Laser intelligent observation instrument light path fine adjustment system with automatic calibration function

Abstract

The invention relates to the technical field of laser intelligent measurement, discloses a laser intelligent observation instrument light path fine adjustment system with an automatic calibration function, and realizes real-time monitoring and automatic accurate adjustment of a laser observation instrument light path through combination of multi-dimensional sensor monitoring, intelligent light path analysis, a self-adaptive compensation algorithm and precise mechanical adjustment. The system adopts an adaptive Kalman filtering algorithm to process four-quadrant photoelectric detector signals, establishes a multi-sensor timestamp alignment mechanism and a hierarchical data fusion architecture, and obtains high-precision fusion sensor data; real-time optical path deviation analysis is carried out by adopting an edge computing architecture and a special FPGA (Field Programmable Gate Array) chip; a hybrid drive adjusting mechanism is adopted, a voice coil motor is used for coarse adjustment, piezoelectric ceramic stacks are used for fine adjustment, and nanoscale adjustment precision is achieved; a multi-target distance calibration verification mechanism is established, and the calibration effect is verified through cross validation of a physical target and a virtual target. According to the invention, automatic calibration can be carried out in real time in the normal working process of equipment.

Description

Technical Field

[0001] This invention relates to the field of laser intelligent measurement technology, and more specifically, to a laser intelligent observation instrument optical path fine-tuning system with automatic calibration function. Background Technology

[0002] Laser intelligent observation instruments, as modern precision optical measurement devices, play a vital role in military reconnaissance, target location, fire correction, and civilian range finding. Traditional laser observation instruments employ pulsed laser range finding technology, calculating target distance by measuring the flight time of the laser pulse. However, in practical use, factors such as changes in ambient temperature, mechanical vibration, laser aging, and thermal expansion and contraction of optical components can easily cause misalignment of the laser emission and reception paths, leading to decreased range finding accuracy and severely impacting the reliability and effectiveness of the equipment.

[0003] Current technologies primarily address optical path misalignment issues through manual calibration or periodic factory maintenance. Manual calibration requires specialized technicians to operate using standard targets; the calibration process is complex and time-consuming, and cannot be performed in real-time during equipment use. While periodic factory maintenance can completely resolve the problem, it is time-consuming, costly, and cannot respond promptly to changes in the optical path. These traditional methods all suffer from limitations such as the inability to perform real-time calibration, reliance on specialized personnel, and limited calibration accuracy, failing to meet the high precision, high reliability, and high intelligence requirements of modern laser observation equipment.

[0004] While existing automatic calibration technologies have solved the problems of manual calibration to some extent, they still suffer from technical defects such as insufficient calibration accuracy, poor environmental adaptability, and low level of intelligence. There is an urgent need for a new technology solution that can achieve real-time unattended automatic calibration. Summary of the Invention

[0005] This invention provides a laser intelligent observation instrument optical path fine-tuning system with automatic calibration function, which solves the technical problems of insufficient calibration accuracy, poor environmental adaptability and low level of intelligence in related technologies.

[0006] This invention provides a laser intelligent observation instrument optical path fine-tuning system with automatic calibration function, comprising: The sensor monitoring module is used to acquire the raw signals and environmental change data of the laser intelligent observation instrument. It uses adaptive Kalman filtering to fuse multi-source data to obtain optical path status data. The data processing module is used to perform real-time optical path deviation analysis on the optical path status data using a distributed processing method based on an edge computing architecture, and to obtain the optical path deviation analysis results. The hybrid drive adjustment module is used to achieve rapid optical path adjustment based on the optical path deviation analysis results, and obtain the optical path adjustment results. The calibration and verification module is used to acquire the optical path adjustment results, perform multi-target distance verification on the ranging performance of the adjusted laser intelligent observation instrument, and obtain full-range calibration and verification results. The reliability assessment module is used to obtain full-range calibration verification results, combine them with historical operating data of the laser intelligent observation instrument, and establish a reliability model using digital twin technology to obtain assurance data; The intelligent control module is used to learn the relationship between environment and performance using a long short-term memory network based on the protection data and the history of environmental changes, so as to obtain an intelligently optimized environment adaptive calibration strategy.

[0007] In a preferred embodiment, applying adaptive Kalman filtering to the original signal includes: Acquire photocurrent signal intensity values ​​in the four quadrants as observations; calculate the positional offsets in the X and Y directions; Initialize the filter state; perform state prediction, and calculate the Kalman gain based on the state estimate from the previous time step and the predicted state value for the current time step; The state estimate is updated by correcting the residuals between the observed and predicted state values ​​and then updating the state.

[0008] In a preferred embodiment, the multi-source data fusion in the sensor monitoring module further includes: Establish a multi-sensor timestamp alignment mechanism. The main controller triggers the mechanism uniformly, and each sensor records its original timestamp and compensates for the response delay to obtain the aligned timestamp. A sensor fault detection and isolation mechanism is constructed. The average value and standard deviation of all sensor measurements are calculated. If the deviation between the sensor measurement value and the average value exceeds three times the standard deviation, it is judged as abnormal and removed.

[0009] In a preferred embodiment, the multi-source data fusion processing in the sensor monitoring module adopts a layered fusion architecture: The first layer performs data fusion of similar sensors by weighted averaging of data from multiple temperature sensors and normalizing the weights of each sensor before weighted fusion. The second layer performs heterogeneous sensor data fusion, normalizes and compensates the data from various sensors, and then performs weighted fusion based on the sensor measurement accuracy.

[0010] In a preferred embodiment, the distributed processing method based on an edge computing architecture includes: A hierarchical processing architecture is established, with the main processor responsible for overall coordination and decision-making, a dedicated FPGA chip responsible for high-speed signal processing, and a DSP chip responsible for digital filtering and numerical calculation. Parallel data exchange is achieved, with each processing unit exchanging data via a high-speed bus to enable parallel computing; Design the internal processing core of the FPGA. The FPGA integrates multiple parallel processing cores, each responsible for its own computational tasks. A pipeline architecture is constructed, and the process of calculating the centroid position of the light spot is divided into three stages: data preprocessing, centroid calculation, and coordinate transformation. Each stage is executed in parallel.

[0011] In a preferred embodiment, the real-time optical path deviation analysis in the data processing module includes: Constructing a fast algorithm for optical path deviation detection: A method for calculating the rate of change is established to obtain the angle deviation values ​​between the current moment and the previous moment. The rate of change of optical path deviation is then calculated by the change in angle deviation and the time interval. Angle deviation calculation is performed, which is obtained by calculating the arctangent function relationship between the spot position offset and the equivalent focal length of the receiving optical system. Implement a dynamic threshold adjustment mechanism to automatically adjust the deviation detection threshold according to environmental conditions; An algorithm for identifying optical path deviation types was established, which classifies deviations into three types: angular deviation, positional deviation, and power deviation based on the directionality, periodicity, and amplitude characteristics of the deviation.

[0012] In a preferred embodiment, the hybrid drive adjustment in the hybrid drive adjustment module includes: The design incorporates a hybrid drive structure, with coarse adjustment using a voice coil motor and fine adjustment using a piezoelectric ceramic stack drive. A dual closed-loop control system is established, with the outer loop being the position control loop, using the deviation between the target position and the actual position as the input; and the inner loop being the current control loop, using the deviation between the target current and the actual current as the input. Implement a predictive control algorithm, obtain the current control output, calculate the rate of change of the control output, set the prediction coefficient and prediction time according to the system's response characteristics, obtain the prediction compensation amount based on the rate of change of the control output, the prediction coefficient and the prediction time, and obtain the final predictive control output based on the current control output and the prediction compensation amount.

[0013] In a preferred embodiment, the multi-target distance verification in the calibration verification module includes: Design a variable-distance built-in calibration target system, including an electric guide rail, a standard reflector, and a position encoder; A virtual target calibration method was established, which uses fiber optic delay lines to simulate laser echo signals at different distances; The system calculates the length of the fiber delay line, determines the simulated distance, and obtains the total optical path of the laser signal based on the simulated distance; it acquires the refractive index parameter of the fiber used, and obtains the actual propagation speed of the light signal in the fiber based on the refractive index parameter; and outputs the required fiber delay line length. Constructing an optical fiber delay line system includes: a laser coupler that couples the laser signal into the optical fiber, an optical fiber disk that provides the required delay length, and a photodetector that converts the delayed optical signal into an electrical signal.

