An engineering surveying and mapping road surface flatness detection device and method

Abstract

The present invention discloses an engineering surveying and mapping road surface flatness detection device and method, including a survey vehicle, in which an in-vehicle industrial control computer is installed; a laser array module is fixedly installed at the front end of the chassis of the survey vehicle, a multi-axis inertial compensation unit is installed at the center of gravity of the chassis of the survey vehicle, a vibration vision fusion sensor group is installed on the survey vehicle, and a spatio-temporal alignment central module is integrated in the in-vehicle industrial control computer; a multi-modal fusion processing module is also integrated in the in-vehicle industrial control computer; a blockchain evidence storage interface is provided on the in-vehicle industrial control computer, and the blockchain evidence storage interface communicates with the cloud server through the in-vehicle 5G module; the blockchain evidence storage interface generates a unique hash value for each detected road section. The present invention relates to the technical field of engineering surveying and mapping. The present invention solves the problems existing in the traditional road surface flatness detection technology, such as low efficiency, limited accuracy, insufficient data processing and analysis capabilities, and lack of a long-term data management and traceability mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering surveying and mapping, and particularly to an engineering surveying and mapping pavement evenness detection device and method. Background Art

[0002] In the field of road engineering, pavement evenness is a key indicator for measuring road quality and service performance. It is not only directly related to the comfort and safety of driving, but also closely linked to vehicle wear, fuel consumption, and the service life of the road. With the continuous growth of traffic flow and the increasing driving speed of vehicles, the need for accurate and efficient detection of pavement evenness has become more urgent. However, traditional pavement evenness detection technologies have many limitations:

[0003] Low detection efficiency: When using tools such as three-meter straightedges and continuous evenness meters for detection, manual operation is required and the speed is slow. When facing long-distance road detection tasks, it consumes a large amount of manpower, material resources, and time costs, and cannot meet the requirements of large-area and high-frequency road detection under the rapid development of modern transportation.

[0004] Limited detection accuracy: Such methods are easily interfered by human factors and environmental factors. For example, differences in manual measurement techniques, and under different weather and lighting conditions, there may be deviations in the judgment of measurement results, making it difficult to accurately obtain microscopic unevenness information of the road surface and unable to meet the requirements of refined pavement detection.

[0005] Insufficient data processing and analysis capabilities: The data obtained by traditional detection means mostly rely on manual recording and simple calculation and analysis, and it is difficult to achieve the fusion processing of multi-source data. Facing a large amount of detection data, it is impossible to timely and comprehensively mine the pavement quality problems reflected behind the data, and it is also difficult to establish an effective pavement condition evaluation system, thus affecting the scientific formulation of road maintenance and repair decisions.

[0006] Lack of long-term data management and traceability mechanism: The storage methods of traditional detection data are relatively scattered and non-standard, and the data security and integrity are difficult to guarantee. During the long-term use of the road, it is impossible to conveniently trace historical detection data, which is not conducive to the quality control and management of the entire life cycle of the road.

[0007] In the current trend of intelligent transportation and digital construction, the existing pavement evenness detection technologies can no longer meet the new development needs. There is an urgent need for an engineering surveying and mapping pavement evenness detection device and method to achieve efficient and high-precision pavement evenness detection, and at the same time have strong data processing, analysis, and long-term management capabilities, providing reliable technical support for the planning, construction, and maintenance of road engineering. Summary of the Invention

[0008] In order to solve the problems of low efficiency, limited accuracy, insufficient data processing and analysis capabilities, and lack of long-term data management and traceability mechanism existing in traditional road surface flatness detection technology, the purpose of the present invention is to provide an engineering surveying and mapping road surface flatness detection device and method.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions: An engineering surveying and mapping road surface flatness detection device includes a measurement vehicle, in which an in-vehicle industrial computer is installed for comprehensive data processing; a laser array module is fixedly installed at the front end of the chassis of the measurement vehicle. The laser array module is fixedly installed at the front end of the chassis of the measurement vehicle and includes three laser emitting units distributed in a triangular shape.

