Sodar system for wind resource exploration

Abstract

The application discloses a sound radar system for wind resource detection and relates to the technical field of radar systems.A index generation module acquires environmental parameters of a wind resource detection area in real time during operation of the sound radar system, generates an environmental index after preprocessing the environmental parameters, and analyzes a plurality of operation parameters of the sound radar system based on a linear regression analysis model to output a device index.A transmission frequency dynamic adjustment module substitutes the environmental index and the device index into a fusion model, the fusion model outputs a correction coefficient, and the current sound wave pulse transmission frequency is dynamically adjusted according to the correction coefficient.The sound radar system can dynamically adjust the sound wave pulse transmission frequency in combination with the actual environmental parameters and the state of the sound radar system during operation of the sound radar system, thereby guaranteeing stable operation of the sound radar system and improving the accuracy of wind speed and wind direction detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar systems, in particular to a SODAR system for wind resource detection. BACKGROUND

[0002] A SODAR (Sonic-Detection-and-Ranging) system is a technology for detecting meteorological parameters such as wind speed, wind direction and turbulence in the atmosphere using sound waves. The SODAR system transmits sound wave pulses into the atmosphere. When the sound waves encounter inhomogeneities or turbulence in the atmosphere, scattering occurs. The system receiver receives the sound wave signals reflected from these inhomogeneities. Based on the frequency shift (Doppler effect) of the received signals, the wind speed and direction can be calculated.

[0003] The prior art has the following disadvantages:

[0004] Existing SODAR systems usually use a fixed sound wave pulse transmission frequency for wind resource detection. In actual operation, when the transmission frequency is too high, the workload and operating cost of the SODAR system will increase, which may cause the SODAR system to malfunction. When the transmission frequency is too low, the detection resolution of the sound wave pulses for small-scale turbulence and wind field changes will be low, so that subtle changes in wind speed and direction cannot be accurately captured, affecting the accurate calculation of wind speed.

[0005] Therefore, the present application proposes a SODAR system for wind resource detection, which can dynamically adjust the sound wave pulse transmission frequency during the operation of the SODAR system in combination with the state of the SODAR system itself and actual environmental parameters, ensuring stable operation of the SODAR system while improving the accuracy of wind speed and direction detection. SUMMARY

[0006] The purpose of the present application is to provide a SODAR system for wind resource detection to solve the problems in the background art.

[0007] To achieve the above purpose, the present application provides the following technical solution: a SODAR system for wind resource detection, comprising a control strategy generation module, an index generation module and a transmission frequency dynamic adjustment module.

[0008] The control strategy generation module: before the SODAR system is used, automatically collects multiple operating parameters of itself, analyzes the multiple test operating parameters based on a linear regression analysis model, judges whether to support operation, and generates a corresponding control strategy according to the judgment result.

[0009] Index generation module: During the operation of the acoustic radar system, environmental parameters of the wind resource detection area are acquired in real time. After preprocessing the environmental parameters, an environmental index is generated. In addition, multiple operating parameters of the system are collected in real time and analyzed based on a linear regression analysis model to output the equipment index.

[0010] Dynamic frequency adjustment module: The environmental index and equipment index are substituted into the fusion model. The fusion model outputs correction coefficients. The current acoustic pulse emission frequency is dynamically adjusted according to the correction coefficients. Wind resources are detected based on the adjusted acoustic pulse emission frequency. The detection results are sent to the management platform.

[0011] In a preferred embodiment, a trial run is conducted before the acoustic radar system is used. The control strategy generation module automatically collects multiple operating parameters of itself, including the high-frequency signal free space path loss coefficient, the signal-to-noise ratio, and the receiver sensitivity degradation rate.

