High-precision laser ranging calibration method and system

Abstract

The invention relates to the technical field of laser ranging calibration, and discloses a high-precision laser ranging calibration method and system. The system comprises a laser data acquisition module, a noise filtering processing module, an environment compensation module, a dynamic calibration module, a precision verification module, a re-calibration triggering module, a data integration module, a safety verification module, a report generation module and a closed-loop feedback module. When high-precision laser ranging is carried out, by sensing and compensating influences of environmental parameter changes of temperature, humidity and atmospheric pressure on the laser propagation speed in real time, ranging systematic deviation caused by environmental fluctuation can be eliminated, accuracy and reliability of ranging results under different working conditions are guaranteed, environmental adaptability of a ranging system is improved, and the range finding precision is improved. When laser ranging signals are processed, the advanced signal decomposition and threshold filtering technology is adopted, signal noise introduced by equipment and external interference can be restrained, and ranging waveform distortion and feature extraction errors are avoided.

Description

Technical Field

[0001] This invention relates to the field of laser ranging calibration technology, specifically a high-precision laser ranging calibration method and system. Background Technology

[0002] With the continuous advancement of laser technology, laser ranging has been widely used in many fields such as military, surveying and mapping, and industrial automation, becoming a mainstream ranging technology. Compared with traditional optical ranging systems, laser ranging has advantages such as high measurement accuracy, high resolution, strong anti-interference ability, small size and light weight.

[0003] Currently, in high-precision laser ranging, due to the complexity and variability of environmental interference factors, laser ranging systems cannot perceive and compensate for the effects of changes in atmospheric temperature, humidity, and pressure on laser propagation speed in real time when collecting raw data. When environmental parameters fluctuate and are not corrected in time, it will lead to systematic deviations in the calculation of laser ranging values, and the accuracy and reliability of the ranging results cannot be guaranteed.

[0004] Therefore, a high-precision laser ranging calibration method and system are proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a high-precision laser ranging calibration method and system, which solves the problem mentioned in the background art of systematic deviations in the calculation of laser ranging values, which cannot guarantee the accuracy and reliability of ranging results.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a high-precision laser ranging calibration method and system, the method comprising the following steps: S1. Collect raw laser ranging data, including emitting a laser beam to the target object through a laser transmitter and receiving the reflected signal using a laser receiver to generate raw laser ranging waveform data; S2. Perform noise filtering on the laser ranging data. Based on the wavelet transform algorithm, perform noise reduction analysis on the original waveform data of the laser ranging to generate filtered laser ranging data. S3. Measure the environmental parameters of the laser ranging system, including temperature, humidity and atmospheric pressure data, and generate environmental parameter compensation data; S4. Based on the filtered laser ranging data and the environmental parameter compensation data, perform dynamic calibration processing of laser ranging, and adjust the ranging parameters in real time through an adaptive Kalman filter algorithm to generate preliminary calibration laser ranging data. S5. Perform laser ranging accuracy verification processing, compare the preliminary calibrated laser ranging data with the reference standard ranging data, and generate laser ranging error analysis data; S6. Based on the laser ranging error analysis data, when the laser ranging error exceeds the preset threshold, the laser ranging system is recalibrated. The calibration parameters are optimized based on the neural network algorithm to generate optimized calibration laser ranging data. S7. Construct a summary data of laser ranging calibration, integrate the optimized calibration laser ranging data, environmental parameter compensation data and error analysis data, and generate high-precision laser ranging calibration results.

[0007] Preferably, the acquisition of raw laser ranging data in step S1 includes the following steps: S11. A high-precision laser rangefinder equipped with a multi-band laser emitter emits laser beams of different wavelengths toward the target object, and a high-speed photoelectric detector receives the reflected signals to generate original waveform data for multi-band laser ranging. S12. Import the original polar waveform data of the multi-band laser ranging into the data acquisition platform, and use the time series analysis method to extract the peak and valley characteristics of each waveform to generate the original laser ranging data matrix, wherein the rows of the data matrix represent time points and the columns represent laser band parameters; S13. Based on the original laser ranging data matrix, perform a data integrity check, remove outliers, and fill in missing data.

[0008] Preferably, the noise filtering process in S2 includes the following steps: S21. Obtain the original laser ranging data matrix, apply the discrete wavelet transform algorithm to perform multi-resolution analysis, and decompose the data into approximation coefficients and detail coefficients. S22. Soft threshold filtering is applied to detail coefficients using a thresholding method to remove high-frequency noise components, and the signal is reconstructed to generate filtered laser ranging data. S23. Calculate the signal-to-noise ratio (SNR) of the data before and after filtering. If the SNR improvement rate is less than 10%, repeat steps S21 to S22 until the requirements are met.

