1632 results about "Road engineering" patented technology

The invention relates to the technical field of road engineering detection, in particular to a highway pavement crack image intelligent detection system and method, and the system comprises an image acquisition module, a deep learning module, an image enhancement module, a crack measurement and calculation module, a crack development trend prediction module and an information visualization module. The core innovation of the invention lies in that a differential geometry theory is introduced to construct a crack development trend prediction module, and the module comprises a crack characterization model based on a differential manifold, a multi-scale crack evolution tensor field analysis model and a nonlinear space-time crack development prediction model. A pavement is regarded as a two-dimensional differential manifold, cracks are represented as singular curves on the manifold, a multi-scale tensor field analysis technology and a non-linear kinetic equation are combined, accurate prediction of the future development trend of the cracks is achieved, the system supports multiple image acquisition modes, transverse, longitudinal and net cracks can be accurately detected, and the detection precision is high. The method adapts to complex illumination and background conditions, and predicts the expansion rate and severity change of the crack.

The invention belongs to the crossing field of road engineering technology, artificial intelligence and engineering mechanics, and particularly relates to a road structure design method based on a large model and reinforcement learning. A technical closed loop of'natural language input-parameter automatic mapping-specification standard value acquisition-finite element verification-reinforcement learning optimization 'is constructed: by constructing a load parameter mapping table and a material semantic encoder, the system can automatically convert natural language input into engineering parameters such as load, material, layer thickness and the like, and the problem of adaptability to non-standard working conditions is solved. And a deviation characteristic space is constructed by further combining a mechanical theory solution and finite element simulation, and the reinforcement learning agent is driven to quickly optimize under the condition of meeting theoretical constraints. Compared with a traditional trial calculation method and an existing intelligent optimization scheme, the method has the advantages that the number of design iterations and the material cost are greatly reduced, the safety and compliance of an output scheme are improved, and intelligent spanning from experience driving to theory guiding is achieved.

The invention relates to the technical field of carbon emission accounting, in particular to a road engineering carbon emission analysis method based on multi-source heterogeneous data fusion. The method comprises the following steps: collecting carbon emission factor multi-source heterogeneous data; performing multi-source heterogeneous data anomaly identification and correction processing on the carbon emission factor multi-source heterogeneous data to generate carbon emission factor multi-source heterogeneous standard data; performing carbon emission factor cross-modal global connection fusion processing on the carbon emission factor multi-source heterogeneous standard data to generate carbon emission factor global fusion feature data; establishing an optimization relation model of actual working condition carbon emission accounting based on the carbon emission factor global fusion feature data, and generating an optimization carbon emission accounting relation model; and performing carbon emission intelligent accounting operation on the road construction project based on the optimized carbon emission accounting relation model. According to the method, the accurate accounting of the carbon emission of the road engineering is realized by carrying out fusion analysis on the multi-source heterogeneous data.

The invention relates to the technical field of measurement and monitoring of road engineering variables, in particular to an asphalt pavement paving quality management system, which comprises a data acquisition module configured to acquire real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; the viscosity analysis module is used for determining the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and the temperature parameters of the mixture; the temperature control module is used for regulating and controlling the mixing temperature according to the viscosity change of the asphalt material and generating a target paving temperature curve; the power optimization module is used for matching a dynamic adjustment strategy of the heating power of the screed of the paver based on the target paving temperature curve; by means of the system, meteorological and construction data are monitored in real time, parameters such as asphalt viscosity and screed power are dynamically regulated and controlled, closed-loop optimization is achieved, the paving quality is improved, and intelligent construction is achieved.

The invention relates to the technical field of road engineering safety monitoring, in particular to an expressway slope crack identification method based on unmanned aerial vehicle laser point cloud visualization, which comprises the following steps: multi-source data collaborative acquisition: an unmanned aerial vehicle carrying a laser radar scanner and a high-resolution optical camera flies along a multi-angle combined route, and the unmanned aerial vehicle carries a laser radar scanner and a high-resolution optical camera; synchronously acquiring side slope three-dimensional laser point cloud data and an orthoimage sequence; data fusion preprocessing: denoising and filtering the data to generate an exposed slope triangular mesh curved surface model, and mapping textures to generate a high-precision live-action three-dimensional model; performing multi-dimensional feature fusion recognition, extracting curvature and texture features, inputting the curvature and texture features into a pre-training classification model, judging and outputting a crack pixel-level position; and carrying out visual output, superposing crack information rendering and generating a quantitative report containing the length and width of the spatial position. The method is high in coverage precision and identification accuracy, and provides a reliable basis for slope safety assessment.

