., m 0 ＝m 1 -δ；
S7: calculating a new payload initial value m 'according to a gradient updating method' 0 ＝m 0 -δ(v c0 -v need )/(v c1 -v c0 ) And proceeds to S4 again.
The invention converts rocket carrying capacity calculation into a speed loss calculation problem under the maximum effective load mass. Firstly, establishing a flight data sample under the multi-configuration and multi-task conditions; then, a radial basis function neural network is adopted to accurately fit the most important gravity loss item which is also the most complicated to calculate in the velocity loss, so that the rapid mapping from the multitask terminal state to the gravity velocity loss can be realized; finally, the carrying capacity is changed into a single-parameter searching problem of the mass of the effective load, and the solving efficiency of the carrying capacity is improved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (5)
1. A rocket carrying capacity intelligent solving method based on speed loss calculation is characterized in that: the method comprises the following steps:
s1: establishing a multi-configuration and multi-task carrier rocket model flight sample library, and obtaining the intelligent relationship mapping between the flight state and the gravity velocity loss through radial basis function neural network learning as follows:
ΔV g ＝Net(X) (1)
in formula (1):
ΔV g is the loss of gravitational velocity;
net (-) is a neural network model of learning completion;
x is the rocket flight state quantity;
s2: calculating the speed increment v required by the mission according to the flight mission parameters need ；
S3: calculating the ideal speed increment delta V of the rocket according to the basic overall parameters of the rocket;
s4: calculating each speed loss term v of the carrier rocket c0 The method comprises the following steps:
at payload initial value m 0 Under the condition, calculating aerodynamic drag speed loss and nozzle pressure speed loss according to an empirical formula, and inputting flight state quantity required by a task terminal into a radial basis function neural network to calculate gravity speed loss;
s5: according to | v c0 -v need If | is smaller than a threshold epsilon, judging whether the calculation process is finished;
if | v c0 -v need If | < ε, then the payload initial value m 0 Namely the maximum carrying capacity of the rocket, and the process is finished;
if | v c0 -v need If | ≧ epsilon, give the initial value m of the payload 0 Plus a small increment delta, defined as the current payload m 1 I.e. m 1 ＝m 0 +δ；
S6: at the current payload m 1 Under the condition, calculating aerodynamic drag speed loss and nozzle pressure speed loss according to an empirical formula, and inputting flight state quantity required by a task terminal into a radial basis function neural network to calculate gravity speed loss; and calculating the actual speed increment v of the rocket c1 And simultaneously recovering the initial value of the payload, i.e. m 0 ＝m 1 -δ；
S7: calculating a new payload initial value m 'according to a gradient updating method' 0 ＝m 0 -δ(v c0 -v need )/(v c1 -v c0 ) And proceeds to S4 again.
2. A rocket carrying capacity intelligent solution method based on velocity loss calculation according to claim 1, characterized in that: the calculation formula of the speed increment required by the task in S2 is as follows:
in formula (2):
v need a speed increment required for the task;
V t an entry speed determined for the track mission;
ω e is the earth rotation angular rate;
R 0 the radius of the earth at the emission point;
i f the inclination angle of the task track is;
wherein: the determined track entry speed of the track task is determined according to the following formula:
in formula (3):
mu is an earth gravity constant;
r f the height of the ground center of the track entering point is set;
a f is a semi-major axis of the mission orbit.
3. A rocket carrying capacity intelligent solution method based on velocity loss calculation according to claim 2, characterized in that: s3 the calculation formula of the rocket ideal speed increment is as follows:
in formula (4):
delta V is the rocket ideal speed increment;
I spv vacuum specific impulse is adopted for the rocket;
μ K is the ratio of the mass of the rocket at the shutdown point to the mass of the takeoff.
4. A rocket carrying capacity intelligent solution method based on velocity loss calculation according to claim 3, characterized in that: s4, the formula of the aerodynamic drag speed loss and the nozzle pressure speed loss is as follows:
in formula (5):
ΔV D is aerodynamic drag speed loss;
ΔV T is the jet pressure velocity loss;
K D is the drag loss coefficient;
C XPD is a coefficient of resistance;
S M is a pneumatic reference area;
m 0 the first-level takeoff mass;
K T is the pressure loss coefficient;
μ K the ratio of the mass of the shutdown point of the rocket to the mass of the takeoff;
μ I is the specific impulse ratio.