[0009] Preferably, the environmental parameter measurement in S3 includes the following steps: S31. Deploy an environmental sensor network, including temperature sensors, humidity sensors, and barometric pressure sensors, to collect environmental parameters around the laser ranging device in real time. S32. Align the environmental parameter data with the laser ranging timestamp to generate a time-synchronized environmental parameter sequence; S33. Based on the environmental parameter sequence, a multiple linear regression model is applied to calculate the compensation coefficient of the environment on the laser velocity, generating environmental parameter compensation data. The compensation formula is as follows: ; in For the compensated laser velocity, The original laser velocity, These represent the changes in temperature, humidity, and pressure, respectively. These are the regression coefficients for temperature, humidity, and pressure, respectively.

[0010] Preferably, the dynamic calibration process in S4 includes the following steps: S41. Input the filtered laser ranging data and environmental parameter compensation data into the adaptive Kalman filter algorithm. The algorithm's state equation and observation equation are respectively expressed as: ; ; in For system status, For the observed values, Here is the state transition matrix. To control the input matrix, For the observation matrix, and For process noise and observation noise, For discrete-time indexing; S42. Adjust the predicted value in real time through Kalman gain to generate preliminary calibration laser ranging data and calculate the calibration residual; S43. When the residual variance exceeds the threshold, the noise covariance matrix is ​​automatically updated to achieve adaptive adjustment.

[0011] Preferably, the accuracy verification process in S5 includes the following steps: S51. Obtain reference standard distance measurement data, which is obtained by measuring with a high-precision total station under the same conditions and stored in the standard database; S52. Align the preliminary calibration laser ranging data with the reference standard ranging data according to time, calculate the absolute error and relative error values, and generate laser ranging error analysis data. S53. Based on the laser ranging error analysis data, draw an error distribution map, and statistically analyze the error mean, standard deviation and confidence interval to evaluate the calibration effect.

[0012] Preferably, the recalibration process in S6 includes the following steps: S61. When the laser ranging error analysis data shows that the error exceeds the preset threshold, the recalibration process is triggered. S62. Construct a neural network model, with inputs including historical calibration data, environmental parameters, and real-time error, and outputs the optimized calibration parameters. S63. The neural network model adopts a convolutional neural network structure, and the training process uses the backpropagation algorithm. The loss function is the mean squared error. S64. Through iterative training, generate optimized calibration laser ranging data and verify its accuracy improvement rate.

[0013] Preferably, the step S7 of constructing the laser ranging calibration summary data includes the following steps: S71. Combine the optimized calibration laser ranging data, environmental parameter compensation data, and laser ranging error analysis data into a multi-dimensional data vector to construct laser ranging calibration summary data. S72. The laser ranging calibration summary data is compressed using a data compression algorithm to reduce storage space and is encrypted and stored on a cloud platform. S73. Generate a calibration report, including calibration time, accuracy indicators, and recommended usage range, for users to download and view.

[0014] Preferably, the method further includes a data security verification step after constructing the laser ranging calibration summary data, specifically including: S91. Perform digital signature processing on the laser ranging calibration summary data, generate a data digest using a hash algorithm, and attach a timestamp using asymmetric encryption technology; S92. Verify data integrity by comparing the hash values ​​before and after transmission to ensure that the data has not been tampered with. S93. Only verified data is allowed to be used for the final calibration result output; otherwise, an alarm will be triggered and the calibration process will be re-executed.

[0015] Preferably, the system includes: The laser data acquisition module receives external commands, emits a laser beam toward the target object through the laser emitting unit, collects the reflected signal using the laser receiving unit, and obtains temperature, humidity and atmospheric pressure data through the environmental sensing unit to generate raw laser ranging data. The noise filtering module receives the raw laser ranging data, performs multi-resolution analysis and threshold denoising through the wavelet transform algorithm unit, and generates filtered laser ranging data. The environmental compensation module receives the raw laser ranging data and generates environmental parameter compensation data through a multiple linear regression calculation unit. The dynamic calibration module receives the filtered laser ranging data and environmental parameter compensation data, and performs real-time parameter adjustment through the adaptive Kalman filter algorithm unit to generate preliminary calibration laser ranging data. The accuracy verification module receives the preliminary calibration laser ranging data, compares it with the reference standard ranging data through the error calculation unit, and generates laser ranging error analysis data. The recalibration trigger module receives the laser ranging error analysis data. When the error exceeds a preset threshold, it optimizes the calibration parameters through a neural network algorithm unit to generate optimized calibration laser ranging data. The data integration module receives the optimized calibration laser ranging data, environmental parameter compensation data, and laser ranging error analysis data, and constructs a summary laser ranging calibration data through a multi-dimensional data fusion unit. The security verification module receives the laser ranging calibration summary data, ensures data integrity through a digital signature unit and a hash verification unit, and outputs the verification result. The report generation module receives the verified calibration summary data and generates a calibration report through a visualization unit, including accuracy indicators and usage recommendations. The closed-loop feedback module receives the laser ranging error analysis data and verification results, and dynamically corrects the configuration parameters of the laser data acquisition module and the dynamic calibration module through the parameter adjustment unit, forming a closed-loop control of the calibration process.

