Wind speed calculation method and device, equipment, storage medium and product

Abstract

The invention discloses a wind speed calculation method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the wind speed calculation method comprises the steps: obtaining historical wind speed measurement data; filtering the historical wind speed measurement data, and calculating to obtain a wind speed estimation value at the current moment; a first data matrix corresponding to the wind speed estimation value is projected, a projection wind speed vector is obtained, the projection wind speed vector is obtained after noise and wind speed signals in the first data matrix are separated, and the projection wind speed vector is used for representing the wind speed state at the current moment. According to the invention, the wind speed state at the current moment is accurately calculated.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a wind speed calculation method, device, equipment, storage medium and computer program product. Background Art

[0002] During the installation of wind power projects, the installation of large wind turbine components such as towers and wind turbine blades is greatly affected by the wind speed at the construction site. However, wind speed is affected by air pressure, temperature, clouds, topography, etc., and will fluctuate greatly, change rapidly, and be unstable. It is impossible to obtain accurate wind speed by relying solely on sensor measurements, so timely and accurate wind speed calculation has become an urgent and important issue in actual work.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present application is to provide a wind speed calculation method, device, equipment, storage medium and computer program product, aiming to solve the technical problem in the related art that accurate wind speed cannot be obtained by relying solely on sensor measurement.

[0005] To achieve the above purpose, the present application proposes a wind speed calculation method, which includes:

[0006] Get historical wind speed measurement data;

[0007] Filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment;

[0008] The first data matrix corresponding to the wind speed estimation value is projected to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

[0009] In one embodiment, the historical wind speed measurement data includes a plurality of measurement data matrices, and the step of filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment includes:

[0010] Performing subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix;

[0011] Correcting the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix;

[0012] The modified data matrix is ​​estimated and calculated to obtain an estimated wind speed value at the current moment.

[0013] In one embodiment, the step of performing subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix includes:

[0014] Aligning subspaces of measurement data matrices at adjacent moments by presetting orthogonal rotation matrices to obtain a plurality of first rotation matrices;

[0015] An expected matrix of each of the first rotation matrices is calculated, and Schmidt orthogonalization is performed on the expected matrix to obtain an average rotation matrix.

[0016] In one embodiment, the step of correcting the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix includes:

[0017] The measured data matrix at the current moment is multiplied by the average rotation matrix to obtain a corrected data matrix.

[0018] In one embodiment, after the step of projecting the first data matrix corresponding to the wind speed estimation value to obtain the projected wind speed vector, the step further includes:

[0019] When the wind speed measurement data is acquired at the next time point, the average rotation matrix and the projected wind speed vector are updated in real time based on the wind speed measurement data.

[0020] In one embodiment, the step of updating the average rotation matrix and the projected wind speed vector in real time based on the wind speed measurement data includes:

[0021] Based on the wind speed measurement data and the average rotation matrix, an updated average rotation matrix is ​​calculated, wherein the calculation formula of the updated average rotation matrix is ​​as follows:

[0022]

[0023] in, is the average rotation matrix at the previous time point, d(n+1) is the wind speed measurement data at the current time point, and α is the preset coefficient;

[0024] Calculating the wind speed estimate based on the updated average rotation matrix to obtain an updated wind speed estimate;

[0025] Based on the matrix data corresponding to the updated wind speed estimation value and the wind speed measurement data, an updated projection matrix is ​​calculated, wherein the calculation formula of the updated projection matrix is ​​as follows:

[0026]

[0027] Among them, Pd(n+1) is the updated projection matrix, P(n) is the projection matrix at the previous time point, and d(n+1) is the wind speed measurement data at the current time point;

[0028] The updated projection matrix is ​​multiplied by the wind speed measurement data to obtain an updated projection wind speed vector.

[0029] In addition, to achieve the above purpose, the present application also proposes a wind speed calculation device, the wind speed calculation device comprising:

[0030] An acquisition module is used to obtain historical wind speed measurement data;

[0031] A processing module, used for filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment;

[0032] A projection processing module is used to project the first data matrix corresponding to the wind speed estimation value to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a wind speed calculation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the wind speed calculation method described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the wind speed calculation method described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the wind speed calculation method described above are implemented.

