Fan monitoring data transmission method, device and equipment and storage medium
By combining data serialization encapsulation and data augmentation models in wind turbines, the efficiency and accuracy issues of long-distance monitoring data transmission for wind turbines have been resolved, enabling the safe and efficient operation of wind turbines.
Patent Information
- Application Number
- CN202511320121.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-16
- Publication Date
- 2025-12-12
AI Technical Summary
In remote areas and offshore wind farms where communication infrastructure is weak, it is difficult to achieve efficient and real-time transmission of monitoring data from wind turbines over long distances, making it impossible for maintenance personnel to accurately grasp the equipment status and posing safety hazards.
Data augmentation technology based on artificial intelligence models is adopted to serialize and encapsulate data by generating request instructions, improve data accuracy by using data augmentation models, and transmit data in conjunction with the BeiDou communication system.
It improved data transmission efficiency and accuracy, enhanced channel utilization, and ensured the safe operation of wind turbine units.
Smart Images
Figure CN121125873A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data transmission, and in particular to a wind turbine monitoring data transmission method, a wind turbine monitoring data transmission device, an electronic device, and a computer-readable storage medium. Background Technology
[0002] With the rapid development of wind power technology, real-time monitoring of wind turbine operating data has become a core element in ensuring equipment safety and power generation efficiency. Analyzing monitoring data reveals the operating status of wind turbines, enabling timely maintenance in case of equipment failure and ensuring safe operation. However, in remote areas and offshore wind farms with weak communication infrastructure, wind turbine monitoring data needs to be transmitted over long distances to the receiving end. Limited by bandwidth resources and transmission costs in long-distance communication, it is difficult to transmit wind turbine monitoring data to the receiving end in real time and efficiently. This results in maintenance personnel being unable to accurately grasp the operating status of the wind turbines, posing risks to their safe operation. Summary of the Invention
[0003] The purpose of this invention is to provide a wind turbine monitoring data transmission method, a wind turbine monitoring data transmission device, an electronic device, and a computer-readable storage medium, which are applied in the field of data transmission. This method is based on artificial intelligence models to enhance data, solves the problems of small data capacity and low frequency in long-distance data transmission, improves data transmission efficiency and data accuracy, and improves channel utilization efficiency by serializing and encapsulating data through request instructions.
[0004] To solve the above-mentioned technical problems, the present invention provides a wind turbine monitoring data transmission method, comprising:
[0005] The monitoring data receiving end generates a request instruction and sends the request instruction to the monitoring data sending end, so that the monitoring data sending end can parse the request instruction to obtain the serialization method of the wind turbine monitoring data, encapsulate the wind turbine monitoring data based on the serialization method to obtain a response instruction, and send the response instruction to the monitoring data receiving end;
[0006] When the monitoring data receiving end receives the response instruction, the monitoring data receiving end parses the response instruction based on the deserialization method to obtain parsed data;
[0007] The monitoring data receiving end acquires the data augmentation model trained based on historical wind turbine monitoring data, and inputs the parsed data into the trained data augmentation model to obtain wind turbine monitoring augmentation data.
[0008] Optionally, the wind turbine monitoring data is encapsulated based on the serialization method to obtain a response command, including:
[0009] When the request instruction is a full data instruction, the monitoring data sending end encapsulates the full data of the wind turbine monitoring data into the response instruction;
[0010] When the request instruction is a specified data instruction, the monitoring data sending end determines the target data from the full data of the wind turbine monitoring data based on the data point name in the request instruction, and encapsulates the target data into the response instruction;
[0011] When the request instruction is a control instruction, the monitoring data sending end executes the wind turbine control instruction based on the control value in the request instruction, determines the target data from the full data of the wind turbine monitoring data based on the data point name in the request instruction, and encapsulates the target data into the response instruction after the control command is successfully executed.
[0012] Optionally, the request header of the request instruction includes a request signature and a request sequence number; the response header of the response instruction includes a response signature, a response sequence number, and a response status code.