[0010] A multi-axis inertial compensation unit is installed at the center of gravity of the chassis of the measurement vehicle.

[0011] A vibration vision fusion sensor group is installed on the measurement vehicle. The vibration vision fusion sensor group includes a piezoelectric vibration sensor arranged along the diagonal of the measurement vehicle and a polarization vision module installed on the roof of the vehicle.

[0012] A space-time alignment central module is integrated in the in-vehicle industrial computer. The space-time alignment central module is connected to the laser array module, the multi-axis inertial compensation unit, the piezoelectric vibration sensor, and the polarization vision module respectively through a PCIE expansion slot.

[0013] A multi-modal fusion processing module is also integrated in the in-vehicle industrial computer. The multi-modal fusion processing module is connected to the space-time alignment center through a PCIE expansion slot and is used to receive multi-source data aligned by the space-time alignment center.

[0014] A blockchain evidence storage interface is provided on the in-vehicle industrial computer. The blockchain evidence storage interface communicates with the cloud server through an in-vehicle 5G module.

[0015] The space-time alignment central module performs three-level space-time calibration: hardware clock synchronization, space calibration, and dynamic time warping.

[0016] The multi-modal fusion processing module fuses laser curvature, vibration energy spectrum, and visual crack features through a gated attention mechanism.

[0017] The blockchain evidence storage interface generates a unique hash value for each detected road section.

[0018] Preferably, the laser array module is installed at the front bumper of the measurement vehicle. The three laser emitting units are distributed in an equilateral triangle with a side length of 80 cm; the laser wavelengths emitted by the three laser emitting units are 1550 nm, 1310 nm, and 980 nm respectively; the emission frequencies of the three laser emitting units are 10 kHz, 15 kHz, and 20 kHz respectively.

[0019] Preferably, the multi-axis inertial compensation unit is embedded at the center of gravity of the measurement vehicle chassis and integrated with a six-degree-of-freedom MEMS sensor to output the vehicle pose quaternion in real time.

[0020] Preferably, the piezoelectric vibration sensor is installed on the four-wheel suspension bearing seat, with its axis aligned with the suspension movement direction, and the detection frequency band is 0.1 - 500 Hz; an electric lifting bracket is installed on the top of the measurement vehicle, and the polarization vision module is installed on the telescopic end of the electric lifting bracket, and the polarization vision module is equipped with a four-way rotating polarization filter.

[0021] Preferably, when the spatio-temporal alignment central module performs hardware clock synchronization, it uses the IEEE1588 time protocol to align the clocks of each sensor; when the spatio-temporal alignment central module performs spatial calibration, it solves the transformation matrix from the laser point cloud to the vision coordinate system; dynamic time warping aligns the non-uniformly sampled data stream by improving the DTW algorithm.

[0022] Preferably, the hardware implementation of the improved DTW algorithm includes a rough alignment unit, a refinement unit, and a post-processing unit. The rough alignment unit downsamples to 1 / 4 resolution and calculates the initial path; the refinement unit searches for the optimal path within ±5 points in the neighborhood of the initial path; the post-processing unit generates a continuous alignment sequence through cubic spline interpolation.

[0023] Preferably, the multi-modal fusion processing module includes a laser feature encoder, a vibration feature encoder, and a vision feature encoder; the laser feature encoder extracts curvature features; the vibration feature encoder generates an energy spectrum; the vision feature encoder is used for vision feature generation.

