47 results about "Road surface temperature" patented technology

1. The name of the design product: integrated traffic meteorological monitor. 2. The use of the design product: for measuring visibility, road conditions, road temperature, wet slip coefficient, air temperature, relative humidity, wind speed, wind direction, air pressure, rainfall and other data. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view.

The application discloses a kind of high-temperature phase change cooling-self-repairing dual-function microcapsules and its application, belong to intelligent road material field.The preparation method of the microcapsule includes: high-temperature phase change core material, aromatic oil asphalt regenerant and polyisocyanate compound are mixed to prepare oil phase;Afterwards, the oil phase is added to aqueous phase, and after mixing, it is reacted under the condition of 60~70 DEG C, after reaction is completed, the precipitate is collected, and the precipitate is washed with water and ethanol, and then dried to obtain the microcapsule;The preparation of the high-temperature phase change core material includes: mixing tricosane and tetracosane under the condition of 60~70 DEG C, and then cooling to room temperature to obtain high-temperature phase change core material;The aqueous phase is non-ionic emulsifier aqueous solution.The application effectively reduces the temperature of pavement by phase change endothermic, and relieves rut and aging;At the same time, the regenerant in core material can be released when crack occurs, to realize self-repairing.The application significantly improves the rut resistance, durability and service life of asphalt pavement in high-temperature area.

The application discloses a highway pavement temperature inversion method and system fusing meteorological and traffic information, and the method comprises the following steps: acquiring meteorological information and traffic information of a target highway section, performing time synchronization, space matching and preprocessing on multi-source data, and constructing an inversion input dataset; performing feature extraction on meteorological information, road category information and traffic time series information based on a multi-input branch neural network, comprehensively processing in a fusion layer, and establishing a pavement temperature inversion model; introducing a physical loss function with embedded energy balance constraints in the model training process to enhance the physical consistency of the inversion result; and using the trained model to perform inversion calculation on the pavement temperature of the target section at the corresponding time. The method can comprehensively consider the differences between meteorological conditions and road operating environments, improve the accuracy and stability of pavement temperature inversion, and is suitable for highway pavement temperature monitoring and traffic safety guarantee.

The invention provides an expressway surface low-temperature ice condition short-term forecasting method and system, and relates to the technical field of road surface temperature forecasting, and the method comprises the steps: obtaining a mesoscale numerical forecasting product, and providing basic meteorological data of an expressway; performing spatial interpolation and preprocessing on the basic meteorological data by adopting a bilinear interpolation method to form road surface meteorological data; establishing a meteorological parameterized model, and converting the road surface meteorological data into road surface meteorological parameters; calculating the road surface heat flux according to the road surface meteorological parameters through an earth surface heat balance equation; a road surface temperature hourly forecasting model is constructed, the current road surface temperature is obtained, a road surface temperature hourly forecasting value is calculated through derivation by means of a thermodynamic equation in combination with road surface heat flux, and a road surface low-temperature ice condition short-term forecasting result is generated in combination with a continuous road temperature inversion model based on a thermal spectrum map, so that a road surface low-temperature ice condition forecasting result is obtained through an energy conservation method and a refined parameterization scheme. And the accuracy and timeliness of short-term forecasting of the low-temperature ice condition of the expressway surface in a complex environment are remarkably improved.

The present invention relates to a method for predicting the expected road surface temperature for at least one road section, in which the surface temperature of the road surface in the road section is measured via at least one sensor, ambient environmental information is associated with the measurement and transmitted to the backend along with the surface temperature, and the relationship between the ambient environmental information and the surface temperature is learned by machine learning, thereby enabling the estimation of the expected road surface temperature. According to the present invention, a range including the maximum and minimum possible road surface temperatures that can occur depending on the location is generated as an output value.