[0013] Optionally, obtain a data augmentation model trained based on historical wind turbine monitoring data, including:
[0014] The monitoring data receiving end acquires the data augmentation model trained based on the historical wind turbine monitoring data; wherein, the data augmentation model is trained based on dataset samples and sample labels; the dataset samples are first sampled data obtained from the historical wind turbine monitoring data based on a first sampling period; the sample labels are second sampled data obtained from the historical wind turbine monitoring data based on a second sampling period; the first sampling period is greater than the second sampling period.
[0015] Optionally, the effective data sampling time within the first sampling period is the same as the period interval of the second sampling period; the sampling interval of the second sampling period is zero.
[0016] Optionally, the data augmentation model is an LSTM model.
[0017] Optionally, the monitoring data sending end and the monitoring data receiving end transmit data via BeiDou communication.
[0018] To address the aforementioned technical problems, this invention provides a wind turbine monitoring data transmission device, applied at a monitoring data receiving end, comprising:
[0019] The first module is used to generate a request instruction, send the request instruction to the monitoring data sending end, so that the monitoring data sending end can parse the request instruction to obtain the serialization method of the wind turbine monitoring data, encapsulate the wind turbine monitoring data based on the serialization method to obtain a response instruction, and send the response instruction to the monitoring data receiving end.
[0020] The second module is used to parse the response instruction based on deserialization to obtain parsed data after the monitoring data receiving end receives the response instruction.
[0021] The third module is used to obtain a data augmentation model trained based on historical wind turbine monitoring data, and input the parsed data into the trained data augmentation model to obtain wind turbine monitoring augmentation data.
[0022] To solve the above-mentioned technical problems, the present invention provides an electronic device, comprising:
[0023] Memory, used to store computer programs;
[0024] A processor is used to implement the wind turbine monitoring data transmission method described above when executing the computer program.
[0025] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned wind turbine monitoring data transmission method.
[0026] As can be seen, in this invention, the monitoring data receiving end generates a request command and sends it to the monitoring data sending end. The sending end then parses the request command to obtain the serialization method of the wind turbine monitoring data. Based on the serialization method, it encapsulates the wind turbine monitoring data to obtain a response command, which is then sent to the monitoring data receiving end. Upon receiving the response command, the receiving end parses it using deserialization to obtain parsed data. The receiving end then acquires a data augmentation model trained on historical wind turbine monitoring data and inputs the parsed data into the trained model to obtain enhanced wind turbine monitoring data. This invention uses an artificial intelligence model for data augmentation, solving the problems of small data capacity and low frequency in long-distance data transmission, improving data transmission efficiency and accuracy. Furthermore, by using request commands to achieve data serialization and encapsulation, it improves channel utilization efficiency. Attached Figure Description
[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0028] Figure 1 A flowchart illustrating a wind turbine monitoring data transmission method provided in an embodiment of the present invention;
[0029] Figure 2 This is an example diagram of the architecture of a monitoring data receiving end and a monitoring data sending end provided in an embodiment of the present invention;
[0030] Figure 3 This is an example diagram illustrating the serialization of a request instruction and a response instruction provided in an embodiment of the present invention;
[0031] Figure 4 Example diagrams of a request header for a request instruction and a response header for a response instruction provided in an embodiment of the present invention;
[0032] Figure 5 An example diagram of data augmentation provided in an embodiment of the present invention;
[0033] Figure 6 This is a structural block diagram of a wind turbine monitoring data transmission device provided in an embodiment of the present invention. Detailed Implementation
[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0035] With the rapid development of wind power technology, real-time monitoring of wind turbine operation has become a core element in ensuring equipment safety and power generation efficiency. In remote areas and offshore wind farms where communication infrastructure is weak, data exchange is generally carried out through long-distance data transmission systems. For example, the BeiDou satellite communication system, due to its wide-area coverage, has become an important means of data transmission between wind turbine controllers and monitoring servers.
