Anemometer calibration method and device, storage medium and electronic device

Abstract

The invention discloses a calibration method for a wind meter, and the method comprises the steps: obtaining the collected environment data in a wind field environment, and the wind power data, outputted by the wind meter, of the wind field environment; comparing the wind power data with preset standard wind power data, and determining an initial compensation coefficient of the wind meter; determining an influence factor corresponding to the environmental data, and determining an environmental compensation parameter according to the influence factor; wherein the influence factor is used for representing a parameter of the influence degree of the environmental data on the output of the wind meter; and obtaining a target compensation coefficient based on the environment compensation coefficient and the initial compensation coefficient, and calibrating the wind meter according to the target compensation coefficient. The problem that the calibration accuracy is low in the calibration process of the wind meter in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of meteorological measurement technology, and in particular to a calibration method and device for an anemometer, a storage medium, and an electronic device. Background Art

[0002] In the field of meteorological measurement technology, especially when it comes to wind speed and direction measurement, anemometers play a vital role. Anemometers, such as ultrasonic anemometers, use the principle that the speed of ultrasonic waves in the air is affected by wind speed to measure. Their accuracy is of great significance to many fields such as weather forecasting, wind farm operations, aviation safety, environmental monitoring and scientific research.

[0003] With the advancement of science and technology and the increasing demand for meteorological observation, the accuracy and reliability of anemometers have become the focus of the industry. In actual use, the working environment of anemometers is complex and changeable. Slight changes in environmental factors such as temperature, humidity and air pressure may affect the measurement results of wind speed and direction. These factors will not only lead to deviations in measurement data, but also increase the maintenance cost of the equipment and reduce its service life.

[0004] At present, most of the calibration methods of anemometers are static calibration under standard environment, that is, the wind parameters measured by the anemometer are compared with the set standard parameters to obtain a set of calibration parameters. The calibration effect of this static calibration method is poor, which affects the measurement accuracy of the anemometer.

[0005] Therefore, how to improve the calibration accuracy during the anemometer calibration process to improve the measurement accuracy of the anemometer has become a problem that needs to be solved urgently. Summary of the invention

[0006] The embodiment of the present application provides a calibration method for an anemometer, so as to at least solve the problem in the related art that the calibration accuracy of the anemometer during calibration is low, which affects the measurement accuracy of the anemometer.

[0007] According to an embodiment of the embodiments of the present application, a calibration method for an anemometer is provided, comprising: acquiring collected environmental data in a wind field environment, and wind data of the wind field environment output by the anemometer; determining an initial compensation coefficient of the anemometer based on comparing the wind data with pre-set standard wind data; determining an influencing factor corresponding to the environmental data, and determining an environmental compensation parameter based on the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; obtaining a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient, and calibrating the anemometer based on the target compensation coefficient.

[0008] In an exemplary embodiment, the wind data includes wind direction data and wind speed data; the initial compensation coefficient of the anemometer is determined based on the comparison of the wind data with pre-set standard wind data, including: comparing the wind data with pre-set standard wind data to determine the wind error; comparing the wind direction data with pre-set standard wind speed data to determine the wind speed error; and weighting based on the wind error and wind weight, and the wind speed error and wind speed weight to obtain the initial compensation coefficient of the anemometer.

[0009] In an exemplary embodiment, determining the influencing factor corresponding to the environmental data includes: inputting the environmental data into a pre-trained neural network model, and determining the influencing factor corresponding to the environmental data based on the neural network model.

[0010] In an exemplary embodiment, the environmental data includes temperature, humidity and pressure; the determining the influencing factors corresponding to the environmental data based on the neural network model includes: calculating the temperature, humidity and wind data based on the neural network model to obtain predicted wind data; comparing the predicted wind data with standard wind data to determine an error; matching the error with preset environmental impact information to determine the influencing factor, wherein the environmental impact information includes multiple preset errors and preset influencing factors corresponding to the multiple preset errors.

[0011] In an exemplary embodiment, the training steps of the neural network model include: obtaining multiple groups of sample environmental data; wherein each group of sample environmental data includes sample temperature, sample humidity and sample pressure; inputting the multiple groups of sample environmental data into the neural network model to be trained, and the neural network model to be trained calculates the corresponding training wind data for each group of sample environmental data; comparing each of the training wind data with the preset sample wind data to determine the error corresponding to each group of sample environmental data; if any error does not meet the set loss condition, adjusting the parameters of the neural network model to be trained according to the error until the preset condition is met, thereby obtaining a trained neural network model.

[0012] In an exemplary embodiment, obtaining a target compensation coefficient based on the environment compensation coefficient and the initial compensation coefficient includes: obtaining a target compensation coefficient based on a sum of the environment compensation coefficient and the initial compensation coefficient.

