916 results about "Traffic engineering" patented technology

The invention relates to the technical field of intelligent traffic engineering, and provides a road guardrail intelligent inspection and maintenance planning method based on machine learning, which breaks through the limitation of a traditional single mode in a detection link, fuses laser radar three-dimensional geometrical characteristics and visual texture information, realizes high-precision identification of defects such as microcracks and hidden corrosion, and improves the inspection efficiency. The detection problem in a complex environment is solved, and the problem that a traditional method is high in omission ratio is solved. In the aspect of dynamic planning, a multi-dimensional state space is constructed based on real-time traffic, weather and defect levels, polling path dynamic optimization and multi-vehicle collaborative operation are achieved through a reinforcement learning algorithm, and the current situations that manual planning is low in efficiency and poor in flexibility are changed. In the aspect of maintenance decision, a time sequence data analysis model is used for accurately predicting guardrail maintenance requirements, a scientific maintenance plan is made in combination with an intelligent scheduling algorithm, and efficient utilization of resources is achieved. According to the method, a complete intelligent operation and maintenance closed loop is constructed, and road guardrail operation and maintenance are promoted to be converted from passive low efficiency to active intelligence.

The invention discloses a BIM-based traffic engineering construction progress and resource management optimization system, and the system comprises a three-dimensional cognitive interaction body which adjusts the construction progress and resource configuration in real time, and eliminates the physical difference between a virtual model and an actual environment; the self-adaptive calculation engine is used for realizing butt joint between the virtual model and an actual construction site by utilizing real-time sensing data and a cognitive correction mechanism; the progress prediction simulation module is used for realizing dynamic prediction and optimization of the construction progress through deep learning and a self-adaptive feedback mechanism; the virtual-reality synchronous perception grid is constructed based on the obtained progress dynamic prediction and optimization result, and a virtual model and a construction site are synchronized and dynamically corrected; and the self-optimization module is used for realizing self-adaptive optimization and long-period evolution through a genetic algorithm and multi-objective optimization by utilizing a self-optimization and ecological optimization mechanism. According to the method, the problem of virtual reality distortion is solved, and the difference between virtuality and reality in the construction process is accurately corrected.

The invention relates to the field of carbon emission measurement and calculation, and particularly discloses a railway traffic engineering carbon emission evaluation system and method based on a full life cycle. Carbon emission accounting with day as a unit is carried out on field construction data by using an artificial intelligence technology based on deep learning, and then multi-time-domain-scale-based time sequence analysis is carried out on carbon emission data generated by engineering construction, so that the regularity and tendency characteristics of carbon emission change are mined; therefore, the total carbon emission of the whole life cycle of the railway traffic engineering is intelligently estimated on the basis, and whether a carbon emission standard exceeding early warning prompt is generated or not is determined by combining the expected total carbon emission, so that real-time monitoring and intelligent early warning of the carbon emission of the railway traffic engineering can be realized, measures can be taken in advance, engineering design is optimized, and the economic benefit is improved. Carbon emission is reduced, and green and low-carbon railway traffic engineering is realized.

The invention provides an urban construction and traffic engineering investigation and design enterprise product quality management system and method. The system comprises a cross-project type quality data fusion module, a dynamic defect evolution prediction module, an intelligent layered spot check engine, a three-dimensional quality situation deduction module, a conference knowledge graph construction module, a result influence evaluation module and a self-adaptive file management system. All the modules are connected through a unified data bus to form a closed-loop management process of quality data acquisition, analysis, decision and feedback. According to the system, through the functions of multi-modal data fusion, defect prediction, intelligent spot check, situation deduction, knowledge precipitation, achievement evaluation, file management and the like, the whole-process intelligent management and control of the project quality are realized, and the management efficiency and the decision scientificity are improved.

Described herein are devices, systems, methods, and processes for optimizing network traffic distribution across multiple paths in a manner that is energy-efficient and environmental sustainability-aware. This may be achieved by leveraging time-series analytics and capacity planning based on seasonalities. Data associated with the Layer 3 topology of the network can be collected. Bandwidth can be pre-reserved on an energy-aware traffic engineering tunnel. The time-series data can be used to build a capacity plan based on the seasonalities. Nodes may be clustered based on usage patterns and network utilization seasonality. The data can be used to make decisions about when and where to combine or shut down paths for energy efficiency, while maintaining optimal network performance. A hysteresis mechanism may be incorporated to avoid oscillation when changing active links. Power savings can be achieved by fully turning off or depowering certain network components when they are not needed.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention provides an SDN (Software Defined Network) inter-domain traffic engineering method based on reinforcement learning, which comprises the following steps of: deploying a data traffic demand monitoring platform and a control system, and constructing a global network topological graph; calculating a short link identifier for the link in the network and distributing the short link identifier to each network device; flow judgment is carried out, upward notification is carried out according to requirements, and pre-operation of intelligent routing is cooperatively completed; deploying a reinforcement learning model in the total intelligent body, outputting an optimal cross-domain path strategy to the cooperative controller, disassembling the optimal cross-domain path strategy into flow table rules which can be executed by each domain, and issuing the flow table rules to local controllers of related domains; and each local controller pushes the flow table configuration to the domain switching equipment to complete the forwarding decision of the flow. According to the method, a complete closed-loop process of flow measurement, intelligent decision making, cross-domain control and path issuing is realized, feasible reference is provided for actual deployment of an intelligent network, and the method has good engineering popularization value and is suitable for intelligent scheduling scenes such as an operator backbone network, an industrial internet and metro edge cloud.

The invention discloses a slope stability analysis method based on rock and soil characteristics, belongs to the technical field of traffic engineering modeling, and aims to solve the technical problems as follows: exploring a slope instability mechanism; analyzing the reliability of the slope sliding surface system; the influence of the hydraulic effect on the slope stability is avoided. The method specifically comprises the steps that the geological structure, the rock-soil body type and the distribution characteristics of the side slope are surveyed in detail; performing indoor and outdoor tests to obtain mechanical parameters of the rock-soil body; the influence of underground water level change, rainfall infiltration and underground water flow on the slope stability is investigated, then main factor parameters influencing the slope stability are determined, a slope two-dimensional finite element model is established on the basis, investigation parameters are input, and subsequent feedback and improvement are carried out. Therefore, the performance of the slope stability under the influence of complex geological conditions and external factors is explored.

The invention discloses a traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention relates to the technical field of traffic engineering, and discloses an urban air traffic vertical take-off and landing field site selection optimization method based on demand maximization, and the method comprises the following steps: S1, obtaining and preprocessing online car-hailing original data; s2, dividing grids for the research area range, and taking the grid center as an alternative address of a vertical take-off and landing field; s3, screening out online car-hailing travels which are impossible to turn to urban air traffic; s4, constructing a vertical take-off and landing field site selection model based on potential demand coverage maximization; s5, outputting the selected vertical take-off and landing field address and the corresponding urban air traffic travel demand; and S6, outputting the current optimal site selection and demand as a final solution of the model. According to the method, under the condition of the specific number of vertical takeoff and landing fields, the vertical takeoff and landing field addresses which enable potential requirements of urban air traffic to achieve maximum coverage are selected, an effective solution is provided for development of the urban air traffic, and construction of a more efficient, convenient and environment-friendly urban traffic system is facilitated.

The invention discloses an urban rail transit station site selection multi-objective optimization method and a related device, and belongs to the crossing field of urban planning and traffic engineering, and the method comprises the following steps: according to preprocessed urban road network data, screening crossing points of urban three-level roads and roads above the three-level roads as candidate stations; the coordinates of the candidate sites are coded; based on the preprocessed crowd travel origin and destination data, POI data, building data and land utilization data, quantifying the service people flow, facility accessibility and land development intensity of each candidate site, and forming a feature matrix of each candidate site; and constructing a multi-target traffic station site selection model, screening candidate stations meeting distance constraints through a greedy search algorithm according to the feature matrix, inputting the candidate stations to NSGA-II, and solving a multi-target optimization function of the multi-target traffic station site selection model to obtain an optimal planning result. The method can solve the problems that in the prior art, the target is single during site selection, and the inter-site distance constraint is not considered.

The invention provides a key flow routing optimization method based on deep reinforcement learning, and belongs to the field of flow engineering. Under the constraint of routing overhead, a part of the total flow is selected as a key flow and rerouting is carried out, so that the purposes of minimizing the maximum link utilization rate and reducing the time delay are achieved. And splicing the traffic matrix, the topological matrix and the feature matrix as input, and carrying out feature extraction with higher dimension through a graph neural network containing a K-hop peripheral sub-graph. For the discrete action space domain and maximum entropy problem, the optimal selection of the key flow is obtained by using a discrete soft behavior person-reviewer, and finally, the optimal routing scheme is obtained by solving through linear programming constraint. Simulation results show that compared with a traditional algorithm and other reinforcement learning algorithms, the provided algorithm has better time delay performance and ability to adapt to dynamic network topology.