The present application relates to the technical field of computer-aided design, in particular to a road surface material construction state remote communication monitoring system, the system comprises: a construction environment sensing module collects voltage and pulse signals and converts them into road surface temperature and wind speed to construct a monitoring data set, a heat flux dynamic calculation module generates a convective heat transfer coefficient in combination with the wind speed and calculates the surface heat dissipation flux, an internal state deduction module constructs a virtual grid model and configures material parameters, calculates the heat energy exchange amount of the nodes according to the heat dissipation flux to generate a core temperature gradient sequence, and a construction window monitoring module extracts the central node temperature, and if it is less than a threshold value, a stop rolling pre-warning instruction is generated. In the present application, the internal heat energy exchange is deduced by constructing a virtual grid model, deep analysis from surface data to core temperature is realized, the problem that the internal state cannot be grasped only by surface monitoring is solved, the compaction operation timing is accurately controlled, and the road surface forming quality is significantly improved.

The application discloses a kind of for thin surface layer asphalt pavement structure modulus inversion method, it is related to road engineering technical field.The related information and initial population of the present application are obtained, the modulus and speed of particle in population are initialized, the interlayer stiffness coordination coefficient calculation method is determined, interlayer stiffness coordination coefficient value is calculated using pavement structure information and road surface temperature, the fitness of each particle in population is evaluated in combination with pavement structure information and road surface deflection basin information, the fitness of optimal individual in population and corresponding modulus are determined, initial temperature is calculated, the fitness of each particle is compared with individual historical optimal fitness, individual historical optimal and group historical optimal are updated, the group learning object of particle is determined and the velocity and modulus of each particle are updated, the fitness of each particle is recalculated, optimal solution accuracy is updated again, and each structure layer modulus is output.The present application can accurately obtain thin surface layer asphalt pavement structure inversion solution.

This invention relates to the field of road engineering materials technology and discloses a low-temperature sensitive lightweight aggregate asphalt concrete and its preparation method. The asphalt concrete comprises asphalt binder, coarse aggregate, fine aggregate, mineral filler, and composite phase change lightweight aggregate. The key to its preparation lies in: firstly, impregnating a phase change energy storage medium (such as paraffin wax or fatty acids) into a porous carrier material (such as silica or diatomaceous earth) to form a shaped composite phase change material; then, loading this material into the pores of a porous lightweight matrix aggregate (such as expanded shale) using a vacuum impregnation method to form the composite phase change lightweight aggregate; finally, incorporating this aggregate as a functional component into the asphalt mixture. The phase change material in this composite aggregate undergoes a phase change when the road surface temperature changes, absorbing heat at high temperatures and releasing heat at low temperatures, effectively regulating the road surface temperature field, thereby significantly improving the high-temperature rutting resistance, low-temperature crack resistance, and temperature stability of the asphalt concrete, and extending the service life of the road surface.

The present application relates to a kind of winter asphalt pavement surface temperature prediction method based on integrated deep learning, belong to highway traffic meteorological monitoring technical field.The method includes: according to correlation coefficient method and expert knowledge, the meteorological factor that is accumulated to the road surface temperature Influence is selected;Sliding window method is used to cut time series data, and supervised learning sample is constructed;Supervised learning sample is obtained, and the winter asphalt pavement surface temperature prediction model of integrated deep learning is trained;Winter asphalt pavement surface temperature prediction method based on integrated deep learning includes with LSTM neural network module, GRU neural network module, feature fusion layer and fully connected layer;The trained model is used to predict hourly pavement surface temperature.The present application is through the cumulative influence of the integration of deep learning network of multi-dimensional meteorological factor and pavement surface temperature periodicity, improves the accuracy and robustness of pavement surface temperature, with data-driven, self-learning, multi-granularity prediction and other technical characteristics.