The invention belongs to the field of data processing, and provides a road engineering low-carbon data credible interaction method and system, and the method comprises the steps: constructing a full life cycle stage division model, generating corresponding low-carbon data according to the business activity of each stage, and adding a stage identifier, a timestamp and a spatial position label to the low-carbon data; based on a preset engineering logic, a data causal relationship and a carbon emission factor model, constructing a data context tree comprising a plurality of low-carbon data nodes, and organizing the low-carbon data nodes in different space-time ranges and life cycle stages into a life cycle data context tree with a staged conduction relationship; before data interaction is carried out, multi-dimensional verification operation is carried out on target data based on the data context tree; and calculating a credible score of the target data based on a structural path verification result, a space-time consistency verification result and a carbon factor mapping verification result, and controlling the interactive access permission of the low-carbon data according to a score result.

The invention discloses a heavy traffic asphalt pavement damage evolution prediction system, and relates to the technical field of road engineering, and the system comprises a data acquisition module which is used for obtaining continuous multi-frame time sequence images of pavement cracks, a vehicle-mounted axle load spectrum and environment temperature and humidity data; the image processing and space-time modeling module is used for performing multi-scale fractal processing and dynamic time warping on the time sequence image and calculating a cross-scale fractal dimension sequence; the material performance association module is used for establishing and updating a probability association model of the fractal dimension and the shear modulus in real time according to the test data of the dynamic shear rheometer; the multi-physics field data fusion module is used for performing multi-physics field feature fusion through the fractal attention network to generate a multi-physics field collaborative damage correction factor; and the damage prediction and early warning module is used for predicting a fractal dimension growth trend through a fractal domain adaptive transfer learning algorithm, and outputting damage evolution early warning based on a viscoelasticity phase consistency verification result.

The invention relates to the field of performance testing of road engineering materials, and discloses an emulsified asphalt in-situ cold-recycled mixture high-temperature creep test method which comprises the following steps: preparing a test piece: presetting an old material-emulsified asphalt standard test piece, and embedding a moisture regulation and control device to generate a controllable gradient; multi-field loading: coupling an unsteady-state temperature field, a moisture gradient field and a time-varying attenuation stress field to construct a composite loading environment; synchronously aligning data: synchronously acquiring macro strain and micropore data, and realizing cross-scale alignment through a space-time matrix; performing parameter inversion: inverting nonlinear constitutive parameters by combining macro and micro data, and constraining a solution space by using a thermodynamic equation; and intelligent termination output: triggering a termination mechanism based on a multi-dimensional criterion and outputting damage evolution data. Through multi-field coupling loading, cross-scale data fusion and intelligent criterion cooperation, the problems of traditional test environment distortion, parameter deviation and criterion lag are solved, and accurate analysis of the high-temperature creep behavior of the emulsified asphalt reclaimed material and reliable prediction of the performance degradation law are achieved.

The invention discloses an asphalt pavement base crack identification method and device based on a ground penetrating radar, and belongs to the technical field of road engineering pavement maintenance. The method comprises the following steps: 1) carrying out crack image identification on a radar slice map through a YOLOv8n model; 2) based on an identification result, extracting A-scan signals of a crack area, and classifying time-frequency features by using a Light GBM model; and 3) clustering classification results through a'multi-segment clustering + PCA 'analysis method, and filtering false detection cracks. The device is used for executing the method. According to the method, the map and the time-frequency characteristics are fused, the problem that the false detection rate is high due to subjective labeling or objective interference of a single identification method is solved, experiments show that the false detection rate is reduced to 2.8% from 11.2%, and the accuracy and engineering applicability of crack identification are improved.

The invention discloses an asphalt pavement maintenance strategy intelligent generation method and system, and relates to the technical field of road engineering.The maintenance strategy generation method comprises the steps that traffic analysis data information and traffic load data information are extracted from a multi-source traffic monitoring system, and the traffic analysis data information and the traffic load data information are obtained through a laser scanning and image intelligent recognition algorithm; analyzing the pavement structural disease level in each grid unit; according to the road surface structural disease level, the road condition risk level of each grid unit under the traffic load and damage superimposed effect is analyzed, a risk response area is divided, and a service life loss analysis instruction is generated; analyzing an accelerated decline trend of pavement structure deterioration of each grid unit in the risk response area to determine a life reduction value of each grid unit in the risk response area; after comparison, generating a pavement maintenance strategy of a corresponding grade; the maintenance strategy generation method has the advantages of being high in real-time performance, fine in area recognition and dynamically adjustable in strategy response.

