Beam surface paving laser positioning and tracking system and method

Abstract

The invention relates to the technical field of positioning and tracking, in particular to a beam surface paving laser positioning and tracking system and method. Comprising a dynamic wavelength calibration module, a multi-path signal separation module, a heterogeneous communication protocol bridging module, a quantum noise suppression module, a high-precision clock synchronization module, a dynamic buffer management module, a condensation self-repairing module and a heterogeneous data fusion module. The dynamic wavelength calibration module is used for wavelength real-time monitoring and dynamic compensation. According to the system, mirror surface condensation is actively eliminated through condensation self-repairing, dynamic buffer area management and multi-source data fusion are combined, millimeter-level paving control can be fully automatically completed through the system, the actually-measured paving surface flatness error is stabilized within + / -2mm, the construction efficiency is improved by 40% or above, and the problems that a traditional method is insufficient in precision, poor in interference resistance and depends on manpower are thoroughly solved.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning and tracking, and specifically provides a laser positioning and tracking system and method for beam surface paving. Background Technique

[0002] Beam surface paving refers to the process of constructing an asphalt concrete or cement concrete paving layer on the surface of a bridge structure. Its core objective is to ensure that the thickness, flatness, and transverse and longitudinal slope accuracy of the paving layer meet the design requirements. This process directly affects the driving comfort, structural durability, and drainage performance of the bridge, and has extremely high requirements for the positioning accuracy and dynamic control ability during construction.

[0003] Generally, traditional methods mainly rely on total station manual measurement and mechanical guiding systems. Before construction, a large number of control points need to be arranged on the beam surface. The elevation is measured point by point with a total station and reference lines are marked. The paver operator manually adjusts the height of the screed according to the marked lines. Some high-end equipment uses mechanical contact sensors or single-frequency laser receivers for rough positioning by detecting physical contact signals of a preset reference plane or a single laser plane.

[0004] However, traditional methods have low efficiency in manual measurement and are prone to introducing human errors, and cannot meet the requirements of long-distance continuous paving; mechanical contact sensors are easily affected by beam surface vibrations, resulting in measurement data jumps; single-frequency laser systems have poor anti-interference ability and are prone to being interfered by multipath reflections and losing accuracy in areas with dense steel bars. In addition, incompatible heterogeneous device protocols will cause control instruction delays, and there are no effective means to suppress problems such as laser wavelength drift and power supply harmonic noise caused by environmental temperature and humidity changes. Eventually, the flatness fluctuation of the paving surface exceeds ±5 mm, making it difficult to meet the requirements of high-grade bridges.

[0005] In summary, it is necessary to propose a laser positioning and tracking system and method for beam surface paving to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a laser positioning and tracking system and method for beam surface paving to solve the problems raised in the above background technique.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] The present invention proposes a laser positioning and tracking system for beam surface paving, including a dynamic wavelength calibration module, a multipath signal separation module, a heterogeneous communication protocol bridging module, a quantum noise suppression module, a high-precision clock synchronization module, a dynamic buffer management module, a condensation self-repair module, and a heterogeneous data fusion module;

[0009] The dynamic wavelength calibration module is used for real-time wavelength monitoring and dynamic compensation;

[0010] The multi-path signal separation module is used for spatial beamforming and feature recognition;

[0011] The heterogeneous communication protocol bridging module is used for protocol parsing and data encapsulation;

[0012] The quantum noise suppression module is used for harmonic spectrum analysis and noise cancellation;

[0013] The high-precision clock synchronization module is used for dual-source timekeeping and phase fine-tuning;

[0014] The dynamic buffer management module is used for memory pre-allocation and priority cleaning;

[0015] The condensation self-repair module is used for dew point prediction and nano-coating activation;

[0016] The heterogeneous data fusion module is used for sampling rate normalization and spatio-temporal alignment.

[0017] Preferably, the dynamic wavelength calibration module further includes a wavelength real-time monitoring unit and a dynamic compensation unit;

[0018] The wavelength real-time monitoring unit uses an adaptive Kalman filtering algorithm to track the output wavelength change of the laser emitter in real time, and is used to achieve millisecond-level dynamic detection of wavelength offset;

[0019] The dynamic compensation unit drives the laser voltage-controlled oscillator based on the wavelength locking method, and is used to adjust the laser wavelength to the preset reference value in real time to eliminate the influence of temperature drift or aging.

[0020] Preferably, the multi-path signal separation module further includes a spatial beamforming unit and a feature recognition unit;

[0021] The spatial beamforming unit suppresses the reflected signals from steel bars and formworks through spatial adaptive filtering, and is used to extract the main signal of the direct laser path;

[0022] The feature recognition unit uses a convolutional pulse neural network to recognize the time-frequency features of the real laser signal, and is used to separate multi-path interference signals.

[0023] Preferably, the heterogeneous communication protocol bridging module further includes a protocol parsing unit and a data encapsulation unit;

[0024] The protocol parsing unit uses a dynamic protocol conversion engine to parse heterogeneous protocol instructions such as CAN bus and RS485 in real time, and is used to unify the control instruction format;

[0025] The data encapsulation unit inserts synchronization marks into the instruction stream based on timestamp injection, and is used to eliminate the instruction delay caused by protocol conflicts.

[0026] Preferably, the quantum noise suppression module further includes a harmonic spectrum analysis unit and a noise cancellation unit;

[0027] The harmonic spectrum analysis unit detects the energy distribution in the power harmonic frequency band through quantum wavelet transform, and is used to locate the interference source frequency band;

[0028] The noise cancellation unit uses adaptive quantum entanglement filtering to reconstruct the reverse harmonic signal, and is used to cancel the interference of the power supply noise on the laser.

[0029] Preferably, the high-precision clock synchronization module further includes a dual-source time service unit and a phase fine-tuning unit;

[0030] The dual-source time service unit obtains dual clock source signals through the GPS-cesium atomic clock redundancy synchronization algorithm, and is used to eliminate the cumulative error of a single clock source;

[0031] The phase fine-tuning unit dynamically adjusts the controller clock phase based on sub-nanosecond phase alignment, and is used to achieve time synchronization between the laser and the controller.

[0032] Preferably, the dynamic buffer management module further includes a memory pre-allocation unit and a priority cleaning unit;

[0033] The memory pre-allocation unit pre-divides the point cloud data storage area through a block circular queue, and is used to avoid high-frequency data overflow;

[0034] The priority cleaning unit automatically cleans the expired data blocks based on the time-weighted elimination strategy, and is used to maintain the stable operation of the buffer.

[0035] Preferably, the condensation self-repair module further includes a dew point prediction unit and a nano-coating activation unit;

[0036] The dew point prediction unit predicts the risk of condensation on the lens surface through the microenvironment temperature and humidity field modeling, and is used to trigger the protection mechanism in advance;

[0037] The nano-coating activation unit controls the deformation of the nano-materials on the lens surface based on electrostatic drive hydrophobicity, and is used to remove the condensation water film in real time.

[0038] Preferably, the heterogeneous data fusion module further includes a sampling rate normalization unit and a spatio-temporal alignment unit;

[0039] The sampling rate normalization unit aligns the data streams of the laser and the tilt sensor through non-uniform interpolation resampling, and is used to eliminate the sampling rate mismatch error;

[0040] The spatio-temporal alignment unit unifies the spatio-temporal reference of the data based on the multi-sensor Lie group synchronization, and is used to achieve the precise fusion of the attitude control signals.

[0041] Based on the above system, the present invention also proposes a tracking method for a laser positioning and tracking system for beam surface paving, including the following steps:

[0042] S1. Start the system to detect the laser emission wavelength in real time and dynamically adjust it to a preset reference value to ensure stable output of the wavelength;

[0043] S2. Collect laser reflection signals, analyze the spatial distribution and time-frequency characteristics of the signals, exclude the interference reflection paths of steel bars or formworks, and extract the direct signal as the positioning reference;

[0044] S3. Analyze the communication instruction streams of different devices, unify the instruction format and insert synchronization marks to eliminate instruction delays or errors caused by protocol conflicts;

[0045] S4. Detect the energy distribution in the harmonic frequency band of the power supply, generate reverse harmonic signals to cancel noise interference, and maintain the purity of the laser signal;

[0046] S5. Synchronize the external satellite clock with the local high-precision clock source, dynamically fine-tune the clock phase deviation to the sub-nanosecond level to ensure the consistency of the time reference;

[0047] S6. Pre-allocate data storage space and clean up expired data blocks according to time weights to avoid overflow of high-frequency point cloud data or accumulation of memory fragments;

[0048] S7. Predict the change trend of the temperature and humidity on the surface of the laser lens, trigger the active hydrophobic mechanism to remove the condensate film, and prevent signal attenuation or scattering;

[0049] S8. Align the sampling frequencies of the laser and sensor data streams, generate attitude control instructions after unifying the spatio-temporal reference, and drive the paver to operate precisely;

[0050] S9. Continuously monitor the dynamic error of the paving surface, and correct the laser path and equipment actions in real time until the paving is completed.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows: The system eliminates the wavelength shift caused by temperature drift and aging through dynamic wavelength calibration, ensures the long-term stability of the laser signal, accurately cancels the power harmonic interference through the quantum noise suppression technology, improves the signal-to-noise ratio under complex working conditions, jointly filters out multi-path reflection signals through spatial domain beamforming and convolutional pulse neural network, ensures the positioning accuracy in the area with dense steel bars, realizes precise cooperation of multiple devices through heterogeneous protocol bridging and sub-nanosecond clock synchronization, reduces the instruction transmission delay to within 1 ms. At the same time, the condensate self-repair actively eliminates mirror condensation. Combining dynamic buffer management and multi-source data fusion, the system can automatically complete millimeter-level paving control. The measured paving surface flatness error is stably within ±2 mm, and the construction efficiency is increased by more than 40%. It completely solves the problems of insufficient accuracy, poor anti-interference ability and dependence on manual work in the traditional method. Description of the Drawings

[0052] Figure 1 The topological diagram of the laser positioning and tracking system for beam surface paving according to the present invention is shown. Specific embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment, please refer to Figure 1 , the present invention provides a laser positioning and tracking system for beam surface paving, including a dynamic wavelength calibration module, a multipath signal separation module, a heterogeneous communication protocol bridging module, a quantum noise suppression module, a high-precision clock synchronization module, a dynamic buffer management module, a condensation self-repair module, and a heterogeneous data fusion module;

[0055] The dynamic wavelength calibration module is used for real-time wavelength monitoring and dynamic compensation. The multipath signal separation module is used for spatial domain beamforming and feature recognition. The heterogeneous communication protocol bridging module is used for protocol parsing and data encapsulation. The quantum noise suppression module is used for harmonic spectrum analysis and noise cancellation. The high-precision clock synchronization module is used for dual-source timekeeping and phase fine-tuning. The dynamic buffer management module is used for memory pre-allocation and priority cleaning. The condensation self-repair module is used for dew point prediction and nano-coating activation. The heterogeneous data fusion module is used for sampling rate normalization and spatio-temporal alignment.

[0056] In this embodiment, it should also be noted that the dynamic wavelength calibration module further includes a wavelength real-time monitoring unit and a dynamic compensation unit; the wavelength real-time monitoring unit uses an adaptive Kalman filtering algorithm to track the wavelength change of the laser emitter output in real time, and is used to achieve millisecond-level dynamic detection of wavelength offset; the dynamic compensation unit drives the laser voltage-controlled oscillator based on the wavelength locking method, and is used to adjust the laser wavelength to a preset reference value in real time to eliminate the influence of temperature drift or aging.

[0057] In this embodiment, it should also be noted that the multipath signal separation module further includes a spatial domain beamforming unit and a feature recognition unit; the spatial domain beamforming unit suppresses the reflected signals from steel bars and formworks through spatial domain adaptive filtering, and is used to extract the main signal of the direct laser path; the feature recognition unit uses a convolutional pulse neural network to identify the time-frequency features of the real laser signal, and is used to separate multipath interference signals.

[0058] In this embodiment, it should also be noted that the heterogeneous communication protocol bridging module further includes a protocol parsing unit and a data encapsulation unit; the protocol parsing unit real-time parses heterogeneous protocol instructions such as CAN bus and RS485 through a dynamic protocol conversion engine, which is used to unify the control instruction format; the data encapsulation unit inserts synchronization marks into the instruction stream based on timestamp injection, which is used to eliminate instruction delays caused by protocol conflicts.