[0013] According to another embodiment of the embodiment of the present application, a calibration system for an anemometer is also provided, the system comprising a wind source unit, an anemometer, a compensation unit and an environment collection unit; the wind source unit is used to provide a wind source to the anemometer in a wind field environment; the environment collection unit is used to collect environmental data in the wind field environment and send the environmental data to the compensation unit; the anemometer receives the wind source of the wind field environment and outputs the wind force data of the wind field environment to the compensation unit; the compensation unit is used to obtain the environmental data in the wind field environment output by the environment collection unit and the wind force data of the wind field environment output by the anemometer; based on the comparison between the wind force data and pre-set standard wind force data, an initial compensation coefficient of the anemometer is determined; an influencing factor corresponding to the environmental data is determined, and an environmental compensation parameter is determined according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; based on the environmental compensation coefficient and the initial compensation coefficient, a target compensation coefficient is obtained, and the anemometer is calibrated according to the target compensation coefficient.

[0014] According to another embodiment of the embodiment of the present application, a calibration device for an anemometer is also provided, including: an acquisition module, used to acquire collected environmental data in a wind field environment, and wind data of the wind field environment output by the anemometer; a compensation module, used to determine the initial compensation coefficient of the anemometer based on the comparison between the wind data and pre-set standard wind data; the compensation module is also used to determine the influencing factor corresponding to the environmental data, and determine the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; the compensation module is also used to obtain a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient, and calibrate the anemometer according to the target compensation coefficient.

[0015] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0016] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0017] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0018] In the embodiment of the present application, environmental data collected in the wind field environment and wind data of the wind field environment output by the anemometer are obtained; the initial compensation coefficient of the anemometer is determined based on the comparison between the wind data and the pre-set standard wind data; the influence factor corresponding to the environmental data is determined, and the environmental compensation parameter is determined according to the influence factor; wherein the influence factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; based on the environmental compensation coefficient and the initial compensation coefficient, the target compensation coefficient is obtained, and the anemometer is calibrated according to the target compensation coefficient. In the calibration process of the anemometer, the target calibration coefficient is obtained by combining the static calibration parameters and the environmental compensation coefficient generated by the environmental data, thereby improving the calibration accuracy of the anemometer, thereby ensuring that the anemometer can provide accurate and reliable wind data under various environmental conditions, which is crucial for data accuracy in the fields of meteorological observation and wind energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for calibrating an anemometer according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for calibrating an anemometer according to an embodiment of the present application;

[0022] Figure 3 is a structural block diagram of a calibration system for an anemometer according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of a calibration device for an anemometer according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure diagram of a method for calibrating an anemometer according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for calibrating an anemometer is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flow chart of a method for calibrating an anemometer according to an embodiment of the present application, the flow chart comprising the following steps:

[0030] Step S202, acquiring the collected environmental data in the wind farm environment and the wind force data of the wind farm environment output by the anemometer.

[0031] It is understandable that the environmental data may be data obtained by the environmental acquisition unit under the wind farm environment, and the environmental data may include temperature, humidity, and air pressure. The environmental acquisition unit may be integrated with a temperature and humidity sensor and an air pressure sensor, which are designed to monitor and record key meteorological parameters in the wind farm environment where the anemometer is located in real time, including air temperature, air humidity, and air pressure. These parameters have a direct or indirect impact on the measurement of wind speed and wind direction.

[0032] While acquiring environmental data, the anemometer can also output wind data in the wind field environment, such as wind speed and wind direction data, through its own measurement principle (such as ultrasonic method). These data reflect the measurement results of the anemometer under the current environmental conditions and are key data points that need to be verified and corrected during the calibration process. Wind data refers to the wind information actually measured by the anemometer, including wind direction data and wind speed data. Wind direction data indicates the angle of wind direction, while wind speed data indicates the size of wind speed. In the static calibration stage, these data are provided to the anemometer by the standard wind speed source device and the standard wind direction device in a standard windless environment. The purpose is to compare the measurement performance of the anemometer under standard conditions.

[0033] It should be noted that the wind data output by the anemometer may be a wind source generated by a wind source unit, which includes a standard wind speed source device (such as a wind turbine with adjustable wind speed or a wind speed simulator) and a standard wind direction device. The standard wind speed source device can generate a standard wind speed, and the standard wind direction device can set a standard wind direction.