[0012] In a preferred embodiment, the control strategy generation module substitutes the high-frequency signal free space path loss coefficient, signal-to-noise ratio, and receiver sensitivity degradation rate into a linear regression analysis model, the model expression of which is:

[0013] In the formula, For equipment index, Signal-to-noise ratio (SNR) This represents the free space path loss coefficient for high-frequency signals. The rate of decrease in receiver sensitivity. , , Let be the regression coefficient, and , , All are greater than 0;

[0014] The acquired device index is compared with a preset anomaly threshold. The anomaly threshold is used to determine whether the acoustic radar system supports operation. If the device index is less than or equal to the anomaly threshold, the acoustic radar system is determined to support operation. If the device index is greater than the anomaly threshold, the acoustic radar system is determined to not support operation.

[0015] In a preferred embodiment, during the operation of the acoustic radar system, the index generation module acquires environmental parameters of the wind resource detection area in real time, including the critical wind speed rate cumulative coefficient and the wind direction fluctuation frequency.

[0016] In a preferred embodiment, the index generation module normalizes the critical wind speed rate accumulation coefficient and the wind direction fluctuation frequency, mapping the value range of the critical wind speed rate accumulation coefficient and the wind direction fluctuation frequency to the range of [0,1], obtaining the wind speed accumulation normalized value and the wind direction fluctuation normalized value, and summing the wind speed accumulation normalized value and the wind direction fluctuation normalized value to obtain the environmental index.

[0017] In a preferred embodiment, the dynamic frequency adjustment module inputs environmental and equipment indices into a fusion model, which outputs correction coefficients. The model expression is as follows: In the formula, For correction factors, For equipment index, For environmental indices, 、 These are the proportional coefficients for the equipment index and the environmental index, respectively. , All are greater than 0.

[0018] In a preferred embodiment, the transmission frequency dynamic adjustment module compares the correction coefficient with a preset coefficient threshold. If the coefficient threshold is used to determine whether the current sound pulse transmission frequency needs to be adjusted, if the correction coefficient is less than or equal to the coefficient threshold, it is determined that the current sound pulse transmission frequency does not need to be adjusted, and if the correction coefficient is greater than the coefficient threshold, it is determined that the current sound pulse transmission frequency needs to be adjusted.

[0019] When it is determined that the current sound pulse emission frequency needs to be adjusted, the current sound pulse emission frequency is dynamically adjusted according to the correction coefficient. The adjustment algorithm is as follows: In the formula, The adjusted sound wave pulse emission frequency, This is the current sound wave pulse emission frequency. This is the correction factor.

[0020] In a preferred embodiment, the logic for obtaining the critical wind speed rate accumulation coefficient is as follows: when the real-time wind speed is lower than a preset wind speed threshold, the wind speed threshold is subtracted from the real-time wind speed to obtain the wind speed difference; when the wind speed difference is less than the wind speed difference threshold, the monitoring time period is divided into multiple sub-time periods, the wind speed increase rate of each sub-time period is calculated, and the wind speed increase rates of all sub-time periods are accumulated to obtain the critical wind speed rate accumulation coefficient, expressed as: In the formula, This is the cumulative coefficient for critical wind speed. Number of sub-time periods It is the first Wind speed at the end of each time period It is the first Wind speed at the start of the time period.

[0021] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0022] 1. This invention utilizes an index generation module to acquire environmental parameters of the wind resource detection area in real time during the operation of the acoustic radar system. After preprocessing the environmental parameters, an environmental index is generated. Simultaneously, multiple operating parameters of the system are collected in real time and analyzed using a linear regression analysis model to output an equipment index. A dynamic frequency adjustment module integrates the environmental index and the equipment index into a fusion model, which outputs correction coefficients. Based on these correction coefficients, the current acoustic pulse transmission frequency is dynamically adjusted. This acoustic radar system can dynamically adjust the acoustic pulse transmission frequency by combining the system's own state and actual environmental parameters during operation, ensuring stable operation while improving the accuracy of wind speed and direction detection.