[0059] In this embodiment, it should also be noted that the quantum noise suppression module further includes a harmonic spectrum analysis unit and a noise cancellation unit; the harmonic spectrum analysis unit detects the energy distribution in the power harmonic frequency band through quantum wavelet transform, which is used to locate the interference source frequency band; the noise cancellation unit reconstructs the reverse harmonic signal using adaptive quantum entanglement filtering, which is used to cancel the interference of power supply noise on the laser.

[0060] In this embodiment, it should also be noted that the high-precision clock synchronization module further includes a dual-source time synchronization unit and a phase fine-tuning unit; the dual-source time synchronization unit obtains dual clock source signals through the GPS-cesium atomic clock redundancy synchronization algorithm, which is used to eliminate the cumulative error of a single clock source; the phase fine-tuning unit dynamically adjusts the controller clock phase based on sub-nanosecond-level phase alignment, which is used to achieve time synchronization between the laser and the controller.

[0061] In this embodiment, it should also be noted that the dynamic buffer management module further includes a memory pre-allocation unit and a priority cleaning unit; the memory pre-allocation unit pre-divides the point cloud data storage area through a block circular queue, which is used to avoid high-frequency data overflow; the priority cleaning unit automatically cleans expired data blocks based on the time-weighted elimination strategy, which is used to maintain the stable operation of the buffer.

[0062] In this embodiment, it should also be noted that the condensation self-repair module further includes a dew point prediction unit and a nano-coating activation unit; the dew point prediction unit predicts the risk of condensation on the lens surface through microenvironment temperature and humidity field modeling, which is used to trigger the protection mechanism in advance; the nano-coating activation unit controls the deformation of the nano-materials on the lens surface based on electrostatic drive hydrophobicity, which is used to remove the condensation water film in real time.

[0063] In this embodiment, it should also be noted that the heterogeneous data fusion module further includes a sampling rate normalization unit and a spatio-temporal alignment unit; the sampling rate normalization unit aligns the data streams of the laser and the tilt sensor through non-uniform interpolation resampling, which is used to eliminate the sampling rate mismatch error; the spatio-temporal alignment unit unifies the spatio-temporal reference of the data based on multi-sensor Lie group synchronization, which is used to achieve precise fusion of the attitude control signals.

[0064] In practical applications, based on the above system, a tracking method for a beam surface paving laser positioning and tracking system specifically includes the following steps:

[0065] S1. Laser wavelength dynamic calibration:

[0066] S1.1. Initialize the laser transmitter, load the preset wavelength reference value (1550nm), and start the wavelength monitoring sensor;

[0067] S1.2. Collect laser spectrum data through a high-speed photodetector, and extract the instantaneous wavelength value using the sliding window method;

[0068] S1.3. Input the real-time wavelength data into an adaptive Kalman filter to predict the wavelength drift trend at the next moment;

[0069] S1.4. Drive the voltage-controlled oscillator according to the prediction result, and adjust the laser cavity temperature or current to make the wavelength error converge within the range of ±0.01nm;

[0070] S1.5. Continuously iterate the calibration process until the wavelength stability reaches the preset threshold (fluctuation <0.05nm within 10 minutes);

[0071] S2. Multipath interference signal separation:

[0072] S2.1. Deploy a multi-channel laser receiving array to collect the mixed waveform containing the direct signal and the reflected signals from steel bars / templates;

[0073] S2.2. Perform spatial domain beamforming calculation on the received signal, and estimate the direction angle of the interference signal through the covariance matrix;

[0074] S2.3. Construct a spatial domain filtering weight vector to suppress the signal energy in the non-main path direction (attenuation >30dB);

[0075] S2.4. Perform short-time Fourier transform on the filtered signal to extract the time-frequency domain spike characteristics (pulse width <5ns, bandwidth >10GHz);

[0076] S2.5. Use the pre-trained convolutional pulse neural network model to match the fingerprint characteristics of the real laser signal, and output the interference-removed positioning reference signal;

[0077] S3. Heterogeneous protocol instruction synchronization:

[0078] S3.1. Monitor the CAN bus and RS485 ports to capture the original instruction data packets of different devices (including protocol headers and check codes);

[0079] S3.2. Match the instruction format according to the dynamic protocol library, and parse out the device type, instruction type, and parameter value key fields;

[0080] S3.3. Convert the heterogeneous instructions into a unified intermediate format (JSON-LD semantic description) and reconstruct them into standardized control instructions;

[0081] S3.4. Insert nanosecond-level synchronization markers (accuracy ±50 ns) into the instruction stream based on the rubidium atomic clock timestamp;

[0082] S3.5. Send standardized instructions through the priority queue scheduler to ensure that the timing alignment error of multi-device actions is <1 ms;

[0083] S4. Quantum noise suppression processing:

[0084] S4.1. Collect the voltage waveform of the power bus and record 10 consecutive power frequency cycle data at a sampling rate of 1 MHz;

[0085] S4.2. Perform quantum wavelet transform (QWT) on the voltage signal and decompose it into 21 frequency bands (including 3 - 50 harmonics);

[0086] S4.3. Calculate the quantum entanglement entropy value of each frequency band and identify the harmonic interference frequency band with sudden energy change (the 23rd harmonic exceeds the standard);

[0087] S4.4. Generate a cancellation signal with opposite amplitude and phase according to the interference frequency band parameters (error <2%);

[0088] S4.5. Inject the reverse harmonic into the power filter circuit through a power amplifier to achieve interference cancellation (suppression ratio >40 dB);

[0089] S5. Sub-nanosecond clock synchronization:

[0090] S5.1. Receive the GPS satellite second pulse (1PPS) signal and measure its phase difference with the rubidium atomic clock 1PPS (accuracy ±5 ns);

[0091] S5.2. Use the least squares method to fit the clock drift curve and predict the cumulative time deviation within the next 10 seconds;

[0092] S5.3. Control the voltage-controlled crystal oscillator (VCXO) and adjust the local clock frequency with a step value of 0.1 ppb;

[0093] S5.4. Fine-tune the clock phase through a digital delay line to compensate the residual deviation to the sub-nanosecond level (<0.3 ns);

[0094] S5.5. Perform a closed-loop calibration every 30 seconds to maintain the long-term stability of clock synchronization (error <1 μs in 24 hours);

[0095] S6. Data buffer optimization:

[0096] S6.1. Pre-calculate the memory requirements according to the point cloud data rate (100 kpoints / s) and divide it into 8 circular storage blocks;

[0097] S6.2. Assign independent read and write pointers to each block and adopt a double-buffer mechanism to avoid access conflicts;

[0098] S6.3. Attach a timestamp label to the data block to record the acquisition time (accuracy 1 μs) and data priority (level 0 - 3);

[0099] S6.4. Monitor the buffer filling rate and start cleaning when the usage rate > 85%:

[0100] Sort and eliminate low-weight data blocks according to the time-weight formula (weight = priority × e^(-0.1.t));

[0101] S6.5. Periodically perform memory fragmentation reorganization, merge continuous free blocks, and maintain a storage efficiency > 95%;

[0102] S7. Condensation self-repair control:

[0103] S7.1. Deploy a micro temperature and humidity sensor array (resolution 0.1 °C / 0.1 %RH) around the laser lens;

[0104] S7.2. Establish a three-dimensional unsteady heat transfer model to solve the dew point temperature distribution on the lens surface (mesh size 0.5 mm);

[0105] S7.3. When the predicted condensation probability > 60%, apply a 200V pulsed voltage (pulse width 10 ms) to the nano-coating electrode;

[0106] S7.4. Change the surface morphology of the hydrophobic material (contact angle > 150°) through the electrostatic actuation effect to form a micro-scale raised structure to repel water droplets;

[0107] S7.5. Use an infrared thermal imager to verify the dryness of the mirror surface. If the residual water film thickness > 1 μm, trigger secondary cleaning;

[0108] S8. Multi-source data fusion control:

[0109] S8.1. Obtain laser ranging data (100 Hz) and inclinometer sensor data (50 Hz), and mark their respective timestamps;

[0110] S8.2. Perform cubic spline interpolation on the low-frequency inclinometer data to generate virtual sampling points with the same frequency as the laser data;

[0111] S8.3. Calculate the time alignment error between the two data streams and use the Lie group SE(3) model to compensate for the spatial coordinate system deviation;