[0024] An engineering surveying and mapping road surface flatness detection method includes the following steps:

[0025] S1, multi-source data acquisition:

[0026] a1, emitting a frequency-modulated signal through the laser array module, and solving the original elevation through the echo phase difference. The road surface elevation point cloud is , then the road surface elevation is measured by triangulation :

[0027] , where is the ranging value of each laser unit;

[0028] a2, the multi-axis inertial compensation unit calculates the vehicle roll angle and pitch angle in real time and generates laser matrix dynamic compensation parameters:

[0029] ;

[0030] At the same time, the vehicle pose quaternion is output;

[0031] a3. The piezoelectric vibration sensor collects acceleration data in the frequency band of 0.1 - 500 Hz;

[0032] a4. The polarization vision module captures four-way polarized images ;

[0033] S2. The spatio-temporal alignment central module performs spatio-temporal alignment processing:

[0034] b1. Hardware clock synchronization: Align the clocks of each sensor;

[0035] b2. Spatial calibration: Unify the coordinate system through the Lie group SE(3) transformation matrix:

[0036] , where ;

[0037] b3. Dynamic time warping. For non-uniformly sampled data, through the improved dynamic time warping DTW algorithm, the cumulative distance matrix is: , the path search constraint window width , where T is the sequence length;

[0038] S3. The multi-modal fusion processing module performs multi-modal feature fusion:

[0039] c1. Laser feature extraction, local curvature ;

[0040] c2. Vibration feature encoding, generating a 16-subband energy spectrum through wavelet packet decomposition;

[0041] c3. Visual feature generation, the DeepLabv3+ network outputs a pixel-level crack probability map;

[0042] c4. Gated attention fusion, weighting each modal feature according to the weight:

[0043] weight ; where is the laser modal feature vector, is the vibration modal feature vector, is the visual modal feature vector, is the weight matrix, is the attention score vector;

[0044] S4. Flatness determination and decision output:

[0045] d1. Calculate the flatness index , where L is the length of the detection section, m is the half-width of the window, and N is the number of sampling points;

[0046] d2, Calculate the local defect index ;

[0047] d3, Defect grading:

[0048] First-level defect: IRI > 8 and LDI > 0.8;

[0049] Second-level defect: 6 ≤ IRI ≤ 8 or 0.6 > LDI ≥ 0.8;

[0050] Third-level defect: IRI < 6 and LDI ≤ 0.6;

[0051] d4, Generate a blockchain deposit certificate hash ;

[0052] d5, Output the maintenance decision.

[0053] Preferably, the maintenance decision includes a first-level defect decision, a second-level defect decision, and a third-level defect decision.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0055] 1. In the present invention, multi-sensors work together. The laser array module obtains accurate elevation data through lasers with different wavelengths and frequencies. The multi-axis inertial compensation unit compensates in real time for the influence of the vehicle attitude on the measurement. The piezoelectric vibration sensor and the polarization vision module collect data from different dimensions. After spatio-temporal alignment and multi-modal fusion processing, the detection accuracy of the road surface flatness is significantly improved, and tiny road surface defects can be accurately identified.

[0056] 2. In the present invention, the spatio-temporal alignment central module performs three-level spatio-temporal calibration, synchronizes the clock using the time protocol, unifies the coordinate system with the transformation matrix, and improves the DTW algorithm to process non-uniformly sampled data, ensuring the accurate alignment of multi-source data and laying a foundation for subsequent fusion analysis; the multi-modal fusion processing module uses the gated attention mechanism to fuse laser, vibration, and visual features, fully excavates the data value, and comprehensively reflects the road surface condition.

[0057] 3. In the present invention, by calculating the flatness index and the local defect index, and grading the defects according to the set standards, a clear quantitative basis is provided for road surface maintenance; the generated maintenance decision is highly targeted, and the maintenance resources can be reasonably arranged according to different defect levels, improving the maintenance efficiency and economy.