[0023] 2. This invention uses a control strategy generation module to conduct trial runs of the acoustic radar system before its use. It automatically collects multiple operating parameters of the system, analyzes these parameters based on a linear regression analysis model, determines whether the system supports operation, and generates corresponding control strategies based on the results, thereby further ensuring the stability of the acoustic radar system. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0025] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1: Please refer to Figure 1 As shown, the acoustic radar system for wind resource detection described in this embodiment includes a control strategy generation module, an index generation module, and a transmission frequency dynamic adjustment module.

[0028] Control strategy generation module: Before the acoustic radar system is used, a trial run is conducted. It automatically collects multiple operating parameters of itself, analyzes the multiple trial run parameters based on a linear regression analysis model, determines whether it supports operation, and generates a corresponding control strategy based on the judgment result. The control strategy is then sent to the index generation module.

[0029] Index generation module: During the operation of the acoustic radar system, environmental parameters of the wind resource detection area are acquired in real time. After preprocessing the environmental parameters, an environmental index is generated. In addition, multiple operating parameters of the system are collected in real time and analyzed based on a linear regression analysis model. The system outputs the equipment index, environmental index and equipment index and sends them to the transmission frequency dynamic adjustment module.

[0030] Dynamic frequency adjustment module: The environmental index and equipment index are substituted into the fusion model. The fusion model outputs correction coefficients. The current acoustic pulse emission frequency is dynamically adjusted according to the correction coefficients. Wind resources are detected based on the adjusted acoustic pulse emission frequency. The detection results are sent to the management platform.

[0031] This application utilizes an index generation module to acquire environmental parameters of the wind resource detection area in real time during the operation of the acoustic radar system. After preprocessing the environmental parameters, an environmental index is generated. Simultaneously, multiple operating parameters of the system are collected in real time and analyzed using a linear regression analysis model to output an equipment index. A dynamic frequency adjustment module integrates the environmental index and the equipment index into a fusion model, which outputs correction coefficients. Based on these correction coefficients, the current acoustic pulse transmission frequency is dynamically adjusted. This acoustic radar system can dynamically adjust the acoustic pulse transmission frequency during operation, combining the system's own state and actual environmental parameters, ensuring stable operation while improving the accuracy of wind speed and direction detection.

[0032] This application uses a control strategy generation module to conduct trial runs of the acoustic radar system before its use. It automatically collects multiple operating parameters of the system, analyzes these parameters based on a linear regression analysis model, determines whether the system supports operation, and generates corresponding control strategies based on the results, thereby further ensuring the stability of the acoustic radar system's operation.

[0033] The specific workflow of the acoustic radar system is as follows:

[0034] Before use, the acoustic radar system undergoes trial operation, automatically collecting multiple operating parameters. These parameters are then analyzed using a linear regression analysis model to determine if operation is supported. Based on the results, a corresponding control strategy is generated. During operation, the system acquires environmental parameters of the wind resource detection area in real time. These parameters are preprocessed to generate an environmental index. The system also collects and analyzes multiple operating parameters using a linear regression analysis model, outputting an equipment index. The environmental and equipment indices are then fused into a model, which outputs correction coefficients. The acoustic radar system dynamically adjusts the current acoustic pulse emission frequency based on these coefficients and performs wind resource detection accordingly. The detection results are then sent to the management platform.

[0035] Example 2: Before using the acoustic radar system, a trial run is conducted. The control strategy generation module automatically collects multiple operating parameters of itself, analyzes the multiple trial run parameters based on a linear regression analysis model, determines whether the system supports operation, and generates a corresponding control strategy based on the determination result.

[0036] Before the acoustic radar system is used, a trial run is conducted. The control strategy generation module automatically collects multiple operating parameters, including the high-frequency signal free space path loss coefficient, signal-to-noise ratio, and receiver sensitivity degradation rate.