[0014] In a preferred embodiment, the calibration verification module further includes: Construct a cross-validation mechanism that uses both physical and virtual targets for calibration and validation; To achieve consistency assessment, the ranging results of the two methods are compared, and the correlation coefficient method is used to evaluate the consistency of the calibration effect and obtain the correlation coefficient. A statistical analysis algorithm is implemented to evaluate the calibration stability through the statistical characteristics of multiple measurements. The ranging stability index is obtained based on the average and variance of multiple ranging results.

[0015] In a preferred embodiment, the digital twin technology in the reliability assessment module includes: Establish a digital twin model of the equipment and construct a comprehensive digital model that includes an optical system model, a mechanical system model, an electronic system model, and an environmental impact model; A mechanical wear prediction model was constructed. Based on the frequency of use of the adjustment mechanism, the load size and environmental conditions, the Weibull distribution model was used to predict the wear degree and remaining life of key components. Design an adaptive calibration strategy to dynamically adjust calibration parameters and calibration frequency based on the aging degree and performance change trend of the equipment; Establish a performance degradation compensation mechanism to reduce the slow degradation of hardware performance through hardware compensation. Establish an equipment health status assessment system, calculate equipment health scores through comprehensive analysis, and output system reliability assessment results and maintenance recommendations.

[0016] The beneficial effects of this invention are as follows: Real-time unattended automatic calibration of the laser observation instrument's optical path system has been achieved. By integrating multi-dimensional sensor monitoring, intelligent optical path analysis, adaptive compensation algorithms, and precision mechanical adjustment mechanisms, the system can detect optical path deviations in real time and automatically perform accurate calibration during normal operation of the equipment without interrupting normal operation or requiring professional personnel to operate it, and the calibration accuracy is improved. The system enhances the adaptability and stability of the laser observation instrument in harsh environments. It establishes a multi-parameter fusion-based optical path deviation detection model and an environmental adaptive compensation mechanism, enabling predictive compensation based on changes in environmental parameters such as temperature, humidity, air pressure, and vibration. This ensures stable operation in both high-temperature and low-temperature environments. Through deep learning-based intelligent algorithms and digital twin technology, the system possesses self-learning and predictive capabilities, proactively optimizing calibration strategies based on historical data and environmental trends. This results in long-term stable, high-precision ranging performance, improving equipment availability and reliability. Attached Figure Description

[0017] Figure 1 This is a block diagram of a laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to the present invention; Figure 2 This is a comparison chart of the calibration effects of the present invention at different distances; Figure 3 This is a radar chart showing the environmental adaptability of the present invention. Detailed Implementation

[0018] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0019] At least one embodiment of the present invention discloses a laser intelligent observation instrument optical path fine-tuning system with automatic calibration function, such as Figure 1 As shown, it includes: The sensor monitoring module is used to acquire the raw signals and environmental change data of the laser intelligent observation instrument. It uses adaptive Kalman filtering to fuse multi-source data to obtain optical path status data. Acquire raw signals from each sensor and environmental change data under normal operating conditions of the laser observation instrument. The raw signals from each sensor include the photocurrent signal output by the four-quadrant photodetector, the temperature distribution signal output by the temperature sensor, the ambient light intensity signal output by the light intensity sensor, and the vibration signal output by the triaxial accelerometer. Environmental change data includes real-time acquisition of laser base temperature, optical platform temperature, and ambient temperature gradient by temperature sensors; ambient background light intensity, instantaneous scintillation, and spectral distribution synchronously recorded by light intensity sensors; relative humidity and dew point temperature provided by humidity sensors; air pressure change rate output by barometric pressure sensors; platform vibration spectrum, impact amplitude, and attitude angular rate provided by a combination of triaxial accelerometers and gyroscopes; ambient electromagnetic field strength and spectrum detected by electromagnetic sensors; and environmental change data aligned with the original signals using a unified timestamp. An adaptive Kalman filter algorithm is used to process the signals from the four-quadrant photodetector; specifically, this includes: A signal model of a four-quadrant photodetector is established, using the photocurrent signals of the laser echo spot in each of the four quadrants as the observables. The centroid position shift of the spot is calculated by analyzing the differences between the signals in each quadrant. The specific calculation steps are as follows:

[0020] The raw signals from the four-quadrant photodetector are acquired, and the photocurrent signal intensity values ​​in the first, second, third, and fourth quadrants are recorded respectively. The positional offset in the X direction is calculated. The photocurrent signals in the first and fourth quadrants are added together to obtain the total signal intensity in the right quadrant. The photocurrent signals in the second and third quadrants are added together to obtain the total signal intensity in the left quadrant. The difference between the total signal intensity on the right and the total signal intensity on the left is calculated. This difference is then divided by the sum of the photocurrent signals in the four quadrants to obtain the normalized X-direction offset ratio. Finally, this ratio is... Multiply by the calibration coefficient in the X direction to obtain the actual position offset in the X direction; calculate the position offset in the Y direction by adding the photocurrent signals in the first and second quadrants to obtain the total signal intensity in the upper quadrant; add the photocurrent signals in the third and fourth quadrants to obtain the total signal intensity in the lower quadrant; calculate the difference between the total signal intensity in the upper quadrant and the total signal intensity in the lower quadrant; then divide this difference by the sum of the photocurrent signals in the four quadrants to obtain the normalized Y-direction offset ratio; finally, multiply this ratio by the calibration coefficient in the Y direction to obtain the actual position offset in the Y direction. The position offset of the centroid of the output spot in the X and Y directions is used as input data for subsequent filtering processing.

[0021] An adaptive Kalman filter algorithm is used to filter the position offset signal, dynamically adjusting the filter parameters to adapt to changes in ambient light. The specific steps of the filtering process are as follows:

[0022] Initialize the filter state by setting initial values ​​for the initial state estimate, initial error covariance matrix, system noise covariance matrix, and observation noise covariance matrix. Perform state prediction by predicting the current state value based on the previous state estimate and the system dynamic model. Simultaneously, calculate the prediction error covariance matrix for the current time step based on the system noise characteristics. Calculate the Kalman gain and the product of the observation matrix and the prediction error covariance matrix. Calculate the combined term of the observation matrix, prediction error covariance matrix, and observation noise covariance matrix. Perform matrix inversion on this combined term. Finally, calculate the prediction error... The Kalman gain is obtained by multiplying the covariance matrix by the transpose of the observation matrix and then multiplying by the inverse of the result. The state estimate is updated by obtaining the current observation value, i.e., the spot position offset of the output of the four-quadrant detector. The residual between the observation value and the predicted state value is calculated. The residual is multiplied by the Kalman gain to obtain the state correction. Finally, the state correction is added to the predicted state value to obtain the optimal state estimate at the current time. The error covariance matrix is ​​updated by calculating the difference between the identity matrix and the product of the Kalman gain and the observation matrix, and multiplying this difference by the prediction error covariance matrix to obtain the updated error covariance matrix. The offset of the output filtered light spot position.

[0023] The adaptive adjustment mechanism updates the noise covariance matrix by analyzing the statistical characteristics of the observed residuals. The specific adjustment steps are as follows:

[0024] Ambient light intensity is detected by real-time monitoring of the current ambient light intensity using a light intensity sensor. An ambient light influence factor is calculated by multiplying the current ambient light intensity by a preset ambient light influence coefficient to obtain the influence factor of ambient light on the noise covariance. The noise covariance matrix is ​​adjusted by multiplying the baseline noise covariance value by an adjustment factor, where the adjustment factor equals 1 plus the ambient light influence factor, to obtain a noise covariance matrix adapted to the current ambient light conditions. An adjustment strategy is applied: when the ambient light is strong, the noise covariance value is increased to reduce the weight of the observed data and mitigate the impact of strong light interference; when the ambient light is weak, the noise covariance value is decreased to increase the weight of the observed data and fully utilize weak signal information. A multi-sensor timestamp alignment mechanism is established, ensuring simultaneous sampling by all sensors through hardware synchronization trigger signals, and eliminating sensor response time differences using a software time compensation algorithm. The specific steps of time alignment are as follows: Hardware synchronization triggering: The system's main controller sends a unified synchronization trigger signal to all sensors, ensuring that each sensor starts data acquisition at the same time. Recording original timestamps: Each sensor records its original timestamp when it begins data acquisition after receiving the trigger signal. Calculating response time differences: Based on the technical specifications of each sensor and actual test results, the response delay time from receiving the trigger signal to actually starting data acquisition is determined for each sensor. Applying time compensation: The original timestamp of each sensor is added with the corresponding time compensation amount to obtain a corrected aligned timestamp, ensuring the consistency of all sensor data in the time dimension. Verifying the alignment effect: By comparing the consistency of the aligned timestamps of each sensor, the accuracy of time synchronization is verified, ensuring that the time deviation is controlled within the system's required accuracy range. Designing a hierarchical data fusion architecture: The first layer performs data fusion of similar sensors, weighted averaging of data from multiple temperature sensors; the second layer performs data fusion of dissimilar sensors, comprehensively processing location information, temperature information, vibration information, etc. The calculation steps for the fusion weights are as follows: The process involves acquiring sensor accuracy information, determining the measurement uncertainty of each sensor through sensor calibration experiments (this uncertainty reflects the sensor's measurement accuracy level), calculating the square of the reciprocal of the accuracy, squaring the reciprocal of the measurement uncertainty for each sensor to obtain the accuracy weighting factor for each sensor, summing the accuracy weighting factors of all participating sensors, and then normalizing the weights by dividing the accuracy weighting factor of each sensor by the sum of the weighting factors to obtain the normalized weight of that sensor in the data fusion. The weight allocation principle is to assign a larger fusion weight to sensors with higher measurement accuracy (lower uncertainty) and a smaller fusion weight to sensors with lower measurement accuracy (higher uncertainty), ensuring that high-precision data dominates the fusion result. Before calculating the fusion weights, the measurement uncertainties of each sensor need to be standardized and preprocessed. The specific preprocessing steps are as follows:

[0025] The process involves identifying differences in uncertainty dimensions and analyzing the measurement uncertainty units of various sensors, such as millimeters for position sensors, degrees Celsius for temperature sensors, and acceleration for vibration sensors. It also involves obtaining the sensor measurement range to determine the effective measurement range of each sensor, representing the numerical range it can accurately measure. The relative uncertainty is calculated by dividing the absolute uncertainty of each sensor by its corresponding measurement range to obtain a dimensionless relative uncertainty, thus eliminating the influence of different physical quantity dimensions. Finally, the relative uncertainty is applied in subsequent weighting calculations to replace the original absolute uncertainty, ensuring comparability between different types of sensors during the fusion process. Before calculating the weights, the sensor data needs to be preprocessed. The specific steps for data preprocessing are as follows: Data normalization is performed to determine the minimum and maximum values ​​of each sensor's output data. The minimum value is subtracted from the original sensor output value to obtain the offset value. The offset value is then divided by the difference between the maximum and minimum values ​​to obtain the normalized data. Finally, the output values ​​of different sensors are mapped to a unified numerical range of 0 to 1. Temperature compensation is performed by obtaining the current ambient temperature. The difference between the current temperature and the reference temperature is calculated. This temperature difference is multiplied by the sensor's temperature coefficient to obtain the temperature influence factor. The temperature influence factor is then incremented by 1 to obtain the temperature compensation coefficient. Finally, the normalized data is multiplied by the temperature compensation coefficient to obtain the final temperature-compensated data, eliminating the influence of ambient temperature on the sensor output. A sensor fault detection and isolation mechanism is constructed. By analyzing the statistical characteristics of the sensor output, abnormal sensors are identified and their data is automatically removed. The specific steps for fault detection are as follows:

[0026] Sensor data is collected, acquiring the current measurement values ​​of all participating sensors to form a sensor dataset. Statistical parameters are calculated, including the arithmetic mean of all sensor measurements, which serves as the central tendency of the dataset. Simultaneously, the standard deviation of all sensor measurements is calculated to reflect the data dispersion. The 3σ criterion is applied to calculate the absolute deviation of each sensor's measurement from the mean. This absolute deviation is compared to three times the standard deviation; if the absolute deviation is greater than three times the standard deviation, the sensor is considered abnormal. Abnormal sensors are isolated, removing detected abnormal sensors from the data fusion process to prevent them from participating in subsequent weight calculations and data fusion, thus avoiding the impact of abnormal data on the accuracy of the fusion results. Dynamic monitoring continuously monitors the output status of isolated sensors, and once their output returns to normal, they are reintegrated into the data fusion process. We obtain high-precision, low-noise fused sensor data, including accurate spot position offset, reliable temperature distribution information, and effective vibration data.

[0027] Furthermore, a multi-scale signal processing method based on wavelet transform can be used to replace traditional Kalman filtering. Wavelet transform can analyze signals simultaneously in the time and frequency domains, better handling non-stationary signals. The specific implementation steps of wavelet denoising are as follows:

[0028] Wavelet basis function selection: Based on the characteristics of the four-quadrant photodetector signal, a suitable wavelet basis function, such as the Daubechies wavelet or the Biorthogonal wavelet, is selected to ensure that the wavelet basis function matches the signal characteristics. Multi-scale decomposition: The original signal of the four-quadrant photodetector is decomposed into multiple wavelet coefficients at different frequency scales. High-frequency coefficients mainly contain noise components, while low-frequency coefficients mainly contain useful signal components. Threshold determination: An adaptive threshold selection method is used to dynamically determine the threshold based on the statistical characteristics of each layer of wavelet coefficients. The standard deviation of each layer of wavelet coefficients is calculated, and the standard deviation is multiplied by... The adjustment factor serves as the threshold for this layer; soft thresholding is applied to the wavelet coefficients of each layer. When the absolute value of a wavelet coefficient is greater than the threshold, the coefficient is subtracted from the threshold while keeping the sign unchanged; when the absolute value of a wavelet coefficient is less than or equal to the threshold, the coefficient is set to zero, effectively removing noise components; signal reconstruction involves performing inverse wavelet transform on the wavelet coefficients of each layer after thresholding to reconstruct the denoised four-quadrant photodetector signal, improving signal quality; the purpose of using a multi-scale signal processing method based on wavelet transform is to improve signal processing capabilities in strong noise environments, especially suitable for environments with severe electromagnetic interference such as anti-interference training grounds.

[0029] The data processing module is used to perform real-time optical path deviation analysis on the optical path status data using a distributed processing method based on an edge computing architecture, and to obtain the optical path deviation analysis results. Real-time optical path deviation analysis is performed based on fused sensor data and an edge computing architecture. The complex optical path analysis algorithm is decomposed into multiple parallel processing units; the main processor is responsible for overall coordination and decision-making, the dedicated FPGA chip is responsible for high-speed signal processing, and the DSP chip is responsible for digital filtering and numerical calculation. The processing units exchange data via a high-speed bus to achieve parallel computing.

[0030] Design a dedicated optical path analysis FPGA chip to achieve hardware-accelerated real-time signal processing; the FPGA integrates multiple parallel processing cores, each responsible for a specific computational task; the calculation of the spot centroid position adopts a pipeline architecture, dividing the calculation process into three stages: data preprocessing, centroid calculation, and coordinate transformation, with each stage executed in parallel within different clock cycles.

[0031] A hierarchical processing strategy is established; when an optical path deviation exceeds the emergency threshold, a simplified algorithm is used to quickly generate calibration instructions; when the deviation is within the normal range, a complete algorithm is used for precise analysis. The specific steps for setting the emergency threshold are as follows:

[0032] Determine the normal operating threshold: Based on system design requirements and actual test results, determine the maximum allowable deviation angle of the laser optical path under normal operating conditions; this angle is used as the normal operating threshold. Calculate the emergency threshold: Multiply the normal operating threshold by a safety factor of 2 to obtain the emergency handling threshold; when the optical path deviation exceeds this emergency threshold, the system immediately activates the rapid calibration mode.

[0033] Threshold comparison and judgment: The current detected optical path deviation is compared in real-time with the emergency threshold, and an appropriate processing strategy is selected based on the comparison result. A fast optical path deviation detection algorithm is constructed. Deviation prediction is performed by analyzing the rate of change of the spot centroid position. The specific steps for calculating the rate of change are as follows: Acquire time series data, record the angle deviation values ​​between the current moment and the previous moment, and record the corresponding timestamps to form a time series data pair; calculate the angle deviation difference by subtracting the angle deviation of the previous moment from the angle deviation of the current moment to obtain the change in angle deviation, which reflects the magnitude of the change in optical path deviation. The time interval is calculated by subtracting the timestamp of the previous moment from the current timestamp. This time interval is used to calculate the rate of change. The rate of change is calculated by dividing the change in angular deviation by the time interval. This rate of change represents the speed at which the optical path deviation changes per unit time. The rate of change analysis determines the development trend of the optical path deviation based on the magnitude and direction of the rate of change, providing a basis for subsequent prediction and calibration. The angular deviation is calculated from the spot position offset. The specific calculation steps are as follows: The process involves: acquiring positional offsets by measuring the X and Y direction offsets of the laser spot on the receiver, reflecting the degree of deviation of the spot from its ideal position; calculating the squares of the offsets in the X and Y directions to prepare for subsequent vector synthesis; calculating the composite offset by adding the squares of the X and Y direction offsets and taking the square root of the sum to obtain the composite offset of the laser spot position, representing the total distance the spot deviates from its ideal position; calculating the angular deviation by dividing the composite offset by the equivalent focal length of the receiving optical system to obtain the ratio of offset to focal length; and performing an arctangent operation on this ratio to obtain the angular deviation value, which represents the deflection angle of the laser path relative to the ideal optical axis. A dynamic threshold adjustment mechanism is implemented to automatically adjust the deviation detection threshold based on environmental conditions such as temperature and humidity. The specific steps for dynamic threshold adjustment are as follows:

[0034] The system acquires environmental parameters, monitors the current temperature and humidity values ​​in real time, and records the reference temperature and humidity values ​​used in the system design. It calculates environmental changes by subtracting the reference temperature from the current temperature and humidity from the reference humidity. It also calculates influence factors by multiplying the temperature change by the temperature influence coefficient and the humidity change by the humidity influence coefficient. Finally, it calculates the total influence coefficient by adding the temperature and humidity influence factors and then adding a baseline value of 1. Finally, it calculates the dynamic threshold by multiplying the baseline detection threshold by the total influence coefficient to obtain a dynamically adjusted detection threshold based on current environmental conditions, ensuring appropriate detection sensitivity under different environmental conditions. An algorithm for identifying optical path deviation types was established. Based on the directionality, periodicity, and amplitude characteristics of the deviation, the deviations were classified into three types: angular deviation, positional deviation, and power deviation. Angular deviation is mainly manifested as a systematic shift in the position of the laser spot; positional deviation is manifested as the translation of the laser relative to the optical axis; and power deviation is manifested as a change in the laser output power.

[0035] The results of real-time optical path deviation analysis are obtained, including deviation type, deviation degree, trend of change and calibration priority.

[0036] Furthermore, a neural network-based intelligent optical path analysis method can replace traditional algorithms. A deep convolutional neural network model is constructed, using multi-sensor fusion data as input, to directly output the type and degree of optical path deviation. The neural network model includes an input layer, multiple convolutional layers, pooling layers, fully connected layers, and an output layer. The network parameters are trained using a large amount of historical data, enabling the model to automatically learn the complex characteristic patterns of optical path deviation. The goal of the neural network-based intelligent optical path analysis method is to improve the accuracy and robustness of deviation identification, making it particularly suitable for complex and variable environmental conditions.

[0037] The hybrid drive adjustment module is used to achieve rapid optical path adjustment based on the optical path deviation analysis results, and obtain the optical path adjustment results. Based on the optical path deviation analysis results, a hybrid drive adjustment mechanism is adopted to achieve rapid optical path adjustment; The design incorporates a hybrid drive adjustment mechanism. Coarse adjustment utilizes a voice coil motor, characterized by fast response and large stroke; fine adjustment employs a piezoelectric ceramic stack drive, offering high precision and resolution. The voice coil motor's adjustment range is ±5mm with a resolution of 1μm; the piezoelectric ceramic stack's adjustment range is ±100μm with a resolution of 1nm.

[0038] A dual closed-loop control system is established. The outer loop is a position control loop, using the deviation between the target position and the actual position as input; the inner loop is a current control loop, using the deviation between the target current and the actual current as input. The outer loop controller uses a PID algorithm, and the inner loop controller uses a PI algorithm. The output of the outer loop PID controller serves as the input reference for the inner loop PI controller.

[0039] A predictive control algorithm is implemented to predict the deviation value at future moments based on the changing trend of optical path deviation, and to initiate adjustment actions in advance to reduce adjustment delay. The specific steps for calculating the predictive control output are as follows:

[0040] Acquire the current control output and record the system's control output value at the current moment. This value represents the current driver's control voltage in volts. Calculate the control output change rate by analyzing the changes in the control output over the most recent sampling periods. This rate reflects the trend of the control signal. Determine the prediction parameters by setting the prediction coefficient based on the system's response characteristics, with a value range of 0.1-0.5. Simultaneously determine the prediction time, typically set to 10-50 milliseconds, selected based on the system's dynamic response characteristics. Calculate the prediction compensation by multiplying the control output change rate by the prediction coefficient and then by the prediction time. This compensation is used to compensate for the system's response delay in advance. A predictive control output is generated by adding the current control output to the predictive compensation amount to obtain the final predictive control output. This output can respond to the changing trend of the system in advance and reduce the adjustment delay.

[0041] The selection of prediction coefficients is based on the system's step response characteristics. For fast-response systems, smaller prediction coefficient values ​​are chosen to avoid over-prediction; for slow-response systems, larger prediction coefficient values ​​are chosen to improve response speed. The specific steps for adaptive adjustment of prediction coefficients are as follows:

[0042] A baseline prediction coefficient is set based on the system's fundamental response characteristics, serving as the starting point for adaptive adjustment. Steady-state error is measured and monitored in real-time; this error, representing the deviation remaining after the system reaches a stable state, reflects the system's control accuracy. An error influence factor is calculated by multiplying the absolute value of the steady-state error by the adjustment rate coefficient, determining the degree of influence of the error on the prediction coefficient. The adjustment rate coefficient determines the sensitivity of adaptive adjustment. An exponential decay factor is calculated by taking the negative value of the error influence factor and performing an exponential operation, resulting in a factor that decreases as the steady-state error increases. Finally, an adaptive prediction coefficient is calculated by multiplying the baseline prediction coefficient by the exponential decay factor, yielding the adaptively adjusted prediction coefficient. When the steady-state error is large, the prediction coefficient automatically decreases to improve system stability; when the steady-state error is small, the prediction coefficient remains large to maintain rapid response capability. An adaptive PID control algorithm is adopted to adjust the PID parameters in real time according to the system's response characteristics, thereby improving control performance. The parameter adjustment adopts a fuzzy logic method, with deviation and deviation change rate as inputs and PID parameter adjustment amount as output. The fuzzy rule base includes 25 rules, covering various deviation conditions. A vibration suppression mechanism is established by detecting external vibrations using a triaxial accelerometer and employing a feedforward compensation method to suppress the impact of vibrations on adjustment accuracy. The specific steps for calculating the vibration compensation amount are as follows:

[0043] The system acquires vibration signals by measuring acceleration values ​​in the X, Y, and Z directions in real time using a triaxial accelerometer. These acceleration values ​​reflect the degree of influence of external vibration on the system. Signal filtering involves low-pass filtering of the measured acceleration signals to remove high-frequency noise and retain low-frequency vibration components that affect the system's adjustment accuracy. A compensation coefficient is determined based on the system's mechanical characteristics and vibration transfer function; this coefficient reflects the relationship between vibration acceleration and the required compensation amount. The compensation amount is calculated by multiplying the measured acceleration value by the vibration compensation coefficient to obtain the initial compensation amount. The initial compensation amount is then negatively evaluated to obtain the final vibration compensation amount, which is used to offset the adverse effects of vibration on the system. The calculated vibration compensation amount is then applied to the control system output to achieve active compensation for vibration interference, improving the system's adjustment accuracy and stability. The adjustment process is monitored in real time using position sensors to ensure adjustment accuracy and stability. When an adjustment anomaly is detected, the system automatically switches to a backup adjustment mechanism or employs a software compensation method.