The invention relates to a traffic engineering BIM (Building Information Modeling) and VR (Virtual Reality) technology combined interactive design and simulation system, which relates to the field of traffic engineering and comprises an interactive configuration module used for selecting simulation configuration to obtain user feedback information; the route mining module is used for performing networking frequent mining to obtain a mobile route case set; the virtual driving module is used for starting simulated driving and obtaining a three-dimensional dynamic time sequence diagram of a vehicle and heart rate monitoring time sequence information of a user; the safety scoring module is used for receiving a user driving safety score through the user side; the risk evaluation module is used for obtaining a driving risk evaluation value set; and the safety identification module is used for performing safety identification and then sending the information to the traffic engineering design end. Through combination of BIM and VR technologies, virtual simulation acquisition and analysis of user driving behaviors are realized, so that the driving risk of a design scheme is evaluated in a targeted manner, traffic engineering optimization design is guided, and the safety of traffic engineering design is improved.

The invention discloses a high-speed rail seamless track displacement real-time detection system based on Beidou navigation, and relates to the technical field of traffic engineering, a target locking module is used for receiving Beidou observation data by using a Beidou GNSS receiver, locking GNSS lock losing signals at various monitoring moments based on the Beidou observation data, and transmitting the locked GNSS lock losing signals to the target locking module; the GNSS lock losing signal comprises a GNSS interruption starting point, a signal lock losing time period and a corresponding track space position; the interruption compensation module is used for initializing a mapping vector according to a track state before a signal lock losing time period to form a track estimation sequence, and judging whether IMU drift tends to systematic deviation or not after modal residual verification; the reconstruction module is used for performing error compensation on track position points at all moments in a signal lock losing time period if the deviation tends to systematic deviation; and the identification module is used for carrying out feature extraction in a signal lock losing time period based on the reconstruction module, and delimiting a dominant source of the current displacement in a form of a spatial domain angle.

The invention relates to a train anti-snaking shock absorber installation loosening identification method, in particular to the field of railway traffic engineering, a camera is installed to obtain train anti-snaking shock absorber image data, and the overall and contrast of the train anti-snaking shock absorber image data are changed by enhancing the brightness difference, scaling the brightness range and stretching the gray level, so that train anti-snaking shock absorber installation loosening identification is achieved. The method comprises the following steps: positioning an object boundary through a gradient violent change region of a pixel gray value in train anti-snaking damper image data, carrying out edge detection, corner detection and spot detection, carrying out classification on each candidate frame through a random forest algorithm, judging detection features, and carrying out feature tracking; a Z-score algorithm is utilized to realize conversion of train anti-snaking damper image data and judgment condition data into standard normal distribution with a zero mean value and a unit standard deviation, the standard normal distribution is selectively stored in a cloud and a local database, and an alarm is sent to a train management worker according to the degree of loosening and abnormal conditions.

The invention discloses a ballastless track concrete bonding interface mechanical state testing method, belongs to the technical field of track traffic engineering, and realizes a multi-field coupling loading test on a ballastless track interlayer test piece by constructing an interlayer mechanical testing device comprising a servo loading mechanism, a DIC technology and finite element mechanical analysis. A high-speed camera is adopted to synchronously record the whole process of interlayer interface damage, an interface stress distribution cloud picture is obtained in combination with a distributed strain patch, and a load-displacement curve and interlayer slippage data are synchronously collected. A refined finite element model containing a cohesion unit is established based on measured data, and iterative fitting of a simulation result and test data is achieved by dynamically adjusting interface damage parameters. According to the method, the limitation of a traditional single-point stress test is broken through, space-time multi-scale analysis of interlayer mechanical behaviors is achieved, the technical problems that an interlayer interface failure mechanism is not clear and simulation model parameters are difficult to calibrate are solved, and a novel test is provided for durability evaluation of a ballastless track structure.

An edge device is for a distributed traffic engineering system with quality of service control of a plurality of flow groups routed over a set of overlay links. The edge device is configured to receive one or more service-level agreement requirements for the plurality of flow groups from a network controller, receive first control information from one or more other edge devices in the distributed traffic engineering system, obtain monitoring information for the plurality of flow groups and the set of overlay links, and adjust a plurality of queuing parameters for the plurality of flow groups based on at least one of the first control information, the monitoring information, or the one or more SLA requirements.