The invention discloses a method for determining a service temperature domain of each structural layer of a full-thickness LSAM-50 pavement, which comprises the following steps: firstly, determining a'surface layer + binding layer + flexible base layer 'full-thickness structure of the pavement, collecting hourly air temperature and solar radiation data, and classifying according to day and night; calculating the temperature of each time point of the surface layer through a road surface temperature model, and counting an extreme value to determine a surface layer temperature domain; combining meteorological data and road surface temperature, segmentally calculating temperatures of the top and the bottom of the junction layer and the shallow layer and the deep layer of the flexible base layer, and respectively counting extreme values to determine a temperature domain of a corresponding layer; according to the method, fine analysis of the full-depth temperature field of the full-thickness LSAM-50 pavement is realized for the first time through structure layering, data classification and segmented modeling, accurate temperature boundary conditions are provided for material selection, full-thickness structure thickness design and maintenance decision, the disease risk and engineering cost caused by misalignment of full-thickness temperature parameters are effectively reduced, and the method is suitable for large-scale popularization and application of the full-depth temperature field of the full-thickness LSAM-50 pavement. And the method has remarkable engineering application value and technical innovation.

The invention relates to the technical field of computer aided design, in particular to a pavement material construction state remote communication monitoring system, which comprises a construction environment sensing module, a data processing module, a data processing module and a data processing module, the heat flux dynamic calculation module generates a convective heat transfer coefficient in combination with wind speed and calculates surface heat dissipation flux, the internal state deduction module constructs a virtual grid model and configures material parameters, node heat energy exchange capacity is calculated according to the heat dissipation flux so as to generate a core temperature gradient sequence, and the construction window monitoring module extracts the temperature of a center node; if yes, a rolling stopping early warning instruction is generated. According to the method, internal heat energy exchange is deduced by constructing the virtual grid model, deep analysis from surface data to the core temperature is achieved, the problem that the internal state cannot be mastered only through surface monitoring is solved, the compaction operation opportunity is accurately controlled, and the pavement forming quality is remarkably improved.

The invention discloses an LSAM-50 pavement temperature-time probability distribution construction method based on an EM-GMM algorithm, which flexibly describes single-peak, double-peak and three-peak distribution through linear combination of a plurality of Gaussian components in a Gaussian mixture model (GMM), and performs iterative optimization on parameters such as a mean value, a variance and a weight in combination with an expectation maximization algorithm (EM). And the fitting precision of complex multi-peak data is obviously improved. Meanwhile, Kolmogorov-Smirnov (K-S) test is introduced to quantitatively verify the model fitting effect, it is ensured that a cumulative distribution function (CDF) is highly matched with measured data, and accurate modeling of monthly and annual scale multimodal temperature distribution is achieved. According to the method, the certainty limitation of a traditional model is broken through, refined data containing the temperature occurrence probability is provided for LSAM-50 pavement design, construction time selection, maintenance strategy making and material performance optimization can be effectively guided, and the technical blank of complex multi-peak distribution probabilistic description in the field of road engineering is filled up.

The invention discloses a solar intelligent demisting, deicing and warning all-in-one machine for a road surface. The all-in-one machine comprises a solar power supply and energy storage module; a multi-sensor fusion detection module (temperature and humidity, visibility, road surface temperature and icing); a PTC hot air demisting and ice melting execution module; an automatic warning yellow light module; a 4G / 5G remote data and video return module; the equipment collects environmental parameters in real time, when fog or road icing is judged, hot air and a warning lamp are automatically turned on, data and pictures are uploaded to a control center at the same time, and full-automatic, unattended and large-range networking road safety guarantee is achieved.

The invention relates to the technical field of road asphalt pavement maintenance, in particular to a low-carbon fine thin hot paving overlay integrated maintenance process which comprises the following steps: 1, heating the surface layer of an old pavement, and monitoring the heating temperature; the heating temperature is 170 + / -5 DEG C; 2, the heated disease layer is stripped, foreign matter on the stripped surface layer is cleaned, and after the disease layer is stripped, the temperature of the remaining old pavement is 150 + / -5 DEG C; 3, immediately paving a new asphalt mixture on the peeled clean and hot-state interface, and controlling the loose paving thickness through a loose paving coefficient; fourthly, the paved road surface is compacted in a stepped mode; and step 5, carrying out spot check on the pavement subjected to stepped compaction, detecting indexes such as compactness, a water seepage coefficient, a structural depth, a swing value and a transverse force coefficient SFC of the pavement, and judging whether the maintained pavement meets the standard or not. The maintenance process provided by the invention has the advantages that the construction speed is high, disease treatment, interface activation and overlay paving are synchronously completed, and long-acting and stable recovery of the surface performance of the aged pavement is facilitated.