[0037] The control strategy generation module substitutes the high-frequency signal free space path loss coefficient, signal-to-noise ratio, and receiver sensitivity degradation rate into the linear regression analysis model. The model expression is as follows:

[0038] In the formula, For equipment index, Signal-to-noise ratio (SNR) This represents the free space path loss coefficient for high-frequency signals. The rate of decrease in receiver sensitivity. , , Let be the regression coefficient, and , , All are greater than 0;

[0039] The acquired device index is compared with a preset anomaly threshold. The anomaly threshold is used to determine whether the acoustic radar system supports operation. If the device index is less than or equal to the anomaly threshold, the acoustic radar system is determined to support operation. If the device index is greater than the anomaly threshold, the acoustic radar system is determined to not support operation.

[0040] The logical components of the equipment index used in this invention are as follows: taking the influence of operating parameters on the acoustic radar system as an example, the first is the index, that is, the factors that lead to the decrease in the stability of the acoustic radar system (in this invention, the influence of operating parameters on the acoustic radar system); the second is the weight of these indicators, that is, the proportion of each major influencing data when it is generated; the third is the calculation equation, that is, the mathematical calculation process to obtain the result, and the equipment index obtained by calculating the indicators with their respective weights through the calculation equation.

[0041] The main impact data obtained from the sample were transformed and processed into data language recognizable by computer software. Secondly, these evaluation factors were analyzed using SPSS software through Logistic Regression to identify factors and their weights that were significantly correlated with the results. Thirdly, the evaluation factors and weights were input into the Logistic Regression equation to obtain the results, specifically:

[0042] First, ensure the integrity of the key influencing data by handling missing and outlier values. Transform the data into a format that SPSS can recognize, typically by storing it in CSV, XLSX, or similar formats. Then import it into SPSS. Open SPSS, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, then select the "Regression" tab and choose the "Bivariate Logistic" option. In the dialog box, add the dependent variable (outcome) and independent variables (key influencing data) to the corresponding boxes. SPSS will then fit a Logistic regression based on the selected variables. The model's output will show information such as the model's index, standard error, and p-value. Examining the index and p-value helps determine which variables are significantly correlated with the outcome. Typically, a p-value less than 0.05 is considered significant. While fitting the model, variable selection methods, such as stepwise regression, are used to help screen for the most relevant factors. Based on the index of the logistic regression model, the magnitude of the index reflects the degree of influence of each factor on the outcome, and the sign of the index indicates the direction of influence. After obtaining the significant factors and their indices, the logistic regression equation is obtained. This equation is used to calculate the probability of each sample and thus predict the outcome.

[0043] The expression for calculating the free space path loss coefficient of high-frequency signals is as follows:

[0044] In the formula, It is the free space path loss coefficient for high-frequency signals. It is the distance between the transmitter and the receiver. It is the frequency of the signal. It is the speed of light, with a value of 3 × 10⁻⁶. 8The high-frequency signal free space path loss coefficient indicates that the larger the high-frequency signal free space path loss coefficient, the more severe the anomaly in the acoustic radar system. Specifically:

[0045] 1. Signal strength is too low: Increased free space path loss indicates that the signal is severely attenuated during propagation, resulting in a significant reduction in the received signal strength;

[0046] 2. Inaccurate measurement data: A high FSPL means that the received signal is very weak, which affects the accuracy and reliability of the data, reduces data quality, and may lead to incorrect measurement results for wind speed, wind direction or other meteorological parameters;

[0047] 3. Significant noise interference: When the signal strength is low, the relative noise level may become significant, increasing the impact of noise interference on the data. High noise levels may mask or interfere with weak signals, further affecting measurement accuracy.

[0048] 4. Increased system power consumption and heat: To compensate for higher path loss, it may be necessary to increase the transmission power, which will lead to increased system power consumption and generate more heat. Increased power consumption and heat may put additional stress on the equipment and affect the long-term operational stability of the equipment.

[0049] 5. Risk of equipment overload or damage: Long-term high-power transmission can lead to equipment overload, especially the transmitter or power amplifier. Equipment overload may cause hardware damage, shorten the service life of the equipment, or even cause system failure.