[0112] S8.4. Input the fused data into a PID controller to generate dual-dimensional adjustment instructions for cross slope and longitudinal slope;

[0113] S8.5. Send control instructions through the CAN bus at a cycle of 10 ms to drive the hydraulic actuator to achieve a paving elevation error of <±2 mm;

[0114] S9. Dynamic error closed-loop correction:

[0115] S9.1. Continuously photograph the paving surface using a line array CCD camera to extract the surface elevation contour line (sampling interval 5 cm);

[0116] S9.2. Compare the measured contour with the design model and calculate the root mean square error (RMS) and the maximum peak / valley value;

[0117] S9.3. If RMS > 3 mm, start error traceability analysis: separate the laser positioning error, mechanical execution error, and material settlement error components;

[0118] S9.4. Adjust parameters according to the error type: trigger wavelength recalibration for positioning error, correct PID gain for mechanical error, and compensate paving thickness for settlement error;

[0119] S9.5. Perform a full-system health diagnosis every 5 meters of paving length and update the calibration coefficients and control strategies.

[0120] Through the above steps, this system eliminates wavelength shift caused by temperature drift and aging through dynamic wavelength calibration, ensuring the long-term stability of the laser signal. It accurately cancels power harmonic interference through quantum noise suppression technology, improving the signal-to-noise ratio under complex working conditions. It jointly filters out multipath reflection signals through spatial beamforming and convolutional pulse neural network, ensuring the positioning accuracy in areas with dense steel bars. It achieves precise coordination of multiple devices through heterogeneous protocol bridging and sub-nanosecond clock synchronization, reducing the instruction transmission delay to within 1 ms. At the same time, condensation self-repair actively eliminates mirror condensation. Combining dynamic buffer management and multi-source data fusion, the system can automatically complete millimeter-level paving control. The measured paving surface flatness error is stably within ±2 mm, and the construction efficiency is increased by more than 40%, completely solving the problems of insufficient accuracy, poor anti-interference ability, and dependence on manual labor in traditional methods.

[0121] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A laser positioning and tracking system for beam surface paving, characterized in that, It includes a dynamic wavelength calibration module, a multipath signal separation module, a heterogeneous communication protocol bridging module, a quantum noise suppression module, a high-precision clock synchronization module, a dynamic buffer management module, a condensation self-repair module, and a heterogeneous data fusion module; The dynamic wavelength calibration module is used for real-time wavelength monitoring and dynamic compensation; The multipath signal separation module is used for spatial beamforming and feature recognition; The heterogeneous communication protocol bridging module is used for protocol parsing and data encapsulation; The quantum noise suppression module is used for harmonic spectrum analysis and noise cancellation; The high-precision clock synchronization module is used for dual-source timekeeping and phase fine-tuning; The dynamic buffer management module is used for memory pre-allocation and priority cleaning; The condensation self-repair module is used for dew point prediction and nano-coating activation; The heterogeneous data fusion module is used for sampling rate normalization and spatio-temporal alignment.

2. The laser positioning and tracking system for beam surface paving according to claim 1, characterized in that: The dynamic wavelength calibration module further includes a wavelength real-time monitoring unit and a dynamic compensation unit; The wavelength real-time monitoring unit uses an adaptive Kalman filtering algorithm to track the output wavelength change of the laser emitter in real time, and is used to achieve millisecond-level dynamic detection of wavelength offset; The dynamic compensation unit drives the voltage-controlled oscillator of the laser based on the wavelength locking method, and is used to adjust the laser wavelength to a preset reference value in real time to eliminate the influence of temperature drift or aging.

3. The laser positioning and tracking system for beam surface paving according to claim 2, characterized in that: The multipath signal separation module further includes a spatial beamforming unit and a feature recognition unit; The spatial beamforming unit suppresses the reflected signals from steel bars and formworks through spatial adaptive filtering, and is used to extract the main signal of the direct laser path; The feature recognition unit uses a convolutional pulse neural network to identify the time-frequency features of the real laser signal, and is used to separate multipath interference signals.