The invention discloses a novel concrete pavement hardness detection device, and belongs to the technical field of detection. The invention discloses a novel concrete pavement hardness detection device, and belongs to the technical field of road engineering detection. The device comprises a springback tester, an ultrasonic tester and an analysis system, and equipment credibility parameters are generated through a springback confidence model (quantitative impact energy / angular deviation) and an ultrasonic confidence model (quantitative frequency / probe centering error); combining a pavement characteristic model (analyzing water content / temperature / steel bar density) to output an environment adaptation coefficient; further dynamically calculating a springback risk coefficient and an ultrasonic risk coefficient based on a risk model (fused vibration / carbonization depth); and finally, outputting a calibration intensity value through the intensity correction model. The method solves the defects of weak anti-interference capability and insufficient precision of a traditional method, effectively improves the detection efficiency, and is suitable for high-precision scenes such as expressways and airport runways.

The invention discloses an automatic auditing system and method for road engineering completion settlement. The system comprises a data acquisition and integration module; a data preprocessing module; a settlement index construction module; an intelligent auditing engine; an abnormity early warning and tracing module; a settlement result management module; and a data analysis and optimization module. The auditing method comprises the following steps: S1, collecting and integrating multi-source settlement data; s2, big data preprocessing; s3, constructing a settlement auditing index system; s4, performing intelligent automatic auditing; s5, performing abnormal early warning and tracing; s6, managing settlement results; and S7, performing big data driving optimization. According to the invention, multi-source data intelligent integration, full-process automatic auditing, abnormal accurate tracing and dynamically optimized big data driven settlement auditing can be realized; the method is applied to whole-process management of road engineering completion settlement, and data-driven intelligent auditing and continuous optimization control are realized in the process.

The invention relates to the technical field of road engineering, in particular to a DSR-based method for rapidly predicting low-temperature performance of asphalt, which comprises the following steps: preparing an asphalt cylinder sample; performing a strain amplitude scanning test to obtain a linear viscoelasticity interval; performing frequency scanning test to obtain asphalt dynamic modulus and phase angle data at different frequencies at low temperature; according to the prediction model, converting the dynamic modulus and the phase angle into a creep stiffness modulus and a creep stiffness modulus change rate; and finally, according to a low-temperature performance grade evaluation standard, obtaining the low-temperature grade of the asphalt sample. According to the method, the low-temperature performance of the asphalt can be rapidly predicted, a tedious bending beam rheometer test is not needed, and the test efficiency is greatly improved. The method also has the characteristics of low sample demand, simple operation, accurate result and the like, can be used for rapidly predicting the low-temperature performance of the laboratory asphalt, and also can be applied to asphalt extracted from a pavement core sample, so that the purpose of rapidly obtaining the low-temperature performance of the asphalt is achieved.

The invention discloses a roadbed and pavement compactness detection and regulation method for road engineering. The method comprises the following steps: deploying an intrusive sensor array to monitor an internal physical state and water-heat-force coupling phase change data; synchronously acquiring operation data of intelligent compaction equipment and scanning data of nondestructive detection equipment, and identifying a phase change critical point of the material; a disturbance propagation coefficient is calculated through a mechanical transmission chain model after data fusion, a target point is determined, and automatic micro sampling is carried out; and fusing all data to establish a macro-medium-micro three-scale fused compaction prediction model, generating a real-time regulation and control instruction to realize closed-loop control, and finally generating a three-dimensional compaction degree distribution digital twin model. According to the method, multi-source data are integrated, material phase change and mechanical transfer rules are associated, and the stability and the intelligent level of compaction quality are improved.

The utility model discloses a detection device and a control system for settlement of a lap joint surface of a new roadbed and an old roadbed, and relates to the technical field of road engineering monitoring, the detection device comprises a first support plate, a second support plate, two solar panels, two vibration sensors and two storage batteries, a placing box is mounted on the supporting rod, a laser displacement sensor and an industrial camera are mounted in the placing box, a detection plate is mounted on the second supporting plate, and a fluorescent graduated scale is mounted on the detection plate; according to the utility model, the traditional threaded rod and threaded cylinder adjusting mode is replaced by optimizing the adjusting mechanisms, the problems that a threaded structure is easy to wear and block and the manual operation efficiency is low are effectively solved, and a composite monitoring mode of the laser displacement sensor, the industrial camera and the fluorescent graduated scale is adopted, so that the monitoring precision is improved. And a multi-dimensional auxiliary assembly is combined to improve the reliability of the system.

