1100 results about "Road engineering" patented technology

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 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 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 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 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 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 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 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 relates to the technical field of intelligent construction of road engineering, and discloses an expressway asphalt pavement construction data analysis and processing system, the system performs digital modeling on an asphalt mixture and anchors the asphalt mixture to a pavement three-dimensional coordinate by creating a material state vector including a static attribute, a dynamic state and a process resume, and the system adopts a heat transfer model to construct an asphalt pavement construction data analysis and processing system. And the predicted temperature of the target vector at the future interception moment of the road roller is updated in real time and forwards simulated. Meanwhile, the system calculates a dynamic compactability window according to the compaction history and the material type, compares the predicted temperature with the window, generates a feedforward control instruction of a standard, priority or waiting operation, converts the feedforward control instruction into visual guidance, and issues the visual guidance to the vehicle-mounted terminal, so that prospective control is realized. According to the invention, the conversion of construction control from passive lag to active feedforward is realized, the fineness of the compaction process is improved, and a reliable digital quality tracing mechanism is established.

The invention belongs to the technical field of road engineering roadbed nondestructive testing, discloses a roadbed humidity nondestructive testing method based on wireless radio frequency signal multi-parameter inversion, and aims to solve the problems that a traditional roadbed humidity testing method is destructive, low in efficiency and discontinuous in monitoring or detection parameters are easily interfered. The method specifically comprises the following steps of: firstly, embedding an RFID tag and acquiring an on-site received signal strength indication value; then establishing a relation model of the received signal strength indication value and the roadbed moisture content, and extracting characteristic parameters only influenced by the moisture content; then, constructing a neural network model, taking the extracted characteristic parameters as input, and utilizing field data to train the model to invert the roadbed moisture content; and finally, the spatial distribution of the moisture content of the roadbed is obtained by combining the distribution of the test points and the roadbed structure information, so that the efficient, non-contact and continuous monitoring of the humidity state of the roadbed is realized.

The invention provides rubber-plastic composite modified asphalt suitable for low-temperature regions and a preparation method thereof, and belongs to the technical field of modified asphalt preparation. The modified asphalt is prepared from matrix asphalt, rubber powder, polyethylene and a compatibilizer, the content of the rubber powder is 15%-25% of the weight of the matrix asphalt, the content of the polyethylene is 4%-6% of the weight of the matrix asphalt, and the content of the compatibilizer is 5%-15% of the weight of the matrix asphalt; the preparation method comprises the following steps: heating the matrix asphalt to a flowing state, adding the rubber powder, stirring and swelling for 60 minutes, adding the polyethylene and the compatibilizer, stirring, and shearing at a high speed of 5000r / min for 45-60 minutes. Through rubber and plastic compound modification, the low-temperature crack resistance, the high-temperature stability and the water stability are excellent, the storage stability is good, the cost and pollution are reduced by utilizing waste materials, the process is simple, and the low-temperature road pavement material is suitable for road engineering in low-temperature areas.

The invention discloses a white and black pavement structure exterior and interior disease one-key plotting method oriented to pavement health management, and relates to the technical field of road engineering detection and maintenance. Reflection crack tracing is realized based on an interlayer disease conduction rule by establishing a cement board joint coordinate system; the method comprises the following steps: determining the types of broken slabs and slab corner fractures by utilizing the spatial geometrical relationship between cracks and joints, calculating indexes such as a load transfer coefficient, a reflection crack rate, a slab breakage rate and a pavement condition index, and generating a visual result containing disease distribution and performance parameters in a linkage manner. According to the method, the traditional single structure layer analysis limitation is broken through, the reflection crack recognition accuracy rate exceeds 92%, the broken plate judgment accuracy rate reaches 94% or above, the batch data processing time complexity is optimized to O (n + m), the single road section analysis time is shortened to be within 5 minutes from 2 hours, the problems of low manual detection efficiency, large errors and the like are effectively solved, and quantitative support is provided for precise maintenance of white and black pavements.