[0050] The calculation logic for signal-to-noise ratio (SNR) is as follows: obtain the signal power and noise power, and divide the signal power by the noise power to obtain the SNR. The smaller the SNR, the more severe the anomaly in the acoustic radar system. Specifically:

[0051] 1. Severe signal attenuation: The signal attenuates severely after propagation, possibly due to high free space path loss, harsh environmental conditions, excessively high signal frequency, or insufficient signal strength, making it difficult for the receiver to extract the effective signal from the noise.

[0052] 2. High environmental noise interference: High environmental noise levels or proximity of noise sources increase the impact of noise on the signal. High noise levels reduce the signal-to-noise ratio, making the signal difficult to identify and interpret.

[0053] 4. System failure or damage: Failure or aging of internal system components (such as transmitters, receivers, signal processing units) leads to a decrease in signal quality, increased noise, or weakened signal strength.

[0054] The calculation logic for the receiver sensitivity decay rate is as follows: Obtain the receiver sensitivity at the current moment and the receiver sensitivity at the previous moment; subtract the receiver sensitivity at the previous moment from the current moment's sensitivity to obtain the sensitivity difference; subtract the previous moment from the current moment to obtain the monitoring duration; divide the sensitivity difference by the monitoring duration to obtain the receiver sensitivity decay rate. The larger the receiver sensitivity decay rate, the more severe the anomaly in the acoustic radar system. Specifically:

[0055] 1. Equipment aging or damage: Receiver components (such as sensors, amplifiers, etc.) may experience performance degradation due to long-term use, wear and tear, or malfunction. Decreased sensitivity can cause the system to be unable to effectively receive weak signals, affecting measurement accuracy.

[0056] 2. Internal receiver fault: Faulty internal circuitry or components of the receiver (such as a failed amplifier, a damaged sensor, etc.) impair signal amplification or processing, resulting in decreased sensitivity;

[0057] 3. Changes in environmental conditions: Changes in environmental conditions such as temperature and humidity may affect the performance of the receiver. Changes in signal propagation characteristics caused by environmental factors can affect the sensitivity of the receiver.

[0058] 4. Power supply issues: Unstable or insufficient power supply to the receiver can cause unstable receiver operation, thereby reducing sensitivity.

[0059] Example 3: During the operation of the acoustic radar system, the index generation module acquires environmental parameters of the wind resource detection area in real time, generates an environmental index after preprocessing the environmental parameters, and collects multiple operating parameters of its own in real time for analysis based on a linear regression analysis model to output the equipment index.

[0060] During the operation of the acoustic radar system, the index generation module acquires environmental parameters of the wind resource detection area in real time, including the critical wind speed rate cumulative coefficient and the wind direction fluctuation frequency.

[0061] The index generation module normalizes the critical wind speed rate accumulation coefficient and the wind direction fluctuation frequency, mapping the value range of the critical wind speed rate accumulation coefficient and the wind direction fluctuation frequency to [0,1], obtaining the wind speed accumulation normalized value and the wind direction fluctuation normalized value, and summing the wind speed accumulation normalized value and the wind direction fluctuation normalized value to obtain the environmental index.

[0062] The calculation logic for wind direction fluctuation frequency is as follows: Obtain the number of wind direction changes within the monitoring period, and divide this number by the monitoring duration to obtain the wind direction fluctuation frequency. When the wind direction fluctuation frequency is low, it indicates that the wind direction change is not significant, and the acoustic pulse emission frequency needs to be reduced. Specifically:

[0063] Reduced detection requirements: When wind direction changes are not significant, the need for high-resolution data is lower. High-frequency signals are mainly used to detect subtle changes, but their advantages are not obvious when the wind direction is stable.

[0064] System optimization: Reducing the transmission frequency can lower the system's power consumption and processing burden, while also reducing the workload on the equipment.