The invention discloses a roadbed compactness laser radar point cloud evaluation system and method, and belongs to the technical field of intelligent construction of road engineering. The system comprises a laser radar point cloud acquisition module, a self-adaptive multi-scale octree space segmentation module, a differential geometric feature extraction module, a compaction degree field construction module, a compaction degree visualization module and a vibration control module. Differential geometric features are extracted, multi-scale features are fused to construct a compaction degree field, a color coded contour line image is generated, and finally a vibration path is planned and vibration parameters of the road roller are controlled. According to the system, through laser radar point cloud analysis, accurate evaluation and intelligent control of the roadbed compaction degree are achieved, the defects that a traditional compaction method depends on artificial experience, real-time evaluation cannot be achieved, compaction is uneven and the like are overcome, the roadbed compaction quality and construction efficiency are remarkably improved, and the system has important engineering application value.

The invention belongs to the technical field of comprehensive utilization of industrial solid wastes, and particularly relates to a sawing mud-graphite tailing road base material as well as a preparation method and application thereof. The road base material is prepared by mixing saw mud, graphite tailings, a red mud-based cementing material, a reinforcing agent and water. The sawn mud-graphite tailing road base material is applied to a heavy traffic load road base with the strength requirement of 4.0-6.0 MPa. According to the sawing mud-graphite tailing road base material, all aggregates in a traditional road base material are jointly replaced by sawing mud-graphite tailings, and the red mud-based cementing material is adopted for curing, so that the utilization efficiency of solid waste recycling is greatly improved; and the problems of shortage of sand and gravel resources and high material cost of construction engineering in the road engineering construction process are relieved.

The invention relates to the technical field of road engineering, in particular to a new highway subgrade stability evaluation method and system based on data analysis, and the method comprises the steps: obtaining underground goaf data of a target engineering region, new highway subgrade design data and highway network data of an existing highway; carrying out underground goaf modeling, new highway subgrade modeling and highway network modeling by utilizing a three-dimensional modeling technology, and constructing a space coupling three-dimensional model; evaluating the stability of the underground goaf under the coupling influence of the existing highway; evaluating the stability of the newly-built highway subgrade according to an underground goaf stability evaluation result in combination with the space coupling three-dimensional model; newly-built highway subgrade stability early warning is conducted according to the newly-built highway subgrade stability evaluation result. According to the method, the stability influence of the existing highway on the underground goaf and the space-time coupling effect of the underground goaf on the newly-built highway subgrade are quantified, so that the accuracy and reliability of stability evaluation of the newly-built highway subgrade are effectively improved.

The invention discloses a quick-curing polyurethane seamless expansion joint material for roads and a preparation method, and belongs to the technical field of road engineering materials. The material is prepared from polyether polyol, polycaprolactone glycol, a bisphenol A epoxy-polyether block isocyanate terminated prepolymer, a methyl phenyl silicon-boron hybrid hydroxyl chain extender, diphenylmethane diisocyanate, calcium carbonate, wollastonite, a hindered amine anti-aging agent and dioctyl phthalate. Wherein the bisphenol A epoxy-polyether block isocyanate terminated prepolymer is prepared by the following steps: reacting bisphenol A with epoxy chloropropane to generate epoxy resin, reacting with polytetramethylene ether glycol, and introducing isophorone diisocyanate; the methyl phenyl silicon-boron hybrid hydroxyl chain extender is prepared by carrying out hydrosilylation and ethylene glycol reaction on methyl trimethoxy silane, 3-glycidyl ether oxypropyl trimethoxy silane and allylboronic acid pinacol ester; and the methyl phenyl silicon-boron hybrid hydroxyl chain extender is prepared by carrying out hydrosilylation on methyl trimethoxy silane, 3-glycidyl ether oxypropyl trimethoxy silane and allylboronic acid pinacol ester. The material has the advantages of quick curing property, weather resistance and high-temperature stability, and is suitable for road seamless expansion joint scenes.