5. A rocket carrying capacity intelligent solving method based on speed loss calculation according to claim 4, characterized in that: s4 the calculation of the gravitational velocity loss includes the steps of:
loss of gravitational velocity Δ V g Fitting learning is carried out by adopting a radial basis function neural network;
the radial basis function neural network is a three-layer neural network comprising an input layer, a single hidden layer and an output layer;
the input state quantity of the radial basis function neural network is as follows: the height, the speed of a launching system and the local trajectory inclination angle, and the input dimension is 3;
the output of the radial basis function neural network is a gravity velocity loss value, and the output dimension is 1;
the number, the center distance and the weight coefficient from the single hidden layer to the output layer of the nodes of the single hidden layer are obtained through model learning, and the activation function of the nodes of the single hidden layer adopts a Gaussian kernel function:
in formula (6):
φ jk is an activation function;
x j is the jth input quantity;
c k is the center of the kth neuron kernel function in the single hidden layer;
σ is a width parameter of the function;
the output of the radial basis function neural network is:
in formula (7):
y is the output of the radial basis function neural network;
h is the number of neurons in a single hidden layer;
ω k is the k-th weight coefficient from the hidden layer to the output layer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210142466.XA CN114491819B (en) | 2022-02-16 | 2022-02-16 | Rocket carrying capacity intelligent solving method based on speed loss calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210142466.XA CN114491819B (en) | 2022-02-16 | 2022-02-16 | Rocket carrying capacity intelligent solving method based on speed loss calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114491819A CN114491819A (en) | 2022-05-13 |
| CN114491819B true CN114491819B (en) | 2022-08-02 |
Family
ID=81479556
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210142466.XA Active CN114491819B (en) | 2022-02-16 | 2022-02-16 | Rocket carrying capacity intelligent solving method based on speed loss calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114491819B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112084581A (en) * | 2020-09-24 | 2020-12-15 | 中国人民解放军国防科技大学 | Spacecraft low-thrust perturbation intersection trajectory optimization method and system |
| US10866789B1 (en) * | 2016-12-02 | 2020-12-15 | The Mathworks, Inc. | Variant modeling elements in graphical programs |
| CN112989549A (en) * | 2019-12-18 | 2021-06-18 | 星河动力(北京)空间科技有限公司 | Method for rapidly calculating low-orbit effective load of solid carrier rocket |
| CN113486564A (en) * | 2021-06-10 | 2021-10-08 | 北京航天控制仪器研究所 | Unmanned ship propeller fault diagnosis system and method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10061481B2 (en) * | 2013-02-28 | 2018-08-28 | The Boeing Company | Methods and devices for visually querying an aircraft based on an area of an image |
-
2022
- 2022-02-16 CN CN202210142466.XA patent/CN114491819B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10866789B1 (en) * | 2016-12-02 | 2020-12-15 | The Mathworks, Inc. | Variant modeling elements in graphical programs |
| CN112989549A (en) * | 2019-12-18 | 2021-06-18 | 星河动力(北京)空间科技有限公司 | Method for rapidly calculating low-orbit effective load of solid carrier rocket |
| CN112084581A (en) * | 2020-09-24 | 2020-12-15 | 中国人民解放军国防科技大学 | Spacecraft low-thrust perturbation intersection trajectory optimization method and system |
| CN113486564A (en) * | 2021-06-10 | 2021-10-08 | 北京航天控制仪器研究所 | Unmanned ship propeller fault diagnosis system and method |
Non-Patent Citations (2)
| Title |
|---|
| 固体运载火箭轨迹/总体参数一体化优化设计研究;胡凡等;《固体火箭技术》;20101215(第06期);全文 * |
| 土星V月球过渡轨道有效载荷能力的提高;R.C.AFRICANO等;《导弹与航天运载技术》;19831231(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114491819A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220147820A1 (en) | Optimal rescue orbital elements online decision-making method based on rbfnn for launch vehicles under thrust drop fault | |
| Koehl et al. | Aerodynamic modelling and experimental identification of a coaxial-rotor UAV | |
| CN106021784B (en) | A kind of full track mark optimum design method based on bilevel optimization strategy | |
| CN111258302B (en) | Aircraft thrust fault online identification method based on LSTM neural network | |
| CN113867143B (en) | Safety soft landing analysis obstacle avoidance guidance method for extraterrestrial celestial body | |
| CN111924139B (en) | Small celestial body landing obstacle avoidance constant thrust control method based on expansion early warning area | |
| CN108875244A (en) | A kind of orbit prediction accuracy improvements method based on random forest | |
| CN109774974A (en) | A kind of rail design method close for space junk | |
| CN113619815B (en) | Spacecraft cluster dynamic path planning method | |
| CN113189870A (en) | Trajectory re-planning method for elliptical rescue orbit under rocket thrust descent fault | |
| Carzana et al. | Locally optimal control laws for Earth-bound solar sailing with atmospheric drag | |
| CN105116905A (en) | Aircraft attitude control method | |
| CN114491819B (en) | Rocket carrying capacity intelligent solving method based on speed loss calculation | |
| CN105912020A (en) | Spacecraft formation flying method in weak-stability boundary area | |
| Viavattene et al. | Artificial neural network for preliminary multiple NEA rendezvous mission using low thrust | |
| Brauer et al. | Program to optimize simulated trajectories (POST). Volume 1: Formulation manual | |
| DAMARIO et al. | Interplanetary trajectory design for the Galileo mission | |
| CN112009727B (en) | Optimal low-thrust transfer sectional design method for translation point orbit | |
| Pallone et al. | Design methodology and performance evaluation of new generation sounding rockets | |
| Xie et al. | A novel migrant PSO algorithm for vehicle reentry trajectory optimization | |
| CN109976362B (en) | Rocket upper-level trajectory re-planning method using differential evolution algorithm | |
| Zhu et al. | The Intelligent Trajectory Optimization of Multistage Rocket with Gauss Pseudo-Spectral Method. | |
| Cheng et al. | Trajectory Correction of the Rocket for Aerodynamic Load Shedding Based on Deep Neural Network and the Chaotic Evolution Strategy with Covariance Matrix Adaptation | |
| Birge III | A computational intelligence approach to the Mars precision landing problem | |
| CN116258066A (en) | Method for estimating low thrust intersection fuel consumption by using neural 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |