Aerosol radar light path calibration method based on digital twin empowerment

Abstract

The present application relates to the technical field of radar detection, and more particularly to an aerosol radar light path calibration method based on digital twinning empowerment, which dynamically predicts radar detection data of different atmospheric regions by combining real-time environmental data and aerosol concentration information through digital twinning technology, so as to perform real-time light path calibration according to environmental changes; the actual correlation degree of environmental temperature, laser power and aerosol concentration is considered, and calibration compensation is performed based on the dynamic changes of these factors, thereby reducing the negative impact of the external environment on the performance of the radar; the stability of the radar light path is comprehensively evaluated by calculating the correlation degree deviation, combining the time domain deviation and the frequency domain deviation; two indexes, the thermal-optical coupling instability index and the signal-to-noise ratio degradation surface, are introduced, and the accuracy of the light path calibration is further improved through collaborative verification relationship; by constructing a collaborative stability boundary vector and calculating the Euclidean two norm, the risk of light path calibration failure can be detected in time.

Description

Technical Field

[0001] This invention relates to the field of radar detection technology, and in particular to a method for calibrating the optical path of aerosol radar based on digital twin technology. Background Technology

[0002] Aerosol radar, a key instrument for atmospheric sounding, is widely used in atmospheric composition analysis, meteorological monitoring, and environmental protection. Aerosol radar acquires vertical atmospheric profile data by detecting aerosol particles in the air. However, the detection accuracy and stability of aerosol radar under different atmospheric conditions are affected by various environmental factors, making optical path calibration a crucial issue for improving radar performance. Existing radar calibration methods typically rely on static environmental parameters, but with the dynamic nature of aerosol concentration and atmospheric changes, these methods cannot cope with varying environmental conditions, leading to low accuracy of radar detection data and even error accumulation.

[0003] Traditional radar optical path calibration methods typically ignore the impact of dynamic environmental changes on detection results, employing conventional calibration methods based on static environmental parameters. This approach cannot reflect real-time atmospheric changes in a timely manner, leading to lag and inaccuracy in calibration results, making it difficult to meet the requirements of high-precision detection. Existing radar calibration schemes fail to fully consider the interaction of factors such as temperature, laser power, and aerosol concentration at different times, which may result in distortion of equipment and detection data. Traditional methods do not perform dynamic regional compensation for different atmospheric types and cannot adjust the calibration scheme for changing environmental conditions. For the stability assessment of optical path calibration, existing technologies mostly use a single indicator for judgment, lacking comprehensive stability prediction methods, and cannot effectively detect potential optical path failures in advance. Summary of the Invention

[0004] The main objective of this invention is to provide a digital twin-enabled aerosol radar optical path calibration method. By combining real-time environmental data and aerosol concentration information with digital twin technology, it dynamically predicts radar detection data for different atmospheric regions, thereby performing real-time optical path calibration based on environmental changes. It considers the actual correlation between ambient temperature, laser power, and aerosol concentration, and performs calibration compensation based on the dynamic changes of these factors, thus reducing the negative impact of the external environment on radar performance. Through K-means clustering, the atmosphere is divided into different regions, and personalized compensation is applied to the radar detection data for each region, improving radar detection performance under different environmental conditions. The accuracy of the measurement is improved by calculating the correlation deviation and combining the time-domain and frequency-domain deviations to comprehensively evaluate the stability of the radar optical path. Two indices, the thermal-optical coupling instability index and the signal-to-noise ratio degradation surface, are introduced, and the accuracy of optical path calibration is further improved through collaborative verification relationships. Particularly in stability assessment, by constructing a collaborative stability boundary vector and calculating the Euclidean norm 2, the risk of optical path calibration failure can be detected in a timely manner. When optical path calibration fails, this scheme can calculate the remaining service life of the radar based on the Euclidean norm 2 of the collaborative stability boundary vector, providing a scientific basis for equipment maintenance and replacement, and effectively extending the radar's service life.

[0005] The technical solution of the present invention is as follows:

[0006] Firstly, a digital twin-enabled aerosol radar optical path calibration method is proposed, which includes the following steps:

[0007] S1. Acquire radar detection data, ambient temperature data, laser power data, and aerosol concentration data. Based on the ambient temperature data and laser power data, calculate the temperature-component actual correlation index. Based on the aerosol concentration data, use the K-means clustering algorithm to classify atmospheric regions and generate dynamic region identifiers.

[0008] S2. Based on atmospheric region type, perform digital twin prediction on radar detection data of different atmospheric regions, output the radar detection data predicted by digital twin, and perform regional compensation on different atmospheric regions, output the radar detection data predicted by the compensated digital twin.

[0009] S3. Based on the actual temperature-component correlation index, calculate the ideal temperature-component correlation index for different atmospheric regions in the digital twin state, obtain the deviation between the ideal correlation index and the actual correlation index for different atmospheric region types, and further obtain the comprehensive correlation deviation.

[0010] S4. Calculate the integrated time-domain deviation and integrated frequency-domain deviation based on radar detection data and radar detection data predicted by the compensated digital twin. Further combine the integrated correlation deviation to calculate the coupling deviation and make a preliminary judgment on the optical path calibration status.

[0011] S5. When the optical path calibration status is normal, based on ambient temperature data, laser power data, temperature-component actual correlation index, deviation between ideal correlation index and actual correlation index for different atmospheric region types, and comprehensive correlation deviation, calculate the thermal-optical coupling instability index and signal-to-noise ratio-efficiency degradation surface and verify whether the cooperative relationship is effective.

[0012] S6. Based on the thermal-optical coupling instability index, signal-to-noise ratio-efficiency degradation surface, collaborative verification results, and comprehensive correlation deviation, construct a collaborative stability boundary vector and calculate the Euclidean norm 2 to determine whether the optical path calibration state is stable. When the optical path calibration is determined to be in failure, calculate the remaining service life of the radar.

[0013] A further improvement of the present invention is that step S1 includes the following specific steps:

[0014] S11. Acquire radar detection data, ambient temperature data, laser power data, and aerosol concentration data at n altitude levels;

[0015] S12. Based on ambient temperature data and laser power data, calculate the temperature-component actual correlation index. The calculation formula is as follows:

[0016] ;

[0017] Where t represents a time node, and m is the total number of time nodes contained in a time window. The temperature-component correlation index at time point t. Let t be the ambient temperature. The average ambient temperature over a time window. Let be the laser power at time node t. The average laser power over a time window;

[0018] S13. Extract aerosol concentration data, aerosol concentration gradient data, and aerosol concentration curvature data from n altitude layers, and then extract the aerosol concentration data from altitude layer i. Aerosol concentration gradient data and aerosol concentration curvature data Combined into feature vectors From a set of n feature vectors, three feature vectors are randomly selected as the initial cluster centers for the K-means clustering algorithm. For each feature vector... Calculate the Euclidean distance from each feature vector to the initial cluster center, and assign it to the cluster corresponding to the initial cluster center with the smallest distance; calculate the mean of all feature vectors within a single cluster, update the initial cluster centers, and then assign each feature vector... The process continues until a preset number of iterations is reached, at which point the computation stops, completing the atmospheric region type classification and generating dynamic region identifiers. Each cluster represents an atmospheric region type, which includes the near-surface layer, the boundary layer, and the free troposphere. The value of i is 1-n, and j represents the atmospheric region type, which is 1-3.

[0019] A further improvement of the present invention is that step S2 includes the following specific steps:

[0020] S21. Based on atmospheric region type, perform digital twin prediction on radar detection data for different atmospheric regions and output the predicted radar detection data using digital twins. The digital twin prediction is based on the optical transmission equation, and the formula is:

[0021] ;

[0022] in, Radar detection data predicted by a digital twin for atmospheric region type j at time node t. For radar transmission power, Let be the atmospheric attenuation coefficient of atmospheric region type j at altitude z at time node t. For the transmission path length, To effectively receive the area, For detection distance;

[0023] S22. Regional compensation is performed for different atmospheric regions. The specific formula is as follows:

[0024] ;

[0025] in, This represents the compensation coefficient for atmospheric region type j at time node t. The ambient temperature is the reference value. This is the laser power reference value. Let be the average aerosol concentration at time node t for n altitude layers. This is the baseline value for aerosol concentration. , , All are calibration coefficients for atmospheric region type j;

[0026] S23. Calculate and output the compensated digital twin-predicted radar detection data. The calculation formula is: .

[0027] A further improvement of the present invention is that step S3 includes the following specific steps:

[0028] S31. Calculate the temperature-component ideal correlation index for different atmospheric regions under digital twin conditions. The calculation formula is as follows:

[0029] ;

[0030] in, The temperature-component ideal correlation index is used for atmospheric region type j at time node t. Let t be the ideal laser power for atmospheric region type j in a digital twin state. The average ideal laser power over a time window;

[0031] S32. The deviation between the ideal correlation index and the actual correlation index for different atmospheric region types is calculated using the following formula: ; The deviation between the ideal correlation index and the actual correlation index for atmospheric region type j at time node t is further used to obtain the comprehensive correlation deviation, calculated using the following formula: ; This represents the weight of atmospheric region type j.

[0032] A further improvement of the present invention is that step S4 includes the following specific steps:

[0033] S41, Based on radar detection data Radar detection data predicted by digital twins after compensation for different atmospheric regions The comprehensive time-domain deviation is calculated using the following formula:

[0034] ;

[0035] in, The total time-domain deviation at time node t;

[0036] S42, Regarding radar detection data Radar detection data predicted by digital twins after compensation for different atmospheric regions Perform an FFT Fourier transform to obtain the amplitude of the radar detection data at N frequency points. Simultaneously, the amplitude of radar detection data predicted by digital twins at N frequency points after compensation for different atmospheric regions is obtained. The formula for calculating the overall frequency domain deviation is as follows:

[0037] ;

[0038] in, The overall frequency domain deviation is denoted by t, and N is the number of FFT points.

[0039] S43, Based on comprehensive time-domain deviation Comprehensive frequency domain deviation and overall correlation deviation Calculate coupling deviation The calculation formula is:

[0040] ;

[0041] S44. Make a preliminary fault judgment. If and only if the overall time domain deviation is less than the time domain deviation threshold, the overall frequency domain deviation is less than the frequency domain deviation threshold, and the coupling deviation is less than the coupling deviation threshold, the optical path calibration status is judged to be normal.

[0042] A further improvement of the present invention is that step S5 includes the following specific steps:

[0043] S51. When the optical path calibration is normal, based on ambient temperature data... Laser power data Temperature-component actual correlation index Deviation between ideal and actual correlation indices for different atmospheric regional types and overall correlation deviation The thermal-optical coupling instability index is calculated using the following formula:

[0044] ;

[0045] in, is the optical path transmission efficiency, and is the ratio of radar received power to radar transmitted power; Let be the ambient temperature gradient at time t. As the baseline value for the ambient temperature gradient, Let be the laser power stability at time node t, and be the standard deviation of the laser power data within a time window. For the maximum permissible laser power stability;

[0046] S52. Calculate the signal-to-noise ratio-efficiency degradation surface. The calculation formula is as follows:

[0047] ;

[0048] in, The threshold for laser power stability. Let be the standard deviation of laser power stability at time node t. The standard Gaussian error function, The radar signal-to-noise ratio attenuation exponent at time node t is calculated using the following formula: ; Let be the radar signal-to-noise ratio at time t. This is the radar signal-to-noise ratio reference value;

[0049] S53, if and only if When the thermo-optical coupling instability index and the signal-to-noise ratio-efficiency degradation surface are synergistically effective, the synergistic verification results are output.

[0050] A further improvement of the present invention is that step S6 includes the following specific steps:

[0051] S61, Based on the thermo-optical coupling instability index Signal-to-noise ratio-efficiency degradation surface Collaborative validation results and overall correlation deviation Construct a collaborative stability boundary vector, denoted as: ;

[0052] S62. Calculate the Euclidean norm 2 of the cooperative stability boundary vector. The formula is as follows:

[0053] ;

[0054] in, Let be the Euclidean norm 2 of the collaborative stability boundary vector at time node t, when The optical path calibration state is considered stable when the value is less than the first instability threshold. When the first instability threshold is reached but less than the second instability threshold, the optical path calibration state is determined to be unstable and a mild warning is issued. The optical path calibration is deemed to have failed when the second instability threshold is reached.

[0055] S63. When the optical path calibration is determined to have failed, the remaining service life of the radar is calculated using the following formula:

[0056] ;

[0057] in, The remaining service life of the radar at time point t. The attenuation coefficient is 0.01.