[0036] However, existing systems face significant technical bottlenecks. For example, the BeiDou communication protocol, limited by bandwidth resources and transmission costs, requires that a single data packet be strictly controlled within 229 bytes (BeiDou-3, Level 2 card), and the transmission frequency is limited to once per minute. This rule results in a severe shortage of data that can be carried in a single transmission, making it difficult to meet the high-frequency monitoring needs of multiple wind turbine parameters (such as speed, temperature, vibration, etc.). In traditional solutions, the use of a general serialization format for encapsulation further exacerbates the problem of wasted transmission capacity due to its redundant text structure. On the other hand, limited by a fixed acquisition cycle of 1 minute, the data granularity obtained by the monitoring server is relatively coarse, making it difficult to capture transient anomalies in the wind turbine's status in a timely manner (such as short-term bearing overheating or vibration abrupt changes caused by microcracks in the blades). Although existing data interpolation methods (such as linear interpolation and cubic splines) can generate fine-grained data, they rely on fixed mathematical models and cannot adapt to the nonlinear temporal characteristics of wind turbine operating parameters, resulting in a high error rate in the interpolation results, which cannot meet the accuracy requirements for fault prediction.
[0037] Against this backdrop, there is an urgent need for a solution that balances transmission efficiency and data accuracy. Under the constraints of bandwidth resources and transmission costs, this solution can indirectly overcome the information volume limit by improving the utilization rate of single-frame data payload, and enhance the equivalent monitoring granularity based on server data augmentation, while avoiding the cost burden of hardware upgrades.
[0038] The following combination Figure 1 , Figure 1 A flowchart of a wind turbine monitoring data transmission method provided in an embodiment of the present invention, the method may include:
[0039] S101: The monitoring data receiving end generates a request command and sends the request command to the monitoring data sending end, so that the monitoring data sending end can parse the request command to obtain the serialization method of the wind turbine monitoring data, encapsulate the wind turbine monitoring data based on the serialization method to obtain a response command, and send the response command to the monitoring data receiving end.
[0040] In this embodiment, the monitoring data receiver and the monitoring data sender can transmit data via command control. This embodiment does not limit the communication method between the monitoring data receiver and the monitoring data sender; data transmission can generally be performed using BeiDou communication (third-generation short message service).
[0041] This embodiment does not limit the specific method of data interaction between the monitoring data receiving end and the monitoring data sending end, and it can generally be as follows: Figure 2As shown, the monitoring data sending end can collect wind turbine monitoring data, and the monitoring data receiving end generates a request command and sends the request command to the monitoring data sending end. When the monitoring data sending end receives the request command, it parses the request command to obtain the serialization method of the wind turbine monitoring data, encapsulates the wind turbine monitoring data based on the serialization method to obtain a response command, and sends the response command to the monitoring data receiving end to complete the data transmission.
[0042] The serialization method in this embodiment refers to the method of constructing communication messages. This embodiment does not limit the specific way of encapsulating wind turbine monitoring data to obtain response instructions based on serialization. Generally, the serialization method is related to the type of request instruction. This embodiment does not limit the specific type of request instruction, which can generally include full data instructions, specified data instructions, and control instructions.
[0043] Generally, the full data command can transmit all wind turbine monitoring data; the specified data command can transmit specified data of wind turbine monitoring data; and the control command can enable the monitoring data receiver to send control commands to the monitoring data sender, so as to control the wind turbine through the control commands and transmit the specified data of wind turbine monitoring data after the control commands take effect.
[0044] Specifically, when the request instruction is a full data instruction, the monitoring data sending end encapsulates the full data of the wind turbine monitoring data into a response instruction.
[0045] When the request instruction is a specified data instruction, the monitoring data sending end determines the target data from the full amount of wind turbine monitoring data based on the data point name in the request instruction, and encapsulates the target data into a response instruction.