The invention provides a temperature control method and device for a low-temperature test road, and the method comprises the steps: regulating and controlling the cold plate power of each wheel track unit based on the test requirement of a continuous test task for the road surface temperature, and determining a target wheel track unit corresponding to the test task based on the test range of the test task of a current test vehicle, and determining an associated wheel track unit corresponding to the associated test task based on the test range of the associated test task, and in the test process of the current test vehicle, determining the associated wheel track unit. Determining a thermal disturbance unit in the target wheel track unit based on the target wheel track unit, the associated wheel track unit, the real-time planned path of the current test vehicle in the future preset time period and the historical path of the vehicle corresponding to the associated test task in the past preset time period, according to the method, the temperature influence value of the associated test task on the thermal disturbance unit and the additional cooling capacity demand are determined, so that the cold plate power of the thermal disturbance unit is adjusted based on the additional cooling capacity demand, and the road surface can be maintained at the uniform target temperature in a multi-vehicle continuous test scene.

The application relates to a material-equipment collaborative crack repair design method and equipment based on fusion digital twinning, and the method comprises the following steps: establishing an electromagnetic-thermal field coupling model to obtain a pavement temperature field distribution, the electromagnetic-thermal field coupling model being a system combined by a microwave heating device, an asphalt pavement, a crack and a repair material; based on the mapping relationship between the pavement temperature and the dynamic modulus, a pavement compaction model under the action of a road roller is established through finite element simulation; actual pavement parameters are obtained, and the key parameter combination of the crack repair material and the microwave heating device is inversed and optimized according to the pavement compaction model, the key parameter combination comprising an electromagnetic loss parameter E, a heating power W, a heating width b and a heating time t; and the crack repair material and the microwave heating device are designed based on the finally selected key parameter combination. Compared with the prior art, the application has the advantages of precise repair, convenient construction, high repair efficiency, small environmental disturbance and the like.

The utility model discloses a temperature monitoring and early warning device for an asphalt pavement. The temperature monitoring and early warning device comprises a mounting seat, a traction piece, a road detector, a power supply board, a marking device, an early warning device and a monitoring sensor, the marking device is arranged, the infusion pump is started to pump pigment into the liquid injection disc, so that the liquid injection disc can store the pigment, it is guaranteed that the liquid injection disc can provide the pigment for the marking mechanism, a problem road section is marked through the marking mechanism, a worker can quickly find a defective road section, and the working efficiency is improved. Defect omission caused by insufficient light is avoided, the marking device is matched with the road tester to operate, the marking device can mark a striking maintenance mark in real time, the situation that road sections are omissed in the follow-up road section maintenance process of road administration personnel is avoided, and the problem that in original equipment, due to time change, the maintenance time is shortened is solved. The problem that some defects are not striking enough and missing defects exist due to the fact that the intensity of light rays goes by time is solved.

The invention discloses a self-circulation expressway cooling system, which comprises a pipeline used for being laid under an expressway pavement; a phase change material is arranged in the pipeline; the TEG power generation module comprises a heating end and a cold end, the heating end is used for making contact with concrete of an expressway, and the cold end is used for making contact with a pipeline; the underground fin condensation section is positioned below the pipeline; one end of the ascending pipe is connected with one end of the pipeline, and the other end of the ascending pipe is connected with one end of the underground fin condensation section; one end of the descending pipe is connected with the other end of the pipeline, and the other end of the descending pipe is connected with the other end of the underground fin condensation section; and the circulating pump is driven by the electric power generated by the TEG power generation module so as to pump the circulating working medium of the underground fin condensation section into the pipeline. According to the invention, pump-free complete circulation can be realized, continuous and stable operation of the system is ensured, and adaptive regulation and control of the pavement temperature are realized.