[0044] High-precision, fast-response optical path adjustment results were obtained, with laser position adjustment accuracy reaching ±10nm and angle adjustment accuracy reaching ±0.1 arcseconds.

[0045] Furthermore, intelligent damping adjustment mechanisms based on magnetorheological technology can replace traditional mechanical adjustment mechanisms. Magnetorheological dampers adjust damping characteristics by changing the magnetic field strength, achieving precise adjustment of variable damping. This mechanism has advantages such as fast response speed, large adjustment range, and no mechanical wear. By controlling the current in the electromagnetic coil, the damping coefficient can be changed within milliseconds, achieving precise control of the adjustment process. The purpose of intelligent damping adjustment mechanisms based on magnetorheological technology is to improve the reliability and service life of adjustment mechanisms, making them particularly suitable for applications requiring long-term continuous operation.

[0046] The calibration and verification module is used to acquire the optical path adjustment results, perform multi-target distance verification on the ranging performance of the adjusted laser intelligent observation instrument, and obtain full-range calibration and verification results. Based on the optical path adjustment results, a multi-target distance verification method is used to verify the calibration effect across the entire range. A variable-distance built-in calibration target system is designed, comprising a motorized guide rail, a standard reflector, and a position encoder. The motorized guide rail uses a precision ball screw drive with a travel range of 50mm-1000mm and a positioning accuracy of ±0.1mm. The standard reflector is made of a high-reflectivity material with a reflectivity greater than 95%. The position encoder uses a grating ruler with a resolution of 1μm.

[0047] A virtual target calibration method is established, utilizing fiber optic delay lines to simulate laser echo signals at different distances. The specific steps for calculating the fiber optic delay line length are as follows:

[0048] The process involves several steps: First, determine the simulated distance. Based on calibration requirements, identify the target distance to be simulated. This target distance represents the measurement range that the laser ranging system needs to verify. Calculate the optical path difference. Since the laser signal needs to travel back and forth, multiply the simulated target distance by 2 to obtain the total optical path of the laser signal. This path is equivalent to the distance the laser signal travels back and forth in a vacuum. Consider the fiber refractive index. Obtain the refractive index parameter of the fiber used. For single-mode fiber, the refractive index is approximately 1.46. This parameter determines the propagation speed of the optical signal in the fiber. Calculate the speed of light in the fiber by dividing the speed of light in a vacuum by the refractive index of the fiber. Calculate the required fiber length by multiplying the total optical path by the refractive index of the fiber and then dividing by the speed of light in a vacuum. This results in the required fiber delay line length, which produces a time delay equivalent to the target distance. The fiber delay line system includes a laser coupler, a fiber optic disk, and a photodetector. The laser coupler couples the laser signal into the fiber, the fiber optic disk provides the required delay length, and the photodetector converts the delayed optical signal into an electrical signal. To simulate different distances, a switchable combination of fiber delay lines is used, with optical switches selecting fiber paths of varying lengths. The switching time of the optical switches is less than 1 millisecond, ensuring rapid switching between different simulated distances.

[0049] A cross-validation mechanism is constructed, using both physical and virtual targets for calibration verification. The consistency of the calibration effect is evaluated by comparing the ranging results of the two methods. The consistency assessment uses the correlation coefficient method, and the specific calculation steps are as follows:

[0050] Collect measurement data by performing multiple distance measurements using both physical and virtual targets, recording the results of each measurement to form two corresponding distance measurement data sequences. Calculate the average values: calculate the arithmetic mean of the distance measurement results for both the physical and virtual targets; these two average values ​​represent the central tendency of their respective methods. Calculate the deviation values: for each measurement, calculate the deviation between the physical target distance measurement result and its average value, and the deviation between the virtual target distance measurement result and its average value. Calculate the covariance: multiply the deviation values ​​of the physical and virtual targets in each measurement, sum over all measurements to obtain the numerator of the covariance. Calculate the standard deviation product: calculate the sum of squares of the deviation values ​​of the physical and virtual targets, multiply the two sums and take the square root to obtain the standard deviation product. Calculate the correlation coefficient: divide the covariance by the standard deviation product to obtain the correlation coefficient, which reflects the degree of consistency between the results of the two calibration methods, ranging from -1 to 1; the closer to 1, the better the consistency. A statistical analysis algorithm is implemented to calculate the ranging accuracy and stability indices through multiple repeated measurements. The ranging accuracy is expressed as the root mean square error, and the specific calculation steps are as follows:

[0051] Collect distance measurement data: Perform multiple repeated measurements, record the distance measurement results each time, and obtain the actual distance value as a reference standard; Calculate distance measurement error: For each measurement, calculate the difference between the distance measurement result and the actual distance value to obtain the error value for each measurement; Calculate the square of the error: Square the error value of each measurement to eliminate the mutual cancellation effect of positive and negative errors; Calculate the average square of the error: Add all the squared error values ​​and divide by the number of measurements to obtain the average squared error value; Calculate the root mean square error: Take the square root of the average squared error value to obtain the root mean square error, which reflects the overall accuracy level of the distance measurement system; Distance measurement stability is represented by standard deviation, and the specific calculation steps are as follows: Calculate the average distance measurement value: Sum all distance measurement results and divide by the number of measurements to obtain the average distance measurement value; Calculate the deviation value: For each measurement, calculate the difference between the distance measurement result and the average distance measurement value to obtain the deviation value for each measurement; Calculate the squared deviation: Square the deviation value for each measurement to prepare for subsequent variance calculation; Calculate the variance: Sum all squared deviation values ​​and divide by the number of measurements minus 1 to obtain the sample variance; Calculate the standard deviation: Take the square root of the variance to obtain the standard deviation of distance measurement stability, which reflects the reproducibility and consistency of distance measurement results; Establish a calibration quality scoring mechanism. Considering indicators such as distance measurement accuracy, stability, and response time, calculate the comprehensive calibration quality score:

[0052] The overall calibration quality score is calculated using the following steps: The evaluation criteria are as follows: 1. Determine evaluation indicators: Accuracy, stability, and response time are selected as the main evaluation indicators for calibration quality. These indicators comprehensively reflect the performance level of the calibration system. 2. Set weighting coefficients: Based on actual application needs and the importance of the indicators, weighting coefficients are set for accuracy, stability, and response time. The sum of these weighting coefficients equals 1 to ensure the rationality of the scoring results. 3. Obtain standardized scores: The measurement results of each indicator are standardized, converting indicators with different dimensions into dimensionless scores between 0 and 1 for easier subsequent calculations. 4. Calculate weighted products: The standardized score of each indicator is multiplied by its corresponding weighting coefficient to obtain a weighted score for each indicator, reflecting the importance of different indicators. 5. Calculate the comprehensive score: The weighted scores of all indicators are summed to obtain the comprehensive calibration quality score, which is used to evaluate the overall performance level of the calibration system. 6. Establish a calibration effect recording and traceability mechanism: All calibration processes and results are recorded in detail in the database, including calibration time, environmental conditions, adjustment parameters, verification results, etc., providing data support for subsequent analysis and optimization.

[0053] The full-range calibration effect verification results are obtained, including the ranging accuracy, stability index and calibration quality score of each distance point.