[0036] The present application proposes a wind speed calculation method, device, equipment, storage medium and computer program product. The present application obtains historical wind speed measurement data, and based on the historical wind speed measurement data, calculates the wind speed estimate at the current moment, and the calculated wind speed estimate also contains noise. Then, a first data matrix corresponding to the wind speed estimate is projected to obtain a projected wind speed vector. After the first data matrix corresponding to the wind speed estimate is projected, the noise and wind speed signal in the first data matrix can be separated, so as to obtain the denoised wind speed state at the current moment. Therefore, the measurement error of the wind speed is reduced, and an accurate and timely wind speed calculation process is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the wind speed calculation method of the present application;

[0040] Figure 2 A schematic diagram of a flow chart provided for the second embodiment of the wind speed calculation method of the present application;

[0041] Figure 3 This is a schematic diagram of the overall process involved in the wind speed calculation method of this application;

[0042] Figure 4 This is a schematic diagram of the module structure of the wind speed calculation device according to an embodiment of the present application;

[0043] Figure 5 Schematic diagram of the equipment structure of the hardware operating environment involved in the wind speed calculation method in the embodiment of the present application.

[0044] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0046] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0047] The main solutions of the embodiments of this application are:

[0048] Get historical wind speed measurement data;

[0049] Filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment;

[0050] The first data matrix corresponding to the wind speed estimation value is projected to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

[0051] In the prior art, during the hoisting construction of wind power projects, the hoisting of large wind turbine components such as towers and wind turbine blades is greatly affected by the wind speed at the construction site. When the wind speed is too high, the hoisting operation is very likely to have safety problems, so timely and accurate wind speed prediction has become an urgent and important issue in actual work.

[0052] In the prior art, there are three commonly used methods for processing time series: (1) ARIMA (autoregressive differential moving average model), (2) RNN (recurrent neural network), and (3) LSTM (long short-term memory network).

[0053] In the prior art, the wind speed prediction results obtained by prediction or calculation through models have large errors, for example:

[0054] 1. The ARIMA model attempts to extract the time series patterns hidden behind the data through the autocorrelation and difference of the data, and then use these patterns to predict future data. ARIMA makes predictions through historical data regression, which requires the data to have strong regression and stationarity. When non-stationary time series appear, the error is often relatively large.

[0055] 2. RNN recurrent neural network does not have a hidden state update mechanism, and the hidden state has the characteristic of preserving the essential laws of time series, so RNN can usually only predict time series with relatively simple data changes. For time series with relatively large oscillations, the error will increase.

[0056] 3. LSTM overcomes the shortcomings of RNN to a certain extent. By introducing hidden states, it can model time series with more complex changes. However, neural network methods such as RNN and LSTM have a relatively large amount of computation and are not suitable for use in large-scale field environments with relatively small computing power and relatively low costs.

[0057] In summary, a calculation method with less calculation amount and smaller error is needed.

[0058] The present application proposes a wind speed calculation method, device, equipment, storage medium and computer program product. The present application obtains historical wind speed measurement data, and based on the historical wind speed measurement data, calculates the wind speed estimate at the current moment, and the calculated wind speed estimate also contains noise. Then, a first data matrix corresponding to the wind speed estimate is projected to obtain a projected wind speed vector. After the first data matrix corresponding to the wind speed estimate is projected, the noise and wind speed signal in the first data matrix can be separated, so as to obtain the denoised wind speed state at the current moment. Therefore, the measurement error of the wind speed is reduced, and an accurate and timely wind speed calculation process is realized.

[0059] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a wind speed calculation device, etc. The following takes a wind speed calculation device as an example to illustrate this embodiment and the following embodiments.

[0060] Based on this, the present application embodiment provides a method for calculating wind speed, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the wind speed calculation method of the present application.

[0061] In this embodiment, the wind speed calculation method includes steps S10 to S40:

[0062] Step S10, obtaining historical wind speed measurement data;

[0063] It should be noted that the historical wind speed measurement data may be the wind speed data measured by a sensor at different times when the same wind speed passes, for example, the wind speed data measured when level one or level two wind passes. In order to improve the reference of the measured wind speed data, the historical wind speed measurement data is the wind speed data measured from a historical preset time period to the current time point. For example, if the current time is the Nth moment, the historical wind speed measurement data is d(1), d(2)...d(n).