[0046] When the request instruction is a control instruction, the monitoring data sending end executes the wind turbine control instruction based on the control value in the request instruction, determines the target data from the full amount of wind turbine monitoring data based on the data point name in the request instruction, and encapsulates the target data into a response instruction after the control command is successfully executed.
[0047] In this embodiment, the request instruction is the request message generated by the monitoring data receiving end, and the response instruction is the response message generated by the monitoring data sending end based on the request message.
[0048] In this embodiment, the serialization method for the request command and the response command can be as follows: Figure 3As shown, the full data command includes a message header (request header) and empty data, while its corresponding response command includes a message header (response header) and the value of the full data. The specified data command includes a message header and a data point name (the name of the specified data point), while its corresponding response command includes a message header, the specified data point name, and the value of the specified data point name. The control command includes a message header, a control point name, a control value, and a point name, while its corresponding response command includes a message header, the specified data point name, and the value of the specified data point name. The control point name is used to send control commands. The monitoring data sender can parse the control value to obtain the corresponding control command, send the control command to the wind turbine, and after the command is successfully executed, encapsulate the specified target data into a response command and send it to the monitoring data receiver.
[0049] In this embodiment, the structures of the request header of the request instruction and the response header of the response instruction can be as follows: Figure 4 As shown, the request header of the request instruction contains a request signature and a request sequence number; the response header of the response instruction contains a response signature, a response sequence number, and a response status code.
[0050] In a request instruction, the request signature is used to distinguish different data instructions, and the request sequence number marks the communication sequence of this instruction. In a response instruction, the response signature corresponds to the request signature, the response sequence number corresponds to the request sequence number, and the response status code is used to indicate the validity of the data.
[0051] S102: When the monitoring data receiving end receives the response command, the monitoring data receiving end parses the response command based on the deserialization method to obtain the parsed data.
[0052] S103: The monitoring data receiving end acquires the data augmentation model trained based on historical wind turbine monitoring data, and inputs the parsed data into the trained data augmentation model to obtain wind turbine monitoring augmentation data.
[0053] In this embodiment, after the monitoring data receiving end receives the response instruction, it can parse the response instruction to obtain the deserialization method, and then parse the response instruction to obtain the parsed data.
[0054] In this embodiment, after obtaining the parsed data, array augmentation can be performed on the parsed data using a data augmentation model to obtain enhanced wind turbine monitoring data. The data augmentation in this embodiment aims to perform interpolation processing on the parsed wind turbine monitoring data to improve data accuracy, thereby making the wind turbine operating status analyzed based on the wind turbine monitoring data more accurate.
[0055] This embodiment does not limit the training method of the data augmentation model. Generally, the data augmentation model is trained based on the dataset samples and sample labels. The dataset samples are the first sampled data obtained from the historical wind turbine monitoring data based on the first sampling period. The sample labels are the second sampled data obtained from the historical wind turbine monitoring data based on the second sampling period. The first sampling period is longer than the second sampling period.
[0056] This embodiment does not limit the specific type of data augmentation model, but can generally be an LSTM (Long Short-Term Memory) model.
[0057] This embodiment does not limit the specific setting of the first sampling period and the second sampling period. In this embodiment, the effective data sampling time in the first sampling period can be the same as the period interval of the second sampling period, and the sampling interval in the second sampling period can be zero.
[0058] For example, if the first sampling period is set to 60 seconds and the second sampling period to 20 seconds, the effective data sampling time within the first sampling period is 20 seconds. That is, within each first sampling period, monitoring data exists for only 20 seconds. In this embodiment, the entire second sampling period can be considered the effective data sampling time. Figure 5 As shown, after the model training is complete, data with a sampling period of 60 seconds is input into the data augmentation model. The sampling interval is 40 seconds, meaning only 20 seconds of effective sampling data are available. The LSTM model interpolates the data to obtain augmented data with a sampling period of 20 seconds, and the sampling interval within each sampling period is 0. In other words, the 40-second sampling interval is filled by interpolation using data with a sampling period of 20 seconds, thus improving data accuracy.