The invention relates to the field of rock and road engineering, and discloses a slope stability analysis method based on data-physical model fusion. Comprising the following steps of S1, multi-source data acquisition and preprocessing, S2, physical model construction and initial simulation, S3, data-physical fusion modeling, S4, model training and optimization, and S5, stability analysis and early warning output. The method aims at deeply fusing a physical mechanism and monitoring data and making up the defects that a pure physical model is sensitive to parameters and a pure data driving model is poor in generalization and interpretability, so that slope stability evaluation and early warning which are higher in precision and generalization ability and have definite physical significance are achieved.

The invention provides a road detection vehicle pavement disease detection method and system based on multi-sensor fusion, and relates to the technical field of road engineering detection, and the method comprises the steps: 1, configuring at least one two-dimensional image collection unit and at least one three-dimensional image collection unit on a detection vehicle to obtain a configuration result; 2, according to the configuration result, in the driving process of the detection vehicle, a geometric reference area used for multi-source data space alignment is established based on lane marking lines in a to-be-detected road surface area; and step 3, performing structured division on the geometric reference area to obtain regularly distributed detection areas, and calculating spatial geometric feature changes generated by vehicle composite motion in the detection areas based on distribution features of the detection areas in combination with translational motion and rotary composite motion states existing in the driving process of the detection vehicle so as to obtain the spatial geometric feature changes of the detection areas. And obtaining a rigid body space composite kinematics characteristic resolving result of the detection area. According to the invention, efficient, accurate and full-dimensional detection of pavement diseases is realized.

The invention discloses basalt fiber reinforced low-carbon environment-friendly asphalt and a construction method of the basalt fiber reinforced low-carbon environment-friendly asphalt on a high and cold road surface, and relates to the technical field of road engineering. The basalt fiber is subjected to ultrasonic treatment by a silane coupling agent KH-550 hydrolysate and dried, and the low-carbon additive is prepared from waste rubber powder and biological asphalt and is matched with an anti-stripping agent; adding biological asphalt, stirring at a high speed, adding basalt fibers in three times, stirring at a high speed, and finally adding an anti-stripping agent to prepare an asphalt composition; adding an asphalt composition for wet mixing, and then adding fibers for continuous mixing to obtain a mixture; after the lower bearing layer is preheated, a crawler-type paver paves at the temperature of 155-165 DEG C, and the speed and the loose paving coefficient are controlled; initial pressing, re-pressing and final pressing are carried out, and a double-steel-wheel and rubber-wheel road roller is used for rolling according to the temperature and the speed; and after compaction, the heat-insulating tarpaulin is covered. The low-temperature crack resistance and freeze-thaw resistance of the basalt fiber reinforced asphalt, the carbon emission reduction of the waste rubber powder and the biological asphalt and the construction quality guarantee through dynamic temperature control and intelligent compaction are realized, and the construction method is adaptive to the alpine regions and meets the requirements of green road construction.

The invention relates to the technical field of road engineering, in particular to an all-solid waste rubber-based composite modified foam cold recycled asphalt mixture and a preparation method thereof, and the all-solid waste rubber-based composite modified foam cold recycled asphalt mixture comprises the following components in parts by mass: 150-200 parts of graded precisely-controlled recycled aggregate, 10-25 parts of recycled mineral activity enhancing powder, 5-10 parts of an industrial solid waste reinforcing agent and 20-40 parts of solid waste-based recycled foam asphalt. The mixture utilizes various solid wastes for recycling, has excellent pavement performance, high strength, crack resistance and durability, can be constructed at normal temperature or low temperature, and is low in energy consumption and environment-friendly. Through composite modification of foamed asphalt and rubber powder, early strength and stability are significantly improved, the problem of slow development of early strength of a cold recycled material is overcome, the construction period is shortened, and traffic is quickly opened. The method is simple in process, low in cost and convenient to construct, meets the requirements of green and low-carbon construction, provides a new way for the double-carbon target in the traffic field, and is remarkable in economic and social benefits and wide in application prospect.

The invention relates to the technical field of crossing of road engineering and computer vision, and discloses a densely-stacked asphalt mixture particle roundness distribution prediction method, which comprises the following steps of: performing instance segmentation on particles in an image by adopting a visual basic model to obtain a mask image of each particle in the image; analyzing each particle mask image, extracting morphological parameters including the area, the equivalent circle diameter, the equivalent ellipse long axis and the like, constructing a particle geometric feature set, constructing a distribution curve for each morphological parameter according to the number of particles, and performing equal-interval sampling on the curve to form a multi-dimensional feature vector for machine learning input; and constructing a recurrent neural network model, and training the model by taking the multi-dimensional feature vector as an input and the known particle roundness distribution as a label to realize prediction of the particle roundness number distribution in the new image. And the segmentation applicability and the roundness distribution prediction precision of the densely-stacked asphalt mixture image are obviously improved.

The invention discloses a large-scale visualized direct shear test device and method for geosynthetics. The device comprises a main frame, an upper shear box, a lower shear box, a normal cyclic loading system, a horizontal cyclic shear loading system, a data acquisition system and a visualized monitoring system. The direct shear test device is characterized in that the direct shear test device can perform normal and horizontal cyclic loading at the same time, and normal stress with different frequencies and amplitudes can be applied according to needs to simulate a test of the characteristics of the reinforcement-soil interface under the cyclic loading effect; one side wall of the upper shear box and one side wall of the lower shear box are transparent observation windows, and a high-speed camera and a digital image analysis method are combined, so that the shear band evolution process can be visually observed and quantitatively analyzed; the shear box is large in size, and the size effect is effectively overcome. According to the invention, the interface characteristics under the cyclic load effect in the road engineering can be reflected more truly and comprehensively, the size and the function of the shear box are comprehensive, and the interface mechanical characteristics and the interaction mechanism under various working conditions can be simulated.

The invention discloses a road intelligent diagnosis method based on an AI large model, and relates to the technical field of road engineering detection, and the method comprises the following steps: building a unified time base line, constructing a phase reference field, injecting a calibration pulse, separating dynamic shadow time sequence texture and crack initial features in a road image sequence, and obtaining a road image sequence; generating prior information of dynamic shadows and cracks; under the constraint of prior information, polarization spectrum three-dimensional coding imaging is carried out, polarization angle distribution and spectrum gradient are extracted, a feature dictionary of dynamic shadows and cracks is constructed, and phase drift in the prior information is calibrated. According to the method, a unified time base line and a phase reference field are constructed, and accurate distinguishing of dynamic shadows and cracks is realized by combining polarization spectrum imaging and anti-fact distillation; the point cloud and acceleration data are fused for geometric reconstruction, and through phase conjugate extinguishing and closed-loop feedback, the continuity and accuracy of diagnosis are significantly improved, and maintenance error decisions caused by misjudgment are avoided.

The invention discloses a comprehensive design method for an anti-freezing pavement of a highway in a high-altitude severe cold area, relates to the technical field of road engineering, and solves the problems of high energy consumption, short service life, unfriendly environment or poor system reliability and the like generally existing in the prior art. And the design of combining structures, materials, energy and intelligent control is lacked. A comprehensive design method for an anti-freezing pavement of a highway in a high-altitude severe cold area comprises the following steps that S1, a multi-layer composite pavement structure is constructed, and the multi-layer composite pavement structure is composed of an anti-sliding and ice-inhibiting functional layer, an energy active layer and a heat preservation structure layer from top to bottom; and S2, a distributed state sensing network is arranged in the multi-layer composite pavement structure and on the roadside of the multi-layer composite pavement structure. Passive ice suppression, active ice melting, intelligent sensing and green energy are organically integrated to form a complete'sensing-decision-execution-energy supply 'closed-loop system, and the limitation of a single technology is broken through.

The invention relates to the technical field of road engineering, in particular to a detection method for evaluating cohesion characteristics of river-crossing shield tunnel waste slag improved soil for roadbed filling, which comprises the following steps: preparing a solidified soil sample; calculating coupling parameters; obtaining a static cohesion force and a dynamic cohesion force; obtaining a water stability coefficient; based on the collected roadbed filling characteristic parameters, the static cohesion, the dynamic cohesion and the water stability coefficient, constructing a dynamic resilience modulus correlation model; grading the cohesion characteristics of the solidified shield soil according to the coupling parameters and the dynamic rebound modulus; detecting actual values of the coupling parameters and the dynamic resilience modulus of the to-be-evaluated solidified soil, and determining the grade of the to-be-evaluated solidified soil; according to the cohesion characteristic detection method, the characteristics of shield tunnel soil particles, mineral composition and roadbed filling scenes are fitted, actual working conditions are simulated, and the solidification effect is quantified, so that a scientific basis is provided for engineering application of shield tunnel soil solidification improved roadbed, and the problems in the background technology are effectively solved.

The present invention relates to the technical field of road engineering, and provides a deflection basin parameter determination method and apparatus, a road testing device, a medium and a product. The method comprises: by means of a ground penetrating radar, acquiring a real-time deflection basin evaluation index value of any point in a road segment to be tested; and inputting the real-time deflection basin evaluation index value into a deflection basin parameter prediction model, to obtain a predicted deflection basin parameter value, wherein the deflection basin parameter prediction model is obtained by performing training on the basis of at least one deflection basin parameter value and a deflection basin evaluation index value which correspond to a reference point in a reference road segment, the at least one deflection basin parameter value is determined by means of a falling weight deflectometer, and the deflection basin evaluation index value is determined by the ground penetrating radar. In the present invention, a deflection basin parameter can be predicted simply by obtaining a real-time deflection basin evaluation index value by a ground penetrating radar, thereby improving the efficiency of deflection basin parameter determination.

The invention discloses nano-composite high-viscosity and high-stability modified asphalt and a preparation method thereof, and belongs to the technical field of road engineering materials. The modified asphalt is prepared from the following raw materials in parts by weight: 100 parts of matrix asphalt, 3 to 10 parts of star-shaped SBS (Styrene Butadiene Styrene), 5 to 25 parts of waste tire rubber powder, 2 to 6 parts of C5 / C9 petroleum resin, 2 to 8 parts of naphthenic oil, 0.1 to 0.5 part of sulfur, 1 to 3 parts of organic montmorillonite, and 0.5 to 2 parts of optional maleic anhydride grafted polypropylene and / or 0.1 to 0.5 part of silane coupling agent. The core of the preparation method is that through a three-stage process of swelling and nano dispersion, development and interface reaction and cross-linking stabilization, the technical problems of poor compatibility and easy segregation of an SBS / rubber powder composite system are fundamentally solved by utilizing the physical barrier effect of a nano sheet layer and the synergistic effect of vulcanization / interface chemical bonding. The obtained product has ultrahigh viscosity (dynamic viscosity gt at 60 DEG C, 80000 Pa.s), excellent storage stability (segregation error 1t at 48 hours, 2.0 DEG C) and excellent aging resistance, and the comprehensive performance of the product is far better than that of the prior art.

The invention relates to the technical field of road engineering construction, and discloses a quality monitoring method and system for road construction, and the method comprises the steps: obtaining a time sequence image sequence of a construction region, carrying out the processing of the image sequence based on an improved Canny edge detection process, obtaining a binary image containing texture features, and carrying out the processing of the binary image. The method comprises the steps of calculating edge offset data of edge line segments between adjacent frames based on a dynamic time warping algorithm, constructing a pavement texture change model based on the edge offset data, identifying an abnormal region and calculating an area proportion of the abnormal region, and obtaining a construction quality evaluation index based on an edge expansion index and fusing data collected by an Internet of Things sensor. And comparing the construction quality evaluation index with a quality preset threshold value, triggering a grading early warning mechanism, and synchronously pushing the grading early warning mechanism to a highlight area of the construction site model. Based on the improved algorithm, the road surface texture noise interference is effectively inhibited, the edge detection stability is improved, the compactness prediction accuracy is improved, and the road construction quality monitoring efficiency is improved.