[0016] Compared with the prior art, the present invention provides a high-precision laser ranging calibration method and system, which has the following beneficial effects: 1. In this invention, when performing high-precision laser ranging, the influence of changes in environmental parameters such as temperature, humidity and atmospheric pressure on laser propagation speed is sensed and compensated in real time. This can eliminate systematic deviations in ranging caused by environmental fluctuations, ensure the accuracy and reliability of ranging results under different working conditions, and improve the environmental adaptability of the ranging system.

[0017] 2. In this invention, when processing laser ranging signals, advanced signal decomposition and threshold filtering techniques are used to suppress signal noise introduced by the device itself and external interference, avoid ranging waveform distortion and feature extraction errors, and at the same time, a closed-loop verification mechanism ensures that the noise processing effect always meets the requirements, thereby improving the signal-to-noise ratio and overall accuracy of the ranging data.

[0018] 3. In this invention, during the operation of the laser ranging system, an intelligent closed-loop optimization mechanism is constructed to automatically diagnose the system status and dynamically adjust the calibration parameters based on the real-time error analysis results. When abnormal errors occur, a self-correction process can be quickly triggered to ensure that the calibration model continuously matches the actual measurement conditions, thereby ensuring the long-term stability and robustness of the ranging system. Attached Figure Description

[0019] Figure 1 This is a flowchart of a high-precision laser ranging calibration method according to the present invention; Figure 2 This is a schematic diagram of the high-precision laser ranging calibration system of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] For specific implementation examples, please refer to: Figure 1-2 A high-precision laser ranging calibration method and system, the method comprising the following steps: S1. Collect raw laser ranging data, including emitting a laser beam to the target object through a laser transmitter and receiving the reflected signal using a laser receiver to generate raw laser ranging waveform data; S2. Perform noise filtering on the laser ranging data. Based on the wavelet transform algorithm, perform noise reduction analysis on the original waveform data of laser ranging to generate filtered laser ranging data. S3. Measure the environmental parameters of the laser ranging system, including temperature, humidity and atmospheric pressure data, and generate environmental parameter compensation data; S4. Based on the filtered laser ranging data and environmental parameter compensation data, perform dynamic calibration processing of laser ranging. Adjust the ranging parameters in real time through the adaptive Kalman filter algorithm to generate preliminary calibration laser ranging data. S5. Perform laser ranging accuracy verification processing, compare the preliminary calibrated laser ranging data with the reference standard ranging data, and generate laser ranging error analysis data; S6. Based on the laser ranging error analysis data, when the laser ranging error exceeds the preset threshold, the laser ranging system is recalibrated. The calibration parameters are optimized based on the neural network algorithm to generate optimized calibration laser ranging data. S7. Construct a summary of laser ranging calibration data, integrate and optimize the calibration laser ranging data, environmental parameter compensation data, and error analysis data, and generate high-precision laser ranging calibration results.

[0022] The process of acquiring raw laser ranging data in S1 includes the following steps: S11. A high-precision laser rangefinder equipped with a multi-band laser emitter emits laser beams of different wavelengths toward the target object, and a high-speed photoelectric detector receives the reflected signals to generate original waveform data for multi-band laser ranging. S12. Import the raw polar waveform data of multi-band laser ranging into the data acquisition platform, and use time series analysis to extract the peak and valley features of each waveform to generate a raw laser ranging data matrix. The rows of the data matrix represent time points, and the columns represent laser band parameters. The specific operations for extracting peak and valley features using time series analysis include: S121. Divide the data points of each waveform into a sliding window in chronological order, with the window length being an integer multiple of the sampling period; S122. Calculate the first-order difference sequence within each window, and identify the critical point where the difference value changes from positive to negative as the peak value and the critical point where it changes from negative to positive as the valley value. S123. Record the timestamps and amplitudes corresponding to the peak and trough values ​​to form a feature vector; S124. Align the feature vectors of different waveforms according to time and combine them into a two-dimensional data matrix; S13. Based on the original laser ranging data matrix, perform data integrity checks, remove outliers and fill in missing data to ensure that the data quality meets the calibration requirements.

[0023] The noise filtering process in S2 includes the following steps: S21. Obtain the original laser ranging data matrix and apply the discrete wavelet transform algorithm for multi-resolution analysis, decomposing the data into approximation coefficients and detail coefficients. The discrete wavelet transform algorithm includes the following specific steps: S211. Select wavelet basis functions. The db4 wavelet in the Daubechies wavelet system is adopted as the basis function because it has compact support and symmetry, and is suitable for time-frequency analysis of laser ranging signals. S212. Perform multi-resolution decomposition. Input the original laser ranging data matrix according to the time series, and decompose it into approximation coefficients and detail coefficients through convolution operation. Set the decomposition level to 5 levels to balance accuracy and computational efficiency. S213. Apply Mallat algorithm to implement fast wavelet transform, reduce computational complexity and ensure real-time processing requirements through iterative filtering and downsampling operations; S214. Output the decomposed coefficient matrix, where the approximate coefficients represent the low-frequency components of the signal and the detail coefficients represent the high-frequency noise components, providing a basis for subsequent thresholding. S22. Soft thresholding is applied to the detail coefficients using a thresholding method to remove high-frequency noise components and reconstruct the filtered laser ranging data. The soft thresholding in the thresholding method includes the following specific steps: S221. Calculate the threshold for detail coefficients. Adaptively determine the global threshold using the Stein unbiased risk estimation method. The formula is: ; in This is the global threshold for the detail coefficients. The standard deviation of noise. This represents the number of training samples; S222, For each detail coefficient The soft threshold function is applied for processing, and the specific calculation method is as follows: when If the filtered value is zero, then the filtered value is zero. The filtered value is: This allows for the suppression of noise interference while preserving the effective signal trend. S223. Perform coefficient reconstruction by combining the filtered detail coefficients and approximate coefficients using inverse wavelet transform to generate smoothed laser ranging data. The inverse wavelet transform synthesis includes the following specific steps: S2231. Filter selection: The same Daubechies wavelet basis as the forward transform is used, and its reconstruction filter coefficients are... and Derived from wavelet functions; S2232, Upsampling operation: Upsample the filtered detail coefficients and approximation coefficients by a factor of 2, and insert zero values ​​to extend the data length; S2233, Convolutional Reconstruction: The upsampled coefficients are convolved with the reconstruction filter. The formula is as follows: ; in For the reconstructed data, These are the approximate coefficients after upsampling. These are the detail coefficients after upsampling. For low-pass reconstruction filter, For high-pass reconstruction filters; S2234. Perform boundary correction on the reconstructed data to eliminate edge distortion caused by the limited data length during the inverse transformation process, and ensure the smoothness of the generated laser ranging data over the entire mileage range. S23. Calculate the signal-to-noise ratio (SNR) of the data before and after filtering. If the SNR improvement rate is less than 10%, repeat steps S21 to S22 until the requirements are met.

[0024] The environmental parameter measurement in S3 includes the following steps: S31. Deploy an environmental sensor network, including temperature sensors, humidity sensors, and barometric pressure sensors, to collect environmental parameters around the laser ranging device in real time. S32. Align the environmental parameter data with the laser ranging timestamp to generate a time-synchronized environmental parameter sequence; S33. Based on the environmental parameter sequence, a multiple linear regression model is applied to calculate the compensation coefficient of the environment on the laser velocity, generating environmental parameter compensation data. The compensation formula is as follows: ; in For the compensated laser velocity, The original laser velocity, These represent the changes in temperature, humidity, and pressure, respectively. These are the regression coefficients for temperature, humidity, and pressure, respectively.

[0025] The dynamic calibration process in S4 includes the following steps: S41. Input the filtered laser ranging data and environmental parameter compensation data into the adaptive Kalman filter algorithm. The algorithm's state equation and observation equation are expressed as follows: ; ; in For system status, For the observed values, Here is the state transition matrix. To control the input matrix, For the observation matrix, and For process noise and observation noise, For discrete-time indexing; S42. Adjust the predicted value in real time through Kalman gain to generate preliminary calibration laser ranging data and calculate the calibration residual; S43. When the residual variance exceeds the threshold, the noise covariance matrix is ​​automatically updated to achieve adaptive adjustment. The specific implementation of the adaptive Kalman filter algorithm includes the following sub-steps: S431. Initialize the state vector and covariance matrix, and set the state vector. Given the initial ranging values, the error covariance matrix... It is the identity matrix; S432. Prediction steps: Calculate the prior state estimate and prior covariance based on the state equation. ; ; in This represents the prior state estimate. This represents the estimate of the previous state. Denotes the prior covariance matrix. Indicates control input, The process noise covariance matrix is... Representation matrix transpose; S433, Update Step: Calculate the Kalman gain using the observation equation and update the posterior estimate: ; ; ; in For Kalman gain, For the observation matrix, For matrix transpose, To observe the noise covariance, and These are the posterior state estimate and the covariance matrix, respectively. It is the identity matrix; S434. Adaptive adjustment, monitoring residual sequence. When the residual variance continuously exceeds the threshold Then, the maximum likelihood estimation is used to update the noise covariance: ; ; in The noise covariance matrix of the new process. The new observation noise covariance matrix, For time points The residual, For time points Kalman gain, To enable real-time optimization of the sliding window size, This is the index of the time point within the sliding window.