The invention relates to the technical field of road engineering detection, in particular to a road disease recognition method and system based on electromagnetic waves and photographic imaging, and the method comprises the steps: firstly obtaining electromagnetic wave detection data and photographic imaging data of a road region, then synchronously detecting the distribution position and thickness of a road thin water film, generating a rasterized water film feature map, and finally obtaining a road disease recognition result. Then, according to the water film characteristic pattern, dynamically correcting bimodal data interference, adjusting a reflected wave amplitude value and a time domain delay parameter for electromagnetic wave detection data, compensating a gray difference and an edge gradient value for photographic imaging data by applying a local reflection suppression algorithm, and finally, fusing the corrected data and associating the state of the water film characteristic pattern to eliminate coupling misjudgment. The system comprises a data acquisition unit, a water film detection unit, a double-domain correction unit and a fusion decision unit, the problem of insufficient identification precision caused by a thin water film is solved, the road disease identification precision is improved, and a reliable basis is provided for road maintenance.

The present invention relates to a compaction monitoring device and method that integrates 3D scanning and image perception. The device comprises a sensing component comprised of a 3D road surface texture scanning module, a camera, a vibration damping module, and a locator; a data analysis component, which performs image positioning, image stitching, time synchronization, and calculation of a compaction index based on texture features; and a control strategy component, which controls the number of compaction attempts based on the surface distribution of the compaction index. Compared to existing technologies, this invention introduces a new information source and evaluation basis for road compaction, enabling dynamic, real-time monitoring and feedback control of compaction levels, improving the quality of road construction projects and enabling compaction monitoring based on 3D scanning and image perception.

The invention provides a three-order angular distance extraction-based emulsified asphalt polymer distribution uniformity evaluation method, which comprises the following steps of: acquiring a fluorescent microphotograph of an emulsified asphalt polymer modified sample, carrying out RGB three-channel separation, constructing a joint probability matrix with a gray vector on polar coordinates, and simultaneously carrying out normalization processing; and further calculating a first-order angular moment, a second-order angular moment and a third-order angular moment of the fluorescent microscopic image of the emulsified asphalt polymer modified sample according to the joint probability matrix of the gray vector, and evaluating the distribution uniformity of the polymer in the emulsified asphalt matrix. The method can accurately quantify the distribution uniformity of the polymer in the emulsified asphalt, is small in test sampling amount and high in test speed, can be widely applied to quality detection of the emulsified asphalt in road engineering construction and maintenance, and provides an effective technical means for improving the modification effect of the polymer of the emulsified asphalt and guiding optimization of a preparation process of the emulsified asphalt.

The invention relates to the technical field of road engineering, in particular to a high-performance warm-mixing high-modulus dense water pavement structure and a preparation method thereof. The waterproof bonding layer is prepared by mixing an epoxy resin main agent, a microencapsulated amine curing agent, a functional diluent and filler according to the mass ratio of (50-60): (25-35): (5-8): (12-18), and the filler is expanded vermiculite powder; the mixture lower layer is prepared by mixing a high modulus agent, a lower layer warm mixing agent, asphalt, aggregate and mineral powder according to the mass ratio of (0.4-0.8): (0.3-1.1): (5-6.5): (80-85): (2-5); the high-modulus agent is prepared by mixing POE (Polyolefin Elastomer), modified sepiolite, an interfacial compatibilizer and an anti-aging agent according to the mass ratio of (72-78): (16-20): (4-6): (1-2); a non-stick wheel bonding layer; preparing a mixture upper layer; a curing agent is added into epoxy resin in a microcapsule mode, construction heat triggers microcapsule breaking to achieve accurate epoxy curing, molten POE forms a barb structure in vermiculite gradient pores, sepiolite fibers puncture to construct an anchor chain framework, the three components cooperate to eliminate an interface weak area, and stripping resistance is improved in a leap-over mode.

The invention relates to the field of road engineering, in particular to a multispectral imaging evaluation system and method for the aging degree of an asphalt pavement, and the method comprises the steps that a multispectral imaging module collects pavement spectral data; the topological feature extraction module is used for performing multi-scale feature decomposition on the spectral data and extracting topological structure features; the spectral line attention calculation module calculates weight coefficients of different spectral lines and generates weighted feature vectors; the time sequence feature learning module processes the time sequence change of the feature vector and captures an aging evolution rule; the life prediction module determines the current aging state based on the state vector, generates a plurality of possible aging evolution paths, and determines the pavement aging grade and the remaining life; the decision support module generates a maintenance decision suggestion according to the aging grade and the remaining life, and the multi-spectral imaging technology and the deep learning algorithm are innovatively combined, so that high-precision evaluation of the aging degree of the asphalt pavement and accurate prediction of the remaining life are realized, the scientificity of the maintenance decision is remarkably improved, and the maintenance cost is reduced.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The invention discloses a pavement crack identification method and system based on YOLOv8-Seg, and belongs to the technical field of road engineering pavement maintenance. The method comprises the following steps: identifying a pavement crack slice image by using a trained YOLOv8-Seg segmentation model, and generating a mask area of a crack; extracting attribute information of the crack, wherein the attribute information comprises a center point coordinate, a bounding box, a crack area and a mask binary image; performing space-time continuity analysis on a plurality of images continuously acquired in the same road range, and judging whether cracks with similar forms and similar positions exist or not; and if cracks with similar forms and similar positions exist in the images of the at least two different point positions, determining that the cracks are real cracks, and outputting an identification result. According to the method, crack pixel-level segmentation and attribute extraction are realized, cross-image space-time continuity analysis is combined, crack authenticity is judged, the false detection rate is effectively reduced, and the identification accuracy and engineering applicability are improved.