[0064] It should be noted that the historical wind speed measurement data will be converted into a data matrix during calculation, and the data matrix is ​​defined as follows:

[0065]

[0066] Step S20, filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment;

[0067] It should be noted that the filtering process includes two steps:

[0068] Step 1 is to convert the historical wind speed measurement data into a data matrix, and then calculate the average rotation matrix through the data matrix, and then correct the data matrix through the average rotation matrix to obtain a corrected data matrix to reduce the noise in the data matrix;

[0069] Step 2 is to calculate the projection matrix at the current moment through a filter, that is, the estimated wind speed value, where the true wind speed value is the actual wind speed value, but the true value cannot be obtained directly or indirectly through measurement means. The estimated value of the true value can be obtained by calculating and processing the measured value, and the estimated value is closer to the true value than the measured value. Similarly, for the wind speed calculation problem, the true value of the wind speed cannot be obtained directly or indirectly through measurement means.

[0070] The projection estimate is an estimate of the true value, which is obtained by calculating and processing the measured value and historical data (including historical measured values ​​and historical estimated values). It is called the estimate of the true value, or wind speed estimate for short.

[0071] In a feasible implementation manner, the historical wind speed measurement data includes a plurality of measurement data matrices, and the step S20 of filtering the historical wind speed measurement data to calculate the wind speed estimation value at the current moment includes:

[0072] Performing subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix;

[0073] It should be noted that multiple repeated measurements are performed on the same process (for example, when the wind speed is level one) to obtain multiple measurement data matrices, namely: 1 (n),D 2 (n),...,D t (n), by performing subspace rotation on each measurement data matrix, we get the average rotation matrix The average rotation matrix can suppress the measurement noise to a certain extent.

[0074] Correcting the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix;

[0075] It should be noted that the average rotation matrix is ​​used to correct the data matrix D(n) by the following method: 1 (n) Right multiply by the average rotation matrix: Get the corrected data matrix The corrected data matrix is ​​a new data matrix after suppressing the influence of measurement noise.

[0076] It should be noted that the average rotation matrix modifies the data matrix, suppresses the noise caused by the measurement, and reduces the error.

[0077] The modified data matrix is ​​estimated and calculated to obtain an estimated wind speed value at the current moment.

[0078] It should be noted that the estimation calculation process is the step of calculating the projection matrix. The estimated value filter used here is where the estimated value filter coefficient vector w m It is expressed by the following formula:

[0079]

[0080] Assuming that sampling starts at a certain moment, at the nth moment, a total of n sensor sampling values ​​are obtained, then the maximum value of m can be n. In normal use, m is not limited to n, and can be less than or equal to n. Among them, the relationship between the estimated value filter coefficient vector and the data matrix is ​​as follows:

[0081]

[0082] The estimated value vector is expressed as follows:

[0083] Abbreviated as

[0084] The matrix form of the filter can be written as:

[0085] It should be noted that the method for calculating the projection matrix at the Nth moment may be to use the least squares estimation method to obtain remember in is the sensor measurement vector, abbreviated as d(n), For the matrix The Moore-Penrose inverse (generalized inverse matrix, ), abbreviated as MP inverse, and then the projection matrix corresponding to the wind speed estimate is calculated as:

[0086]

[0087] In a feasible implementation manner, the step of performing subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix includes:

[0088] Aligning subspaces of measurement data matrices at adjacent moments by presetting orthogonal rotation matrices to obtain a plurality of first rotation matrices;

[0089] It should be noted that the preset orthogonal rotation matrix Q(n) is a special orthogonal matrix, which not only maintains the basic properties of the orthogonal matrix, but also changes the direction of the coordinate system through rotation transformation. Among them, Q(n) satisfies the condition of QQT=QTQ=I, where I is the unit matrix.