[0058] 4. In the present invention, the blockchain deposit certificate interface generates a unique hash value for each detected road section, and uploads it to the cloud server in combination with the in-vehicle 5G module, ensuring the non-tampering and traceability of the detection data, improving the data security and credibility, facilitating subsequent query and quality traceability, and providing strong support for the long-term management of the road surface. Brief Description of the Drawings

[0059] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0060] Figure 1 It is a schematic diagram of a device system for detecting the evenness of a road surface in engineering surveying and mapping;

[0061] Figure 2 It is a schematic diagram of the process flow of the method for detecting the evenness of a road surface in engineering surveying and mapping of the present invention. Specific Embodiments

[0062] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in this technology can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0063] Please refer to Figures 1 to 2 . It should be noted that the structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have technical essential meanings. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that the technical content disclosed by the present invention can cover. At the same time, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration, and are not used to limit the scope under which the present invention can be implemented. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope under which the present invention can be implemented.

[0064] Embodiment 1, an apparatus for detecting the evenness of a road surface in engineering surveying and mapping, including a survey vehicle, and a laser array module is installed at the front bumper of the survey vehicle. The module includes three laser emitting units distributed in an equilateral triangle, and the side length is set to 80 cm. The laser wavelengths emitted by the three laser emitting units are 1550 nm, 1310 nm, and 980 nm respectively, and the emission frequencies are 10 kHz, 15 kHz, and 20 kHz respectively. This design enables the laser array module to obtain rich road surface information based on lasers with different wavelengths and frequencies when emitting frequency modulation signals, and accurately calculate the original elevation through the echo phase difference, providing basic data for subsequent road surface evenness analysis. At the same time, the dual wavelengths of 1550 nm and 980 nm eliminate the temperature drift error and adapt to the extreme environment of -20°C to 60°C.

[0065] The multi-axis inertial compensation unit is embedded at the center of gravity position of the survey vehicle chassis. The unit integrates a six-degree-of-freedom MEMS sensor and can output the vehicle pose quaternion in real time . During the vehicle's driving process, by real-time calculating the vehicle's roll angle and pitch angle and generating laser matrix dynamic compensation parameters, the error caused by the vehicle's attitude change to the laser-measured elevation data can be effectively compensated, improving the measurement accuracy.

[0066] The vibration vision fusion sensor group consists of a piezoelectric vibration sensor and a polarization vision module. The piezoelectric vibration sensor is installed on the four-wheel suspension bearing seat, with its axial direction consistent with the suspension movement direction, and the detection frequency band is 0.1 - 500 Hz. Such an installation position and detection frequency band setting can accurately collect the vibration acceleration data caused by road unevenness during the vehicle's driving process. An electric lifting bracket is installed on the top of the measurement vehicle, and the polarization vision module is installed on the telescopic end of the electric lifting bracket and is equipped with a four-way rotating polarization filter. The electric lifting bracket can adjust the height of the polarization vision module according to actual detection requirements, and the four-way rotating polarization filter can capture four-way polarization images of 0°, 45°, 90°, and 135°, obtaining more comprehensive road surface vision information, including defect features such as cracks.

[0067] The four-way polarization filter eliminates the interference of rain, fog, and reflection, and greatly improves the image contrast.

[0068] The in-vehicle industrial computer installed in the measurement vehicle is the data processing core of the entire device. The in-vehicle industrial computer integrates a spatio-temporal alignment central module, which is connected to the laser array module, the multi-axis inertial compensation unit, the piezoelectric vibration sensor, and the polarization vision module respectively through the PCIE expansion slot. At the same time, the in-vehicle industrial computer also integrates a multi-modal fusion processing module, which is connected to the spatio-temporal alignment center through the PCIE expansion slot and is used to receive the multi-source data aligned by the spatio-temporal alignment center. In addition, a blockchain evidence storage interface is set on the in-vehicle industrial computer, and the blockchain evidence storage interface communicates with the cloud server through the in-vehicle 5G module to realize the blockchain evidence storage and remote transmission of the detection data.

[0069] The spatio-temporal alignment central module performs three-level spatio-temporal calibration. In terms of hardware clock synchronization, the IEEE1588 time protocol is used to align the clocks of each sensor to ensure the time consistency of the data collected by different sensors. Spatial calibration is to calculate the transformation matrix from the laser point cloud to the vision coordinate system. Specifically, the Lie group SE(3) transformation matrix is used to unify the coordinate system to realize the unified expression of different sensor data in space. Dynamic time warping uses an improved DTW algorithm to align non-uniformly sampled data streams. Its hardware implementation includes a coarse alignment unit, a refinement unit, and a post-processing unit. The coarse alignment unit downsamples the data to 1 / 4 resolution and calculates the initial path; the refinement unit searches for the optimal path within ±5 points in the neighborhood of the initial path; the post-processing unit generates a continuous alignment sequence through cubic spline interpolation, effectively solving the alignment problem of non-uniformly sampled data and providing an accurate data basis for subsequent multi-modal fusion processing.

[0070] The multimodal fusion processing module includes a laser feature encoder, a vibration feature encoder, and a visual feature encoder. The laser feature encoder extracts local curvature features, calculates the local curvature through a formula, and obtains the microscopic geometric features of the road surface. The vibration feature encoder generates a 16-subband energy spectrum through wavelet packet decomposition, analyzes the vibration energy distribution in different frequency bands, and reflects the impact of road surface flatness on vehicle vibration. The visual feature encoder uses the DeepLabv3+ network to output a pixel-level crack probability map to identify defects such as road surface cracks. Finally, the laser curvature, vibration energy spectrum, and visual crack features are fused through a gated attention mechanism, and each modal feature is weighted according to the weight to improve the comprehensive judgment ability of the road surface condition.

[0071] The blockchain evidence storage interface generates a unique hash value for each detected road section. During the detection process, every time the detection of a road section is completed, the detection data of this section is subjected to a hash operation to generate a unique hash value, which is then uploaded to the cloud server through the in-vehicle 5G module for blockchain evidence storage. This method ensures the immutability and traceability of the detection data, providing a reliable data basis for subsequent road maintenance decision-making and quality assessment.

[0072] Embodiment 2, an engineering surveying and mapping road surface flatness detection method, includes the following steps:

[0073] S1, Multi-source data collection:

[0074] a1, Transmit a frequency-modulated signal through the laser array module, and calculate the original elevation through the echo phase difference. The road surface elevation point cloud is , then the road surface elevation is measured through triangulation :

[0075] , where is the ranging value of each laser unit;

[0076] a2, The multi-axis inertia compensation unit calculates the vehicle roll angle and pitch angle in real time, and generates laser matrix dynamic compensation parameters:

[0077] ;

[0078] At the same time, output the vehicle pose quaternion ;

[0079] a3, The piezoelectric vibration sensor collects acceleration data in the 0.1 - 500 Hz frequency band;

[0080] a4, The polarization vision module captures four-way polarization images ;

[0081] S2, the spatio-temporal alignment central module performs spatio-temporal alignment processing:

[0082] b1, Hardware clock synchronization: Align the clocks of each sensor;

[0083] b2, Spatial calibration: Unify the coordinate system through the Lie group SE(3) transformation matrix:

[0084] , where ;

[0085] b3, Dynamic time warping. For non-uniformly sampled data, through the improved dynamic time warping DTW algorithm, the cumulative distance matrix is: , the path search constraint window width ;

[0086] S3, The multi-modal fusion processing module performs multi-modal feature fusion:

[0087] c1, Laser feature extraction, local curvature ;

[0088] c2, Vibration feature encoding, Generate a 16-subband energy spectrum through wavelet packet decomposition;

[0089] c3, Visual feature generation, The DeepLabv3+ network outputs a pixel-level crack probability map;

[0090] c4, Gated attention fusion, Weight each modal feature according to the weight:

[0091] Weight ; where is the laser modal feature vector, is the vibration modal feature vector, is the visual modal feature vector, is the weight matrix, is the attention score vector;

[0092] S4, Flatness determination and decision output:

[0093] d1, Calculate the flatness index , where L is the length of the detection section, m is the half-width of the window, and N is the number of sampling points;

[0094] d2, Calculate the local defect index ;

[0095] d3, Defect classification:

[0096] First-level defect: IRI > 8 and LDI > 0.8;

[0097] Second-level defect: 6 ≤ IRI ≤ 8, or 0.6 > LDI ≥ 0.8;

[0098] Level 3 defect: IRI < 6 and LDI ≤ 0.6;

[0099] d4, generate the blockchain evidence storage hash ;

[0100] d5, output the maintenance decision.

[0101] The maintenance decision includes the Level 1 defect decision, the Level 2 defect decision, and the Level 3 defect decision.

[0102] Level 1 defect decision: Trigger the drone for re-inspection and generate a 3D disease model.

[0103] Level 2 defect decision: Automatically plan the milling path and output the repair volume.

[0104] Level 3 defect decision: Mark it as the annual maintenance area and generate a material budget list.

[0105] Example 3, Level 1 defect scenario: When the inspection vehicle is patrolling on the highway, it is found that there are serious ruts and deep cracks in a certain section, and the data is collected:

[0106] Laser height sequence: [5.2, 7.8, 9.1, 6.3, 12.4, 8.9, 4.7, 10.2], the maximum height difference ΔD_max = 12.4 - 4.7 = 7.7mm.

[0107] Vibration signal: Peak value of the impact energy spectrum: 82g·s > 50g·s threshold; Main frequency band: 80 - 120Hz, corresponding to the rut resonance frequency;

[0108] Visual image: Proportion of crack pixels: 22% > 15% threshold; Texture anisotropy index A_index = 0.85 > 0.7 threshold;

[0109] Then ;

[0110] ;

[0111] Then IRI = 9.2 > 8.0, LDI = 5.835 > 0.8, so the defect level is a Level 1 defect.

[0112] Level 1 defect decision:

[0113] Trigger the drone for re-inspection and generate a 3D disease model: Rut depth 9.1mm, crack length 3.2m.

[0114] Automatically push an emergency maintenance work order, close the section and start hot in-place recycling repair.

[0115] Marked as "Emergency Event" in blockchain evidence storage, the maintenance unit needs to sign for it within the specified time.

[0116] Example 4, Secondary Defect Scenario: During the inspection of urban secondary arterials, moderate rutting and surface cracks are found in some sections. The collected data:

[0117] Laser height sequence: [3.5, 4.2, 5.8, 6.1, 7.3, 5.9, 4.7, 6.5], maximum height difference ΔD_max = 7.3 - 3.5 = 3.8mm.

[0118] Vibration signal: Peak value of impact energy spectrum: 42g·s, within the threshold range of 20 - 50g·s; Main frequency band: 30 - 60Hz, corresponding to the resonance frequency of rutting;

[0119] Visual image: Proportion of crack pixels: 9%, within the threshold range of 5% - 15%; Texture anisotropy index A_index = 0.62;

[0120] Then ;

[0121] ;

[0122] Then IRI = 6.7 ∈ [6.0, 8.0], LDI = 4.196 ∈ [0.6, 0.8], so the defect level is secondary defect.

[0123] Secondary defect decision: Automatically plan the milling path and output the repair volume.

[0124] Blockchain evidence storage:

[0125] The hash is marked as a yellow alert, and the maintenance unit needs to handle it within the specified time.

[0126] The smart contract automatically generates an electronic work order and assigns it to the nearest maintenance team.

[0127] Example 5, Tertiary Defect Scenario: During the routine inspection of urban arterials, uniform wear is found on the road surface without structural damage. The collected data:

[0128] Laser height sequence: [2.1, 1.9, 2.3, 2.0, 1.8, 2.2, 2.1, 1.9], maximum height difference ΔD_max = 2.3 - 1.8 = 0.5mm.

