CN117889036A - Emergency yaw control method, system and device for wind generating set and storage medium - Google Patents
Emergency yaw control method, system and device for wind generating set and storage medium Download PDFInfo
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Abstract
The invention provides a method, a system, a device and a storage medium for controlling emergency yaw of a wind generating set, and belongs to the field of wind power generation. The method comprises the following steps: acquiring peripheral wind resource data of a wind power plant; establishing a wind farm model according to peripheral wind resource data of the wind farm; extracting wind condition parameters at the position of the fan according to the wind field model; inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result; and generating an emergency yaw signal of the unit according to the yaw prediction result. A wind field model is established based on wind resource data around a wind power plant to simulate wind condition parameters at the position of a fan, yaw prediction is performed based on the simulated wind condition parameters, and an emergency yaw signal of the unit can be generated in advance, so that emergency yaw operation can be performed in advance when the unit does not enter a special emergency state, and safe operation of the unit is ensured.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind generating set emergency yaw control method, a wind generating set emergency yaw control system, a wind generating set emergency yaw control device and a machine-readable storage medium.
Background
In wind power plants, emergency yaw is a critical function. When the wind park is in an abnormal state, emergency yaw measures may need to be taken. The reliability of the emergency yaw system of a wind power plant is therefore critical for the stable operation of the plant and for the health of the plant.
In the current state of the art, typically when the sensor detects that the system needs an emergency yaw, the wind power generator set may already be in a dangerous state, in which the time for executing the yaw strategy is short.
Disclosure of Invention
The invention aims to provide a method, a system, a device and a storage medium for controlling emergency yaw of a wind generating set, wherein a wind field model is built based on wind resource data around the wind generating field to simulate wind condition parameters at a fan position, yaw prediction is performed based on the simulated wind condition parameters, and an emergency yaw signal of the set can be generated in advance, so that emergency yaw operation can be performed in advance when the set does not enter a special emergency state, and safe operation of the set is ensured.
To achieve the above object, a first aspect of the present invention provides a method for controlling emergency yaw of a wind turbine, the method comprising:
acquiring peripheral wind resource data of a wind power plant;
establishing a wind farm model according to peripheral wind resource data of the wind farm;
extracting wind condition parameters at the position of the fan according to the wind field model;
inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result;
and generating an emergency yaw signal of the unit according to the yaw prediction result.
According to the technical means, the wind field model is built based on wind resource data around the wind power plant to simulate wind condition parameters at the position of the fan, yaw prediction is performed based on the simulated wind condition parameters, and emergency yaw signals of the unit can be generated in advance, so that emergency yaw operation can be performed in advance when the unit does not enter a special emergency state, and safe operation of the unit is ensured.
In an embodiment of the present application, obtaining wind resource data around a wind farm includes:
receiving original wind resource data around a wind power plant acquired by a laser anemometer;
and correcting the original wind resource data to obtain wind resource data around the wind power plant.
According to the technical means, the laser anemometer can measure the wind speed and the wind direction at a longer distance, the collected original wind resource data around the wind power plant can be used for predicting the wind condition of the whole wind plant at a longer distance, and the influence of external environmental factors on the measurement result can be eliminated by correcting the original wind resource data, so that the measurement result is more accurate.
In the embodiment of the present application, correcting the original wind resource data to obtain wind resource data around the wind farm includes:
and carrying out temperature correction and humidity correction on the original wind resource data to obtain wind resource data around the wind power plant.
According to the technical means, the influence of external temperature, humidity and other factors on the measurement result can be eliminated, so that the measurement result is more accurate.
In an embodiment of the present application, establishing a wind farm model according to wind resource data around a wind farm includes:
preprocessing peripheral wind resource data of a wind power plant to obtain preprocessed data;
extracting wind condition characteristics from the preprocessed data;
and establishing a wind field model according to the wind condition characteristics and the current meteorological data.
According to the technical means, wind condition characteristics are extracted from wind resource data around the wind power plant, a wind field model is built by combining meteorological data, the situation of the wind turbine when the current wind group reaches the position of the wind turbine can be accurately simulated, and a data basis is provided for yaw control of the wind turbine.
In an embodiment of the present application, extracting wind condition features from the preprocessed data includes:
and calculating the average wind speed, the maximum value of wind direction change and the maximum value of wind speed change of each time interval after dividing according to different time units according to the preprocessed data.
According to the technical means, the average wind speed, the maximum wind direction change and the maximum wind speed change are extracted in different time units, so that the real situation of wind resources can be reflected.
