Automatic tracking management system and method for full-automatic gradienter
Through multi-sensor data processing and intelligent algorithms, dynamic and multi-target tracking of the fully automatic level is achieved, which solves the problem of unstable measurement accuracy of traditional level instruments and improves the applicability and accuracy of measurements.
Patent Information
- Application Number
- CN202510781574.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-09-19
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional levels require manual adjustment, are easily affected by human and environmental factors, have unstable measurement accuracy, are difficult to achieve dynamic target and multi-target measurement, lack automatic tracking strategies, and are insufficiently applicable.
Multi-sensor data acquisition and preprocessing are adopted, combined with the random forest regression algorithm to establish an association model, and PID, model prediction and joint probability data association filtering algorithms are used for target tracking to achieve automatic adjustment in vertical and horizontal directions. Accurate measurement is performed in three modes according to distance.
It achieves high-precision automatic measurement at different distances and target states, improves the reliability and practicality of measurement, and adapts to dynamic and multi-target tracking in complex scenarios.
Smart Images
Figure CN120668104A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of measurement technology, and in particular to an automatic tracking management system and method for a fully automatic level. Background Art
[0002] In the fields of engineering surveying, construction and precision instrument installation, the level plays an indispensable role as a commonly used measuring tool. Its accuracy and ease of operation are crucial.
[0003] However, traditional spirit levels have many limitations. Traditional spirit levels often require manual adjustment, which is not only time-consuming and labor-intensive, but also easily affected by human and environmental factors, resulting in fluctuations and instability in measurement accuracy. Traditional spirit levels are mainly suitable for measuring static targets. When faced with dynamic targets or when multiple targets need to be processed simultaneously, they often seem powerless and find it difficult to achieve efficient and accurate measurements. Currently, most spirit level technologies only achieve automatic tracking in the vertical direction, while there are still obvious deficiencies in automatic tracking in the horizontal direction and coordinated adjustment of the vertical and horizontal directions, which limits their application in complex scenarios. Traditional spirit levels usually adopt a fixed adjustment mode and lack the ability to automatically adjust the strategy according to the distance of the target, resulting in difficulty in maintaining consistent measurement accuracy at different distances, affecting the reliability and practicality of the measurement. Summary of the Invention
[0004] The object of the present invention is to provide an automatic tracking management system and method for a fully automatic level to solve the problems raised in the prior art.
[0005] To achieve the above object, the present invention provides the following technical solutions:
[0006] In a first aspect, the present invention provides an automatic tracking management method for a fully automatic level, comprising:
[0007] Collect data through multiple sensors, including the current position of the laser beam, the deviation of the target position, the actual distance between the beam and the target, and the motion state of the target. Preprocess the collected data, including noise removal, outlier detection, standardization and normalization.
[0008] Based on historical data, a random forest regression algorithm is used to establish a correlation model between light adjustment and target position. The relationship between target position and light adjustment is predicted by analyzing the characteristic parameters in the data, and the model is trained and evaluated through cross-validation.
[0009] According to the target's motion state, it is divided into static target tracking, dynamic target tracking and multi-target tracking. When the target is static, the PID control algorithm is used in combination with the preset threshold for tracking. When the target is dynamic, the tracking path is optimized based on the target's motion characteristics, and the model predictive control algorithm is introduced to achieve the prediction and early adjustment of the target's motion trend. When there are multiple targets, the joint probabilistic data association filtering algorithm is introduced to achieve automatic recognition, differentiation and tracking of multiple targets.
[0010] When the target needs to be adjusted vertically, the rotation mechanism of the level is controlled to adjust the vertical direction of the laser beam. When the target needs to be adjusted horizontally, the motor of the tripod is controlled to extend and retract, driving the level to adjust the position of the laser beam. When both vertical and horizontal adjustments are required, the vertical and horizontal adjustment mechanisms are coordinated to ensure that the beam is accurately aligned with the target.
[0011] The actual distance between the light and the target is determined by a preset threshold, and is divided into three adjustment modes according to different distance conditions, including short-distance mode, medium-distance mode and long-distance mode;
[0012] When the light reaches the target position, the detector emits a long beep signal to indicate that the adjustment is completed.
[0013] In conjunction with the first aspect, in a first implementation of the first aspect of the present application, the data collected by multiple sensors includes the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, and the motion state of the target, and the collected data is preprocessed, including noise removal, outlier detection, standardization and normalization processing, including:
[0014] Use a position sensor to collect the current angle or position information of the laser emitting device; use an image sensor to capture target image information, compare it with the preset reference position through image processing, and calculate the deviation of the target position; use a distance sensor to measure the actual distance between the light and the target; by continuously collecting target position information multiple times, use a differential algorithm to calculate the target's speed and acceleration, and determine the target's motion state;
[0015] The collected data is preprocessed, and the sensor data is smoothed using a filtering algorithm to eliminate the influence of random noise, identify and eliminate abnormal data points, and standardize and normalize the data collected by different sensors.
[0016] In combination with the first aspect, in a second implementation of the first aspect of the present application, the association model between light adjustment and target position is established based on historical data using a random forest regression algorithm; the relationship between the target position and light adjustment is predicted by analyzing characteristic parameters in the data, and the model is trained and evaluated through cross-validation, including:
[0017] A training dataset was constructed by selecting characteristic parameters related to light adjustment and target position from historical data, including the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, the target's motion state, and the corresponding light adjustment parameters. A feature subset was selected from the original feature set using the correlation coefficient method. The training dataset was then learned using the random forest regression algorithm to establish a nonlinear mapping relationship between light adjustment and target position. The random forest algorithm constructed multiple decision trees and integrated the prediction results of each tree to process data and nonlinear relationships.
[0018] The training dataset is randomly divided into K subsets of equal size. One subset is selected as the validation set each time, and the remaining K-1 subsets are used as training sets for model training and validation. This process is repeated K times, and the average of the K validation results is finally taken as the evaluation indicator of model performance. The mean square error is used to evaluate the prediction performance of the model, and the parameters of the random forest algorithm are tuned according to the cross-validation results.