[0058] Secondly, a computer-readable storage medium is proposed, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned aerosol radar optical path calibration method based on digital twin empowerment.

[0059] Thirdly, an electronic device is proposed, including a memory for storing instructions and a processor for executing the instructions, causing the device to perform the above-described aerosol radar optical path calibration method based on digital twin empowerment.

[0060] The technical effects of this invention are as follows:

[0061] A digital twin-enabled aerosol radar optical path calibration method was constructed. By combining real-time environmental data and aerosol concentration information with digital twin technology, radar detection data in different atmospheric regions is dynamically predicted, thereby enabling real-time optical path calibration based on environmental changes. The actual correlation between ambient temperature, laser power, and aerosol concentration is considered, and calibration compensation is performed based on the dynamic changes of these factors, thereby reducing the negative impact of the external environment on radar performance. Through the K-means clustering algorithm, the atmosphere is divided into different regions, and personalized compensation is performed on the radar detection data of each region, improving the radar detection accuracy under different environmental conditions. By calculating the correlation deviation and combining time-domain and frequency-domain deviations, a comprehensive assessment of the stability of the radar optical path is conducted. Two indices, the thermal-optical coupling instability index and the signal-to-noise ratio degradation surface, are introduced. Through collaborative verification, the accuracy of optical path calibration is further improved. Particularly in stability assessment, by constructing a collaborative stability boundary vector and calculating the Euclidean norm 2, the risk of optical path calibration failure can be detected promptly. When optical path calibration fails, this scheme can calculate the remaining service life of the radar based on the Euclidean norm 2 of the collaborative stability boundary vector, providing a scientific basis for equipment maintenance and replacement, and effectively extending the radar's service life. Attached Figure Description

[0062] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0063] Figure 1 This is a schematic flowchart of the aerosol radar optical path calibration method based on digital twin empowerment according to Embodiment 1 of the present invention. Detailed Implementation

[0064] Example 1: This example proposes a digital twin-enabled aerosol radar optical path calibration method. By combining real-time environmental data and aerosol concentration information with digital twin technology, it dynamically predicts radar detection data for different atmospheric regions, thereby performing real-time optical path calibration based on environmental changes. It considers the actual correlation between ambient temperature, laser power, and aerosol concentration, and performs calibration compensation based on the dynamic changes of these factors, thus reducing the negative impact of the external environment on radar performance. Using the K-means clustering algorithm, the atmosphere is divided into different regions, and personalized compensation is applied to the radar detection data of each region, improving the radar's detection performance under different environmental conditions. The accuracy of the measurement is improved by calculating the correlation deviation and combining time-domain and frequency-domain deviations to comprehensively evaluate the stability of the radar optical path. Two indices, the thermal-optical coupling instability index and the signal-to-noise ratio degradation surface, are introduced. Through collaborative verification, the accuracy of optical path calibration is further enhanced, particularly in stability assessment. By constructing a collaborative stability boundary vector and calculating the Euclidean norm 2, the risk of optical path calibration failure can be detected in a timely manner. When optical path calibration fails, this scheme can calculate the remaining service life of the radar based on the Euclidean norm 2 of the collaborative stability boundary vector, providing a scientific basis for equipment maintenance and replacement, and effectively extending the radar's service life. Specifically, for example... Figure 1 As shown, the aerosol radar optical path calibration method based on digital twin empowerment proposed in this embodiment includes the following specific steps:

[0065] S1. Acquire radar detection data, ambient temperature data, laser power data, and aerosol concentration data. Based on the ambient temperature data and laser power data, calculate the temperature-component actual correlation index. Based on the aerosol concentration data, use the K-means clustering algorithm to classify atmospheric regions and generate dynamic region identifiers.

[0066] S2. Based on atmospheric region type, perform digital twin prediction on radar detection data of different atmospheric regions, output the radar detection data predicted by digital twin, and perform regional compensation on different atmospheric regions, output the radar detection data predicted by the compensated digital twin.

[0067] S3. Based on the actual temperature-component correlation index, calculate the ideal temperature-component correlation index for different atmospheric regions in the digital twin state, obtain the deviation between the ideal correlation index and the actual correlation index for different atmospheric region types, and further obtain the comprehensive correlation deviation.