[0026] The accuracy verification process in S5 includes the following steps: S51. Obtain reference standard distance measurement data, which is obtained by measuring with a high-precision total station under the same conditions and stored in the standard database; S52. Align the preliminary calibration laser ranging data with the reference standard ranging data according to time, calculate the absolute error and relative error values, and generate laser ranging error analysis data. S53. Based on the laser ranging error analysis data, draw an error distribution map, and statistically analyze the error mean, standard deviation and confidence interval to evaluate the calibration effect.

[0027] The recalibration process in S6 includes the following steps: S61. When the laser ranging error analysis data shows that the error exceeds the preset threshold, the recalibration process is triggered. S62. Construct a neural network model, with inputs including historical calibration data, environmental parameters, and real-time error, and outputs the optimized calibration parameters. S63. The neural network model adopts a convolutional neural network structure. The training process uses the backpropagation algorithm, and the loss function is the mean squared error. The convolutional neural network includes the following steps: S631. Design the network structure. The input layer receives historical calibration data and environmental parameters. The convolutional layer uses a 3×3 convolutional kernel to extract local features. The pooling layer uses max pooling to reduce the number of parameters. S632, Forward propagation, processes input data through multiple convolutions and activation functions to generate feature maps; S633, fully connected layer integration features, outputs optimized values ​​for calibration parameters; S634. Regularization processing: Use Dropout technology to randomly ignore some neurons to prevent overfitting; The backpropagation algorithm includes the following specific steps: S635. Calculate the gradient of the loss function, using the mean squared error as the loss function. The formula is as follows: ; in The number of training samples. and Representing the first The predicted and actual values ​​of each sample. The sample index in the training dataset; S636. Backpropagation error: Starting from the output layer of the neural network, calculate the partial derivative of each connection weight in reverse order along the network layers. Specifically, gradients are passed layer by layer through the chain rule; S637. Update the weight parameters by adjusting the network weights using gradient descent. The update formula is as follows: ; in For the updated weights, For the old weight, The learning rate controls the step size for parameter updates; S638. Iterative training: Repeat the forward and backward propagation processes until the loss function value converges to the preset threshold and the maximum number of iterations is reached. S64. Through iterative training, generate optimized calibration laser ranging data and verify its accuracy improvement rate.

[0028] The steps involved in constructing the laser ranging calibration summary data in S7 are as follows: S71. Combine the optimized calibration laser ranging data, environmental parameter compensation data, and laser ranging error analysis data into a multi-dimensional data vector to construct a summary laser ranging calibration data. S72. The laser ranging calibration summary data is compressed using a data compression algorithm to reduce storage space and is then encrypted and stored on a cloud platform. The data compression algorithm uses ZIP compression and includes the following specific steps: During the S721 and LZ77 encoding stages, the laser ranging calibration summary data is scanned to find continuously repeating string patterns. When such repeating strings are found, they are replaced with data containing offset and length information to achieve the purpose of data compression. S722, Huffman coding stage, entropy coding is performed on the symbol sequence output by LZ77 to generate a variable-length coding table; S723, Compressed data packaging, combining encoded data and metadata into a ZIP format file; S724. Decompression verification: After compression, the original data is compared through decompression to ensure lossless compression. S73. Generate a calibration report, including calibration time, accuracy indicators, and recommended usage range, for users to download and view.

[0029] The method also includes a data security verification step after constructing the laser ranging calibration summary data, specifically including: S91. Perform digital signature processing on the laser ranging calibration summary data, generate a data digest using a hash algorithm, and append a timestamp using asymmetric encryption technology. The hash algorithm includes the following specific steps: S911. Select a hash function, use the SHA-256 algorithm to generate a data digest, input the laser ranging calibration summary data, and output a 256-bit hash value; S912. Calculate the digest, process the data block through a compression function with multiple iterations to ensure collision resistance; S913. Verify the hash value by comparing the hash values ​​before and after calculation. If they are inconsistent, trigger a data tampering alarm. Asymmetric encryption technology includes the following specific steps: S914. Key generation: Use the RSA algorithm to generate a public and private key pair, with the key length set to 2048 bits. S915. Encryption operation: Encrypt the data digest using the public key to generate a digital signature; S916. Decryption and verification: Decrypt the signature with the private key and compare it with the original digest to verify identity authentication. S917, timestamp integration, binds encrypted data to a trusted time source to ensure data freshness; S92. Verify data integrity by comparing the hash values ​​before and after transmission to ensure that the data has not been tampered with. S93. Only verified data is allowed to be used for the final calibration result output; otherwise, an alarm will be triggered and the calibration process will be re-executed.