[0019] In combination with the first aspect, in a third implementation of the first aspect of the present application, the target tracking is divided into static target tracking, dynamic target tracking, and multi-target tracking according to the target's motion state; when the target is static, a PID control algorithm is used in combination with a preset threshold for tracking, including:
[0020] By continuously collecting target position information multiple times, the target's speed and acceleration are calculated using a differential algorithm to determine the target's motion state. When the target's speed and acceleration are both less than a preset threshold in multiple consecutive sampling periods, the target is determined to be a static target.
[0021] According to historical data, a set of PID control parameters are preset, including the proportional coefficient K p , integral coefficient K i and differential coefficient K d , used for initial control of static target tracking; calculate the deviation between the current position of the laser light and the target position in real time, including vertical deviation and horizontal deviation; according to the PID control algorithm, use the current deviation and the preset PID parameters to calculate the control amount, the formula is as follows:
[0022]
[0023] Among them, u(t) is the control quantity, e(t) is the error signal, is the integral of the error signal from time 0 to the current time t, is the derivative of the error signal e(t) with respect to time t;
[0024] Set an alignment threshold. When the target position deviation decreases to within the threshold range, the light is considered to be aligned with the target and enters a stable state. In the stable state, the target position deviation is continuously monitored. When the deviation changes and exceeds the threshold range, PID control is re-performed until the deviation returns to within the threshold range again.
[0025] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present application, when the target is dynamic, the tracking path is optimized based on the target motion characteristics, and a model predictive control algorithm is introduced to achieve prediction and advance adjustment of the target motion trend, including:
[0026] By continuously collecting target position information multiple times, the target's speed and acceleration are calculated using a differential algorithm to determine the target's motion state. When the target's speed and acceleration exceed the preset threshold in multiple consecutive sampling periods, the target is determined to be a dynamic target.
[0027] Analyze the target's speed and acceleration data, identify the target's motion pattern, including uniform linear motion, uniformly accelerated linear motion, and circular motion, calculate the target's motion parameters based on the identified motion pattern, and use the model predictive control algorithm to predict the target's motion trajectory in the future. Use the A* algorithm to generate a tracking path so that the laser light can move along the path to achieve target tracking; and adaptively adjust the parameters based on the tracking effect.
[0028] In combination with the first aspect, in a fifth implementation of the first aspect of the present application, when there are multiple targets, a joint probabilistic data association filtering algorithm is introduced to achieve automatic identification, differentiation, and tracking of multiple targets, including:
[0029] The motion characteristics of the target collected by the sensor are associated with the known or predicted target trajectory, the association probability between the motion characteristics and the target trajectory is calculated, and the corresponding relationship between the motion characteristics and the target trajectory is established; the association probability is used to update the state of the target, and the Kalman filter algorithm is combined to predict the latest position, velocity and acceleration information of each target based on the current motion characteristic information and the predicted target state;
[0030] Manage existing target trajectories, including initialization, confirmation, maintenance, merging, segmentation, and deletion of trajectories, and handle the appearance, merging, separation, and disappearance of new targets;
[0031] According to the results of multi-target tracking, the output direction and parameters of the laser light are adjusted to achieve automatic tracking of multiple targets.
[0032] In combination with the first aspect, in the sixth implementation manner of the first aspect of the present application, when the target needs to be vertically adjusted, control the rotation mechanism of the level to adjust the vertical direction of the laser beam; when the target needs to be horizontally adjusted, control the motor of the tripod to extend and retract, and drive the level to adjust the position of the laser beam; when vertical and horizontal adjustments are required simultaneously, coordinate the vertical and horizontal adjustment mechanisms to ensure that the beam accurately aligns with the target, including:
[0033] According to the position information of the target collected by the sensor, including the horizontal and vertical coordinates of the target, compare it with the preset target position, and calculate the position deviation of the target in the horizontal and vertical directions; according to the calculated vertical direction position deviation, determine whether the target needs to be adjusted in the vertical direction. When vertical adjustment is required, calculate the rotation angle and speed according to the magnitude and direction of the vertical direction position deviation, and control the rotation mechanism of the level to rotate according to the calculated angle and speed to adjust the vertical direction of the laser beam so that it faces the target;
[0034] According to the calculated horizontal direction position deviation, determine whether the target needs to be adjusted in the horizontal direction; when horizontal adjustment is required, calculate the displacement and speed of the tripod motor to extend and retract according to the magnitude and direction of the horizontal direction position deviation, and control the motor of the tripod to extend and retract to drive the level to move horizontally and adjust the horizontal position of the laser beam so that it faces the target;
[0035] When vertical and horizontal adjustments are required simultaneously, adopt a coordinated control strategy, set priorities according to the actual application scenario, and adjust the vertical and horizontal directions according to the priorities;
[0036] During the adjustment process, continuously monitor the target position information fed back by the sensor, and correct the adjustment command according to the feedback information.
[0037] In combination with the first aspect, in the seventh implementation manner of the first aspect of the present application, judge the actual distance between the beam and the target through a preset threshold, and divide it into three adjustment modes according to different distance situations, including a short-distance mode, a medium-distance mode, and a long-distance mode, including:
[0038] Use the sensor to measure the actual distance d between the beam and the target. According to two preset distance thresholds d1 and d2, where d2>d1>0, divide the distance situation into three modes. When d<d1, enter the short-distance mode; when d1≤d≤d2, it is the medium-distance mode; when d>d2, it is the long-distance mode;
[0039] In close-range mode, the rotation angle and motor extension amount are reduced to improve the accuracy of the control algorithm, reduce the adjustment speed, and ensure that the light is aligned with the target; in medium-range mode, a moderate rotation angle and motor extension amount are used to dynamically adjust the speed and accuracy of the rotating and extension motors according to the specific location and movement trend of the target; in long-range mode, the rotation angle and motor extension amount are increased to increase the adjustment speed;
[0040] When the target distance changes, resulting in mode switching, a smooth transition strategy is adopted, a transition area is set, and the parameters of the rotation angle and motor extension amount are gradually adjusted within the transition area.