[0059] In this embodiment, since there are multiple types of monitoring data, a corresponding data augmentation model can be trained for each type of monitoring data. In other words, multiple types of data can be combined to train a single data augmentation model. The specific settings can be based on the actual application.
[0060] In this embodiment, when the request instruction is a specified data instruction and a control instruction, the parsed data obtained by the monitoring data receiving end is the specified wind turbine monitoring data, and its format may not conform to the input of the data augmentation model. Therefore, in this embodiment, data augmentation can only be performed through the data augmentation model when the request instruction is a full data instruction, that is, when the parsed data is the full wind turbine monitoring data.
[0061] The original wind turbine monitoring data can be real-time, second-level monitoring data. In this embodiment, the full data collected by the monitoring data sending end needs to be synchronized with the sampling method of the model training samples. Therefore, the full data can be the data obtained by sampling the original wind turbine monitoring data according to the first sampling period.
[0062] Based on the above embodiments, this invention uses an artificial intelligence model for data augmentation to solve the problems of small data capacity and low frequency in long-distance data transmission, thereby improving data transmission efficiency and data accuracy. Furthermore, it improves channel utilization efficiency by implementing data serialization and encapsulation through request commands.
[0063] The following combination Figure 6 , Figure 6 This is a structural block diagram of a wind turbine monitoring data transmission device provided in an embodiment of the present invention. The device is applied at a monitoring data receiving end and may include:
[0064] The first module 100 is used to generate a request instruction and send the request instruction to the monitoring data sending end, so that the monitoring data sending end can parse the request instruction to obtain the serialization method of the wind turbine monitoring data, encapsulate the wind turbine monitoring data based on the serialization method to obtain a response instruction, and send the response instruction to the monitoring data receiving end.
[0065] The second module 200 is used to parse the response command based on the deserialization method to obtain parsed data after the monitoring data receiving end receives the response command;
[0066] The third module 300 is used to obtain the data augmentation model trained based on historical wind turbine monitoring data, and input the parsed data into the trained data augmentation model to obtain wind turbine monitoring augmentation data.
[0067] Based on the above embodiments, this invention uses an artificial intelligence model for data augmentation to solve the problems of small data capacity and low frequency in long-distance data transmission, thereby improving data transmission efficiency and data accuracy. Furthermore, it improves channel utilization efficiency by implementing data serialization and encapsulation through request commands.
[0068] Based on the above embodiments, the first module 100 may include:
[0069] The first unit is used to encapsulate the full data of the wind turbine monitoring data into a response command when the request command is a full data command;
[0070] The second unit is used to determine the target data from the full data of wind turbine monitoring data based on the data point name in the request instruction when the request instruction is a specified data instruction, and to encapsulate the target data into a response instruction.
[0071] The third unit is used to execute the wind turbine control command based on the control value in the request command when the request command is a control command, determine the target data from the full data of the wind turbine monitoring data based on the data point name in the request command, and encapsulate the target data into a response command after the control command is successfully executed.
[0072] Based on the above embodiments, the request header of the request instruction includes a request feature code and a request sequence number; the response header of the response instruction includes a response feature code, a response sequence number, and a response status code.
[0073] Based on the above embodiments, the third module 300 may include:
[0074] The fourth unit is used to obtain a data augmentation model trained based on historical wind turbine monitoring data. The data augmentation model is trained based on dataset samples and sample labels. The dataset samples are the first sampled data obtained from historical wind turbine monitoring data based on the first sampling period. The sample labels are the second sampled data obtained from historical wind turbine monitoring data based on the second sampling period. The first sampling period is longer than the second sampling period.
[0075] Based on the above embodiments, the effective data sampling time in the first sampling period is the same as the period interval of the second sampling period; the sampling interval of the second sampling period is zero.
[0076] Based on the above embodiments, the data augmentation model is an LSTM model.