[0034] Step S204, determining an initial compensation coefficient of the anemometer based on comparing the wind data with pre-set standard wind data;

[0035] Among them, when determining the initial compensation coefficient of the anemometer, the standard wind force data is the standard wind speed set by the standard wind speed source device, and the standard wind force set by the standard wind direction device. The standard wind source mechanism generates known wind speed and wind direction (i.e., standard wind force data), such as a standard wind speed source device (such as a wind turbine with adjustable wind speed or a wind speed simulator) generates a standard wind speed, and a standard wind direction device (such as a wind vane with precise angle scale) sets a standard wind direction. The data acquisition module collects and records the wind speed and wind direction data output by the anemometer in real time, and records the standard wind force data of the standard wind source mechanism at the same time. The data processing and analysis module compares the output data of the anemometer with the data of the standard wind source mechanism through a static calibration algorithm, and calculates the zero point offset of the anemometer, that is, the difference between the output data of the anemometer and the actual standard data under the conditions of standard wind speed and wind direction.

[0036] In an exemplary embodiment, the wind data includes wind direction data and wind speed data; the initial compensation coefficient of the anemometer is determined based on the comparison of the wind data with pre-set standard wind data, including: comparing the wind data with pre-set standard wind data to determine the wind error; comparing the wind direction data with pre-set standard wind speed data to determine the wind speed error; and weighting based on the wind error and wind weight, and the wind speed error and wind speed weight to obtain the initial compensation coefficient of the anemometer.

[0037] Specifically, the wind data actually measured by the anemometer can be compared with the standard wind data, and the wind error, that is, the difference between the measured value and the standard value, can be calculated. This error can be expressed in the form of an absolute value or as a percentage relative to the standard value, depending on the requirements and standards in the calibration process. During the comparison process, the wind direction data and wind speed data are compared respectively to determine the wind direction error and wind speed error. The wind direction error refers to the difference between the wind direction angle measured by the anemometer and the standard wind direction angle, while the wind speed error refers to the difference between the wind speed value measured by the anemometer and the standard wind speed value. After obtaining the wind force error and wind speed error, a weighted calculation is performed based on the error and the corresponding weight to obtain the initial compensation coefficient of the anemometer. The weight here reflects the importance of wind direction and wind speed in the overall wind measurement. Different application scenarios may have different requirements for the accuracy of wind direction and wind speed. Therefore, when calculating the compensation coefficient, different weights can be given to wind direction and wind speed. For example, in some cases, the accuracy requirement for wind speed may be higher than that for wind direction. In this case, during weighted calculation, the weight of wind speed error may be higher than that of wind direction error.

[0038] In the above embodiment, the calculated initial compensation coefficient will be used in the subsequent dynamic calibration to compensate for the wind data deviation by adjusting the output data of the anemometer, thereby improving the measurement accuracy and data reliability of the anemometer.

[0039] Step S206, determining the influencing factor corresponding to the environmental data, and determining the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer;

[0040] It is understood that environmental data may include temperature, humidity and air pressure data of the working environment of the anemometer; real-time collection of temperature, humidity and air pressure data of the working environment of the anemometer provides a basis for subsequent environmental impact analysis. The impact factor is a parameter used to quantify the degree of influence of environmental data on the output performance of the anemometer.

[0041] In an embodiment of the present application, the influencing factors can be determined by experimental data, physical models or machine learning algorithms. For example, using historical calibration data, a relationship model between environmental data (such as temperature, humidity, air pressure) and the output error of the anemometer can be established to obtain the degree of influence of each environmental parameter on the output of the anemometer. This step is the basis of the dynamic calibration algorithm, which enables the system to predict and adjust the output deviation of the anemometer according to specific environmental conditions. Based on the determined influencing factors, the system further calculates environmental compensation parameters. Environmental compensation parameters are parameters used to correct the measurement results of the anemometer under specific environmental conditions to improve its accuracy.

[0042] In specific implementation, the calculation of environmental compensation parameters may take into account the cross-influence of multiple environmental variables and their nonlinear effects on the measurement results. Possible algorithms include but are not limited to linear regression, polynomial regression, neural network or support vector machine, etc., to ensure the accuracy and robustness of the calibration.

[0043] In an exemplary embodiment, determining the influencing factor corresponding to the environmental data includes: inputting the environmental data into a pre-trained neural network model, and determining the influencing factor corresponding to the environmental data based on the neural network model.

[0044] In an exemplary embodiment, the environmental data includes temperature, humidity and pressure; the determining the influencing factors corresponding to the environmental data based on the neural network model includes: calculating the temperature, humidity and wind data based on the neural network model to obtain predicted wind data; comparing the predicted wind data with standard wind data to determine an error; matching the error with preset environmental impact information to determine the influencing factor, wherein the environmental impact information includes multiple preset errors and preset influencing factors corresponding to the multiple preset errors.