[0129] Vibration signal: Peak value of impact energy spectrum: 18g·s < 20g·s threshold; Main frequency band: 5 - 15Hz, within the normal driving vibration range;

[0130] Visual image: Proportion of crack pixels: 0.8% < 1% threshold; Texture anisotropy index A_index = 0.35;

[0131] Then ;

[0132] ;

[0133] Then IRI = 4.3 < 6.0, LDI = 2.63 < 0.6, so the defect level is a level-three defect.

[0134] Level-three defect decision: Mark as the annual maintenance area, include it in the annual microsurfacing plan, and generate a material budget list.

[0135] Blockchain evidence preservation:

[0136] Record the detection results in the blockchain evidence preservation and trigger an automatic re-inspection reminder at a specified time.

[0137] Example 6, Technical logic comparison table of Examples 3, 4, and 5:

[0138] Judgment dimension Primary defect Secondary defect Tertiary defect IRI range >8.0 m / km 6.0 - 8.0 m / km <6.0 m / km LDI threshold >0.8 0.6-0.8 ≤0.6 Peak vibration energy >50g·s 20 - 50g·s <20 g·s Ratio of crack area >15% 5%-15% <5% Response timeliness ≤2 hours ≤15 days Annual plan Repair technology Hot regeneration + structural layer repair Surface milling + resurfacing Micro-surfacing prevention Blockchain marker color Red Yellow Blue

[0139] The above examples only illustrate the principles and effects of the present invention, rather than limiting the present invention. Any person familiar with this technology can modify or change the above examples without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for detecting the flatness of a road surface in engineering surveying and mapping, comprising a measuring vehicle, wherein the measuring vehicle is equipped with an on-board industrial computer for comprehensive data processing; characterized in that: A laser array module is fixedly installed at the front end of the chassis of the measuring vehicle. The laser array module is fixedly installed at the front end of the chassis of the measuring vehicle and includes three laser emitting units distributed in a triangle; A multi-axis inertia compensation unit is installed at the center of gravity of the chassis of the measuring vehicle; The measuring vehicle is equipped with a vibration and vision fusion sensor group, which includes a piezoelectric vibration sensor arranged along the diagonal of the measuring vehicle and a polarization vision module installed on the roof; The vehicle-mounted industrial computer is integrated with a time-space alignment central module, and the time-space alignment central module is respectively connected to the laser array module, the multi-axis inertial compensation unit, the piezoelectric vibration sensor and the polarization vision module through the PCIE expansion slot; The vehicle-mounted industrial computer is also integrated with a multimodal fusion processing module, and the PCIE expansion slot of the multimodal fusion processing module is connected to the time-space alignment center to receive multi-source data aligned by the time-space alignment center; The on-board industrial computer is provided with a blockchain evidence storage interface, and the blockchain evidence storage interface communicates with the cloud server through the on-board 5G module; The spatiotemporal alignment hub module performs three levels of spatiotemporal calibration: hardware clock synchronization, spatial calibration, and dynamic time warping; The multimodal fusion processing module fuses laser curvature, vibration energy spectrum and visual crack features through a gated attention mechanism; The blockchain evidence storage interface generates a unique hash value for each detection section; The following steps are included: S1, multi-source data collection: a1, the frequency modulation signal is emitted by the laser array module, and the original elevation is calculated by the echo phase difference. The road elevation point cloud is , the road elevation is measured by triangulation : ,in is the distance value measured by each laser unit; a2, multi-axis inertia compensation unit calculates vehicle roll angle in real time , Pitch angle , and generate laser matrix dynamic compensation parameters: ； Output the vehicle pose quaternion at the same time ; a3, piezoelectric vibration sensor collects acceleration data in the frequency band of 0.1-500Hz; a4, polarized vision module capture Four-way polarization image ; S2, the spatiotemporal alignment central module performs spatiotemporal alignment processing: b1, hardware clock synchronization: align the clocks of each sensor; b2, space calibration: unify the coordinate system through the Lie group SE (3) transformation matrix: ,in ; b3, dynamic time warping, for non-uniformly sampled data, the dynamic time warping DTW algorithm is improved, and the cumulative distance matrix is: , path search constraint window width ; S3, multimodal fusion processing module performs multimodal feature fusion: c1, laser feature extraction, local curvature ; c2, vibration feature coding, generating 16 sub-band energy spectrum through wavelet packet decomposition; c3, visual feature generation, DeepLabv3+ network outputs pixel-level crack probability map; c4, gated attention fusion, weights each modality feature according to the weight: Weight ;in is the laser mode eigenvector, is the vibration mode eigenvector, is the visual modality feature vector, is the weight matrix, is the attention score vector; S4, flatness determination and decision output: d1, calculate the flatness index , where L is the length of the detection section, m is the half-width of the window, and N is the number of sampling points; d2, calculation of local defect index ; d3, defect classification: Level 1 defect: IRI>8 and LDI>0.8; Second level defect: 6≦IRI≦8, or 0.6>LDI≧0.8; Level 3 defect: IRI<6, and LDI≦0.6; d4, generate blockchain evidence hash ; d5, output maintenance decision.