[0030] The system includes: The laser data acquisition module receives external commands, emits a laser beam toward the target object through the laser emitting unit, collects the reflected signal using the laser receiving unit, and obtains temperature, humidity and atmospheric pressure data through the environmental sensing unit to generate raw laser ranging data. The noise filtering module receives the raw laser ranging data, performs multi-resolution analysis and threshold denoising through the wavelet transform algorithm unit, and generates filtered laser ranging data. The environmental compensation module receives raw laser ranging data and generates environmental parameter compensation data through a multiple linear regression calculation unit. The dynamic calibration module receives filtered laser ranging data and environmental parameter compensation data, and performs real-time parameter adjustment through the adaptive Kalman filter algorithm unit to generate preliminary calibration laser ranging data. The accuracy verification module receives the preliminary calibration laser ranging data, compares it with the reference standard ranging data through the error calculation unit, and generates laser ranging error analysis data. The recalibration trigger module receives laser ranging error analysis data. When the error exceeds a preset threshold, it optimizes the calibration parameters through a neural network algorithm unit to generate optimized calibration laser ranging data. The data integration module receives optimized calibration laser ranging data, environmental parameter compensation data, and laser ranging error analysis data, and constructs laser ranging calibration summary data through a multi-dimensional data fusion unit. The security verification module receives the laser ranging calibration summary data, ensures data integrity through a digital signature unit and a hash verification unit, and outputs the verification result. The report generation module receives the verified calibration summary data and generates a calibration report through a visualization unit, including accuracy indicators and usage recommendations. The closed-loop feedback module receives laser ranging error analysis data and verification results, and dynamically corrects the configuration parameters of the laser data acquisition module and the dynamic calibration module through the parameter adjustment unit, forming a closed-loop control of the calibration process.

[0031] The operation steps of this high-precision laser ranging calibration method and system are as follows: Step 1: Multi-source data acquisition and preprocessing This method begins with the comprehensive acquisition of raw laser ranging data. A multi-band laser emitter mounted on a high-precision laser rangefinder emits a laser towards the target object, and a high-speed photodetector receives the reflected signal to generate raw waveform data for multi-band laser ranging. Subsequently, the data is imported into a data acquisition platform, and time series analysis is used to extract the peak and valley features of each waveform. A raw laser ranging data matrix is ​​constructed with time points as rows and laser band parameters as columns. Based on this, data integrity is checked, outliers are removed, and missing data is filled to ensure that the quality of the input data meets the requirements of high-precision calibration.

[0032] Step 2: Real-time sensing and compensation of environmental parameters To overcome environmental interference, the system synchronously deploys a network of temperature, humidity, and air pressure sensors to collect environmental parameters around the ranging device in real time. These environmental parameter data are aligned with the laser ranging timestamp to form a time synchronization sequence. Then, a multiple linear regression model is applied to calculate the compensation coefficient of the laser propagation speed due to environmental changes, generating environmental parameter compensation data. This compensation model can quantify the impact of environmental fluctuations and correct the original laser speed in real time, providing an accurate data basis for subsequent calibration.

[0033] Step 3: Intelligent Filtering of Signal and Noise To suppress noise in the signal, the system employs a discrete wavelet transform algorithm to perform multi-resolution analysis on the original data matrix, decomposing it into approximation coefficients and detail coefficients. For the detail coefficients representing high-frequency noise, an adaptive soft thresholding filtering method based on Stein's unbiased risk estimation is used to process them, removing noise components while preserving signal characteristics. The filtered coefficients are reconstructed through inverse wavelet transform to generate smooth laser ranging data. This process also includes a closed-loop verification step, which evaluates the effect by calculating the signal-to-noise ratio improvement rate of the data before and after filtering. If the target is not met, the parameters are automatically adjusted and reprocessed to ensure the reliability of noise filtering.

[0034] Step 4: Dynamic calibration based on adaptive filtering The core calibration process utilizes an adaptive Kalman filter algorithm. This algorithm takes filtered laser ranging data and environmental parameter compensation data as input, and performs recursive prediction and updating through state equations and observation equations. After initialization, the algorithm calculates the prior state estimate and covariance in the prediction step, and calculates the Kalman gain based on real-time observations in the update step, and updates the posterior state estimate. The algorithm has adaptive capabilities; by continuously monitoring the residual sequence, it automatically updates the covariance matrix of process noise and observation noise when the residual variance exceeds a threshold, thereby achieving dynamic adjustment of calibration parameters and generating preliminary calibrated laser ranging data.

[0035] Step 5: Calibration accuracy verification and error analysis After generating the preliminary calibration results, the system enters the accuracy verification stage. By comparing the preliminary calibration data with the reference standard distance measurement data obtained by the high-precision total station, the absolute error and relative error are calculated, and laser distance measurement error analysis data is generated. This process also includes drawing an error distribution map and statistically analyzing the error mean, standard deviation and confidence interval, thereby comprehensively evaluating the accuracy and stability of the calibration effect.