[0045] It is understandable that the environmental data of the anemometer, including temperature, humidity and air pressure, are collected in real time and input into the pre-trained neural network model. These environmental data are key factors affecting the accuracy of wind measurement because they can change the density and sound speed of the air, thereby affecting the measurement results of the ultrasonic anemometer. After receiving the input environmental data, the neural network model predicts the wind data in the current environment through internal calculations and learning. Among them, the predicted wind data is the estimated value of wind speed and wind direction calculated by the model based on the environmental data, which reflects the wind measurement value after considering environmental factors.

[0046] After obtaining the predicted wind data, compare it with the standard wind data provided by the standard wind source agency to determine the error between the two. The "standard wind data" here refers to the actual wind speed and wind direction values ​​obtained by the standard wind speed source device and the standard wind direction device under known environmental conditions (such as a standard laboratory environment), which is used as a reference benchmark. Match the calculated error with the preset environmental impact information. The environmental impact information is a set of data tables containing multiple preset errors and preset impact factors corresponding to these errors. The preset impact factor is a parameter that is derived by fitting a large amount of experimental data or based on the principles of physics and is used to quantify the degree of influence of changes in environmental data on wind measurement results.

[0047] Through the matching process, the environmental impact factor that matches the current error is determined. This step is actually to reverse the error to find the impact factor and find the preset impact factor closest to the current error. This impact factor reflects the degree of influence of the current environmental data change on the anemometer measurement result. Finally, the determined impact factor is used as the environmental compensation parameter to compensate the measurement result of the anemometer, that is, the impact factor is applied to the output data of the anemometer to adjust the measured values ​​of wind speed and wind direction to eliminate or reduce the measurement error caused by environmental factors, thereby realizing dynamic calibration of the anemometer and improving measurement accuracy.

[0048] In the above embodiment, the nonlinearity and adaptive capabilities of the neural network model are utilized to handle the impact of complex environmental data changes on wind measurement. Through the error feedback mechanism, the influencing factors are automatically adjusted to ensure that the measurement results of the anemometer under different environmental conditions can be compensated in a timely and effective manner, thereby improving the overall measurement accuracy and stability.

[0049] In an exemplary embodiment, the training steps of the neural network model include: obtaining multiple groups of sample environmental data; wherein each group of sample environmental data includes sample temperature, sample humidity and sample pressure; inputting the multiple groups of sample environmental data into the neural network model to be trained, and the neural network model to be trained calculates the corresponding training wind data for each group of sample environmental data; comparing each of the training wind data with the preset sample wind data to determine the error corresponding to each group of sample environmental data; if any error does not meet the set loss condition, adjusting the parameters of the neural network model to be trained according to the error until the preset condition is met, thereby obtaining a trained neural network model.

[0050] Specifically, at the beginning of model training, a large number of environmental data samples need to be collected. These data should cover various working environments that the anemometer may encounter. Including but not limited to wind speed and wind direction measurement data under different temperature, humidity and air pressure conditions. The collection of these sample data is usually carried out in the laboratory, by precisely controlling environmental conditions and using standard equipment to generate known wind data. The collected multiple sets of sample environmental data are used as input and input into the neural network model. The parameters of the neural network model at the beginning are randomly initialized, and it will try to predict the wind data based on the input environmental data.

[0051] The model will process each set of sample environmental data according to the current parameter settings and output the predicted wind data, i.e., the training wind data. The training wind data output by the neural network model will be compared with the preset sample wind data, where the sample wind data is the known accurate wind data generated under experimental conditions. The difference between the two is called the error, which is usually quantified using a loss function. Common loss functions include mean square error (MSE), cross entropy loss, etc. If the error corresponding to any set of sample environmental data does not meet the set loss condition (i.e., the error is too large), the parameters of the neural network model need to be adjusted through the backpropagation algorithm. The backpropagation algorithm calculates the gradient of the network parameters based on the size and direction of the error, and then updates the network parameters through gradient descent or other optimization algorithms to reduce the error.

[0052] Repeat the above steps, that is, continuously input sample environment data, calculate model output, compare errors, and adjust parameters until the model's prediction error meets the preset loss condition on the overall sample data. The preset condition usually means that the total loss of the model reaches a small threshold, or the update of the model parameters changes very little in multiple consecutive iterations, which indicates that the model has converged.

[0053] When the error between the wind data predicted by the model on multiple sets of sample environmental data and the preset sample wind data meets the set loss condition, the model training is considered complete. At this time, the model parameters are optimized and can accurately calibrate the anemometer's measurement results under various temperature, humidity and air pressure conditions.