The invention belongs to the crossing field of traffic engineering and information technology, and discloses a highway continuous longitudinal slope section traffic safety knowledge graph construction method. According to the method, road section features, traffic flow, historical accidents and environmental data are integrated, and a multi-source data set is constructed through cleaning and standardization processing; defining a road section, an accident, an environment and a vehicle entity, and constructing a multi-level knowledge graph comprising a static topology layer, a dynamic attribute layer and a probabilistic reasoning layer; knowledge triples are extracted by using a natural language processing technology, and real-time risk assessment and early warning are realized by dynamically fusing association logic of gradient, environment and accident data in combination with a rule engine; a graph database is adopted to store the graph, and semantic query and dynamic updating are supported. The method breaks through the limitation of traditional single data source analysis, solves the problem of coupling risk assessment of dynamic environmental factors and static features under complex terrains through multi-source fusion and probabilistic reasoning mechanisms, and remarkably improves the traffic safety risk prediction precision.

The invention specifically relates to a tunnel group road section traffic capacity assessment method based on intelligent network connection, which relates to the technical field of traffic engineering, and comprises the steps of constructing an AI-driven dynamic assessment model based on fusion data and a digital twinborn model in combination with tunnel engineering constraints, and calculating the traffic capacity, congestion risk and bottleneck position of each road section of a tunnel group in real time. In the invention, a multi-source data acquisition system is combined with an edge and cloud two-stage fusion architecture to construct an LSTM-XGBoost double-model architecture; the LSTM model can calculate and predict the traffic capacity of four time nodes in the future 10 minutes in real time, the XGBoost model accurately locates the bottleneck position and quantifies the contribution degree of four types of causes, meanwhile, the precision is ensured through multi-dimensional verification, the tunnel group whole domain can be covered, instantaneous traffic changes can be captured, and a panoramic decision basis of real-time data, prediction trend and bottleneck causes is provided for traffic management.

The invention relates to the technical field of data processing, and discloses a data processing method and system for traffic engineering construction and a storage medium. The method comprises the steps that according to multi-source data of a construction site, construction equipment and environment data are processed through self-adaptive filtering, and a quality score table and a preprocessing data set are obtained; carrying out segmented noise reduction and time-frequency analysis based on the data to obtain engineering indexes including equipment vibration, structural response and other characteristics; generating a quality parameter set and a process feature chain through feature recognition; optimizing model parameters by using a case library to obtain an evaluation standard; fusing the model and the parameter set to analyze the construction process to obtain quality features; and finally, optimizing resource configuration to form a decision scheme. The technical problems of insufficient data processing, inaccurate quality evaluation and unreasonable resource scheduling in a traditional construction quality control method are solved.

The invention discloses a data processing method and system for intelligent road detection and related equipment, and relates to the technical field of traffic engineering and artificial intelligence crossing. In the road detection process, multi-sensor fusion equipment with dynamic adjustment is adopted to collect multi-source data; the real-time environment perception model based on deep reinforcement learning dynamically optimizes the parameters of the multi-modal sensor, and improves the data acquisition quality in severe weather and environment. Millimeter-level alignment of multi-source data is realized by adopting a spatio-temporal model, and a generative adversarial network is introduced to enhance the multi-source data after spatio-temporal alignment so as to supplement missing data details and improve data quality. Disease intelligent identification is carried out on multi-source data through a sample learning model and a meta-learning algorithm, the sample learning model solves the problem of insufficient labeling samples of micro-disease data, the meta-learning algorithm is quickly generalized on the basis of a small amount of labeling data, micro-diseases such as early cracks and hidden road structure layer defects are identified, and the accuracy of identification is improved. And the accuracy of road disease identification is improved.

The invention provides a quasi-zero-stiffness base and an adjustable quasi-zero-stiffness local resonance super-structure vibration isolation device, belongs to the field of environmental vibration reduction and isolation in traffic engineering and civil engineering, and aims at solving the problems that in the prior art, a spring quasi-zero-stiffness structure is complex in installation structure and poor in structural stability. The device comprises a bottom plate, a plurality of first beams with a positive stiffness effect, a plurality of second beams with a negative stiffness effect and a plurality of regular triangular prisms, the first beam, the second beam, the regular triangular prism and the bottom plate are integrally formed, on one hand, it is ensured that all connecting pieces are in rigid connection, the situation that connecting points affect the stability of the structure is avoided, on the other hand, the assembling process can be simpler, and the assembling efficiency is improved.

Systems and methods for computing paths through a network are provided. A method, according to one implementation, includes the step of storing and managing a first set of objects including one or more Segment Routing (SR) policies and Traffic Engineering (TE) tunnels, where the first set of objects represents custom behavior received from a network operator for defining intended characteristics of traffic flowing through a network. The method also includes the step of storing and managing a second set of objects representing one or more managed paths through the network, where the managed paths are calculated by a TE technique using the one or more SR policies and TE tunnels.