[0068] S4. Calculate the integrated time-domain deviation and integrated frequency-domain deviation based on radar detection data and radar detection data predicted by the compensated digital twin. Further combine the integrated correlation deviation to calculate the coupling deviation and make a preliminary judgment on the optical path calibration status.

[0069] S5. When the optical path calibration status is normal, based on ambient temperature data, laser power data, temperature-component actual correlation index, deviation between ideal correlation index and actual correlation index for different atmospheric region types, and comprehensive correlation deviation, calculate the thermal-optical coupling instability index and signal-to-noise ratio-efficiency degradation surface and verify whether the cooperative relationship is effective.

[0070] S6. Based on the thermal-optical coupling instability index, signal-to-noise ratio-efficiency degradation surface, collaborative verification results, and comprehensive correlation deviation, construct a collaborative stability boundary vector and calculate the Euclidean norm 2 to determine whether the optical path calibration state is stable. When the optical path calibration is determined to be in failure, calculate the remaining service life of the radar.

[0071] In this embodiment, step S1 includes the following specific steps:

[0072] S11. Acquire radar detection data, ambient temperature data, laser power data, and aerosol concentration data at n altitude levels;

[0073] S12. Based on ambient temperature data and laser power data, calculate the temperature-component actual correlation index. The calculation formula is as follows:

[0074] ;

[0075] Where t represents a time node, and m is the total number of time nodes contained in a time window. The temperature-component correlation index at time point t. Let t be the ambient temperature. The average ambient temperature over a time window. Let be the laser power at time node t. The average laser power over a time window;

[0076] S13. Extract aerosol concentration data, aerosol concentration gradient data, and aerosol concentration curvature data from n altitude layers, and then extract the aerosol concentration data from altitude layer i. Aerosol concentration gradient data and aerosol concentration curvature data Combined into feature vectors From a set of n feature vectors, three feature vectors are randomly selected as the initial cluster centers for the K-means clustering algorithm. For each feature vector... Calculate the Euclidean distance from each feature vector to the initial cluster center, and assign it to the cluster corresponding to the initial cluster center with the smallest distance; calculate the mean of all feature vectors within a single cluster, update the initial cluster centers, and then assign each feature vector... The process continues until a preset number of iterations is reached, at which point the computation stops, completing the atmospheric region type classification and generating dynamic region identifiers. Each cluster represents an atmospheric region type, which includes the near-surface layer, the boundary layer, and the free troposphere. The value of i is 1-n, and j represents the atmospheric region type, which is 1-3.

[0077] In this embodiment, step S2 includes the following specific steps:

[0078] S21. Based on atmospheric region type, perform digital twin prediction on radar detection data for different atmospheric regions and output the predicted radar detection data using digital twins. The digital twin prediction is based on the optical transmission equation, and the formula is:

[0079] ;

[0080] in, Radar detection data predicted by a digital twin for atmospheric region type j at time node t. For radar transmission power, Let be the atmospheric attenuation coefficient of atmospheric region type j at altitude z at time node t. For the transmission path length, To effectively receive the area, For detection distance;

[0081] S22. Regional compensation is performed for different atmospheric regions. The specific formula is as follows:

[0082] ;

[0083] in, This represents the compensation coefficient for atmospheric region type j at time node t. The ambient temperature is the reference value. This is the laser power reference value. Let be the average aerosol concentration at time node t for n altitude layers. This is the baseline value for aerosol concentration. , , All are calibration coefficients for atmospheric region type j;

[0084] S23. Calculate and output the compensated digital twin-predicted radar detection data. The calculation formula is: .

[0085] In this embodiment, step S3 includes the following specific steps:

[0086] S31. Calculate the temperature-component ideal correlation index for different atmospheric regions under digital twin conditions. The calculation formula is as follows:

[0087] ;

[0088] in, The temperature-component ideal correlation index is used for atmospheric region type j at time node t. Let t be the ideal laser power for atmospheric region type j in a digital twin state. The average ideal laser power over a time window;

[0089] S32. The deviation between the ideal correlation index and the actual correlation index for different atmospheric region types is calculated using the following formula: ; The deviation between the ideal correlation index and the actual correlation index for atmospheric region type j at time node t is further used to obtain the comprehensive correlation deviation, calculated using the following formula: ; This represents the weight of atmospheric region type j.