[0054] Furthermore, an ultra-high precision calibration and verification method based on laser interferometry can be used to replace traditional methods. Using a laser interferometer as a distance reference, the calibration accuracy is evaluated by comparing the ranging results of the laser observation instrument with those of the interferometer. The measurement accuracy of the laser interferometer can reach the nanometer level, providing a more accurate calibration reference. This method needs to be performed in a laboratory environment and is suitable for both factory calibration and periodic precision calibration of equipment. The aim of the ultra-high precision calibration and verification method based on laser interferometry is to further improve the accuracy and reliability of calibration and verification.

[0055] The reliability assessment module is used to obtain full-range calibration verification results, combine them with historical operating data of the laser intelligent observation instrument, and establish a reliability model using digital twin technology to obtain assurance data; Based on calibration verification results and historical equipment operation data, a digital twin model is established to ensure the long-term stability of the system.

[0056] A digital twin model of the equipment is established. The model includes an optical system model, a mechanical system model, an electronic system model, and an environmental impact model. The optical system model describes the laser propagation path, optical component characteristics, and optical path alignment; the mechanical system model describes the motion characteristics and wear status of the adjustment mechanism; the electronic system model describes the sensor characteristics and signal processing; and the environmental impact model describes the effects of environmental factors such as temperature, humidity, and vibration on system performance.

[0057] A mechanical wear prediction model was constructed. Based on the usage frequency, load magnitude, and environmental conditions of the regulating mechanism, the wear degree and remaining life of key components were predicted. The wear prediction adopted the Weibull distribution model, and the specific calculation steps are as follows:

[0058] Determine model parameters: Based on historical failure data of the equipment and reliability data provided by the manufacturer, determine the characteristic lifetime parameter and shape parameter of the Weibull distribution. The characteristic lifetime parameter represents the time when the equipment fails, and the shape parameter reflects the characteristics of the failure mode. Calculate the time ratio: Divide the cumulative usage time of the equipment by the characteristic lifetime parameter to obtain a dimensionless time ratio, which reflects the current aging degree of the equipment. Calculate the shape function: Raise the time ratio to the power of the shape parameter to obtain the shape function value, which describes the change in failure probability over time. Calculate the exponential function: Take the negative value of the shape function and perform an exponential operation to obtain the reliability function value, which represents the probability that the equipment can still work normally at the current point in time. Calculate the failure probability: Subtract the reliability function value from 1 to obtain the cumulative probability of the equipment failing within the cumulative usage time. This probability value is used to assess the reliability level of the equipment and formulate maintenance strategies. An adaptive calibration strategy is designed to dynamically adjust calibration parameters and frequency based on equipment aging and performance change trends. The specific calculation steps for adjusting the calibration frequency are as follows:

[0059] Determine the baseline calibration frequency: Based on the initial performance specifications of the equipment and the manufacturer's recommendations, set the baseline calibration frequency, typically once a day, as the standard calibration interval for new equipment; Obtain equipment usage time: Calculate the cumulative usage time of the equipment from its initial use to the present moment. This time reflects the aging and wear condition of the equipment; Determine the aging impact coefficient: Based on the equipment type, working environment, and historical performance data, determine the aging impact coefficient. This coefficient reflects the degree of influence of equipment aging on the calibration frequency requirement, and its value is typically between 0.001 and 0.01; Calculate the aging adjustment factor: Multiply the aging impact coefficient by the cumulative usage time to obtain the aging adjustment factor. This factor quantifies the specific impact of equipment aging on the calibration requirement; Calculate the adjusted calibration frequency: Multiply the baseline calibration frequency by (1 plus the aging adjustment factor) to obtain the calibration frequency after considering equipment aging. As the equipment usage time increases, the calibration frequency will increase accordingly to ensure measurement accuracy.

[0060] A preventative maintenance algorithm is implemented to proactively perform maintenance before significant performance degradation by analyzing equipment performance trends and fault symptoms. Maintenance timing prediction uses a trend analysis method, and the specific calculation steps are as follows:

[0061] Determine the performance threshold: Based on the equipment's technical specifications and usage requirements, set a minimum acceptable performance threshold, typically 80% of the nominal performance, as the critical point to trigger maintenance; Obtain current performance: Obtain the equipment's current performance level through a real-time monitoring system, expressed as a percentage of the nominal performance, reflecting the equipment's actual operating status; Calculate the performance difference: Subtract the current performance level from the performance threshold to obtain the performance difference, which indicates how much further the equipment's performance can degrade before triggering maintenance; Determine the performance degradation rate: Analyze historical performance data and use linear regression or other fitting methods to calculate the equipment's performance degradation rate, which reflects the trend of performance degradation over time; Calculate the predicted maintenance time: Divide the performance difference by the performance degradation rate to obtain the time interval from the current moment to the time when maintenance is required, add the current time to obtain the predicted maintenance time. Establish a performance degradation compensation mechanism to compensate for the slow degradation of hardware performance through software algorithms, thereby extending the equipment's lifespan. The compensation algorithm calculates the compensation amount based on a performance degradation model; the specific calculation steps are as follows:

[0062] Obtain nominal performance values: Obtain nominal performance values ​​from the equipment's design specifications and technical documents. These values ​​represent the performance indicators that the equipment should achieve under ideal conditions. Measure actual performance values: Measure the current actual performance values ​​of the equipment through a real-time monitoring system. These values ​​reflect the true performance level of the equipment in its current state. Calculate performance difference: Subtract the actual performance value from the nominal performance value to obtain the performance difference, which represents the performance loss caused by hardware degradation. Determine compensation coefficient: Determine the compensation coefficient based on the hardware degradation characteristics, the effectiveness of the compensation algorithm, and system stability requirements. The value range is typically from 0.5 to 2.0, and this coefficient controls the intensity of compensation. Calculate compensation amount: Multiply the compensation coefficient by the performance difference to obtain the final performance compensation amount. This compensation amount will be applied to the system through software algorithms to restore or improve the equipment's performance. Establish an equipment health status assessment system. Calculate the equipment health score through comprehensive analysis of various performance indicators. The specific calculation steps are as follows:

[0063] Determine evaluation indicators: Select key performance indicators as the basis for health assessment, including ranging accuracy, response time, power stability, signal strength, etc., and determine the total number of indicators participating in the assessment; Set weight coefficients: Assign weight coefficients to each indicator according to their importance to the overall performance of the equipment. The sum of all weight coefficients equals 1 to ensure the rationality of the assessment results; Obtain indicator measurement values: Obtain the current measurement values ​​of each performance indicator through a real-time monitoring system. These values ​​reflect the actual performance of the equipment in the current state; Calculate indicator ratios: For each performance indicator, divide its current measurement value by the theoretical maximum value or nominal value of the indicator to obtain a standardized indicator ratio, with a value range of 0 to 1; Calculate weighted health score: Multiply the weight coefficient of each indicator by its standardized ratio, sum over all indicators, and obtain the comprehensive health score of the equipment, with a value range of 0 to 1, where 1 represents complete health and 0 represents complete failure.

[0064] Before calculating the health score, the various performance indicators need to be standardized. Since different performance indicators have different dimensions and numerical ranges (e.g., ranging accuracy is measured in meters, response time in seconds, and power stability as a percentage), all performance indicators need to be normalized to the range of 0 to 1. The standardization process involves the following steps:

[0065] Classify indicator attributes: Based on the physical meaning of the indicators, indicators with high expected values ​​are classified as gain-type indicators, including accuracy and stability; indicators with low expected values, such as time, are classified as suppression-type indicators, including error and response time, and mapping strategies are established for each. Obtain indicator boundaries: Using historical operating data or theoretical extreme values, the upper and lower limits of each performance indicator are calculated as the benchmark interval for subsequent dimensionless conversion. Calculate mapping values: Gain-type indicators and suppression-type indicators are linearly mapped to [0,1] to achieve dimension unification.

[0066] This ensures long-term stable optical path calibration performance, allowing the equipment to maintain high-precision ranging capabilities throughout its entire lifespan.