[0090] It should be noted that, by aligning the subspaces of the data matrices measured at two adjacent moments using a preset orthogonal rotation matrix, the data matrices are rotated in sequence to obtain a plurality of first rotation matrices.

[0091] For example, with D 1 (n) and D 2 (n) as an example, after aligning the subspaces of the two data matrices through Q(n), we get Q 1 (n) and Q 2 (n), we can get D 1 (n) and D 2 The first rotation matrix of (n), and so on, we can get Q 1 (n),Q 2 (n),...,Q t-1 (n).

[0092] An expected matrix of each of the first rotation matrices is calculated, and Schmidt orthogonalization is performed on the expected matrix to obtain an average rotation matrix.

[0093] It should be noted that due to repeated measurements, the Q 1 (n),Q 2 (n),...,Q t-1 (n) Bundle expectations And perform Schmidt orthogonalization to obtain the average rotation matrix, denoted as Represents the average rotation matrix of repeated measurements. This matrix is ​​an unbiased estimate of the rotation matrix and can be approximated by repeated measurements. The measurement noise is suppressed to a certain extent, reducing the inaccuracy of the system's multiple measurements of the same process. The rotation matrix is ​​an inherent property of the system, and its changes are relatively small. It does not change significantly due to changes in the number of measurements or specific measurements. Its update is slightly corrected with new measurements.

[0094] In a feasible implementation manner, the step of correcting the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix includes:

[0095] The measured data matrix at the current moment is multiplied by the average rotation matrix to obtain a corrected data matrix.

[0096] It should be noted that the average rotation matrix is ​​used to correct the data matrix D(n) by the following method: 1 (n) Right multiply by the average rotation matrix: Get the corrected data matrix The corrected data matrix is ​​a new data matrix after suppressing the influence of measurement noise.

[0097] Step S30, projecting the first data matrix corresponding to the wind speed estimation value to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

[0098] It should be noted that the first data matrix corresponding to the wind speed estimation value is the projection matrix, which is expressed as P(n). Project d(n) to get the projected wind speed vector At this time, P(n) is the projection matrix of the measurement data matrix D, and its physical meaning is: the sensor estimation value vector It is the projection of the sensor measurement value vector d on the subspace spanned by the measurement data matrix D.

[0099] It should be noted that the purpose of projecting the matrix to obtain the projection vector is to remove the noise in the matrix data by projection. The specific method can be: the collected signal containing noise, denoted as A, is decomposed into the signal subspace and the noise subspace by projection, and the representation of the signal subspace is obtained: signal; and the representation of the noise subspace is obtained: noise. Then the representation of the signal subspace is taken, which is considered to be a more accurate representation of A after noise reduction. The obtained projected wind speed vector is converted into a data format to obtain the current accurate wind speed state, which improves the accuracy of the wind speed calculation result compared to other measured values.

[0100] The present application proposes a wind speed calculation method, device, equipment, storage medium and computer program product. The present application obtains historical wind speed measurement data, and based on the historical wind speed measurement data, calculates the wind speed estimate at the current moment, and the calculated wind speed estimate also contains noise. Then, a first data matrix corresponding to the wind speed estimate is projected to obtain a projected wind speed vector. After the first data matrix corresponding to the wind speed estimate is projected, the noise and wind speed signal in the first data matrix can be separated, so as to obtain the denoised wind speed state at the current moment. Therefore, the measurement error of the wind speed is reduced, and an accurate and timely wind speed calculation process is realized.

[0101] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , after the step S30 of projecting the first data matrix corresponding to the wind speed estimation value to obtain the projected wind speed vector, the method further includes:

[0102] Step S40: When wind speed measurement data is acquired at the next time point, the average rotation matrix and the projected wind speed vector are updated in real time based on the wind speed measurement data.

[0103] It should be noted that in the first embodiment, the wind speed state at the nth moment is calculated. At the next moment, that is, the n+1th moment, the sensor measurement value vector is d(n+1). The wind speed measurement data calculated by the sensor is used to update the average rotation matrix and the projected wind speed vector, thereby realizing dynamic update of the wind speed calculation results.

[0104] It should be noted that after the wind speed forecast for the current moment is completed, the functional matrices involved in the forecast are dynamically updated, including the average rotation matrix and the projection matrix, so that the forecast is dynamically updated according to the time series, reducing the error of the fixed parameters for the forecast.