[0090] In this embodiment, step S4 includes the following specific steps:

[0091] S41, Based on radar detection data Radar detection data predicted by digital twins after compensation for different atmospheric regions The comprehensive time-domain deviation is calculated using the following formula:

[0092] ;

[0093] in, The total time-domain deviation at time node t;

[0094] S42, Regarding radar detection data Radar detection data predicted by digital twins after compensation for different atmospheric regions Perform an FFT Fourier transform to obtain the amplitude of the radar detection data at N frequency points. Simultaneously, the amplitude of radar detection data predicted by digital twins at N frequency points after compensation for different atmospheric regions is obtained. The formula for calculating the overall frequency domain deviation is as follows:

[0095] ;

[0096] in, The overall frequency domain deviation is denoted by t, and N is the number of FFT points.

[0097] S43, Based on comprehensive time-domain deviation Comprehensive frequency domain deviation and overall correlation deviation Calculate coupling deviation The calculation formula is:

[0098] ;

[0099] S44. Make a preliminary fault judgment. If and only if the overall time domain deviation is less than the time domain deviation threshold, the overall frequency domain deviation is less than the frequency domain deviation threshold, and the coupling deviation is less than the coupling deviation threshold, the optical path calibration status is judged to be normal.

[0100] In this embodiment, step S5 includes the following specific steps:

[0101] S51. When the optical path calibration is normal, based on ambient temperature data... Laser power data Temperature-component actual correlation index Deviation between ideal and actual correlation indices for different atmospheric regional types and overall correlation deviation The thermal-optical coupling instability index is calculated using the following formula:

[0102] ;

[0103] in, is the optical path transmission efficiency, and is the ratio of radar received power to radar transmitted power; Let be the ambient temperature gradient at time t. As the baseline value for the ambient temperature gradient, Let be the laser power stability at time node t, and be the standard deviation of the laser power data within a time window. For the maximum permissible laser power stability;

[0104] S52. Calculate the signal-to-noise ratio-efficiency degradation surface. The calculation formula is as follows:

[0105] ;

[0106] in, The threshold for laser power stability. Let be the standard deviation of laser power stability at time node t. The standard Gaussian error function, The radar signal-to-noise ratio attenuation exponent at time node t is calculated using the following formula: ; Let be the radar signal-to-noise ratio at time t. This is the radar signal-to-noise ratio reference value;

[0107] S53, if and only if When the thermo-optical coupling instability index and the signal-to-noise ratio-efficiency degradation surface are synergistically effective, the synergistic verification results are output.

[0108] In this embodiment, step S6 includes the following specific steps:

[0109] S61, Based on the thermo-optical coupling instability index Signal-to-noise ratio-efficiency degradation surface Collaborative validation results and overall correlation deviation Construct a collaborative stability boundary vector, denoted as: ;

[0110] S62. Calculate the Euclidean norm 2 of the cooperative stability boundary vector. The formula is as follows:

[0111] ;

[0112] in, Let be the Euclidean norm 2 of the collaborative stability boundary vector at time node t, when The optical path calibration state is considered stable when the value is less than the first instability threshold. When the first instability threshold is reached but less than the second instability threshold, the optical path calibration state is determined to be unstable and a mild warning is issued. The optical path calibration is deemed to have failed when the second instability threshold is reached.

[0113] S63. When the optical path calibration is determined to have failed, the remaining service life of the radar is calculated using the following formula:

[0114] ;

[0115] in, The remaining service life of the radar at time point t. The attenuation coefficient is 0.01.

[0116] The threshold and weight settings can be based on the default settings of this invention, or they can be set by the operator.

[0117] Example 2: This example provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described aerosol radar optical path calibration method based on digital twin empowerment by calling the computer program stored in the memory.

[0118] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the aerosol radar optical path calibration method based on digital twin empowerment provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted in this embodiment.