[0065] The logic for obtaining the critical wind speed rate accumulation coefficient is as follows: In this application, when the real-time wind speed is lower than a preset wind speed threshold, it indicates that the current environmental wind speed is too low, and the acoustic radar system may not need to detect it. The wind speed difference is obtained by subtracting the real-time wind speed from the wind speed threshold. When the wind speed difference is less than the wind speed difference threshold, it indicates that the real-time wind speed is increasing and approaching the wind speed threshold, requiring monitoring. The monitoring period is divided into multiple sub-periods. After calculating the wind speed increase rate for each sub-period, the wind speed increase rates for all sub-periods are accumulated to obtain the critical wind speed rate accumulation coefficient, expressed as: In the formula, This is the cumulative coefficient for critical wind speed. Number of sub-time periods It is the first Wind speed at the end of each time period It is the first The wind speed at the start of each sub-time period, and the critical wind speed rate cumulative coefficient, represent the total increase in wind speed over the entire monitoring period. A smaller critical wind speed rate cumulative coefficient means that the increase in wind speed during the monitoring period is not significant, possibly indicating a small change in wind speed. Therefore, it is necessary to reduce the acoustic pulse emission frequency, specifically:

[0066] Reduced signal resolution requirements: When wind speed changes are not significant, the system has lower requirements for detecting subtle changes in wind speed. The high resolution provided by high-frequency signals may no longer be necessary.

[0067] Reduce system load: Lowering the transmission frequency can reduce the computational and processing burden on the system, reduce power consumption and the workload of the equipment.

[0068] During the operation of the acoustic radar system, the control strategy generation module automatically collects multiple operating parameters, including the high-frequency signal free space path loss coefficient, signal-to-noise ratio, and receiver sensitivity degradation rate.

[0069] The control strategy generation module substitutes the high-frequency signal free space path loss coefficient, signal-to-noise ratio, and receiver sensitivity degradation rate into a linear regression analysis model to obtain the real-time device index. The model expression is as follows:

[0070] In the formula, For equipment index, Signal-to-noise ratio (SNR) This represents the free space path loss coefficient for high-frequency signals. The rate of decrease in receiver sensitivity. , , Let be the regression coefficient, and , , All are greater than 0.

[0071] The dynamic frequency adjustment module inputs environmental and equipment indices into the fusion model. The fusion model outputs correction coefficients. Based on the correction coefficients, the current acoustic pulse emission frequency is dynamically adjusted. Wind resources are then detected based on the adjusted acoustic pulse emission frequency, and the detection results are sent to the management platform.

[0072] The dynamic frequency adjustment module inputs environmental and equipment indices into the fusion model, which outputs correction coefficients. The model expression is as follows: In the formula, For correction factors, For equipment index, For environmental indices, 、 These are the proportional coefficients for the equipment index and the environmental index, respectively. 、 All are greater than 0.

[0073] The dynamic frequency adjustment module compares the correction coefficient with the preset coefficient threshold. If the coefficient threshold is used to determine whether the current sound pulse transmission frequency needs to be adjusted, if the correction coefficient is less than or equal to the coefficient threshold, it is determined that the current sound pulse transmission frequency does not need to be adjusted, and if the correction coefficient is greater than the coefficient threshold, it is determined that the current sound pulse transmission frequency needs to be adjusted.

[0074] In this application, the acoustic radar system initially selects the maximum acoustic pulse emission frequency for wind resource detection. The larger the correction coefficient, the more it indicates that the acoustic radar system is in a state of decline or that the wind resources in the environment are not changing significantly, and the maximum acoustic pulse emission frequency needs to be reduced.

[0075] When it is determined that the current sound pulse emission frequency needs to be adjusted, the current sound pulse emission frequency is dynamically adjusted according to the correction coefficient. The adjustment algorithm is as follows: In the formula, The adjusted sound wave pulse emission frequency, This is the current sound wave pulse emission frequency. This is the correction factor.