[0041] In combination with the first aspect, in an eighth implementation of the first aspect of the present application, when the light reaches the target position, the detector emits a long beep signal to indicate that the adjustment is completed, including:
[0042] The deviation between the laser beam and the target position is monitored in real time. When the deviation value is less than the preset alignment threshold, it is considered that the light has reached the target position, triggering the detector to emit a long beep signal to remind the operator that the light has been successfully aligned with the target and the adjustment process is completed. The current position is locked and the measurement data is recorded.
[0043] The frequency and duration of the long-beep signal are configured according to actual application requirements to adapt to different working environments and operating habits.
[0044] In a second aspect, the present invention provides an automatic tracking management system for a fully automatic level, comprising:
[0045] Data acquisition module: includes a sensor acquisition unit and a data preprocessing unit. The sensor acquisition unit uses multiple sensors to collect and calculate the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, and the motion state of the target. The data preprocessing unit removes noise, detects outliers, and performs standardization and normalization on the collected data.
[0046] Data analysis and modeling module: includes feature selection unit, model training unit and model evaluation unit; the feature selection unit uses the correlation coefficient method to select feature subsets from the original feature set; the model training unit uses the random forest regression algorithm to establish a correlation model between light adjustment and target position and integrate the prediction results; the model evaluation unit uses cross-validation to train and evaluate the model;
[0047] Target tracking module: includes target state recognition unit, static target tracking unit, dynamic target tracking unit and multi-target tracking unit; among them, the target state recognition unit divides the target into static target, dynamic target and multi-target according to the target's motion state; when the target is static, the static target tracking unit adopts PID control algorithm and combines preset threshold to track; when the target is dynamic, the dynamic target tracking unit combines the target motion characteristics to optimize the tracking path and introduces model predictive control algorithm to realize the prediction and advance adjustment of the target motion trend; when there are multiple targets, the multi-target tracking unit introduces joint probability data association filtering algorithm to realize automatic recognition, differentiation and tracking of multiple targets;
[0048] Adjustment control module: includes vertical adjustment unit, horizontal adjustment unit and coordination adjustment unit. When the target needs vertical adjustment, the vertical adjustment unit controls the rotation mechanism of the level to adjust the vertical direction of the laser beam. When the target needs horizontal adjustment, the horizontal adjustment unit controls the motor of the tripod to extend and retract, driving the level to adjust the position of the laser beam. When both vertical and horizontal adjustments are required, the coordination adjustment unit coordinates the vertical and horizontal adjustment mechanisms to ensure that the beam is accurately aligned with the target.
[0049] Distance judgment and switching module: includes a distance measurement unit and a mode switching unit. The distance measurement unit determines the actual distance between the light and the target based on a preset threshold. The mode switching unit is divided into three adjustment modes according to different distance conditions, including short-distance mode, medium-distance mode, and long-distance mode, to achieve smooth mode switching.
[0050] Signal prompt module: includes a signal trigger unit, a signal configuration unit and a data recording unit; the signal trigger unit is used to make the detector emit a long beep signal when the light reaches the target position; the signal configuration unit configures the frequency and duration of the long beep signal to adapt to different working environments and operating habits; the data recording unit locks the current position and records the measurement data.
[0051] Compared with the prior art, the present invention has the following beneficial effects:
[0052] 1. The present invention automatically selects the tracking mode according to the motion state of the target, including static target tracking, dynamic target tracking and multi-target tracking, by adopting different algorithms and strategies.
[0053] 2. The present invention can not only perform traditional vertical adjustment, but also realize automatic horizontal adjustment and coordinated adjustment of vertical and horizontal directions.
[0054] 3. The present invention automatically selects the appropriate adjustment mode according to the actual distance between the light and the target, including short-distance mode, medium-distance mode and long-distance mode. BRIEF DESCRIPTION OF THE DRAWINGS
[0055] Figure 1 A schematic diagram of the steps of an automatic tracking management method for a fully automatic level according to the present invention;
[0056] Figure 2 The figure is a system structure diagram of an automatic tracking management system for a fully automatic level according to the present invention. DETAILED DESCRIPTION
[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0058] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.
[0059] like Figure 1 The present invention provides a schematic diagram of the steps of an automatic tracking and management method for a fully automatic level, including:
[0060] Step S100: Collecting data through multiple sensors, including the current position of the laser beam, the deviation of the target position, the actual distance between the beam and the target, and the motion state of the target, and preprocessing the collected data, including noise removal, outlier detection, standardization and normalization;
[0061] Specifically, a position sensor is used to collect the current angle or position information of the laser emitting device; an image sensor is used to capture target image information, and through image processing, the image is compared with a preset reference position to calculate the deviation of the target position; a distance sensor is used to measure the actual distance between the light and the target; by continuously collecting target position information multiple times, a differential algorithm is used to calculate the target's speed and acceleration to determine the target's motion state;
[0062] The collected data is preprocessed, and the sensor data is smoothed using a filtering algorithm to eliminate the influence of random noise, identify and eliminate abnormal data points, and standardize and normalize the data collected by different sensors.
[0063] In a specific embodiment, a construction project in Shanghai was selected for data collection. The current angle of the laser emitting device collected by the position sensor was 30°. The target image information captured by the image sensor was processed and the deviation of the target position was 0.5mm. The actual distance between the light and the target measured by the distance sensor was 10m. After collecting the target position information five times in a row, the target speed was calculated to be 2m / s and the acceleration was 0.1m / s using the differential algorithm. 2 During the preprocessing phase, a mean filter algorithm was used to smooth the sensor data, with a window size of 5, effectively removing the effects of random noise. Three outlier data points with deviations exceeding three times the standard deviation were identified and removed. Data collected by the position sensor, image sensor, and range sensor were normalized and scaled to the [0, 1] range to facilitate subsequent data fusion and algorithm processing.
[0064] Step S200: Based on historical data, a correlation model between light adjustment and target position is established using a random forest regression algorithm; the relationship between target position and light adjustment is predicted by analyzing characteristic parameters in the data, and the model is trained and evaluated through cross-validation;
[0065] Specifically, a training data set is constructed based on feature parameters related to light adjustment and target position selected from historical data, including the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, the target's motion state, and the corresponding light adjustment parameters. A feature subset is selected from the original feature set using the correlation coefficient method, and the training data set is learned using the random forest regression algorithm to establish a nonlinear mapping relationship between light adjustment and target position. The random forest algorithm constructs multiple decision trees and integrates the prediction results of each tree to process data and nonlinear relationships.