The invention relates to the technical field of intelligent road engineering, in particular to a long-life pavement structure optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining pavement structure data, and constructing a structural mechanical model according to the pavement structure data to obtain the structural mechanical model; performing fatigue loss simulation according to the structural mechanical model to obtain fatigue loss data; carrying out rapid physical simulation according to the fatigue loss data to obtain loss simulation data; and performing physical prior life prediction according to the loss simulation data to obtain life prediction data. According to the method, through fracture entropy field modeling and crack density analysis, accurate quantification of spatial distribution and evolution trend of fatigue damage is realized. In combination with artificial intelligence-driven multi-time frame evolution simulation, the precision of pavement structure life prediction and the pertinence of design optimization can be significantly improved.

The invention discloses a three-dimensional ground penetrating radar detection method, system and equipment for road roadbed diseases and a medium, and relates to the technical field of road engineering detection. Controlling a high-frequency antenna and a low-frequency antenna to emit electromagnetic waves to a road roadbed based on the movement of the detection vehicle so as to receive reflected high-frequency data and low-frequency data; processing the high-frequency data and the low-frequency data based on the positioning attitude data to obtain processed high-frequency data and processed low-frequency data; processing and fusing the processed high-frequency data and the processed low-frequency data to obtain a full-band reflection coefficient sequence; calculating the full-band reflection coefficient sequence to obtain broadband geological radar data; inputting broadband geological radar data into the convolutional neural network model to obtain a disease type and a disease semantic segmentation map; and generating a three-dimensional visual model of the roadbed disease based on the disease semantic segmentation map, and outputting maintenance suggestion data matched with the disease type. By implementing the technical scheme, the accuracy of deep hidden disease recognition is improved.

The invention discloses an automatic construction method for a lane-level road network digital model based on topology and semantics, and the method comprises the steps: analyzing a topological association rule between lane units and a multi-modal semantic constraint relation, constructing a hierarchical expression system with lane segments and lane groups as basic composition units, and constructing a hierarchical model. And carrying out structured modeling on lane marking lines, driving area boundaries and traffic logic relationships, researching and developing a geometric-semantic collaborative automatic generation algorithm, and constructing a lane-level road network. According to the technology, for the problems of insufficient semantic information application and low automation degree in the construction process of an existing lane-level road network digital model, topology and semantic analysis are fused into road engineering modeling, the geometric morphology of road entity elements and traffic semantic information are integrated, the manual calibration requirement in the three-dimensional modeling process is reduced, and the construction efficiency is improved. And the automation and intelligence degree of the construction of the lane-level road network digital model is improved.

The invention discloses an asphalt mixing process dynamic optimization method based on multi-data fusion and digital twinning, and relates to the field of road engineering, and the method comprises the steps: collecting multi-source sensor data in the production process of an asphalt mixing station, carrying out the fusion processing of the multi-source sensor data, and obtaining a state parameter feature set, the multi-source sensor data comprises temperature, humidity, flow, vibration signals, dust concentration and asphalt viscosity; inputting the state parameter feature set into the intelligent optimization model, and outputting an optimized process parameter set value through iterative search and prediction feedback; and the optimized process parameter set value is issued to an asphalt mixing station control system for execution, and a digital twin model is driven based on real-time production data to carry out visual monitoring and closed-loop control. By collecting multi-source data and combining an intelligent optimization algorithm, automatic adjustment and real-time quality prediction of process parameters are achieved, the intelligent level of the production process is improved, stable product quality is ensured, and dynamic working condition changes are coped with.