[0076] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0077] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0078] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0079] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An acoustic radar system for wind resource detection, characterized in that: This includes a control strategy generation module, an index generation module, and a dynamic transmission frequency adjustment module; Control strategy generation module: Before the acoustic radar system is used, a trial run is conducted. The module automatically collects multiple operating parameters, including the high-frequency signal free space path loss coefficient, signal-to-noise ratio, and receiver sensitivity degradation rate. After analyzing the multiple trial run parameters based on a linear regression analysis model, it determines whether the system supports operation and generates corresponding control strategies based on the results. Index generation module: During the operation of the acoustic radar system, environmental parameters of the wind resource detection area are acquired in real time. The environmental parameters include the critical wind speed rate cumulative coefficient and the wind direction fluctuation frequency. After preprocessing the environmental parameters, an environmental index is generated. In addition, multiple operating parameters of the system are collected in real time and substituted into the linear regression analysis model for analysis, and the equipment index is output. Specifically, generating the environmental index involves normalizing the critical wind speed rate cumulative coefficient and the wind direction fluctuation frequency, mapping the value range of the critical wind speed rate cumulative coefficient and the wind direction fluctuation frequency to [0,1], obtaining the wind speed cumulative normalized value and the wind direction fluctuation normalized value, and summing the wind speed cumulative normalized value and the wind direction fluctuation normalized value to obtain the environmental index. The linear regression analysis model expression is as follows: In the formula, For equipment index, Signal-to-noise ratio (SNR) This represents the free space path loss coefficient for high-frequency signals. The rate of decrease in receiver sensitivity. , , Let be the regression coefficient, and , , All are greater than 0; Dynamic frequency adjustment module: The environmental index and equipment index are substituted into the fusion model. The fusion model outputs correction coefficients. The current acoustic pulse emission frequency is dynamically adjusted according to the correction coefficients. Wind resources are detected based on the adjusted acoustic pulse emission frequency. The detection results are sent to the management platform.

2. The acoustic radar system for wind resource detection according to claim 1, characterized in that: The control strategy generation module compares the acquired device index with a preset anomaly threshold. The anomaly threshold is used to determine whether the acoustic radar system supports operation. If the device index is less than or equal to the anomaly threshold, the acoustic radar system is determined to support operation. If the device index is greater than the anomaly threshold, the acoustic radar system is determined to not support operation.

3. The acoustic radar system for wind resource detection according to claim 1, characterized in that: The dynamic frequency adjustment module inputs environmental and equipment indices into a fusion model, which outputs correction coefficients. The model expression is as follows: In the formula, For correction factors, For equipment index, For environmental indices, , These are the proportional coefficients for the equipment index and the environmental index, respectively. , All are greater than 0.

4. The acoustic radar system for wind resource detection according to claim 3, characterized in that: The dynamic adjustment module for transmission frequency compares the correction coefficient with a preset coefficient threshold. If the coefficient threshold is used to determine whether the current sound pulse transmission frequency needs to be adjusted, if the correction coefficient is less than or equal to the coefficient threshold, it is determined that the current sound pulse transmission frequency does not need to be adjusted, and if the correction coefficient is greater than the coefficient threshold, it is determined that the current sound pulse transmission frequency needs to be adjusted. When it is determined that the current sound pulse emission frequency needs to be adjusted, the current sound pulse emission frequency is dynamically adjusted according to the correction coefficient. The adjustment algorithm is as follows: In the formula, The adjusted sound wave pulse emission frequency, This is the current sound wave pulse emission frequency. This is the correction factor.

5. The acoustic radar system for wind resource detection according to claim 1, characterized in that: The logic for obtaining the critical wind speed rate accumulation coefficient is as follows: When the real-time wind speed is lower than a preset wind speed threshold, the wind speed threshold is subtracted from the real-time wind speed to obtain the wind speed difference. When the wind speed difference is less than the wind speed difference threshold, the monitoring period is divided into multiple sub-periods. After calculating the wind speed increase rate of each sub-period, the wind speed increase rates of all sub-periods are accumulated to obtain the critical wind speed rate accumulation coefficient, expressed as: In the formula, This is the cumulative coefficient for critical wind speed. Number of sub-time periods It is the first Wind speed at the end of each time period It is the first Wind speed at the start of the time period.

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