In the embodiment of the application, the yaw prediction model is obtained through training in the following manner:
acquiring historical operation data of each fan in a current wind power plant;
extracting yaw characteristic parameters from historical operation data;
and training a machine learning algorithm based on the yaw characteristic parameters to obtain a yaw prediction model.
According to the technical means, a yaw prediction model is obtained through training according to the historical operation data, and the yaw prediction result under the current wind condition can be obtained through prediction according to the input wind condition characteristics.
In an embodiment of the present application, the method further includes:
and optimizing a yaw prediction model according to the actual operation data of the fan.
According to the technical means, the model can be continuously learned according to actual operation data and updated according to new data, so that the self-learning of yaw control is realized, and the model can adapt to the change of the performance of the fan.
A second aspect of the present application provides a wind turbine generator system emergency yaw control device, the device comprising:
the data acquisition unit is used for acquiring peripheral wind resource data of the wind power plant;
the wind field modeling unit is used for establishing a wind field model according to wind resource data around the wind power plant;
the wind condition parameter extraction unit is used for extracting wind condition parameters at the position of the fan according to the wind field model;
the yaw prediction unit is used for inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result;
and the yaw control unit is used for generating an emergency yaw signal of the unit according to the yaw prediction result.
According to the technical means, the wind field model is built based on wind resource data around the wind power plant to simulate wind condition parameters at the position of the fan, yaw prediction is performed based on the simulated wind condition parameters, and emergency yaw signals of the unit can be generated in advance, so that emergency yaw operation can be performed in advance when the unit does not enter a special emergency state, and safe operation of the unit is ensured.
A third aspect of the present application provides a wind turbine generator system emergency yaw control system, the system comprising:
the laser anemometer is arranged around the wind field, is used for collecting original wind resource data around the wind power generation field and transmitting the data to the emergency yaw system;
the emergency yaw system is connected with the laser anemometer and is used for receiving the original wind resource data around the wind power plant, processing the original wind resource data into the wind resource data around the wind power plant and establishing a wind plant model according to the wind resource data around the wind power plant; extracting wind condition parameters at the position of the fan according to the wind field model; inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result, generating an emergency yaw signal of the unit according to the yaw prediction result, and transmitting the emergency yaw signal to the SCADA system;
the SCADA system is used for receiving the emergency yaw signal of the unit and forwarding the emergency yaw signal to the fan control system;
and the fan control system is used for performing yaw control according to the emergency yaw signal of the unit.
According to the technical means, the laser anemometer can measure the wind speed and the wind direction at a longer distance, the collected original wind resource data around the wind power plant is transmitted to the emergency yaw system, and the emergency yaw system is used for predicting the wind condition around the whole wind power plant, performing yaw prediction according to wind condition parameters at the position of the wind power plant, generating a plant emergency yaw signal and transmitting the plant emergency yaw signal to the SCADA system. In the system, the laser anemometer is directly communicated with an emergency yaw system, the emergency yaw system finishes yaw calculation, can judge and yaw in advance, and then transmits an emergency yaw signal of the unit to a fan control system through a SCADA system, and can act in advance when strong wind does not reach the fan yet. The operation amount of the fan control system is reduced, and the operation load of the fan control system is lightened.
A fourth aspect of the present application provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the method of emergency yaw control of a wind turbine generator set.
According to the technical scheme, the wind field model can be built based on wind resource data around the wind power generation field to simulate wind condition parameters at the position of the fan, yaw prediction is performed based on the simulated wind condition parameters, and emergency yaw signals of the unit can be generated in advance, so that emergency yaw operation can be performed in advance when the unit does not enter a special emergency state, and safe operation of the unit is ensured.
In the system, a laser anemometer can measure the wind speed and the wind direction at a longer distance, the collected raw wind resource data around the wind power plant is transmitted to an emergency yaw system, and the emergency yaw system is used for predicting the wind condition around the whole wind power plant and performing yaw prediction according to wind condition parameters at the position of a wind turbine generator to generate a generator set emergency yaw signal and transmit the generator set emergency yaw signal to an SCADA system. In the system, the laser anemometer is directly communicated with an emergency yaw system, the emergency yaw system finishes yaw calculation, can judge and yaw in advance, and then transmits an emergency yaw signal of the unit to a fan control system through a SCADA system, and can act in advance when strong wind does not reach the fan yet. The operation amount of the fan control system is reduced, and the operation load of the fan control system is lightened.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for controlling emergency yaw of a wind turbine generator set according to an embodiment of the present invention;
FIG. 2 is a block diagram of an emergency yaw control apparatus for a wind turbine generator set according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of an emergency yaw control system for a wind turbine generator system according to an embodiment of the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
FIG. 1 is a flowchart of a method for controlling emergency yaw of a wind turbine generator system according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s1: and acquiring peripheral wind resource data of the wind power plant.