[0067] Furthermore, a blockchain-based equipment maintenance record management system can replace the traditional database. Blockchain technology, with its features of data immutability and decentralized storage, ensures the authenticity and integrity of maintenance records. Each calibration and maintenance operation is recorded as a block on the chain, forming a complete equipment lifecycle traceability chain. The purpose of a blockchain-based equipment maintenance record management system is to improve the credibility and security of maintenance records, making it particularly suitable for military equipment with high data security requirements.

[0068] The intelligent control module is used to learn the relationship between environment and performance using a long short-term memory network based on the protection data and the history of environmental changes, so as to obtain an intelligent and optimized environment adaptive calibration strategy. Based on long-term operational data and historical environmental changes, intelligent learning algorithms are used to achieve adaptive optimization of the environment.

[0069] A deep learning-based environment-performance correlation model is constructed. A Long Short-Term Memory (LSTM) network is employed to learn the complex nonlinear relationship between environmental parameters and optical path performance. The LSTM network consists of an input layer, hidden layers, and an output layer, with the hidden layers containing multiple LSTM units. The network input is the time series of environmental parameters, and the output is the predicted value of the optical path performance.

[0070] Design an online learning algorithm. The system continuously collects new data during operation and updates model parameters through incremental learning. The online learning algorithm employs stochastic gradient descent, and the specific calculation steps are as follows:

[0071] Obtain current model parameters: Extract all trainable parameter vectors from the current deep learning model. These parameters include network weights and bias terms. Calculate predicted output: Using the current model parameters and the time series of input environmental parameters, calculate the model's predicted output value through forward propagation. Calculate loss function: Compare the model's predicted output with the actual optical path performance indicators, calculate the square of the prediction error using the mean squared error loss function, and divide by 2 to obtain the loss value. Calculate gradient vector: Calculate the partial derivative of the loss function with respect to each model parameter using the backpropagation algorithm, and calculate the gradient layer by layer using the chain rule to obtain the complete gradient vector. Update model parameters: Multiply the learning rate by the gradient vector to obtain the parameter update amount, subtract the update amount from the current parameters to obtain the new model parameters. The learning rate controls the step size of parameter updates, ranging from 0.001 to 0.1. Establish multi-scene calibration modes, using specially optimized calibration parameters according to different application scenarios (military operations, shooting training, golf rangefinding, etc.). Scene recognition uses a support vector machine classifier, with environmental parameters and usage patterns as feature vectors.

[0072] A swarm intelligence optimization algorithm is implemented, utilizing operational data from multiple devices to find the globally optimal calibration parameters through particle swarm optimization. Before executing the particle swarm algorithm, the calibration parameters across different dimensions need to be preprocessed for normalization. Since the calibration parameters contain different types of physical quantities (e.g., angle adjustment in arcseconds, position adjustment in micrometers, and power adjustment as a percentage), the numerical ranges of each parameter vary greatly, requiring all parameters to be normalized to the 0-1 range. The normalization process involves the following steps:

[0073] Determine the parameter range: Based on the physical limitations and safe operating range of the equipment, determine the maximum and minimum values ​​of each calibration parameter as the benchmark for normalization calculation; Calculate the normalized value: Subtract the minimum value from the current value of each calibration parameter, and divide by the value range of that parameter to obtain a normalized value between 0 and 1; Verify the normalization result: Ensure that all normalized parameter values ​​are within the range of 0 to 1, and maintain the relative relationship between the original parameters; Execute the optimization algorithm: Use the normalized parameter values ​​to perform particle swarm optimization calculations to ensure the convergence and stability of the algorithm; Inverse transformation of parameter values: After optimization, multiply the normalized result by the value range and add the minimum value to inversely transform it into the actual physical parameter value for actual equipment adjustment.

[0074] The particle swarm optimization algorithm updates velocity and position using the following computational steps: Calculate the inertia term: Multiply the inertia weight by the particle's current velocity to obtain the inertia term. This term keeps the particle moving in its original direction and velocity. The inertia weight ranges from 0.4 to 0.9. Calculate the individual learning term: Multiply the individual learning factor, the random number, and the difference between the individual's best position and the current position to obtain the individual learning term. This term drives the particle to move towards its own historical best position. The individual learning factor is usually set to 2.0. Calculate the social learning term: Multiply the social learning factor, the random number, and the difference between the global best position and the current position to obtain the social learning term. This term drives the particle to move towards the group's best position. The social learning factor is usually set to 2.0. Update the particle velocity: Add the inertia term, the individual learning term, and the social learning term to obtain the particle's new velocity in the next iteration. This velocity determines the particle's direction and speed. Update the particle position: Add the particle's current position to the newly calculated velocity to obtain the particle's new position in the next iteration. This position represents the new combination of calibration parameters.

[0075] An anomaly pattern recognition mechanism is constructed, employing the Isolation Forest algorithm to detect abnormal environmental patterns and equipment states. Before performing anomaly detection, the environmental parameter sample vectors need to be standardized. Since environmental parameters include different types of physical quantities (e.g., temperature in degrees Celsius, humidity as a percentage, vibration acceleration in m / s²), and the dimensions and numerical ranges of each parameter differ, the Z-score standardization method is used to convert all parameters into a standard normal distribution with a mean of 0 and a standard deviation of 1. The standardization process involves the following steps:

[0076] Collect historical data: Obtain a sufficient amount of historical data on environmental parameters to ensure the representativeness and completeness of the data, providing a reliable basis for statistical calculations; Calculate statistical parameters: Calculate the mean and standard deviation of the historical data for each environmental parameter. These statistical parameters reflect the normal distribution characteristics of the parameter; Perform standardization transformation: Subtract the corresponding historical mean from the current environmental parameter value and divide by the corresponding historical standard deviation to obtain the standardized value; Verify the standardization results: Ensure that the standardized parameter values ​​conform to the characteristics of a standard normal distribution, i.e., the overall mean is close to 0 and the standard deviation is close to 1; Apply standardized data: Use the standardized environmental parameter vector as input to the Isolation Forest algorithm for anomaly pattern detection and equipment status assessment; Isolation forests construct decision trees by randomly selecting features and split points, making it easier to isolate outlier samples. The anomaly score calculation uses the following steps:

[0077] Constructing an isolated tree forest: Multiple isolated trees are constructed by randomly selecting features and split points. Each tree divides the data space into different regions through recursive splitting. Abnormal samples are more easily isolated quickly due to their rarity. Calculating path length: The environmental parameter samples to be detected are input into each isolated tree, and the path length from the root node to the leaf node is calculated, i.e., the number of splits required to isolate the sample. Abnormal samples typically have shorter path lengths. Calculating average path length: The arithmetic mean of the path lengths of all isolated trees is calculated to obtain the average path length of the sample in the entire forest. This value reflects the ease with which the sample is isolated. Calculating normalization factor: Based on the total number of samples in the training dataset, the average path length of the binary search tree is calculated as a normalization factor. This factor is used to standardize and compare results from datasets of different sizes. Calculating anomaly score: The average path length is converted into an anomaly score using a negative exponential function of 2. The score ranges from 0 to 1, where close to 1 indicates high anomaly and close to 0 indicates normal. This score is used to determine whether the environmental pattern or device status is abnormal. Establishing an adaptive parameter adjustment mechanism: The parameters of the calibration algorithm are adjusted in real time based on the learned environment-performance relationship. The parameter adjustment adopts a fuzzy control method, with environmental changes and performance deviations as inputs and parameter adjustment values ​​as outputs.

[0078] With an intelligently optimized environment-adaptive calibration strategy, the system can automatically adjust calibration parameters according to environmental changes to maintain optimal calibration results.

[0079] Furthermore, a reinforcement learning-based autonomous calibration strategy optimization method can be used to replace traditional optimization algorithms. A Markov decision process model is constructed, with the environmental state as the state space, the calibration actions as the action space, and the calibration effect as the reward function. A deep Q-network (DQN) algorithm is used to train the agent, enabling it to autonomously select the optimal calibration strategy under different environmental conditions. The goal of the reinforcement learning-based autonomous calibration strategy optimization method is to further improve the intelligence and adaptability of the calibration strategy.