[0066] The training dataset is randomly divided into K subsets of equal size. One subset is selected as the validation set each time, and the remaining K-1 subsets are used as training sets for model training and validation. This process is repeated K times, and the average of the K validation results is finally taken as the evaluation indicator of model performance. The mean square error is used to evaluate the prediction performance of the model, and the parameters of the random forest algorithm are tuned according to the cross-validation results.
[0067] In one specific embodiment, historical data collected over the past month was extracted from the project's database, totaling approximately 5,000 sets of records. These records detailed the status of each laser beam adjustment before and after the adjustment, including: the current position of the laser emitting device, with an average angle accuracy of 0.01°; the target position deviation calculated by the image sensor, with an average deviation of 0.3mm and a standard deviation of 0.1mm; the real-time distance measurement between the light and the target, with an average distance of 15m and a range of 10m to 20m; and the target motion state calculated by the differential algorithm, with an average speed of 0.5m / s and an average acceleration of 0.05m / s. 2 The final light adjustment parameters are adjusted by an average of 0.05°.
[0068] A training dataset containing feature parameters was constructed. The correlation coefficient method was used to analyze the relationship between the features and the final light adjustment. A subset of features highly correlated with the light adjustment was selected, including target position deviation, target motion speed, and the distance between the light and the target. A random forest regression algorithm was used to learn the training dataset. Initial parameters were set, with 100 decision trees and a maximum depth of 10. Multiple decision trees were constructed, each trained using bootstrap sampling and random feature selection. The prediction results of each tree were integrated to effectively handle noise and nonlinear relationships in the data.
[0069] To evaluate and optimize model performance, a K-fold cross-validation method was used, randomly dividing the training dataset into K = 5 subsets. The first subset was selected as the validation set, and the remaining four subsets were used as training sets for model training and validation. The second subset was selected as the validation set, and the remaining four subsets were used as training sets. This process was repeated five times, for a total of five training and validation cycles. After each validation cycle, the mean squared error (MSE) of the model on the validation set was calculated as an evaluation metric. The MSEs obtained from the five cross-validation cycles were 0.022, 0.019, 0.025, 0.021, and 0.023, respectively. The average of these five MSEs, 0.022, was used as the model performance evaluation metric. Based on the cross-validation results, the model's predictive performance was good, but there was still room for improvement. The random forest parameters were adjusted, increasing the number of decision trees to 150 and the maximum depth to 8, and cross-validation was repeated. The average MSE of the optimized model in cross-validation was reduced to 0.018, indicating improved predictive accuracy.
[0070] Step S300: According to the target's motion state, the tracking is divided into static target tracking, dynamic target tracking, and multi-target tracking. When the target is static, the PID control algorithm is used in combination with a preset threshold for tracking. When the target is dynamic, the tracking path is optimized based on the target's motion characteristics, and a model predictive control algorithm is introduced to predict and adjust the target's motion trend in advance. When there are multiple targets, a joint probabilistic data association filtering algorithm is introduced to achieve automatic multi-target identification, differentiation, and tracking.
[0071] Specifically, by continuously collecting target position information multiple times, the target's speed and acceleration are calculated using a differential algorithm to determine the target's motion state. When the target's speed and acceleration in multiple consecutive sampling periods are less than a preset threshold, the target is determined to be a static target.
[0072] According to historical data, a set of PID control parameters are preset, including the proportional coefficient K p , integral coefficient K i and differential coefficient K d , used for initial control of static target tracking; calculate the deviation between the current position of the laser light and the target position in real time, including vertical deviation and horizontal deviation; according to the PID control algorithm, use the current deviation and the preset PID parameters to calculate the control amount, the formula is as follows:
[0073]
[0074] Among them, u(t) is the control quantity, e(t) is the error signal, is the integral of the error signal from time 0 to the current time t, is the derivative of the error signal e(t) with respect to time t;
[0075] Set an alignment threshold. When the target position deviation decreases to within the threshold range, the light is considered to be aligned with the target and enters a stable state. In the stable state, the target position deviation is continuously monitored. When the deviation changes and exceeds the threshold range, PID control is re-performed until the deviation returns to within the threshold range again.
[0076] By continuously collecting target position information multiple times, the target's speed and acceleration are calculated using a differential algorithm to determine the target's motion state. When the target's speed and acceleration exceed the preset threshold in multiple consecutive sampling periods, the target is determined to be a dynamic target.
[0077] Analyze the target's speed and acceleration data, identify the target's motion pattern, including uniform linear motion, uniformly accelerated linear motion, and circular motion, calculate the target's motion parameters based on the identified motion pattern, and use the model predictive control algorithm to predict the target's motion trajectory in the future. Use the A* algorithm to generate a tracking path so that the laser light can move along the path to achieve target tracking; and adaptively adjust the parameters based on the tracking effect.
[0078] The motion characteristics of the target collected by the sensor are associated with the known or predicted target trajectory, the association probability between the motion characteristics and the target trajectory is calculated, and the corresponding relationship between the motion characteristics and the target trajectory is established; the association probability is used to update the state of the target, and the Kalman filter algorithm is combined to predict the latest position, velocity and acceleration information of each target based on the current motion characteristic information and the predicted target state;
[0079] Manage existing target trajectories, including initialization, confirmation, maintenance, merging, segmentation, and deletion of trajectories, and handle the appearance, merging, separation, and disappearance of new targets;
[0080] According to the results of multi-target tracking, the output direction and parameters of the laser light are adjusted to achieve automatic tracking of multiple targets.
[0081] In a specific embodiment, the target's position information is collected multiple times continuously, and the target's velocity and acceleration are calculated using a differential algorithm to determine the target's motion state. The sampling period is set to 0.1s, and data is collected continuously for 10 periods. The calculation shows that the target's velocity is less than 0.01m / s and its acceleration is less than 0.001m / s in 5 consecutive sampling periods. 2 , which is much lower than the preset speed threshold of 0.05m / s and acceleration threshold of 0.005m / s 2 , so the target is determined to be a static target.