[0077] Based on the above embodiments, data transmission between the monitoring data sending end and the monitoring data receiving end is carried out through BeiDou communication.
[0078] Based on the above embodiments, the present invention also provides an electronic device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the device may also include various necessary network interfaces, a power supply, and other components.
[0079] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an execution terminal or processor, can implement the method provided in the embodiments of the present invention; the storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0080] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
Claims
1. A method for transmitting data for wind turbine monitoring, characterized in that, include: The monitoring data receiving end generates a request instruction and sends the request instruction to the monitoring data sending end, so that the monitoring data sending end can parse the request instruction to obtain the serialization method of the wind turbine monitoring data, encapsulate the wind turbine monitoring data based on the serialization method to obtain a response instruction, and send the response instruction to the monitoring data receiving end; When the monitoring data receiving end receives the response instruction, the monitoring data receiving end parses the response instruction based on the deserialization method to obtain parsed data; The monitoring data receiving end acquires the data augmentation model trained based on historical wind turbine monitoring data, and inputs the parsed data into the trained data augmentation model to obtain wind turbine monitoring augmentation data.
2. The wind turbine monitoring data transmission method according to claim 1, characterized in that, The wind turbine monitoring data is encapsulated using the serialization method to obtain a response command, including: When the request instruction is a full data instruction, the monitoring data sending end encapsulates the full data of the wind turbine monitoring data into the response instruction; When the request instruction is a specified data instruction, the monitoring data sending end determines the target data from the full data of the wind turbine monitoring data based on the data point name in the request instruction, and encapsulates the target data into the response instruction; When the request instruction is a control instruction, the monitoring data sending end executes the wind turbine control instruction based on the control value in the request instruction, determines the target data from the full data of the wind turbine monitoring data based on the data point name in the request instruction, and encapsulates the target data into the response instruction after the control command is successfully executed.
3. The wind turbine monitoring data transmission method according to claim 1, characterized in that, The request header of the request instruction includes a request signature and a request sequence number; the response header of the response instruction includes a response signature, a response sequence number, and a response status code.
4. The wind turbine monitoring data transmission method according to claim 1, characterized in that, Obtain a data augmentation model trained based on historical wind turbine monitoring data, including: The monitoring data receiving end acquires the data augmentation model trained based on the historical wind turbine monitoring data; wherein, the data augmentation model is trained based on dataset samples and sample labels; the dataset samples are first sampled data obtained from the historical wind turbine monitoring data based on a first sampling period; the sample labels are second sampled data obtained from the historical wind turbine monitoring data based on a second sampling period; the first sampling period is greater than the second sampling period.
5. The wind turbine monitoring data transmission method according to claim 4, characterized in that, The effective data sampling time within the first sampling period is the same as the period interval of the second sampling period; the sampling interval of the second sampling period is zero.
6. The wind turbine monitoring data transmission method according to claim 1, characterized in that, The data augmentation model is an LSTM model.
7. The wind turbine monitoring data transmission method according to claim 1, characterized in that, The monitoring data sending end and the monitoring data receiving end transmit data via BeiDou communication.
8. A wind turbine monitoring data transmission device, characterized in that, Applications include: (The text abruptly ends here, so the translation also ends here.) The first module is used to generate a request instruction, send the request instruction to the monitoring data sending end, so that the monitoring data sending end can parse the request instruction to obtain the serialization method of the wind turbine monitoring data, encapsulate the wind turbine monitoring data based on the serialization method to obtain a response instruction, and send the response instruction to the monitoring data receiving end. The second module is used to parse the response instruction based on deserialization to obtain parsed data after the monitoring data receiving end receives the response instruction. The third module is used to obtain a data augmentation model trained based on historical wind turbine monitoring data, and input the parsed data into the trained data augmentation model to obtain wind turbine monitoring augmentation data.
9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the wind turbine monitoring data transmission method as described in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the wind turbine monitoring data transmission method as described in any one of claims 1 to 7.