[0080] In one embodiment of the present invention, ranging accuracy tests were conducted under different environmental conditions to verify system performance. The test results are shown in Table 1:

[0081] Table 1: Ranging accuracy test results under different environmental conditions

[0082] The test data above shows that the laser observation instrument equipped with an automatic calibration system can maintain excellent ranging performance under various environmental conditions, with ranging accuracy within ±0.5 meters, which is more than double the ±1 meter accuracy of traditional equipment.

[0083] like Figure 2 The figure shows the calibration performance of the automatic calibration system within a distance range of 100 meters to 1000 meters. The horizontal axis represents the target distance, and the vertical axis represents the ranging error. It can be seen that the automatic calibration system maintains higher ranging accuracy across the entire distance range, with its advantages being particularly pronounced at long distances.

[0084] like Figure 3 As shown, the automatic calibration system's adaptability under different environmental parameters is demonstrated. The chart includes five dimensions: temperature adaptability, humidity adaptability, vibration resistance, response speed, and accuracy maintenance. The system exhibits excellent performance in all dimensions, achieving a comprehensive score of 4.2 out of 5, significantly better than the traditional system's score of 2.8.

[0085] The system's automatic calibration function can perform calibration in real time during equipment use without interrupting normal operation. The calibration cycle is shortened from the traditional 24-hour period to real-time continuous calibration, improving equipment availability and reliability. Simultaneously, the system exhibits excellent environmental adaptability, operating stably within a temperature range of -40°C to +60°C, meeting the needs of various harsh environments.

[0086] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A laser intelligent observation instrument optical path fine-tuning system with automatic calibration function, characterized in that, include: The sensor monitoring module is used to acquire the raw signals and environmental change data of the laser intelligent observation instrument. It uses adaptive Kalman filtering to fuse multi-source data to obtain optical path status data. The data processing module is used to perform real-time optical path deviation analysis on the optical path status data using a distributed processing method based on an edge computing architecture, and to obtain the optical path deviation analysis results. The hybrid drive adjustment module is used to achieve rapid optical path adjustment based on the optical path deviation analysis results, and obtain the optical path adjustment results. The calibration and verification module is used to acquire the optical path adjustment results, perform multi-target distance verification on the ranging performance of the adjusted laser intelligent observation instrument, and obtain full-range calibration and verification results. The reliability assessment module is used to obtain full-range calibration verification results, combine them with historical operating data of the laser intelligent observation instrument, and establish a reliability model using digital twin technology to obtain assurance data; The intelligent control module is used to learn the relationship between environment and performance using a long short-term memory network based on the protection data and the history of environmental changes, so as to obtain an intelligently optimized environment adaptive calibration strategy.

2. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The adaptive Kalman filtering of the original signal includes: Acquire photocurrent signal intensity values ​​in the four quadrants as observations; calculate the positional offsets in the X and Y directions; Initialize the filter state; perform state prediction, and calculate the Kalman gain based on the state estimate from the previous time step and the predicted state value for the current time step; The state estimate is updated by correcting the residuals between the observed and predicted state values ​​and then updating the state.

3. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The multi-source data fusion in the sensor monitoring module also includes: Establish a multi-sensor timestamp alignment mechanism. The main controller triggers the mechanism uniformly, and each sensor records its original timestamp and compensates for the response delay to obtain the aligned timestamp. A sensor fault detection and isolation mechanism is constructed. The average value and standard deviation of all sensor measurements are calculated. If the deviation between the sensor measurement value and the average value exceeds three times the standard deviation, it is judged as abnormal and removed.

4. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The multi-source data fusion processing in the sensor monitoring module adopts a layered fusion architecture: The first layer performs data fusion of similar sensors by weighted averaging of data from multiple temperature sensors and normalizing the weights of each sensor before weighted fusion. The second layer performs heterogeneous sensor data fusion, normalizes and compensates the data from various sensors, and then performs weighted fusion based on the sensor measurement accuracy.

5. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The distributed processing method based on edge computing architecture includes: A hierarchical processing architecture is established, with the main processor responsible for overall coordination and decision-making, a dedicated FPGA chip responsible for high-speed signal processing, and a DSP chip responsible for digital filtering and numerical calculation. Parallel data exchange is achieved, with each processing unit exchanging data via a high-speed bus to enable parallel computing; Design the internal processing core of the FPGA. The FPGA integrates multiple parallel processing cores, each responsible for its own computational tasks. A pipeline architecture is constructed, and the process of calculating the centroid position of the light spot is divided into three stages: data preprocessing, centroid calculation, and coordinate transformation. Each stage is executed in parallel.

6. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The real-time optical path deviation analysis in the data processing module includes: Constructing a fast algorithm for optical path deviation detection: A method for calculating the rate of change is established to obtain the angle deviation values ​​between the current moment and the previous moment. The rate of change of optical path deviation is then calculated by the change in angle deviation and the time interval. Angle deviation calculation is performed, which is obtained by calculating the arctangent function relationship between the spot position offset and the equivalent focal length of the receiving optical system. Implement a dynamic threshold adjustment mechanism to automatically adjust the deviation detection threshold according to environmental conditions; An algorithm for identifying optical path deviation types was established, which classifies deviations into three types: angular deviation, positional deviation, and power deviation based on the directionality, periodicity, and amplitude characteristics of the deviation.

7. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The hybrid drive adjustment in the hybrid drive adjustment module includes: The design incorporates a hybrid drive structure, with coarse adjustment using a voice coil motor and fine adjustment using a piezoelectric ceramic stack drive. A dual closed-loop control system is established, with the outer loop being the position control loop, using the deviation between the target position and the actual position as the input; and the inner loop being the current control loop, using the deviation between the target current and the actual current as the input. Implement a predictive control algorithm, obtain the current control output, calculate the rate of change of the control output, set the prediction coefficient and prediction time according to the system's response characteristics, obtain the prediction compensation amount based on the rate of change of the control output, the prediction coefficient and the prediction time, and obtain the final predictive control output based on the current control output and the prediction compensation amount.

8. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The multi-target distance verification in the calibration and verification module includes: Design a variable-distance built-in calibration target system, including an electric guide rail, a standard reflector, and a position encoder; A virtual target calibration method was established, which uses fiber optic delay lines to simulate laser echo signals at different distances; The system calculates the length of the fiber delay line, determines the simulated distance, and obtains the total optical path of the laser signal based on the simulated distance; it acquires the refractive index parameter of the fiber used, and obtains the actual propagation speed of the light signal in the fiber based on the refractive index parameter; and outputs the required fiber delay line length. Constructing an optical fiber delay line system includes: a laser coupler that couples the laser signal into the optical fiber, an optical fiber disk that provides the required delay length, and a photodetector that converts the delayed optical signal into an electrical signal.

9. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The calibration and verification module also includes: Construct a cross-validation mechanism that uses both physical and virtual targets for calibration and validation; To achieve consistency assessment, the ranging results of the two methods are compared, and the correlation coefficient method is used to evaluate the consistency of the calibration effect and obtain the correlation coefficient. A statistical analysis algorithm is implemented to evaluate the calibration stability through the statistical characteristics of multiple measurements. The ranging stability index is obtained based on the average and variance of multiple ranging results.

10. The laser intelligent observation instrument optical path fine-tuning system with automatic calibration function according to claim 1, characterized in that, The digital twin technology in the reliability assessment module includes: Establish a digital twin model of the equipment and construct a comprehensive digital model that includes an optical system model, a mechanical system model, an electronic system model, and an environmental impact model; A mechanical wear prediction model was constructed. Based on the frequency of use of the adjustment mechanism, the load size and environmental conditions, the Weibull distribution model was used to predict the wear degree and remaining life of key components. Design an adaptive calibration strategy to dynamically adjust calibration parameters and calibration frequency based on the aging degree and performance change trend of the equipment; Establish a performance degradation compensation mechanism to reduce the slow degradation of hardware performance through hardware compensation. Establish an equipment health status assessment system, calculate equipment health scores through comprehensive analysis, and output system reliability assessment results and maintenance recommendations.

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