[0119] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be embodied in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0120] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0121] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for calibrating the optical path of aerosol radar based on digital twin technology, characterized in that: The specific steps include the following: S1. Acquire radar detection data, ambient temperature data, laser power data, and aerosol concentration data. Based on the ambient temperature data and laser power data, calculate the temperature-component actual correlation index. Based on the aerosol concentration data, use the K-means clustering algorithm to classify atmospheric regions and generate dynamic region identifiers. S2. Based on atmospheric region type, perform digital twin prediction on radar detection data of different atmospheric regions, output the radar detection data predicted by digital twin, and perform regional compensation on different atmospheric regions, output the radar detection data predicted by the compensated digital twin. S3. Based on the actual temperature-component correlation index, calculate the ideal temperature-component correlation index for different atmospheric regions in the digital twin state, obtain the deviation between the ideal correlation index and the actual correlation index for different atmospheric region types, and further obtain the comprehensive correlation deviation. S4. Calculate the integrated time-domain deviation and integrated frequency-domain deviation based on radar detection data and radar detection data predicted by the compensated digital twin. Further combine the integrated correlation deviation to calculate the coupling deviation and make a preliminary judgment on the optical path calibration status. S5. When the optical path calibration status is normal, based on ambient temperature data, laser power data, temperature-component actual correlation index, deviation between ideal correlation index and actual correlation index for different atmospheric region types, and comprehensive correlation deviation, calculate the thermal-optical coupling instability index and signal-to-noise ratio-efficiency degradation surface and verify whether the cooperative relationship is effective. S6. Based on the thermal-optical coupling instability index, signal-to-noise ratio-efficiency degradation surface, collaborative verification results, and comprehensive correlation deviation, construct a collaborative stability boundary vector and calculate the Euclidean norm 2 to determine whether the optical path calibration state is stable. When the optical path calibration is determined to be in failure, calculate the remaining service life of the radar.

2. The aerosol radar optical path calibration method based on digital twin empowerment according to claim 1, characterized in that: S1 includes the following specific steps: S11. Acquire radar detection data, ambient temperature data, laser power data, and aerosol concentration data at n altitude levels; S12. Based on ambient temperature data and laser power data, calculate the temperature-component actual correlation index. The calculation formula is as follows: ； Where t represents a time node, and m is the total number of time nodes contained in a time window. The temperature-component correlation index at time point t. Let t be the ambient temperature. The average ambient temperature over a time window. Let be the laser power at time node t. The average laser power over a time window; S13. Extract aerosol concentration data, aerosol concentration gradient data, and aerosol concentration curvature data from n altitude layers, and then extract the aerosol concentration data from altitude layer i. Aerosol concentration gradient data and aerosol concentration curvature data Combined into feature vectors From a set of n feature vectors, three feature vectors are randomly selected as the initial cluster centers for the K-means clustering algorithm. For each feature vector... Calculate the Euclidean distance from each feature vector to the initial cluster center, and assign it to the cluster corresponding to the initial cluster center with the smallest distance; calculate the mean of all feature vectors within a single cluster, update the initial cluster centers, and then assign each feature vector... The process continues until a preset number of iterations is reached, at which point the computation stops, completing the atmospheric region type classification and generating dynamic region identifiers. Each cluster represents an atmospheric region type, which includes the near-surface layer, the boundary layer, and the free troposphere. The value of i is 1-n, and j represents the atmospheric region type, which is 1-3.

3. The aerosol radar optical path calibration method based on digital twin empowerment according to claim 2, characterized in that: S2 includes the following specific steps: S21. Based on atmospheric region type, perform digital twin prediction on radar detection data for different atmospheric regions and output the predicted radar detection data using digital twins. The digital twin prediction is based on the optical transmission equation, and the formula is: ； in, Radar detection data predicted by a digital twin for atmospheric region type j at time node t. For radar transmission power, Let be the atmospheric attenuation coefficient of atmospheric region type j at altitude z at time node t. For the transmission path length, To effectively receive the area, For detection distance; S22. Regional compensation is performed for different atmospheric regions. The specific formula is as follows: ； in, This represents the compensation coefficient for atmospheric region type j at time node t. The ambient temperature is the reference value. This is the laser power reference value. Let be the average aerosol concentration at time node t for n altitude layers. This is the baseline value for aerosol concentration. , , All are calibration coefficients for atmospheric region type j; S23. Calculate and output the compensated digital twin-predicted radar detection data. The calculation formula is: .