[0082] For static target tracking, a set of PID control parameters are preset based on historical data: proportional coefficient K p Set to 1.2, the integral coefficient K i Set to 0.8, differential coefficient K d Set to 0.3. Calculate the deviation between the current laser position and the target position in real time, including vertical and horizontal deviations. In one measurement, the laser beam deviated from the target by 0.5mm vertically and 0.3mm horizontally. The PID control algorithm uses the current deviation and the preset PID parameters to calculate the control variable. The formula is as follows:
[0083]
[0084] Calculation yields the control variable u(t) = 1.2 * 0.5 + 0.8 * 0.05 - 0.3 * 0.1 = 0.63. The alignment threshold is set to 0.1 mm. When the target position deviation decreases to within this threshold, the light is considered aligned with the target and enters a stable state. In this stable state, the target position deviation is continuously monitored. When the deviation increases to 0.2 mm, PID control is re-implemented until the deviation returns to within the threshold.
[0085] For dynamic target tracking, the target position information is collected multiple times continuously and the target speed and acceleration are calculated using a differential algorithm. In one measurement, it was calculated that the target speed exceeded 0.1m / s and the acceleration exceeded 0.01m / s in 5 consecutive sampling periods. 2 , exceeding the preset threshold, the target is determined to be a dynamic target. The target's velocity and acceleration data are analyzed and the target's motion mode is identified as uniformly accelerated linear motion. Based on the identified motion mode, the target's motion parameters are calculated, with an initial velocity of 0.1m / s and an acceleration of 0.02m / s. 2 The model predictive control algorithm predicts the target's trajectory over the next 1 second. Using the A* algorithm, it calculates that the laser beam needs to be adjusted rightward by 0.05° and upward by 0.03° to catch up with the moving target. Based on the tracking results, the prediction time domain is adjusted from 1 second to 0.8 seconds to achieve more accurate tracking.
[0086] In the multi-target tracking scenario, the joint probability data association filtering algorithm is used to process multiple targets. In one measurement, the sensor collects the motion features of 3 targets. These features are associated with the known or predicted target trajectories, the association probability between the motion features and the target trajectories is calculated, and the correspondence between the motion features and the target trajectories is established. The state of the target is updated using the association probability, and combined with the Kalman filter algorithm, the latest position, velocity, and acceleration information of each target is predicted based on the current motion feature information and the predicted target state. The predicted position of target 1 is (10.0m, 5.0m), the velocity is (0.2m / s, 0.1m / s), and the acceleration is (0.01m / s 2 , 0.01m / s 2 The existing target trajectories are managed. When the trajectories of Target 2 and Target 3 are very close and have the same speed and direction, the trajectories of the two targets are merged into a single trajectory. Based on the position information of Target 1 and Target 2, the laser beam is calculated to adjust in increments of 0.1° and 0.2°, respectively, to accurately track both targets.
[0087] Step S400: When the target needs to be adjusted vertically, the rotation mechanism of the level is controlled to adjust the vertical direction of the laser beam. When the target needs to be adjusted horizontally, the motor of the tripod is controlled to extend and retract, driving the level to adjust the position of the laser beam. When both vertical and horizontal adjustments are required, the vertical and horizontal adjustment mechanisms are coordinated to ensure that the beam is accurately aligned with the target.
[0088] Specifically, the position information of the target collected by the sensor, including the horizontal and vertical coordinates of the target, is compared with a preset target position to calculate the position deviation of the target in the horizontal and vertical directions; based on the calculated vertical position deviation, it is determined whether the target needs to be adjusted in the vertical direction; when vertical adjustment is required, the required rotation angle and speed are calculated based on the size and direction of the vertical position deviation, and the rotation mechanism of the level is controlled to rotate according to the calculated angle and speed, thereby adjusting the vertical direction of the laser light so that it is directed toward the target;
[0089] Based on the calculated horizontal position deviation, it is determined whether the target needs to be adjusted horizontally. When horizontal adjustment is required, the required extension and retraction displacement and speed of the tripod motor are calculated based on the size and direction of the horizontal position deviation. The tripod motor is then controlled to extend and retract, driving the level to move horizontally, adjusting the horizontal position of the laser beam so that it faces the target.
[0090] When vertical and horizontal adjustments are required simultaneously, a coordinated control strategy is adopted to set priorities according to the actual application scenario and adjust the vertical and horizontal directions according to the priorities;
[0091] During the adjustment process, the target position information fed back by the sensor is monitored in real time, and the adjustment instructions are corrected based on the feedback information.
[0092] In one specific embodiment, the preset target position is 10.00 m horizontally and 5.00 m vertically. The actual target position detected by the sensor is 10.05 m horizontally and 4.95 m vertically. Calculations show that the target's horizontal position deviation is +0.05 m, indicating that the target is horizontally to the right; the vertical position deviation is -0.05 m, indicating that the target is vertically low.
[0093] Based on the calculated vertical position deviation, determine whether the target needs to be adjusted vertically. Since the vertical position deviation is -0.05m, which exceeds the preset threshold of ±0.02m, vertical adjustment is required. According to the magnitude and direction of the vertical position deviation, the calculated rotation angle is +0.5°, which means rotating upward at a speed of 1° / s. Control the rotation mechanism of the level to rotate according to the calculated angle and speed, and adjust the vertical direction of the laser beam to face the target.
[0094] Based on the calculated horizontal position deviation, determine whether the target needs to be adjusted horizontally. Since the horizontal position deviation is +0.05m, which exceeds the preset threshold of ±0.02m, horizontal adjustment is required. According to the magnitude and direction of the horizontal position deviation, the calculated displacement for the tripod motor to extend or retract is -0.05m, which means moving left at a speed of 0.1m / s. Control the motor of the tripod to extend or retract, driving the level to move horizontally and adjusting the horizontal position of the laser beam to face the target.