In an embodiment of the present application, obtaining wind resource data around a wind farm includes:
firstly, receiving original wind resource data around a wind power plant acquired by a laser anemometer; the original wind resource data around the wind power generation field is mainly natural wind information around the wind field. The conventional system collects data by means of a common anemograph and is arranged at the top of the fan, and when the measured wind speed and direction are inappropriate, the fan is in a severe environment and cannot make pre-judgment in advance. The laser anemometer can measure the wind speed and the wind direction at a longer distance, and the collected raw wind resource data around the wind power plant can be used for predicting the wind condition around the whole wind plant at a longer distance.
And then correcting the original wind resource data to obtain wind resource data around the wind power plant.
In the embodiment of the present application, correcting the original wind resource data to obtain wind resource data around the wind farm includes:
and carrying out temperature correction and humidity correction on the original wind resource data to obtain wind resource data around the wind power plant.
According to the technical means, the influence of external temperature, humidity and other factors on the measurement result can be eliminated, so that the measurement result is more accurate.
According to the technical means, the laser anemometer can measure the wind speed and the wind direction at a longer distance, the collected original wind resource data around the wind power plant can be used for predicting the wind condition of the whole wind plant at a longer distance, and the influence of external environmental factors on the measurement result can be eliminated by correcting the original wind resource data, so that the measurement result is more accurate.
S2: and establishing a wind field model according to wind resource data around the wind power plant.
In an embodiment of the present application, establishing a wind farm model according to wind resource data around a wind farm includes:
preprocessing wind resource data around a wind farm to obtain preprocessed data, wherein in some embodiments, the preprocessing comprises: and processing abnormal values, missing values and noise, and simultaneously performing time synchronization to ensure that wind resource data around the wind power plant collected by different laser anemometers are on the same time reference.
Wind condition features are extracted from the preprocessed data. In an embodiment of the present application, extracting wind condition features from the preprocessed data includes:
and calculating the average wind speed, the maximum value of wind direction change and the maximum value of wind speed change of each time interval after dividing according to different time units according to the preprocessed data. In one embodiment, the time unit may be 5s,10s,1min,5min,10min, etc., specifically, the average wind speed, the maximum wind direction change, the maximum wind speed change of 5 seconds, 10 seconds, 1 minute, 5 minutes, 10 minutes, etc. time may be automatically calculated after the data is imported by using Python programming.
According to the technical means, the average wind speed, the maximum wind direction change and the maximum wind speed change are extracted in different time units, so that the real situation of wind resources can be reflected.
And establishing a wind field model according to the wind condition characteristics and the current meteorological data.
According to the technical means, wind condition characteristics are extracted from wind resource data around the wind power plant, a wind field model is built by combining meteorological data, the situation of the wind turbine when the current wind group reaches the position of the wind turbine can be accurately simulated, and a data basis is provided for yaw control of the wind turbine.
S3: wind condition parameters at the position of the wind turbine are extracted according to the wind field model, and in the embodiment of the application, the wind condition parameters of corresponding coordinates can be directly extracted from the wind field model according to the wind field position parameters.
S4: and inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result.
S5: and generating an emergency yaw signal of the unit according to the yaw prediction result.
In the embodiment of the application, the yaw prediction result is yaw probability, a yaw probability threshold is determined according to historical data, yaw is determined to be needed when the predicted yaw probability is larger than the probability threshold, an emergency yaw signal of the unit is generated, the emergency yaw signal controls a fan to form a 90-degree included angle with the wind direction, and at the moment, the fan is stressed least, so that the unit spends an emergency.
According to the technical means, the wind field model is built based on wind resource data around the wind power plant to simulate wind condition parameters at the position of the fan, yaw prediction is performed based on the simulated wind condition parameters, and emergency yaw signals of the unit can be generated in advance, so that emergency yaw operation can be performed in advance when the unit does not enter a special emergency state, and safe operation of the unit is ensured.
In the embodiment of the application, the yaw prediction model is obtained through training in the following manner:
historical operation data of each fan in the current wind power plant is obtained, wherein the historical operation data comprise information such as wind speed, wind direction, yaw angle and yaw time. These data will be used for learning and model training of the algorithm.
Extracting yaw feature parameters from historical operating data, wherein in the embodiment of the application, the yaw feature parameters comprise: the mean value of wind speed, the change rate of wind direction, the historical data of yaw angle and the like are helpful for the algorithm to better understand the running condition of the fan. The yaw characteristic parameter extraction mode is the same as the wind condition parameter extraction mode.