2. The method for detecting the flatness of a road surface in engineering surveying and mapping according to claim 1, characterized in that: The laser array module is installed at the front bumper of the measuring vehicle. The three laser emitting units are distributed in an equilateral triangle with a side length of 80 cm. The laser wavelengths emitted by the three laser emitting units are 1550nm, 1310nm and 980nm respectively. The emission frequencies of the three laser emitting units are 10kHz, 15kHz and 20kHz respectively.

3. The method for detecting road surface flatness in engineering surveying and mapping according to claim 1, characterized in that: The multi-axis inertial compensation unit is embedded in the center of gravity of the chassis of the measurement vehicle and is integrated with a six-degree-of-freedom MEMS sensor for real-time output of the vehicle posture quaternion.

4. The method for detecting the smoothness of a road surface in engineering surveying and mapping according to claim 1, characterized in that: The piezoelectric vibration sensor is installed on the four-wheel suspension bearing seat, its axial direction is consistent with the suspension movement direction, and the detection frequency band is 0.1-500Hz; an electric lifting bracket is installed on the top of the measuring vehicle, and the polarization vision module is installed on the telescopic end of the electric lifting bracket. The polarization vision module is equipped with a four-way rotating polarization filter.

5. The method for detecting road surface flatness in engineering surveying and mapping according to claim 1, characterized in that: When the space-time alignment central module performs hardware clock synchronization, the IEEE1588 time protocol is used to align the clocks of each sensor; the space-time alignment central module performs spatial calibration to solve the transformation matrix from the laser point cloud to the visual coordinate system; the dynamic time warping aligns non-uniform sampling data streams by improving the DTW algorithm.

6. The method for detecting the smoothness of a road surface in engineering surveying and mapping according to claim 5, characterized in that: The hardware implementation of the improved DTW algorithm includes a coarse alignment unit, a refinement unit and a post-processing unit. The coarse alignment unit downsamples to 1 / 4 resolution and calculates the initial path; the refinement unit searches for the optimal path within ±5 points in the neighborhood of the initial path; and the post-processing unit generates a continuous alignment sequence through cubic spline interpolation.

7. The method for detecting road surface flatness in engineering surveying and mapping according to claim 1, characterized in that: The multimodal fusion processing module includes a laser feature encoder, a vibration feature encoder and a visual feature encoder; the laser feature encoder extracts curvature features; the vibration feature encoder generates a band energy spectrum; and the visual feature encoder is used for visual feature generation.

8. The method for detecting road surface flatness in engineering surveying and mapping according to claim 1, characterized in that: The maintenance decision includes a primary defect decision, a secondary defect decision and a tertiary defect decision.

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