[0095] When vertical and horizontal adjustments are required simultaneously, a coordinated control strategy is adopted. In this construction project, the priority of vertical adjustment is higher than that of horizontal adjustment. Therefore, vertical adjustment is carried out first. After the vertical deviation is reduced to within the threshold range, horizontal adjustment is then carried out. During the adjustment process, the target position information feedback by the sensor is monitored in real time, and the adjustment instructions are corrected according to the feedback information. After vertical adjustment, the target position feedback by the sensor shows that the vertical deviation has been reduced to -0.01m, but the horizontal deviation has become +0.06m due to the movement of the target. According to the new feedback information, recalculate the displacement and speed of horizontal adjustment to ensure that the laser beam accurately aligns with the target.
[0096] Step S500: Judge the actual distance between the light beam and the target through the preset threshold, and divide it into three adjustment modes according to different distance situations, including the close - distance mode, the medium - distance mode and the long - distance mode; when the light beam reaches the target position, the detector emits a long beeping signal to indicate that the adjustment is completed.
[0097] Specifically, use the sensor to measure the actual distance d between the light beam and the target. According to the two preset distance thresholds d1 and d2, where d2 > d1 > 0, divide the distance situation into three modes. When d < d1, enter the close - distance mode; when d1 ≤ d ≤ d2, it is the medium - distance mode; when d > d2, it is the long - distance mode.
[0098] In close-range mode, the rotation angle and motor extension amount are slowed down to improve the accuracy of the control algorithm, reduce the adjustment speed, and ensure that the light is aligned with the target; in medium-range mode, a moderate rotation angle and motor extension amount are used to dynamically adjust the speed and accuracy of the rotating and extension motors according to the specific location and movement trend of the target; in long-range mode, the rotation angle and motor extension amount are increased to increase the adjustment speed;
[0099] When the target distance changes, resulting in mode switching, a smooth transition strategy is adopted, a transition area is set, and the parameters of the rotation angle and motor extension amount are gradually adjusted within the transition area.
[0100] Furthermore, the deviation between the laser beam and the target position is monitored in real time. When the deviation value is less than the preset alignment threshold, it is considered that the light has reached the target position, triggering the detector to emit a long beep signal to inform the operator that the light has been successfully aligned with the target and the adjustment process is completed. The current position is locked and the measurement data is recorded;
[0101] The frequency and duration of the long-beep signal are configured according to actual application requirements to adapt to different working environments and operating habits.
[0102] In a specific embodiment, the actual distance d between the light and the target is 3m as measured by the sensor, d1 is set to 1m, and d2 is set to 5m. Since 1m≤d≤5m, the medium distance mode is entered.
[0103] Using appropriate rotation angles and motor extensions, the speed and accuracy of the rotating and retracting motors are dynamically adjusted based on the target's specific location and motion. The maximum increments for rotation angle and motor extension are set to 1°, and the maximum increments for motor extension are set to 0.1m, with an adjustment speed of 5° / s or 0.1m / s. Based on the target's horizontal and vertical position deviations, the speed and accuracy of the rotating and retracting motors are dynamically adjusted to ensure accurate light alignment.
[0104] When the target distance changes, causing a mode switch, a smooth transition strategy is implemented, setting a transition zone within which the rotation angle and motor extension parameters are gradually adjusted. When the target distance changes from 0.8m close-range mode to 1.2m medium-range mode, a 0.2m transition zone is set at approximately 1m, and the rotation angle and motor extension increments are gradually increased within this zone to prevent sudden changes in parameters from causing unstable light alignment.
[0105] The deviation between the laser beam and the target position is monitored in real time, with an alignment threshold set at 0.01m. When the deviation falls below 0.01m, the detector triggers a long beep, signaling to the operator that the beam has been successfully aligned with the target, completing the alignment process. The current position is locked, and the measurement data is recorded. The beep frequency is set to 440Hz and the duration is 2s to accommodate the noisy environment of a construction site and ensure that the operator can clearly hear the beep.
[0106] like Figure 2 As shown in the system structure diagram of an automatic tracking management system for a fully automatic level, the present invention provides an automatic tracking management system for a fully automatic level, comprising:
[0107] Data acquisition module: includes a sensor acquisition unit and a data preprocessing unit. The sensor acquisition unit uses multiple sensors to collect and calculate the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, and the motion state of the target. The data preprocessing unit removes noise, detects outliers, and performs standardization and normalization on the collected data.
[0108] Data analysis and modeling module: includes feature selection unit, model training unit and model evaluation unit; the feature selection unit uses the correlation coefficient method to select feature subsets from the original feature set; the model training unit uses the random forest regression algorithm to establish a correlation model between light adjustment and target position and integrate the prediction results; the model evaluation unit uses cross-validation to train and evaluate the model;
[0109] Target tracking module: includes target state recognition unit, static target tracking unit, dynamic target tracking unit and multi-target tracking unit; among them, the target state recognition unit divides the target into static target, dynamic target and multi-target according to the target's motion state; when the target is static, the static target tracking unit adopts PID control algorithm and combines preset threshold to track; when the target is dynamic, the dynamic target tracking unit combines the target motion characteristics to optimize the tracking path and introduces model predictive control algorithm to realize the prediction and advance adjustment of the target motion trend; when there are multiple targets, the multi-target tracking unit introduces joint probability data association filtering algorithm to realize automatic recognition, differentiation and tracking of multiple targets;
[0110] Adjustment control module: includes vertical adjustment unit, horizontal adjustment unit and coordination adjustment unit. When the target needs vertical adjustment, the vertical adjustment unit controls the rotation mechanism of the level to adjust the vertical direction of the laser beam. When the target needs horizontal adjustment, the horizontal adjustment unit controls the motor of the tripod to extend and retract, driving the level to adjust the position of the laser beam. When both vertical and horizontal adjustments are required, the coordination adjustment unit coordinates the vertical and horizontal adjustment mechanisms to ensure that the beam is accurately aligned with the target.
[0111] Distance judgment and switching module: includes a distance measurement unit and a mode switching unit. The distance measurement unit determines the actual distance between the light and the target based on a preset threshold. The mode switching unit is divided into three adjustment modes according to different distance conditions, including short-distance mode, medium-distance mode, and long-distance mode, to achieve smooth mode switching.