[0105] In a feasible implementation manner, the step S40 of updating the average rotation matrix and the projected wind speed vector in real time based on the wind speed measurement data includes:

[0106] Based on the wind speed measurement data and the average rotation matrix, an updated average rotation matrix is ​​calculated, wherein the calculation formula of the updated average rotation matrix is ​​as follows:

[0107]

[0108] in, is the average rotation matrix at the previous time point, d(n+1) is the wind speed measurement data at the current time point, and α is the preset coefficient;

[0109] It should be noted that at the new moment, i.e. the n+1th moment, the sensor measurement value vector is:

[0110] Abbreviated as d(n+1)

[0111] So The update is:

[0112] in Represents the orthogonal projection matrix through time n+1 Project d(n+1). Orthogonal projection matrix at time n+1 By calculating P D,d(n+1) Via IP D,d(n+1) Get, where P D,d(n+1) =P(n)+P d(n+1) , P d(n+1) is the newly introduced partial projection matrix. This part of the projection matrix cannot be linearly represented by the original projection matrix, and this part is orthogonal to P(n), so the matrix product is a zero matrix, that is, P(n)P d(n+1) =0.

[0113] Calculating the wind speed estimate based on the updated average rotation matrix to obtain an updated wind speed estimate;

[0114] It should be noted that after the average rotation matrix is ​​updated, the data matrix D(n+1) at the n+1th moment is corrected using the average rotation matrix to obtain a corrected data matrix, that is, an updated wind speed estimate.

[0115] Based on the matrix data corresponding to the updated wind speed estimation value and the wind speed measurement data, an updated projection matrix is ​​calculated, wherein the calculation formula of the updated projection matrix is ​​as follows:

[0116]

[0117] Among them, P d(n+1) is the updated projection matrix, P(n) is the projection matrix at the previous time point, d(n+1) is the wind speed measurement data at the current time point, and the inverted T is expressed as the orthogonal matrix of P(n).

[0118] It should be noted that at the new moment, i.e. the n+1th moment, the sensor measurement value vector is d(n+1), then d(n+1) and D form a new data space, and the projection matrix of this data space is P D,d(n+1) =P(n)+P d(n+1) , where P(n) is the projection matrix of the original data space. Since the introduction of the new sensor measurement value vector d(n+1) brings changes to the original data space, the projection matrix needs to be changed accordingly. Except for the part that can be linearly represented by the original projection matrix, the remaining part cannot be linearly represented by the original projection matrix, which is recorded as P d(n+1) , which is orthogonal to P(n), so the matrix product is a zero matrix, that is, P(n)P d(n+1) =0.

[0119] It should be noted that in the above calculation formula, (P ⊥ (n)d(n+1)) T P ⊥(n)d(n+1) is the inner product of the vector. This formula is the update of the original projection matrix P(n) after introducing the new sensor measurement value vector d(n+1), that is, the projection matrix after including the new information d(n+1). ⊥ (n) is the orthogonal projection matrix, obtained from IP(n). I is the unit matrix, which introduces the new information d(n+1), so that the projection matrix at the n+1th moment contains the new information, can keep up with the changes of the new data, and take into account the previous m historical data.

[0120] The updated projection matrix is ​​multiplied by the wind speed measurement data to obtain an updated projection wind speed vector.

[0121] It should be noted that, according to the updated projection matrix and orthogonal projection matrix obtained above, the sensor estimation value vector at the n+1th moment, that is, the calculation of the wind speed projection vector can be obtained as follows:

[0122]

[0123] Among them, P D,d(n+1) is the updated projection matrix, d(n+1) is the sensor measurement data at the n+1th moment, and after the calculation is completed, the wind speed state calculated by the sensor at the n+1th moment can be obtained.

[0124] In this embodiment, after the wind speed prediction at the current moment is completed, the function matrix involved in the prediction is dynamically updated so that the prediction is dynamically updated according to the time series, thereby reducing the error of the fixed parameters for the prediction.