The invention discloses a ground penetrating radar-based pavement structure layer thickness calculation method and system, and belongs to the technical field of road engineering pavement maintenance. The method comprises the following steps: acquiring a road radar map, and identifying a pavement structure layer by using a YOLOv8 model; calculating the thickness of the structural layer through OpenCV; for the thickness abnormal position, extracting corresponding electromagnetic data and tracking the maximum amplitude point in the horizon range, and correcting the thickness value in combination with DBSCAN clustering; and importing a whole-section thickness result into post-processing software to generate a continuous horizon curved surface, and counting average thickness data of different length sections. And the system executes the steps. The problems that traditional core drilling sampling is low in efficiency and radar data manual processing cost is high are solved, automatic nondestructive detection and anomaly correction of the pavement thickness are achieved, and accurate data support is provided for maintenance design.

The invention belongs to the technical field of road engineering detection, and particularly discloses harbor district asphalt road permeability detection equipment which comprises a permeability detection equipment body, the permeability detection equipment body comprises a detection main body and a detection shell, the detection main body comprises a rotating disc, and a clamping block is arranged below the rotating disc; the detection shell comprises an outer cover shell, the surface of the clamping block is movably installed in the outer cover shell, a sealing material is arranged at the end corner of the bottom in an inner cavity of the outer cover shell, and an extrusion mechanism is arranged on the inner wall of the outer cover shell; through the synergistic effect of the extrusion mechanism and the sealing material, when the clamping block moves downwards, the extrusion mechanism extrudes the sealing material, so that colloid in the sealing material overflows to fill a gap between the bottom of the outer cover shell and the road surface, a flexible sealing layer is formed, the device adapts to microscopic unevenness of the road surface, and the sealing performance of a detection area is ensured; manual sealing material smearing is not needed, operation is simplified, and working efficiency is improved.

The invention particularly relates to a bituminous pavement crack multi-scale segmentation method based on image recognition, which relates to the technical field of crossing of computer vision and road engineering, and comprises the following steps: extracting multi-scale features from shallow details to deep semantics, balancing feature expression capability and vehicle-mounted real-time processing requirements, and providing a multi-level feature source for feature fusion; a channel and space double-branch attention architecture is constructed, features of each coding stage are dynamically screened, noise is suppressed, and refining features are generated through dynamic residual fusion. In the feature fusion stage, a channel and space double-branch attention architecture is adopted, useful features are dynamically screened, details and semantics are balanced through a dynamic threshold correction mechanism, and segmentation imbalance caused by low-quality input is avoided; in the post-processing stage, background false detection, crack breakpoints and edge sawteeth are eliminated through a three-step progressive process, and multi-index comprehensive evaluation such as intersection-to-union ratio and F1-score is combined to ensure that a segmentation result meets the requirements of pavement maintenance engineering on precision and stability.

The invention discloses a method for determining the demulsification speed of emulsified asphalt, and relates to the technical field of online detection and intelligent monitoring of road engineering materials, and by integrating a lightweight time sequence prediction model and a self-adaptive processing mechanism, the practicability and reliability of the determination of the demulsification speed of the emulsified asphalt are remarkably improved; in the aspect of portability, an edge model based on a gating circulation unit structure is adopted, so that the dependence on computing resources is reduced, the method can be operated on handheld equipment, the problem that traditional laboratory equipment is heavy is avoided, and construction personnel can conveniently and quickly deploy the equipment on site; the real-time performance is enhanced, the adaptive sliding window dynamically adjusts the window length and step length according to the data stability to ensure that the inference frequency is matched with the demulsification process, and the model can immediately respond to environmental changes such as temperature fluctuation and output a demulsification speed estimated value without obvious delay in combination with an online learning mechanism, so that the paving and compacting opportunities are guided; and construction interruption is reduced.