[0054] In some embodiments, the specific process of model training can be implemented using various machine learning frameworks, such as TensorFlow, PyTorch, etc., to achieve automated parameter adjustment and training processes by defining model structures, loss functions, optimization algorithms, etc. After training, the neural network model can be integrated into a data processing and analysis module to dynamically calibrate the measurement results of the anemometer in real time or periodically according to environmental changes, thereby improving its measurement accuracy and stability under different environmental conditions.

[0055] Step S208: determining a calibration result of the anemometer based on the marked first temperature image and the marked second temperature image.

[0056] In some exemplary embodiments, obtaining a target compensation coefficient based on the environment compensation coefficient and the initial compensation coefficient includes: obtaining the target compensation coefficient based on a sum of the environment compensation coefficient and the initial compensation coefficient.

[0057] Through the above steps S202-S208, the environmental data collected in the wind field environment and the wind data of the wind field environment output by the anemometer are obtained; the initial compensation coefficient of the anemometer is determined based on the comparison between the wind data and the pre-set standard wind data; the influencing factor corresponding to the environmental data is determined, and the environmental compensation parameter is determined according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; based on the environmental compensation coefficient and the initial compensation coefficient, the target compensation coefficient is obtained, and the anemometer is calibrated according to the target compensation coefficient. In the calibration process of the anemometer, the target calibration coefficient is obtained by combining the static calibration parameters and the environmental compensation coefficient generated by the environmental data, thereby improving the calibration accuracy of the anemometer, thereby ensuring that the anemometer can provide accurate and reliable wind data under various environmental conditions, which is crucial for the data accuracy in the fields of meteorological observation, wind energy utilization, etc.

[0058] In order to better understand the process of the calibration method of the anemometer mentioned above, the calibration method of the anemometer mentioned above is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0059] An anemometer is a device that measures wind speed and direction. For example, the ultrasonic anemometer uses ultrasonic waves to measure wind speed. The principle is that the propagation speed of ultrasonic waves in the air is affected by wind speed. The wind speed and direction are calculated by measuring the propagation time of ultrasonic waves in different directions. When the wind blows, the propagation speed of ultrasonic waves in the downwind direction will increase, while the propagation speed of ultrasonic waves against the wind will slow down. With the continuous development of meteorological observation technology, anemometers are important tools for meteorological observation. Their measurement accuracy and stability are crucial to the accuracy and reliability of meteorological data.

[0060] Based on the existing anemometer calibration technology, during the calibration process of the anemometer, affected by the working environment of the anemometer, such as temperature, humidity and air pressure, the static calibration parameters cannot meet the high-precision calibration requirements. In order to further improve the calibration accuracy of the anemometer, a calibration method for anemometer is proposed. It can compensate for the difference in wind speed and wind direction data caused by changes in temperature, humidity and air pressure in the standard environment state and the current environment state of the anemometer, so as to further improve the calibration accuracy of the anemometer.

[0061] refer to Figure 3 As shown, the calibration method of the anemometer of the present application can be based on Figure 3 The calibration system of the anemometer shown in the figure comprises an anemometer 1, a standard wind source mechanism 2 (i.e., a wind source unit) and a data processing and analysis module 5 (i.e., a compensation unit). The standard wind source mechanism 2 comprises a standard wind speed source device 201 and a standard wind direction device 202, which are used to provide a wind source of standard wind speed and standard wind direction to the anemometer 1 in a windless state.

[0062] The standard wind speed source device 201 may be a wind turbine with adjustable wind speed or a wind speed simulator to generate a wind source with standard wind speed, and the standard wind direction device 202 may be a wind direction disk with precise angle scale to provide standard wind direction.

[0063] The data processing and analysis module 5 is electrically connected to the data acquisition module 3. The data processing and analysis module 5 obtains the real-time standard wind speed and standard wind direction data of the standard wind source mechanism 2 through the data acquisition module 3. The data processing and analysis module 5 collects the wind speed and wind direction data output by the anemometer 1 system in real time through the data acquisition module 3.

[0064] The data processing and analysis module 5 includes a static calibration algorithm 501. The data acquisition module 3 is electrically connected to a pressure sensor 301 and a temperature and humidity sensor 302 (i.e., the sensors of the environment acquisition unit), which are used to collect temperature data, humidity data and pressure data of the wind field environment in real time, and feed them back to the data processing and analysis module 5.

[0065] The static calibration algorithm 501 is used to determine the static calibration parameters by calculating the zero point offset of the anemometer 1 in a standard windless environment by comparing the output data of the anemometer 1 under the working conditions of standard wind speed and standard wind direction with the known standard reference data of the standard wind source mechanism 2.