[0125] For example, to help understand the implementation process of the wind speed calculation method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flow chart of a wind speed calculation method is provided, specifically:

[0126] By obtaining historical wind speed measurement data, performing subspace rotation on the data matrix corresponding to the historical wind speed measurement data, obtaining the average rotation matrix, and then correcting the data matrix through the average rotation matrix to obtain the wind speed estimate. At this time, the wind speed estimate is initially de-noised, and then the projection matrix corresponding to the wind speed estimate is projected to separate the noise and signal subspaces. The noise data in the signal is removed and the projected wind speed vector is obtained, so that the wind speed state at the current moment can be determined.

[0127] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the wind speed calculation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0128] This application also provides a wind speed calculation device, please refer to Figure 4 , the wind speed calculation device comprises:

[0129] An acquisition module 10 is used to acquire historical wind speed measurement data;

[0130] The processing module 20 is used to filter the historical wind speed measurement data and calculate the wind speed estimation value at the current moment;

[0131] The projection processing module 30 is used to project the first data matrix corresponding to the wind speed estimation value to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

[0132] Optionally, the processing module includes:

[0133] A first processing unit, configured to perform subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix;

[0134] A second processing unit is used to correct the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix;

[0135] The first calculation unit is used to perform estimation calculation on the corrected data matrix to obtain the estimated wind speed value at the current moment.

[0136] Optionally, the first processing unit includes:

[0137] A processing subunit, configured to align subspaces of measurement data matrices at adjacent moments by using a preset orthogonal rotation matrix to obtain a plurality of first rotation matrices;

[0138] The first calculation subunit is used to calculate the expected matrix of each of the first rotation matrices, and perform Schmidt orthogonalization on the expected matrix to obtain an average rotation matrix.

[0139] Optionally, the second processing unit includes:

[0140] The second calculation subunit is used to multiply the measurement data matrix at the current moment by the average rotation matrix to obtain a corrected data matrix.

[0141] Optionally, the device further comprises:

[0142] The updating module is used to update the average rotation matrix and the projected wind speed vector in real time based on the wind speed measurement data when the wind speed measurement data is acquired at the next time point.

[0143] Optionally, the update module includes:

[0144] The second calculation unit is used to calculate an updated average rotation matrix based on the wind speed measurement data and the average rotation matrix, wherein the calculation formula of the updated average rotation matrix is ​​as follows:

[0145]

[0146] in, is the average rotation matrix at the previous time point, d(n+1) is the wind speed measurement data at the current time point, and α is the preset coefficient;

[0147] A third calculation unit, configured to calculate the wind speed estimation value based on the updated average rotation matrix to obtain an updated wind speed estimation value;

[0148] The fourth calculation unit is used to calculate an updated projection matrix based on the matrix data corresponding to the updated wind speed estimation value and the wind speed measurement data, wherein the calculation formula of the updated projection matrix is ​​as follows:

[0149]

[0150] Among them, P d(n+1) is the updated projection matrix, P(n) is the projection matrix at the previous time point, and d(n+1) is the wind speed measurement data at the current time point;

[0151] A fifth calculation unit is used to multiply the updated projection matrix by the wind speed measurement data to obtain an updated projection wind speed vector.

[0152] The wind speed calculation device provided by the present application adopts the wind speed calculation method in the above embodiment, which can solve the technical problem of wind speed calculation. Compared with the prior art, the beneficial effects of the wind speed calculation device provided by the present application are the same as the beneficial effects of the wind speed calculation method provided by the above embodiment, and other technical features in the wind speed calculation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0153] The present application provides a wind speed calculation device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the wind speed calculation method in the above-mentioned embodiment 1.

[0154] Reference below Figure 5, which shows a schematic diagram of the structure of a wind speed calculation device suitable for implementing the embodiment of the present application. The wind speed calculation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The wind speed calculation device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0155] like Figure 5 As shown, the wind speed calculation device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the wind speed calculation device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the wind speed calculation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a wind speed calculation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0156] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0157] The wind speed calculation device provided by the present application adopts the wind speed calculation method in the above embodiment to solve the technical problem of wind speed calculation. Compared with the prior art, the beneficial effects of the wind speed calculation device provided by the present application are the same as the beneficial effects of the wind speed calculation method provided by the above embodiment, and other technical features in the wind speed calculation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0158] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0159] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0160] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the wind speed calculation method in the above-mentioned embodiment.