[0112] Signal prompt module: includes a signal trigger unit, a signal configuration unit and a data recording unit; the signal trigger unit is used to make the detector emit a long beep signal when the light reaches the target position; the signal configuration unit configures the frequency and duration of the long beep signal to adapt to different working environments and operating habits; the data recording unit locks the current position and records the measurement data.
[0113] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.
Claims
1. An automatic tracking management method for a fully automatic level, characterized in that: include: Collect data through multiple sensors, including the current position of the laser beam, the deviation of the target position, the actual distance between the beam and the target, and the motion state of the target. Preprocess the collected data, including noise removal, outlier detection, standardization and normalization. Based on historical data, a random forest regression algorithm is used to establish a correlation model between light adjustment and target position. The relationship between target position and light adjustment is predicted by analyzing the characteristic parameters in the data, and the model is trained and evaluated through cross-validation. According to the target's motion state, it is divided into static target tracking, dynamic target tracking and multi-target tracking. When the target is static, the PID control algorithm is used in combination with the preset threshold for tracking. When the target is dynamic, the tracking path is optimized based on the target's motion characteristics, and the model predictive control algorithm is introduced to achieve the prediction and early adjustment of the target's motion trend. When there are multiple targets, the joint probabilistic data association filtering algorithm is introduced to achieve automatic recognition, differentiation and tracking of multiple targets. When the target needs to be adjusted vertically, the rotation mechanism of the level is controlled to adjust the vertical direction of the laser beam. When the target needs to be adjusted horizontally, the motor of the tripod is controlled to extend and retract, driving the level to adjust the position of the laser beam. When both vertical and horizontal adjustments are required, the vertical and horizontal adjustment mechanisms are coordinated to ensure that the beam is accurately aligned with the target. The actual distance between the light and the target is determined by a preset threshold, and is divided into three adjustment modes according to different distance conditions, including short-distance mode, medium-distance mode and long-distance mode; When the light reaches the target position, the detector emits a long beep signal to indicate that the adjustment is completed.
2. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: The data collected by multiple sensors include the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, and the motion state of the target, and the collected data is pre-processed, including noise removal, outlier detection, standardization and normalization, including: Use a position sensor to collect the current angle or position information of the laser emitting device; use an image sensor to capture target image information, compare it with the preset reference position through image processing, and calculate the deviation of the target position; use a distance sensor to measure the actual distance between the light and the target; by continuously collecting target position information multiple times, use a differential algorithm to calculate the target's speed and acceleration, and determine the target's motion state; The collected data is preprocessed, and the sensor data is smoothed using a filtering algorithm to eliminate the influence of random noise, identify and eliminate abnormal data points, and standardize and normalize the data collected by different sensors.
3. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: The method is based on historical data, establishing a correlation model between light adjustment and target position through a random forest regression algorithm; predicting the relationship between target position and light adjustment by analyzing characteristic parameters in the data, and training and evaluating the model through cross-validation, including: A training data set was constructed by selecting characteristic parameters related to light adjustment and target position from historical data, including the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, the target's motion state, and the corresponding light adjustment parameters. A feature subset was selected from the original feature set using the correlation coefficient method, and the training data set was learned using the random forest regression algorithm to establish a nonlinear mapping relationship between light adjustment and target position. The random forest algorithm constructed multiple decision trees and integrated the prediction results of each tree to process data and nonlinear relationships. The training dataset is randomly divided into K subsets of equal size. One subset is selected as the validation set each time, and the remaining K-1 subsets are used as training sets for model training and validation. This process is repeated K times, and the average of the K validation results is finally taken as the evaluation indicator of model performance. The mean square error is used to evaluate the prediction performance of the model, and the parameters of the random forest algorithm are tuned according to the cross-validation results.
4. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: The tracking method is divided into static target tracking, dynamic target tracking and multi-target tracking according to the target's motion state; When the target is static, the PID control algorithm is used in combination with the preset threshold for tracking, including: By continuously collecting target position information multiple times, the target's speed and acceleration are calculated using a differential algorithm to determine the target's motion state. When the target's speed and acceleration are both less than a preset threshold in multiple consecutive sampling periods, the target is determined to be a static target. According to historical data, a set of PID control parameters are preset, including the proportional coefficient K p , integral coefficient K i and differential coefficient K d , used for initial control of static target tracking; calculate the deviation between the current position of the laser light and the target position in real time, including vertical deviation and horizontal deviation; according to the PID control algorithm, use the current deviation and the preset PID parameters to calculate the control amount, the formula is as follows: Among them, u(t) is the control quantity, e(t) is the error signal, is the integral of the error signal from time 0 to the current time t, is the derivative of the error signal e(t) with respect to time t; Set an alignment threshold. When the target position deviation decreases to within the threshold range, the light is considered to be aligned with the target and enters a stable state. In the stable state, the target position deviation is continuously monitored. When the deviation changes and exceeds the threshold range, PID control is re-performed until the deviation returns to within the threshold range again.
5. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: When the target is dynamic, the tracking path is optimized by combining the target motion characteristics and introducing a model predictive control algorithm to achieve prediction and early adjustment of the target motion trend, including: By continuously collecting target position information multiple times, the target's speed and acceleration are calculated using a differential algorithm to determine the target's motion state. When the target's speed and acceleration exceed the preset threshold in multiple consecutive sampling periods, the target is determined to be a dynamic target. Analyze the target's speed and acceleration data, identify the target's motion pattern, including uniform linear motion, uniformly accelerated linear motion, and circular motion, calculate the target's motion parameters based on the identified motion pattern, and use the model predictive control algorithm to predict the target's motion trajectory in the future. Use the A* algorithm to generate a tracking path so that the laser light can move along the path to achieve target tracking; and adaptively adjust the parameters based on the tracking effect.
6. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: When there are multiple targets, a joint probabilistic data association filtering algorithm is introduced to achieve automatic multi-target identification, differentiation and tracking, including: Associate the motion characteristics of the target collected by the sensor with the known or predicted target trajectory, calculate the association probability between the motion characteristics and the target trajectory, and establish the corresponding relationship between the motion characteristics and the target trajectory; use the association probability to update the state of the target, and combine with the Kalman filter algorithm to predict the latest position, speed and acceleration information of each target according to the current motion characteristic information and the predicted target state. Manage the existing target trajectories, including trajectory initialization, confirmation, maintenance, merging, splitting and deletion, and handle the emergence, merging, separation and disappearance of new targets. According to the results of multi-target tracking, adjust the output direction and parameters of the laser beam to achieve automatic tracking of multiple targets.
7. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: When the target needs vertical adjustment, control the rotation mechanism of the level to adjust the vertical direction of the laser beam; when the target needs horizontal adjustment, control the motor of the tripod to extend and retract, and drive the level to adjust the position of the laser beam; when vertical and horizontal adjustments are required simultaneously, coordinate the vertical and horizontal adjustment mechanisms to ensure that the beam is accurately aligned with the target, including: Compare the position information of the target collected by the sensor, including the horizontal and vertical coordinates of the target, with the preset target position, and calculate the position deviation of the target in the horizontal and vertical directions; according to the calculated vertical position deviation, judge whether the target needs to be adjusted in the vertical direction. When vertical adjustment is required, calculate the rotation angle and speed required according to the magnitude and direction of the vertical position deviation, and control the rotation mechanism of the level to rotate according to the calculated angle and speed to adjust the vertical direction of the laser beam so that it faces the target. According to the calculated horizontal position deviation, judge whether the target needs to be adjusted in the horizontal direction; when horizontal adjustment is required, calculate the displacement and speed required for the tripod motor to extend and retract according to the magnitude and direction of the horizontal position deviation, and control the motor of the tripod to extend and retract to drive the level to move horizontally and adjust the horizontal position of the laser beam so that it faces the target. When vertical and horizontal adjustments are required simultaneously, adopt a coordinated control strategy, set the priority according to the actual application scenario, and adjust the vertical and horizontal directions according to the priority. During the adjustment process, monitor the target position information feedback by the sensor in real time, and correct the adjustment instructions according to the feedback information.
8. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: Judge the actual distance between the beam and the target through a preset threshold, and divide it into three adjustment modes according to different distance situations, including the close-range mode, the medium-range mode and the long-range mode, including: Use the sensor to measure the actual distance d between the beam and the target. According to the two preset distance thresholds d1 and d2, where d2 > d1 > 0, divide the distance situation into three modes. When d < d1, enter the close-range mode; when d1 ≤ d ≤ d2, it is the medium-range mode; when d > d2, it is the long-range mode. In close-range mode, the rotation angle and motor extension amount are slowed down to improve the accuracy of the control algorithm, reduce the adjustment speed, and ensure that the light is aligned with the target; in medium-range mode, a moderate rotation angle and motor extension amount are used to dynamically adjust the speed and accuracy of the rotating and extension motors according to the specific location and movement trend of the target; in long-range mode, the rotation angle and motor extension amount are increased to increase the adjustment speed; When the target distance changes, resulting in mode switching, a smooth transition strategy is adopted, a transition area is set, and the parameters of the rotation angle and motor extension amount are gradually adjusted within the transition area.
9. The automatic tracking management method for a fully automatic level according to claim 1, characterized in that: When the light reaches the target position, the detector emits a long beep signal to indicate that the adjustment is completed, including: The deviation between the laser beam and the target position is monitored in real time. When the deviation value is less than the preset alignment threshold, it is considered that the light has reached the target position, triggering the detector to emit a long beep signal to remind the operator that the light has been successfully aligned with the target and the adjustment process is completed. The current position is locked and the measurement data is recorded. The frequency and duration of the long-beep signal are configured according to actual application requirements to adapt to different working environments and operating habits.
10. An automatic tracking and management system for a fully automatic level, using the automatic tracking and management method for a fully automatic level according to any one of claims 1 to 9, characterized in that: include: Data acquisition module: includes a sensor acquisition unit and a data preprocessing unit. The sensor acquisition unit uses multiple sensors to collect and calculate the current position of the laser light, the deviation of the target position, the actual distance between the light and the target, and the motion state of the target. The data preprocessing unit removes noise, detects outliers, and performs standardization and normalization on the collected data. Data analysis and modeling module: includes feature selection unit, model training unit and model evaluation unit; the feature selection unit uses the correlation coefficient method to select feature subsets from the original feature set; the model training unit uses the random forest regression algorithm to establish a correlation model between light adjustment and target position and integrate the prediction results; the model evaluation unit uses cross-validation to train and evaluate the model; Target tracking module: includes target state recognition unit, static target tracking unit, dynamic target tracking unit and multi-target tracking unit; among them, the target state recognition unit divides the target into static target, dynamic target and multi-target according to the target's motion state; when the target is static, the static target tracking unit adopts PID control algorithm and combines preset threshold to track; when the target is dynamic, the dynamic target tracking unit combines the target motion characteristics to optimize the tracking path and introduces model predictive control algorithm to realize the prediction and advance adjustment of the target motion trend; when there are multiple targets, the multi-target tracking unit introduces joint probability data association filtering algorithm to realize automatic recognition, differentiation and tracking of multiple targets; Adjustment control module: includes vertical adjustment unit, horizontal adjustment unit and coordination adjustment unit. When the target needs vertical adjustment, the vertical adjustment unit controls the rotation mechanism of the level to adjust the vertical direction of the laser beam. When the target needs horizontal adjustment, the horizontal adjustment unit controls the motor of the tripod to extend and retract, driving the level to adjust the position of the laser beam. When both vertical and horizontal adjustments are required, the coordination adjustment unit coordinates the vertical and horizontal adjustment mechanisms to ensure that the beam is accurately aligned with the target. Distance judgment and switching module: includes a distance measurement unit and a mode switching unit. The distance measurement unit determines the actual distance between the light and the target based on a preset threshold. The mode switching unit is divided into three adjustment modes according to different distance conditions, including short-distance mode, medium-distance mode, and long-distance mode, to achieve smooth mode switching. Signal prompt module: includes a signal trigger unit, a signal configuration unit and a data recording unit; the signal trigger unit is used to make the detector emit a long beep signal when the light reaches the target position; the signal configuration unit configures the frequency and duration of the long beep signal to adapt to different working environments and operating habits; the data recording unit locks the current position and records the measurement data.