[0066] The data processing and analysis module 5 also includes a dynamic calibration algorithm 502 and an environmental difference analysis and compensation module 51. The environmental difference analysis and compensation module 51 includes a temperature difference compensation algorithm 511, a humidity difference compensation algorithm 512 and an air pressure difference compensation algorithm 513. The environmental difference analysis and compensation module 51 is used to analyze the difference in wind speed and wind direction data caused by changes in temperature, humidity and air pressure of the anemometer 1 in a windless standard environment and a windless current environment, and obtain relevant calibration compensation coefficients. The dynamic calibration algorithm 502 compensates the static calibration parameters according to the calibration compensation coefficients to obtain dynamic calibration parameters. The air temperature of the above-mentioned standard environment is 25°C and the air humidity is 60% RH. The air pressure of the standard environment is the average air pressure of the anemometer 1 in clear and windless weather at the working altitude.

[0067] The data processing and analysis module 5 is electrically connected to the data burning module 6. The data processing and analysis module 5 modifies the system program parameters of the anemometer 1 through the data burning module 6 according to the dynamic calibration parameters to correct the wind speed and wind direction data output by the anemometer 1 system.

[0068] The intelligent anemometer calibration system further includes a storage module 4, which is used to store the standard wind speed and standard wind direction data collected by the data acquisition module 3 and the system wind speed and wind direction data of the anemometer 1, and is also used to store the processed data of the data processing and analysis module 5. The data processing and analysis module 5 is electrically connected to the client software 7, and the client software 7 is used to provide a user operation interface, so that the user can perform calibration operations, view calibration parameters and calibration results, and set parameters.

[0069] The calibration method of the anemometer is as follows:

[0070] Step 1. In a standard windless environment, a wind source with standard wind speed and standard wind direction is provided to anemometer 1 through standard wind source mechanism 2, and then real-time standard wind speed and standard wind direction data of standard wind source mechanism 2 are obtained through data acquisition module 3, and fed back to data processing and analysis module 5. At the same time, wind speed and wind direction data output by anemometer 1 system are collected in real time through data acquisition module 3, and fed back to data processing and analysis module 5. Then, through static calibration algorithm 501 in data processing and analysis module 5, the output data of anemometer 1 under the working state of standard wind speed and standard wind direction are compared with known standard reference data, the zero point offset of anemometer 1 is calculated, and static calibration parameters are obtained.

[0071] Step 2: In the current windless environment when the anemometer 1 is working, a wind source of standard wind speed and standard wind direction is provided to the anemometer 1 through the standard wind source mechanism 2, and then the real-time standard wind speed and standard wind direction data of the standard wind source mechanism 2 are obtained through the data acquisition module 3, and fed back to the data processing and analysis module 5. At the same time, the wind speed and wind direction data output by the anemometer 1 system are collected in real time through the data acquisition module 3, and fed back to the data processing and analysis module 5. At the same time, the temperature data, humidity data and air pressure data of the current environment are collected in real time through the air pressure sensor 301 and the temperature and humidity sensor 302, and fed back to the data processing and analysis module 5. Then, the temperature difference compensation algorithm 511, the humidity difference compensation algorithm 512 and the air pressure difference compensation algorithm 513 in the environmental difference analysis and compensation module 51 are used to calculate the difference in wind speed and wind direction data caused by changes in temperature, humidity and air pressure of the anemometer 1 in the standard environmental state and the current environmental state, and the relevant calibration compensation coefficient is obtained and fed back to the data processing and analysis module 5.

[0072] Step 3: The dynamic calibration algorithm 502 in the data processing and analysis module 5 performs analysis and calculation based on the static calibration parameters and the calibration compensation coefficient to obtain the dynamic calibration parameters, which are: dynamic calibration parameters = static calibration parameters + calibration compensation coefficient.

[0073] It should be noted that the dynamic calibration parameters, static calibration parameters and calibration compensation coefficients are all one data set.

[0074] Step 4: Feedback the dynamic calibration parameters to the data burning module 6 through the data processing and analysis module 5. Finally, the data burning module 6 is used in combination with the relevant system protocol to modify the system program parameters of the anemometer 1 to correct the wind speed and wind direction data output by the anemometer 1 system.

[0075] In combination with the above-mentioned implementation scheme and implementation principle, the intelligent anemometer calibration system and calibration method, in a standard windless environment, compares the output data of the anemometer 1 under the working state of standard wind speed and standard wind direction with the known standard reference data through the static calibration algorithm 501 in the data processing and analysis module 5, calculates the zero point offset of the anemometer 1, and obtains the static calibration parameters;

[0076] Then, in the current environment without wind when the anemometer 1 is working, the temperature data, humidity data and air pressure data of the current environment are collected in real time through the air pressure sensor 301 and the temperature and humidity sensor 302 in the data acquisition module 3, and then the temperature difference compensation algorithm 511, the humidity difference compensation algorithm 512 and the air pressure difference compensation algorithm 513 in the environmental difference analysis and compensation module 51 are used to calculate the difference in wind speed and wind direction data caused by changes in temperature, humidity and air pressure of the anemometer 1 in the standard environmental state and the current environmental state, and obtain the relevant calibration compensation coefficient;