[0161] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0162] The computer-readable storage medium may be included in the wind speed calculation device; or may exist independently without being assembled into the wind speed calculation device.

[0163] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the wind speed calculation device, the wind speed calculation device:

[0164] Get historical wind speed measurement data;

[0165] Filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment;

[0166] The first data matrix corresponding to the wind speed estimation value is projected to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

[0167] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0168] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0169] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0170] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned wind speed calculation method, and can solve the technical problem of wind speed calculation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the wind speed calculation method provided in the above-mentioned embodiment, and will not be described in detail here.

[0171] The present application also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the wind speed calculation method as described above are implemented.

[0172] The computer program product provided in this application can solve the technical problem of wind speed calculation. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the wind speed calculation method provided in the above embodiment, which will not be repeated here.

[0173] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for calculating wind speed, characterized in that: The method comprises: Get historical wind speed measurement data; Filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment; The first data matrix corresponding to the wind speed estimation value is projected to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

2. The method according to claim 1, characterized in that The historical wind speed measurement data includes a plurality of measurement data matrices, and the step of filtering the historical wind speed measurement data to calculate the wind speed estimation value at the current moment includes: Performing subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix; Correcting the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix; The modified data matrix is ​​estimated and calculated to obtain an estimated wind speed value at the current moment.

3. The method according to claim 2, characterized in that The step of performing subspace rotation processing on each of the measurement data matrices to obtain an average rotation matrix comprises: Aligning subspaces of measurement data matrices at adjacent moments by presetting orthogonal rotation matrices to obtain a plurality of first rotation matrices; An expected matrix of each of the first rotation matrices is calculated, and Schmidt orthogonalization is performed on the expected matrix to obtain an average rotation matrix.

4. The method according to claim 2, characterized in that The step of correcting the measurement data matrix at the current moment by using the average rotation matrix to obtain a corrected data matrix includes: The measured data matrix at the current moment is multiplied by the average rotation matrix to obtain a corrected data matrix.

5. The method according to claim 1, characterized in that After the step of projecting the first data matrix corresponding to the wind speed estimation value to obtain a projected wind speed vector, the method further includes: When the wind speed measurement data is acquired at the next time point, the average rotation matrix and the projected wind speed vector are updated in real time based on the wind speed measurement data.

6. The method according to claim 5, characterized in that The step of updating the average rotation matrix and the projected wind speed vector in real time based on the wind speed measurement data comprises: Based on the wind speed measurement data and the average rotation matrix, an updated average rotation matrix is ​​calculated, wherein the calculation formula of the updated average rotation matrix is ​​as follows: in, is the average rotation matrix at the previous time point, d(n+1) is the wind speed measurement data at the current time point, and α is the preset coefficient; Calculating the wind speed estimate based on the updated average rotation matrix to obtain an updated wind speed estimate; Based on the matrix data corresponding to the updated wind speed estimation value and the wind speed measurement data, an updated projection matrix is ​​calculated, wherein the calculation formula of the updated projection matrix is ​​as follows: Among them, P d(n+1) is the updated projection matrix, P(n) is the projection matrix at the previous time point, and d(n+1) is the wind speed measurement data at the current time point; The updated projection matrix is ​​multiplied by the wind speed measurement data to obtain an updated projection wind speed vector.

7. A wind speed calculation device, characterized in that: The wind speed calculation device comprises: An acquisition module is used to obtain historical wind speed measurement data; A processing module, used for filtering the historical wind speed measurement data to calculate the wind speed estimate at the current moment; A projection processing module is used to project the first data matrix corresponding to the wind speed estimation value to obtain a projected wind speed vector, wherein the projected wind speed vector is obtained after separating the noise and wind speed signal in the first data matrix, and the projected wind speed vector is used to characterize the wind speed state at the current moment.

8. A wind speed calculation device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the wind speed calculation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the wind speed calculation method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the wind speed calculation method according to any one of claims 1 to 6 are implemented.

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