4. The aerosol radar optical path calibration method based on digital twin empowerment according to claim 3, characterized in that: S3 includes the following specific steps: S31. Calculate the temperature-component ideal correlation index for different atmospheric regions under digital twin conditions. The calculation formula is as follows: ； in, The temperature-component ideal correlation index is used for atmospheric region type j at time node t. Let t be the ideal laser power for atmospheric region type j in a digital twin state. The average ideal laser power over a time window; S32. The deviation between the ideal correlation index and the actual correlation index for different atmospheric region types is calculated using the following formula: ; The deviation between the ideal correlation index and the actual correlation index for atmospheric region type j at time node t is further used to obtain the comprehensive correlation deviation, calculated using the following formula: ; This represents the weight of atmospheric region type j.

5. The aerosol radar optical path calibration method based on digital twin empowerment according to claim 4, characterized in that: S4 includes the following specific steps: S41, Based on radar detection data Radar detection data predicted by digital twins after compensation for different atmospheric regions The comprehensive time-domain deviation is calculated using the following formula: ； in, The total time-domain deviation at time node t; S42, Regarding radar detection data Radar detection data predicted by digital twins after compensation for different atmospheric regions Perform an FFT Fourier transform to obtain the amplitude of the radar detection data at N frequency points. Simultaneously, the amplitude of radar detection data predicted by digital twins at N frequency points after compensation for different atmospheric regions is obtained. The formula for calculating the overall frequency domain deviation is as follows: ； in, The overall frequency domain deviation is denoted by t, and N is the number of FFT points. S43, Based on comprehensive time-domain deviation Comprehensive frequency domain deviation and overall correlation deviation Calculate coupling deviation The calculation formula is: ； S44. Make a preliminary fault judgment. If and only if the overall time domain deviation is less than the time domain deviation threshold, the overall frequency domain deviation is less than the frequency domain deviation threshold, and the coupling deviation is less than the coupling deviation threshold, the optical path calibration status is judged to be normal.

6. The aerosol radar optical path calibration method based on digital twin empowerment according to claim 5, characterized in that: S5 includes the following specific steps: S51. When the optical path calibration is normal, based on ambient temperature data... Laser power data Temperature-component actual correlation index Deviation between ideal and actual correlation indices for different atmospheric regional types and overall correlation deviation The thermal-optical coupling instability index is calculated using the following formula: ； in, is the optical path transmission efficiency, and is the ratio of radar received power to radar transmitted power; Let be the ambient temperature gradient at time t. As the baseline value for the ambient temperature gradient, Let be the laser power stability at time node t, and be the standard deviation of the laser power data within a time window. For the maximum permissible laser power stability; S52. Calculate the signal-to-noise ratio-efficiency degradation surface. The calculation formula is as follows: ； in, The threshold for laser power stability. Let be the standard deviation of laser power stability at time node t. The standard Gaussian error function, The radar signal-to-noise ratio attenuation exponent at time node t is calculated using the following formula: ; Let be the radar signal-to-noise ratio at time t. This is the radar signal-to-noise ratio reference value; S53, if and only if When the thermo-optical coupling instability index and the signal-to-noise ratio-efficiency degradation surface are synergistically effective, the synergistic verification results are output.

7. The aerosol radar optical path calibration method based on digital twin empowerment according to claim 6, characterized in that: S6 includes the following specific steps: S61, Based on the thermo-optical coupling instability index Signal-to-noise ratio-efficiency degradation surface Collaborative validation results and overall correlation deviation Construct a collaborative stability boundary vector, denoted as: ; S62. Calculate the Euclidean norm 2 of the cooperative stability boundary vector. The formula is as follows: ； in, Let be the Euclidean norm 2 of the collaborative stability boundary vector at time node t, when The optical path calibration state is considered stable when the value is less than the first instability threshold. When the first instability threshold is reached but less than the second instability threshold, the optical path calibration state is determined to be unstable and a mild warning is issued. The optical path calibration is deemed to have failed when the second instability threshold is reached. S63. When the optical path calibration is determined to have failed, the remaining service life of the radar is calculated using the following formula: ； in, The remaining service life of the radar at time point t. The attenuation coefficient is 0.

01.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the aerosol radar optical path calibration method based on digital twin empowerment as described in any one of claims 1-7.

9. An electronic device, characterized in that, Includes a memory for storing instructions; and a processor for executing the instructions, causing the device to perform the aerosol radar optical path calibration method based on digital twin empowerment as described in any one of claims 1 to 7.

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