[0077] Then, the dynamic calibration algorithm 502 in the data processing and analysis module 5 performs analysis and calculation based on the static calibration parameters and the calibration compensation coefficient to obtain the dynamic calibration parameters, which are fed back to the data burning module 6 through the data processing and analysis module 5. Finally, the data burning module 6 is used in combination with the relevant system protocol to modify the system program parameters of the anemometer 1 to correct the wind speed and wind direction data output by the anemometer 1 system. In summary, the difference in wind speed and wind direction data caused by changes in temperature, humidity and air pressure in the standard environment state and the current environment state of the anemometer 1 can be compensated, thereby further improving the calibration accuracy of the anemometer.

[0078] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0079] The present application also provides a structural block diagram of a calibration device for an anemometer. Figure 4 is a structural block diagram of a calibration device for an anemometer according to an embodiment of the present application; Figure 4 As shown, including:

[0080] An acquisition module 42 is used to acquire environmental data collected in a wind farm environment and wind force data of the wind farm environment output by an anemometer;

[0081] A compensation module 44, configured to determine an initial compensation coefficient of the anemometer based on a comparison between the wind data and pre-set standard wind data;

[0082] The compensation module 44 is further used to determine the influencing factor corresponding to the environmental data, and determine the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer;

[0083] The compensation module 44 is further configured to obtain a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient, and calibrate the anemometer according to the target compensation coefficient.

[0084] In an exemplary embodiment, the wind data includes wind direction data and wind speed data; the compensation module 44 is further used to compare the wind data with pre-set standard wind data to determine the wind error; compare the wind direction data with pre-set standard wind speed data to determine the wind speed error; and weight the wind error and wind weight, as well as the wind speed error and wind speed weight, to obtain an initial compensation coefficient of the anemometer.

[0085] In an exemplary embodiment, the compensation module 44 is further configured to input the environmental data into a pre-trained neural network model, and determine the influencing factors corresponding to the environmental data based on the neural network model.

[0086] In an exemplary embodiment, the environmental data includes temperature, humidity and pressure; the compensation module 44 is also used to calculate the wind data of the temperature, humidity and pressure based on the neural network model to obtain predicted wind data; compare the predicted wind data with the standard wind data to determine the error; match the error with preset environmental impact information to determine the impact factor, wherein the environmental impact information includes multiple preset errors and preset impact factors corresponding to the multiple preset errors.

[0087] In an exemplary embodiment, the device also includes a training module; the training module is used to obtain multiple groups of sample environmental data; wherein each group of sample environmental data includes sample temperature, sample humidity and sample pressure; the multiple groups of sample environmental data are input into the neural network model to be trained, and the neural network model to be trained calculates the corresponding training wind data for each group of sample environmental data; each of the training wind data is compared with the preset sample wind data to determine the error corresponding to each group of sample environmental data; if any error does not meet the set loss condition, the parameters of the neural network model to be trained are adjusted according to the error until the preset condition is met, and a trained neural network model is obtained.

[0088] In an exemplary embodiment, the compensation module 44 is further configured to obtain a target compensation coefficient based on the sum of the environmental compensation coefficient and the initial compensation coefficient.

[0089] Through the above device, the environmental data collected in the wind field environment and the wind data of the wind field environment output by the anemometer are obtained; the initial compensation coefficient of the anemometer is determined based on the comparison between the wind data and the pre-set standard wind data; the influence factor corresponding to the environmental data is determined, and the environmental compensation parameter is determined according to the influence factor; wherein the influence factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; based on the environmental compensation coefficient and the initial compensation coefficient, the target compensation coefficient is obtained, and the anemometer is calibrated according to the target compensation coefficient. In the calibration process of the anemometer, the static calibration parameters and the environmental compensation coefficient generated by the environmental data are combined to obtain the target calibration coefficient, which improves the calibration accuracy of the anemometer, thereby ensuring that the anemometer can provide accurate and reliable wind data under various environmental conditions, which is crucial for the accuracy of data in the fields of meteorological observation and wind energy utilization.

[0090] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0091] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0092] S1, acquiring environmental data collected in a wind field environment and wind force data of the wind field environment output by an anemometer;

[0093] S2, determining an initial compensation coefficient of the anemometer based on comparing the wind data with pre-set standard wind data;

[0094] S3, determining the influencing factor corresponding to the environmental data, and determining the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer;

[0095] S4, obtaining a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient, and calibrating the anemometer according to the target compensation coefficient.