And training a machine learning algorithm based on the yaw characteristic parameters to obtain a yaw prediction model. In the training process, firstly, data are divided into a training set and a verification set, model parameters are adjusted, and the performance of a model is monitored. The goal of the model is to predict whether an emergency yaw is required at a future point in time.
According to the technical means, a yaw prediction model is obtained through training according to the historical operation data, and the yaw prediction result under the current wind condition can be obtained through prediction according to the input wind condition characteristics.
In an embodiment of the present application, the method further includes:
and optimizing a yaw prediction model according to the actual operation data of the fan. The current wind condition is monitored in real time and compared with the prediction, so that the fan control system can respond in time and yaw is carried out when required.
According to the technical means, the model can be continuously learned according to actual operation data and updated according to new data, so that the self-learning of yaw control is realized, and the model can adapt to the change of the performance of the fan.
A second aspect of the present application provides an emergency yaw control apparatus for a wind turbine generator system, as shown in fig. 2, the apparatus comprising:
the data acquisition unit is used for acquiring peripheral wind resource data of the wind power plant;
the wind field modeling unit is used for establishing a wind field model according to wind resource data around the wind power plant;
the wind condition parameter extraction unit is used for extracting wind condition parameters at the position of the fan according to the wind field model;
the yaw prediction unit is used for inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result;
and the yaw control unit is used for generating an emergency yaw signal of the unit according to the yaw prediction result.
According to the technical means, the wind field model is built based on wind resource data around the wind power plant to simulate wind condition parameters at the position of the fan, yaw prediction is performed based on the simulated wind condition parameters, and emergency yaw signals of the unit can be generated in advance, so that emergency yaw operation can be performed in advance when the unit does not enter a special emergency state, and safe operation of the unit is ensured.
A third aspect of the present application provides a wind turbine generator system emergency yaw control system, as shown in fig. 3, the system comprising:
the laser anemometer is arranged around the wind field, is used for collecting original wind resource data around the wind power generation field and transmitting the data to the emergency yaw system;
the emergency yaw system is connected with the laser anemometer and is used for receiving the original wind resource data around the wind power plant, processing the original wind resource data into the wind resource data around the wind power plant and establishing a wind plant model according to the wind resource data around the wind power plant; extracting wind condition parameters at the position of the fan according to the wind field model; inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result, generating an emergency yaw signal of the unit according to the yaw prediction result, and transmitting the emergency yaw signal to the SCADA system;
the SCADA system is used for receiving the emergency yaw signal of the unit and forwarding the emergency yaw signal to the fan control system;
and the fan control system is used for performing yaw control according to the emergency yaw signal of the unit.
In actual operation, the laser anemometer may obtain information about particles in the air by emitting a laser beam and measuring the way the laser propagates in the air, and the received laser signal may be filtered, amplified, or otherwise processed to enhance the performance of the laser anemometer and reduce noise. The processed signals are demodulated to extract the information of the luminous wind speed and the wind direction, so that the original wind resource data around the wind power generation field is obtained. Typically, laser anemometers are calibrated periodically either before use or after being put into service to ensure accuracy of the output. Calibration includes comparison to standard wind speed and standard wind direction measurements.
According to the technical means, the laser anemometer can measure the wind speed and the wind direction at a longer distance, the collected original wind resource data around the wind power plant is transmitted to the emergency yaw system, and the emergency yaw system is used for predicting the wind condition around the whole wind power plant, performing yaw prediction according to wind condition parameters at the position of the wind power plant, generating a plant emergency yaw signal and transmitting the plant emergency yaw signal to the SCADA system. In the system, the laser anemometer is directly communicated with an emergency yaw system, the emergency yaw system finishes yaw calculation, can judge and yaw in advance, and then transmits an emergency yaw signal of the unit to a fan control system through a SCADA system, and can act in advance when strong wind does not reach the fan yet. The operation amount of the fan control system is reduced, and the operation load of the fan control system is lightened. The laser anemometer can measure wind speed and wind direction outside hundreds of meters, and when the measured wind speed and wind direction act on the fan, the time of about 20 to 30 seconds can be provided for the wind turbine generator to judge in advance.
In other embodiments, the wind turbine emergency yaw control system is further integrated with an alarm system, which triggers an alarm to notify personnel and take corresponding action when wind conditions are predicted to be unfavorable for wind power generation.
In some embodiments, the cleaned data and analysis results are stored and an appropriate database and management system is built for future reference and analysis.
A fourth aspect of the present application provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the method of emergency yaw control of a wind turbine generator set.