[0036] Step Six: Intelligent Recalibration and Parameter Optimization When error analysis shows that the error exceeds the preset threshold, the system automatically triggers the recalibration process. This process constructs a convolutional neural network model, takes historical calibration data, real-time environmental parameters and error data as input, and optimizes the network weights through forward propagation and iterative training based on backpropagation algorithms. The optimized calibration parameters are then output, and finally, more accurate optimized calibration laser ranging data is generated to meet the calibration challenges under complex working conditions.

[0037] Step 7: Data Integration and Security Verification All key data, including optimized calibration laser ranging data, environmental parameter compensation data, and error analysis data, are integrated to construct a summary laser ranging calibration data. To ensure data security and integrity, the summary data is digitally signed, a hash algorithm is used to generate a data digest, and asymmetric encryption technology is used to add a timestamp. Before and after transmission and storage, the hash value is compared to verify whether the data has been tampered with, thus ensuring the reliability of the calibration results.

[0038] Step 8: Closed-loop feedback and system self-optimization Ultimately, the system forms a closed-loop feedback mechanism. Error analysis results and safety verification results are fed back to the system's front-end data acquisition module and dynamic calibration module, dynamically correcting their configuration parameters. This mechanism enables the entire system to self-optimize based on real-time operating status, continuously improving the accuracy, reliability, and long-term stability of laser ranging, forming a complete intelligent calibration closed loop.

[0039] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-precision laser ranging calibration method, characterized in that: The method includes the following steps: S1. Collect raw laser ranging data, including emitting a laser beam to the target object through a laser transmitter and receiving the reflected signal using a laser receiver to generate raw laser ranging waveform data; S2. Perform noise filtering on the laser ranging data. Based on the wavelet transform algorithm, perform noise reduction analysis on the original waveform data of the laser ranging to generate filtered laser ranging data. S3. Measure the environmental parameters of the laser ranging system, including temperature, humidity and atmospheric pressure data, and generate environmental parameter compensation data; S4. Based on the filtered laser ranging data and the environmental parameter compensation data, perform dynamic calibration processing of laser ranging, and adjust the ranging parameters in real time through an adaptive Kalman filter algorithm to generate preliminary calibration laser ranging data. S5. Perform laser ranging accuracy verification processing, compare the preliminary calibrated laser ranging data with the reference standard ranging data, and generate laser ranging error analysis data; S6. Based on the laser ranging error analysis data, when the laser ranging error exceeds the preset threshold, the laser ranging system is recalibrated. The calibration parameters are optimized based on the neural network algorithm to generate optimized calibration laser ranging data. S7. Construct a summary data of laser ranging calibration, integrate the optimized calibration laser ranging data, environmental parameter compensation data and error analysis data, and generate high-precision laser ranging calibration results.

2. The high-precision laser ranging calibration method according to claim 1, characterized in that: The process of acquiring raw laser ranging data in S1 includes the following steps: S11. A high-precision laser rangefinder equipped with a multi-band laser emitter emits laser beams of different wavelengths toward the target object, and a high-speed photodetector receives the reflected signals to generate original waveform data for multi-band laser ranging. S12. Import the original polar waveform data of the multi-band laser ranging into the data acquisition platform, and use the time series analysis method to extract the peak and valley characteristics of each waveform to generate the original laser ranging data matrix, wherein the rows of the data matrix represent time points and the columns represent laser band parameters; S13. Based on the original laser ranging data matrix, perform a data integrity check, remove outliers, and fill in missing numbers.

3. The high-precision laser ranging calibration method according to claim 1, characterized in that: The noise filtering process in S2 includes the following steps: S21. Obtain the original laser ranging data matrix, apply the discrete wavelet transform algorithm to perform multi-resolution analysis, and decompose the data into approximation coefficients and detail coefficients. S22. Soft threshold filtering is applied to detail coefficients using a thresholding method to remove high-frequency noise components, and the signal is reconstructed to generate filtered laser ranging data. S23. Calculate the signal-to-noise ratio (SNR) of the data before and after filtering. If the SNR improvement rate is less than 10%, repeat steps S21 to S22 until the requirements are met.

4. The high-precision laser ranging calibration method according to claim 1, characterized in that: The environmental parameter measurement in S3 includes the following steps: S31. Deploy an environmental sensor network, including temperature sensors, humidity sensors, and barometric pressure sensors, to collect environmental parameters around the laser ranging device in real time. S32. Align the environmental parameter data with the laser ranging timestamp to generate a time-synchronized environmental parameter sequence; S33. Based on the environmental parameter sequence, a multiple linear regression model is applied to calculate the compensation coefficient of the environment on the laser velocity, generating environmental parameter compensation data. The compensation formula is as follows: ； in For the compensated laser velocity, The original laser velocity, These represent the changes in temperature, humidity, and pressure, respectively. These are the regression coefficients for temperature, humidity, and pressure, respectively.