[0096] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0097] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0098] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0099] S1, acquiring environmental data collected in a wind field environment and wind force data of the wind field environment output by an anemometer;

[0100] S2, determining an initial compensation coefficient of the anemometer based on comparing the wind data with pre-set standard wind data;

[0101] S3, determining the influencing factor corresponding to the environmental data, and determining the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer;

[0102] S4, obtaining a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient, and calibrating the anemometer according to the target compensation coefficient.

[0103] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0104] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0105] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0106] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0107] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0108] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0109] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for calibrating an anemometer, characterized in that: include: Acquiring environmental data collected in a wind farm environment and wind force data of the wind farm environment output by an anemometer; Determining an initial compensation coefficient of the anemometer based on comparing the wind data with pre-set standard wind data; Determine the influencing factor corresponding to the environmental data, and determine the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; Based on the environmental compensation coefficient and the initial compensation coefficient, a target compensation coefficient is obtained, and the anemometer is calibrated according to the target compensation coefficient.

2. The method according to claim 1, characterized in that: The wind data includes wind direction data and wind speed data; The step of comparing the wind data with pre-set standard wind data to determine the initial compensation coefficient of the anemometer comprises: Comparing the wind force data with pre-set standard wind force data to determine the wind force error; Comparing the wind direction data with pre-set standard wind speed data to determine the wind speed error; The initial compensation coefficient of the anemometer is obtained by weighting based on the wind force error and the wind force weight, and the wind speed error and the wind speed weight.

3. The method according to claim 1, characterized in that The determining the influencing factor corresponding to the environmental data includes: The environmental data is input into a pre-trained neural network model, and based on the neural network model, an influencing factor corresponding to the environmental data is determined.

4. The method according to claim 3, characterized in that The environmental data includes temperature, humidity and pressure; and determining the influencing factors corresponding to the environmental data based on the neural network model includes: Calculating the temperature, the humidity and the wind data based on the neural network model to obtain predicted wind data; Comparing the predicted wind data with the standard wind data to determine an error; The error is matched with preset environmental impact information to determine an impact factor, wherein the environmental impact information includes a plurality of preset errors and preset impact factors corresponding to the plurality of preset errors respectively.

5. The method according to claim 3, characterized in that: The training steps of the neural network model include: Acquire multiple sets of sample environment data; wherein each set of sample environment data includes sample temperature, sample humidity and sample pressure; Inputting the plurality of sets of sample environmental data into a neural network model to be trained, and having the neural network model to be trained calculate the corresponding training wind data for each set of sample environmental data; Compare each of the training wind force data with the preset sample wind force data to determine the error corresponding to each set of sample environmental data; In the event that any error does not satisfy the set loss condition, the parameters of the neural network model to be trained are adjusted according to the error until the preset condition is met, thereby obtaining a trained neural network model.

6. The method according to claim 1, characterized in that The obtaining of a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient includes: A target compensation coefficient is obtained based on the sum of the environmental compensation coefficient and the initial compensation coefficient.

7. A calibration system for an anemometer, the system comprising a wind source unit, an anemometer, a compensation unit and an environment acquisition unit, characterized in that: include: The wind source unit is used to provide a wind source to the anemometer in a wind field environment; The environment collection unit is used to collect environmental data under the wind farm environment and send the environmental data to the compensation unit; The anemometer receives the wind source of the wind farm environment, and outputs the wind force data of the wind farm environment to the compensation unit; The compensation unit is used to obtain the environmental data of the wind farm environment output by the environmental acquisition unit and the wind force data of the wind farm environment output by the anemometer; Based on the comparison between the wind data and pre-set standard wind data, the initial compensation coefficient of the anemometer is determined; the influencing factor corresponding to the environmental data is determined, and the environmental compensation parameter is determined according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; based on the environmental compensation coefficient and the initial compensation coefficient, a target compensation coefficient is obtained, and the anemometer is calibrated according to the target compensation coefficient.

8. A calibration device for an anemometer, characterized in that: include: An acquisition module, used to acquire environmental data collected in a wind farm environment, and wind force data of the wind farm environment output by an anemometer; A compensation module, used for determining an initial compensation coefficient of the anemometer based on comparison between the wind data and pre-set standard wind data; The compensation module is further used to determine the influencing factor corresponding to the environmental data, and determine the environmental compensation parameter according to the influencing factor; wherein the influencing factor is a parameter used to characterize the degree of influence of the environmental data on the output of the anemometer; The compensation module is further used to obtain a target compensation coefficient based on the environmental compensation coefficient and the initial compensation coefficient, and calibrate the anemometer according to the target compensation coefficient.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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