Those skilled in the art will appreciate that all or part of the steps in a method for implementing the above embodiments may be implemented by a program stored in a storage medium, where the program includes several instructions for causing a single-chip microcomputer, chip or processor (processor) to perform all or part of the steps in a method according to the embodiments of the invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The alternative embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the embodiments of the present invention are not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the embodiments of the present invention within the scope of the technical concept of the embodiments of the present invention, and all the simple modifications belong to the protection scope of the embodiments of the present invention. In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the various possible combinations of embodiments of the invention are not described in detail.
In addition, any combination of the various embodiments of the present invention may be made, so long as it does not deviate from the idea of the embodiments of the present invention, and it should also be regarded as what is disclosed in the embodiments of the present invention.
Claims (10)
1. A method for emergency yaw control of a wind turbine generator system, the method comprising:
acquiring peripheral wind resource data of a wind power plant;
establishing a wind farm model according to peripheral wind resource data of the wind farm;
extracting wind condition parameters at the position of the fan according to the wind field model;
inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result;
and generating an emergency yaw signal of the unit according to the yaw prediction result.
2. The method of claim 1, wherein obtaining wind resource data around a wind farm comprises:
receiving original wind resource data around a wind power plant acquired by a laser anemometer;
and correcting the original wind resource data to obtain wind resource data around the wind power plant.
3. The method for controlling the emergency yaw of the wind generating set according to claim 2, wherein the correcting the original wind resource data to obtain the wind resource data around the wind generating set comprises:
and carrying out temperature correction and humidity correction on the original wind resource data to obtain wind resource data around the wind power plant.
4. The method of claim 1, wherein building a wind farm model from wind resource data surrounding a wind farm comprises:
preprocessing peripheral wind resource data of a wind power plant to obtain preprocessed data;
extracting wind condition characteristics from the preprocessed data;
and establishing a wind field model according to the wind condition characteristics and the current meteorological data.
5. The method of emergency yaw control of a wind turbine of claim 4, wherein extracting wind condition features from the preprocessed data comprises:
and calculating the average wind speed, the maximum value of wind direction change and the maximum value of wind speed change of each time interval after dividing according to different time units according to the preprocessed data.
6. The method for emergency yaw control of a wind turbine generator set according to claim 1, wherein the yaw prediction model is trained by:
acquiring historical operation data of each fan in a current wind power plant;
extracting yaw characteristic parameters from historical operation data;
and training a machine learning algorithm based on the yaw characteristic parameters to obtain a yaw prediction model.
7. The method of emergency yaw control of a wind turbine generator set of claim 1, wherein the method further comprises:
and optimizing a yaw prediction model according to the actual operation data of the fan.
8. An emergency yaw control device for a wind turbine, the device comprising:
the data acquisition unit is used for acquiring peripheral wind resource data of the wind power plant;
the wind field modeling unit is used for establishing a wind field model according to wind resource data around the wind power plant;
the wind condition parameter extraction unit is used for extracting wind condition parameters at the position of the fan according to the wind field model;
the yaw prediction unit is used for inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result;
and the yaw control unit is used for generating an emergency yaw signal of the unit according to the yaw prediction result.
9. An emergency yaw control system for a wind turbine, the system comprising:
the laser anemometer is arranged around the wind field, is used for collecting original wind resource data around the wind power generation field and transmitting the data to the emergency yaw system;
the emergency yaw system is connected with the laser anemometer and is used for receiving the original wind resource data around the wind power plant, processing the original wind resource data into the wind resource data around the wind power plant and establishing a wind plant model according to the wind resource data around the wind power plant; extracting wind condition parameters at the position of the fan according to the wind field model; inputting wind condition parameters at the position of the fan into the established yaw prediction model to obtain a yaw prediction result, generating an emergency yaw signal of the unit according to the yaw prediction result, and transmitting the emergency yaw signal to the SCADA system;
the SCADA system is used for receiving the emergency yaw signal of the unit and forwarding the emergency yaw signal to the fan control system;
and the fan control system is used for performing yaw control according to the emergency yaw signal of the unit.
10. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the wind turbine emergency yaw control method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311699564.4A CN117889036A (en) | 2023-12-11 | 2023-12-11 | Emergency yaw control method, system and device for wind generating set and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311699564.4A CN117889036A (en) | 2023-12-11 | 2023-12-11 | Emergency yaw control method, system and device for wind generating set and storage medium |
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| Publication Number | Publication Date |
|---|---|
| CN117889036A true CN117889036A (en) | 2024-04-16 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311699564.4A Pending CN117889036A (en) | 2023-12-11 | 2023-12-11 | Emergency yaw control method, system and device for wind generating set and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117889036A (en) |
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2023
- 2023-12-11 CN CN202311699564.4A patent/CN117889036A/en active Pending
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