The invention discloses a road hidden crack detection method and system. The method comprises the steps that road surface temperature time series data, road surface vertical strain time series data and traffic load statistics time series data of a target road section are collected; inputting the pavement temperature time sequence data, the pavement vertical strain time sequence data and the traffic load statistical time sequence data into a pre-trained temperature crack response baseline model to obtain a crack opening and closing index time sequence of each time period on the target road section; generating a plurality of candidate detection time slices from the crack opening and closing index time sequence according to a preset time step length, and calculating a corresponding average crack opening and closing index and predicted traffic flow for each candidate detection time slice; enumerating different lane closing modes and driving speed control combinations in each candidate detection time slice, and screening out candidate combinations meeting preset constraint conditions as recommended detection tasks; and the long-term systematic leak detection risk is obviously reduced.

This invention discloses a method and system for optimizing the thermal resistance performance of composite thermally induced pavement, relating to the field of data processing technology. The method includes: collecting historical meteorological data and pavement thermal response characteristic data to construct a multi-source dataset; performing machine learning to establish a regional climate-pavement temperature correlation model; setting a heat load threshold and, combined with data constraints, performing layered thermal resistance performance optimization to generate an initial pavement thermal resistance performance scheme; executing the initial pavement thermal resistance performance scheme to reconstruct the heat flow path and obtain optimized pavement structural parameters; performing multi-objective thermal resistance simulation on the pavement, generating thermal resistance simulation optimization results, verifying the thermal resistance simulation optimization results, and formulating a composite thermally induced pavement thermal resistance performance optimization scheme. This solves the technical problem in existing technologies where pavement thermal resistance performance design struggles to accommodate different heat loads and regional climate differences, resulting in a lack of adaptability in pavement thermal resistance performance design, and achieves the technical effect of improving the adaptability of pavement thermal resistance performance.

The invention discloses a big data intelligent analysis method and system, and relates to the technical field of big data intelligent analysis, and the method comprises the steps: collecting multi-source monitoring data, such as pavement temperature, humidity, strain and axle load, and calculating a daily average humidity value, a daily maximum temperature gradient value and a normalized strain peak value; constructing a continuous weight based on the strain response, and distinguishing a dry state sample from a wet state sample according to a daily average humidity value; and respectively carrying out weighted linear fitting on the two types of samples to obtain a hysteresis humidity loss temperature amplification index for depicting a humidity effect amplification effect, generating a road section average humidity loss risk index in combination with a continuous weight and a temperature gradient, and realizing road section humidity loss risk sorting according to the road section average humidity loss risk index. According to the invention, automatic quantification and comparative analysis of the moisture damage risk can be realized in a large-scale monitoring data scene, and the accuracy of a road maintenance decision is improved.

The utility model discloses a CAC -20C emulsified asphalt mixture surface layer pavement structure, including foundation layer, the foundation layer top is provided with the layer of penetration, the layer of penetration top is paved with lower seal coat, lower seal coat top is provided with the heat conduction layer, the heat conduction layer bottom end array installation has multiple groups of heat conduction bar, the heat conduction layer upper end is paved with asphalt layer. Advantageous effects lie in: the utility model discloses a graphene as the material of heat conduction layer and array distribution in heat conduction bar, in the use process, because adopt the graphene as the heat conduction material, can in summer, the heat in asphalt layer is transmitted to the foundation layer through heat conduction bar, and then reduces its road surface temperature, avoids appearing vehicle tire burst, and in winter, the heat in foundation layer can be transmitted to asphalt layer through heat conduction bar and heat conduction layer, and then improves its temperature, avoids its icing, guarantees the traffic safety.

The application provides an assisted driving method based on deep learning data fitting and related equipment, and relates to the field of automatic control. In the method, the road surface space-time data set is determined by collecting historical road surface information, collecting time points and road surface position coordinates. Then, based on the road surface space-time data set, the road surface information data group including road surface temperature, humidity, wear state, pothole condition and repair record is taken as input data, and after determining the road surface condition category label, a deep learning model is trained, which can output the road surface condition category matched with the input data. Then, the real-time information of the road surface is obtained through the sensor and the monitoring equipment, and is input into the deep learning model to determine the real-time condition category of the road surface. Finally, the automatic driving strategy of the target vehicle is adjusted according to the weather forecast, the vehicle tire data, the real-time condition category of the road surface and the preset automatic driving behavior optimization model, so that safer and more intelligent driving is realized.