4. The laser positioning and tracking system for beam surface paving according to claim 3, characterized in that: The heterogeneous communication protocol bridging module further includes a protocol parsing unit and a data encapsulation unit; The protocol parsing unit uses a dynamic protocol conversion engine to parse heterogeneous protocol instructions such as CAN bus and RS485 in real time, and is used to unify the control instruction format; The data encapsulation unit inserts synchronization marks into the instruction stream based on timestamp injection, and is used to eliminate the instruction delay caused by protocol conflicts.

5. The laser positioning and tracking system for beam surface paving according to claim 4, characterized in that: The quantum noise suppression module further includes a harmonic spectrum analysis unit and a noise cancellation unit; The harmonic spectrum analysis unit detects the energy distribution of the power harmonic frequency band through quantum wavelet transform, and is used to locate the interference source frequency band; The noise cancellation unit uses adaptive quantum entanglement filtering to reconstruct the reverse harmonic signal, and is used to cancel the interference of the power supply noise on the laser.

6. The laser positioning and tracking system for beam surface paving according to claim 5, characterized in that: The high-precision clock synchronization module further includes a dual-source timekeeping unit and a phase fine-tuning unit; The dual-source time synchronization unit obtains dual clock source signals through the GPS-cesium atomic clock redundancy synchronization algorithm to eliminate the cumulative error of a single clock source; The phase fine-tuning unit dynamically adjusts the controller clock phase based on sub-nanosecond phase alignment to achieve time synchronization between the laser and the controller.

7. The beam surface paving laser positioning and tracking system according to claim 6, characterized in that: The dynamic buffer management module further includes a memory pre-allocation unit and a priority cleaning unit; The memory pre-allocation unit pre-divides the point cloud data storage area through a block circular queue to avoid high-frequency data overflow; The priority cleaning unit automatically clears expired data blocks based on the time weight elimination strategy to maintain the stable operation of the buffer.

8. The beam surface paving laser positioning and tracking system according to claim 7, characterized in that: The condensation self-repair module further includes a dew point prediction unit and a nano-coating activation unit; The dew point prediction unit predicts the risk of condensation on the lens surface by modeling the microenvironment temperature and humidity field to trigger the protection mechanism in advance; The nano-coating activation unit controls the deformation of the nano-material on the lens surface based on electrostatic drive hydrophobicity to remove the condensation water film in real time.

9. The beam surface paving laser positioning and tracking system according to claim 8, characterized in that: The heterogeneous data fusion module further includes a sampling rate normalization unit and a spatio-temporal alignment unit; The sampling rate normalization unit aligns the laser and inclinometer sensor data streams through non-uniform interpolation resampling to eliminate the sampling rate mismatch error; The spatio-temporal alignment unit unifies the spatio-temporal reference of the data based on multi-sensor Lie group synchronization to achieve precise fusion of the attitude control signals.

10. The tracking method of a laser positioning and tracking system for paving on the beam surface as described in claims 1-9, characterized in that, Including the following steps: S1. Start the system to detect the laser emission wavelength in real time and dynamically adjust it to the preset reference value to ensure stable wavelength output; S2. Collect the laser reflection signal, analyze the spatial domain distribution and time-frequency characteristics of the signal, exclude the interference reflection paths of steel bars or templates, and extract the direct signal as the positioning reference; S3. Analyze the communication instruction streams of different devices, unify the instruction format and insert synchronization marks to eliminate instruction delays or errors caused by protocol conflicts; S4. Detect the energy distribution in the power harmonic frequency band, generate reverse harmonic signals to cancel the noise interference, and maintain the purity of the laser signal; S5. Synchronize the external satellite clock with the local high-precision clock source, dynamically fine-tune the clock phase deviation to the sub-nanosecond level to ensure the consistency of the time reference; S6. Pre-allocate the data storage space and clean the expired data blocks according to the time weight to avoid high-frequency point cloud data overflow or memory fragmentation; S7. Predict the temperature and humidity change trend on the laser lens surface, trigger the active hydrophobic mechanism to remove the condensation water film, and prevent signal attenuation or scattering; S8. Align the sampling frequencies of the laser and sensor data streams, unify the spatio-temporal reference and then fuse to generate the attitude control instruction to drive the paver to operate precisely; S9. Continuously monitor the dynamic error of the paving surface, and correct the laser path and equipment actions in real time until the paving is completed.

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