5. The high-precision laser ranging calibration method according to claim 1, characterized in that: The dynamic calibration process in S4 includes the following steps: S41. Input the filtered laser ranging data and environmental parameter compensation data into the adaptive Kalman filter algorithm. The algorithm's state equation and observation equation are respectively expressed as: ； ； in For system status, For the observed values, Here is the state transition matrix. To control the input matrix, For the observation matrix, and For process noise and observation noise, For discrete-time indexing; S42. Adjust the predicted value in real time through Kalman gain to generate preliminary calibration laser ranging data and calculate the calibration residual; S43. When the residual variance exceeds the threshold, the noise covariance matrix is ​​automatically updated to achieve adaptive adjustment.

6. The high-precision laser ranging calibration method according to claim 1, characterized in that: The accuracy verification process in S5 includes the following steps: S51. Obtain reference standard distance measurement data, which is obtained by measuring with a high-precision total station under the same conditions and stored in the standard database; S52. Align the preliminary calibration laser ranging data with the reference standard ranging data according to time, calculate the absolute error and relative error values, and generate laser ranging error analysis data. S53. Based on the laser ranging error analysis data, draw an error distribution map, and statistically analyze the error mean, standard deviation and confidence interval to evaluate the calibration effect.

7. The high-precision laser ranging calibration method according to claim 1, characterized in that: The recalibration process in S6 includes the following steps: S61. When the laser ranging error analysis data shows that the error exceeds the preset threshold, the recalibration process is triggered. S62. Construct a neural network model, with inputs including historical calibration data, environmental parameters, and real-time error, and outputs the optimized calibration parameters. S63. The neural network model adopts a convolutional neural network structure, and the training process uses the backpropagation algorithm. The loss function is the mean squared error. S64. Through iterative training, generate optimized calibration laser ranging data and verify its accuracy improvement rate.

8. The high-precision laser ranging calibration method according to claim 1, characterized in that: The steps involved in constructing the laser ranging calibration summary data in S7 are as follows: S71. Combine the optimized calibration laser ranging data, environmental parameter compensation data, and laser ranging error analysis data into a multi-dimensional data vector to construct laser ranging calibration summary data. S72. The laser ranging calibration summary data is compressed using a data compression algorithm to reduce storage space and is encrypted and stored on a cloud platform. S73. Generate a calibration report, including calibration time, accuracy indicators, and recommended usage range, for users to download and view.

9. The high-precision laser ranging calibration method according to claim 1, characterized in that: The method, after constructing the laser ranging calibration summary data, also includes a data security verification step, specifically including: S91. Perform digital signature processing on the laser ranging calibration summary data, generate a data digest using a hash algorithm, and attach a timestamp using asymmetric encryption technology; S92. Verify data integrity by comparing the hash values ​​before and after transmission to ensure that the data has not been tampered with. S93. Only verified data is allowed to be used for the final calibration result output; otherwise, an alarm will be triggered and the calibration process will be re-executed.

10. A high-precision laser ranging calibration system, characterized in that: The system for implementing the high-precision laser ranging calibration method according to any one of claims 1-9 comprises: The laser data acquisition module receives external commands, emits a laser beam toward the target object through the laser emitting unit, collects the reflected signal using the laser receiving unit, and obtains temperature, humidity and atmospheric pressure data through the environmental sensing unit to generate raw laser ranging data. The noise filtering module receives the raw laser ranging data, performs multi-resolution analysis and threshold denoising through the wavelet transform algorithm unit, and generates filtered laser ranging data. The environmental compensation module receives the raw laser ranging data and generates environmental parameter compensation data through a multiple linear regression calculation unit. The dynamic calibration module receives the filtered laser ranging data and environmental parameter compensation data, and performs real-time parameter adjustment through the adaptive Kalman filter algorithm unit to generate preliminary calibration laser ranging data. The accuracy verification module receives the preliminary calibration laser ranging data, compares it with the reference standard ranging data through the error calculation unit, and generates laser ranging error analysis data. The recalibration trigger module receives the laser ranging error analysis data. When the error exceeds a preset threshold, it optimizes the calibration parameters through a neural network algorithm unit to generate optimized calibration laser ranging data. The data integration module receives the optimized calibration laser ranging data, environmental parameter compensation data, and laser ranging error analysis data, and constructs a summary laser ranging calibration data through a multi-dimensional data fusion unit. The security verification module receives the laser ranging calibration summary data, ensures data integrity through a digital signature unit and a hash verification unit, and outputs the verification result. The report generation module receives the verified calibration summary data and generates a calibration report through a visualization unit, including accuracy indicators and usage recommendations. The closed-loop feedback module receives the laser ranging error analysis data and verification results, and dynamically corrects the configuration parameters of the laser data acquisition module and the dynamic calibration module through the parameter adjustment unit, forming a closed-loop control of the calibration process.

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