This utility model relates to the field of snow and ice melting technology for roads in high-altitude permafrost areas, and particularly to a snow and ice melting device for roads in high-altitude permafrost areas. It includes a permafrost roadbed and an asphalt pavement, with the asphalt pavement located on the upper surface of the permafrost roadbed. A heat insulation system is installed on the side of the asphalt pavement closest to the permafrost roadbed. A snow and ice warning system and a heating system are installed on the side of the heat-insulating, heat-retaining, and anti-radiation aluminum film closest to the asphalt pavement. The snow and ice warning system includes a pavement temperature sensor, a water immersion sensor, and a signal processor. The heating system includes a nano-heat rod, a current regulator, and a roadbed temperature sensor. The heat insulation system includes a heat-insulating, heat-retaining, and anti-radiation aluminum film located on the upper surface of the permafrost roadbed. The snow and ice melting device for roads in high-altitude permafrost areas provided by this utility model has the advantages of snow and ice melting on roads in permafrost areas.

The invention relates to a composite thermal resistance type asphalt pavement structure. The composite thermal resistance type asphalt pavement structure sequentially comprises a colored heat reflection coating, a thermal resistance asphalt mixture layer, a thermal resistance coating and a substrate layer from top to bottom in the vertical direction. Through functional combination of all the layers and reasonable design of components, the composite thermal resistance type asphalt pavement structure can effectively reduce the pavement temperature, improve the use comfort of a road and prolong the service life of the pavement, has good ageing resistance and high temperature resistance and is suitable for construction of various roads.

The invention discloses an expressway pavement temperature inversion method and system fusing weather and traffic information, and the method comprises the steps: obtaining the weather information and traffic information of a target expressway section, carrying out the time synchronization, space matching and preprocessing of multi-source data, and constructing an inversion input data set; feature extraction is carried out on the meteorological information, the road category information and the traffic time sequence information based on a multi-input branch neural network, comprehensive processing is carried out on a fusion layer, and a road surface temperature inversion model is established; introducing a physical loss function embedded with an energy balance constraint in a model training process to enhance the physical consistency of an inversion result; and performing inversion calculation on the road surface temperature of the target road section at the corresponding moment by using the trained model. The method can comprehensively consider the difference between the meteorological condition and the road operation environment, improves the accuracy and stability of road surface temperature inversion, and is suitable for highway road surface temperature monitoring and traffic safety guarantee.

The present invention provides an asphalt mixture that, when used as the surface layer of asphalt pavement, can result in asphalt pavement that suppresses temperature rise due to solar radiation. [Solution] An asphalt mixture containing an asphalt binder and an additive, wherein the additive contains bamboo charcoal powder, and the asphalt mixture contains 0.5 to 4.0 parts by mass of bamboo charcoal powder per 100 parts by mass of the asphalt mixture, and an asphalt pavement for suppressing road surface temperature rise containing the asphalt mixture as the surface layer.

The invention discloses a double-model collaborative complex terrain road icing risk early warning method, which comprises the following steps of: acquiring and fusing multi-source meteorological monitoring data and forecast data in real time, the data comprises live and forecast data of environment temperature, road surface temperature, relative humidity and precipitation from lattice point data and traffic weather stations and regional weather stations, and meanwhile, acquiring risk zoning levels, topographic data and traffic flow data as supplementary input; and when the forecast air temperature meets the trigger condition of less than or equal to 1 DEG C, starting a weighted analytic hierarchy process (WAHP) model, and calculating a road icing risk index 12 hours by 12 hours in the future 72 hours by integrating standardized parameters of six factors including precipitation forecast, air temperature forecast, risk zoning level, traffic flow, terrain and driving time period. And blue, yellow, orange and red four-level short-term risk early warning products are generated.