The invention relates to the field of traffic engineering, and discloses an urban road network toughness management platform and evaluation method based on dynamic cascade failure, and the platform comprises a disturbance scene simulation and random capacity degradation module, a dynamic cascade failure process simulation module, and a multi-dimensional toughness index calculation and uncertainty evaluation module. A toughness three-dimensional vector is formed by integrating system robustness, restorability and performance accumulated loss, and a time-varying dynamic traffic distribution model considering road network structure change is used for depicting a cascade failure dynamic process; a random variable is introduced into a damaged road section capacity index, and a new evaluation method is provided for solving the defect that the existing toughness evaluation method is difficult to comprehensively quantify the robustness and the restorability of a road network. A failure diffusion mechanism in an abnormal scene is truly restored, and an effective method is provided for overcoming the double defects that a traditional model is insufficient in quantification of travel behavior dynamics and a system recovery process.

The utility model relates to the field of railway traffic engineering, and discloses a contact net pillar convenient to overhaul based on a high platform, which comprises a stand column, a stand column base is arranged at the lower end of the stand column, and a cross beam is fixedly connected to the upper end of the side face of the stand column and used for installing a high-voltage wire. A protective wall is arranged on the side face of the stand column base, a handrail is fixedly connected to the upper side face of the protective wall, a riffled plate is arranged at the upper end of the inner wall of the protective wall, and the stand column protective device is arranged between the protective wall and the stand column base. The supporting column can be effectively protected from being affected by the external environment, durability is enhanced, and investment waste is reduced.

A system manages network traffic in a distributed system comprising a plurality of network devices. The network devices are divided into a plurality of network slices, each of the network slices including a subset of the network devices such that there is no overlap of network devices between the network slices. Individual network slices are associated with individual slice controllers, and an individual slice controller is configured to manage network routing of an individual network slice. Each of the individual slice controllers route the network traffic within each respective individual network slice. The network traffic is independently routed based on expected network conditions for each respective individual network slice, and data defining routing decisions is contained within each network slice to limit fault effects between the network slices.

The invention discloses an optimization system and method for SDN traffic engineering, and the method specifically comprises the following steps: S1, obtaining a traffic matrix in a network, and representing the network topology as a graph structure; s2, inputting the traffic matrix and the graph structure into a constructed feature extraction model, and outputting a feature vector; s3, inputting the feature vector into a deep reinforcement learning model for iterative optimization, and outputting an initial flow distribution proportion strategy; and S4, performing constraint optimization on the initial flow distribution proportion strategy, and outputting a final flow distribution strategy.

The invention provides a traffic engineering geological disaster monitoring and early warning system, and the system comprises a data collection module which is used for collecting environment data related to a traffic engineering region in real time; the data transmission module is used for transmitting the acquired environment data to the data processing and analyzing module; the data processing module is used for carrying out cleaning, normalization, feature selection and preprocessing on the input environment data; the disaster prediction module is used for constructing a disaster prediction model based on a support vector machine algorithm, performing disaster prediction on the environment data input in real time through the disaster prediction model, and outputting a prediction result; the early warning decision module is used for carrying out risk assessment according to an output result of the disaster prediction model and triggering early warning; and the alarm execution module is used for executing corresponding emergency response operation according to the early warning signal and notifying related departments and personnel to deal with disasters and deal with the disasters. According to the invention, accurate disaster monitoring and early warning can be provided under different environments and conditions, and disaster response efficiency and public safety guarantee are improved.

The invention relates to the technical field of image communication, in particular to a traffic engineering construction site video monitoring method and system, and the method comprises the steps: obtaining a monitoring frame image of each monitor at each moment in a traffic construction site, screening out the comparison monitor of each monitor at each moment, determining the risk coefficient of each macro block in the monitoring frame image, and determining the risk coefficient of each macro block in the monitoring frame image; a final comprehensive risk coefficient is determined, a plurality of key frame images of each monitor are screened out in combination with pixel point gray value differences between monitoring frame images of the same monitor at adjacent moments, each macro block in the key frame images is equally divided into a plurality of updated macro blocks, and the updated macro blocks are updated. And the compression coding module is used for compressing and coding all the monitored key frame images and transmitting all the monitored compressed data to the database. According to the invention, by introducing a risk assessment mechanism, the risk coefficients of different areas in the construction site monitoring picture are quantified, so that the compression effect of the video is monitored, and the bandwidth consumption is reduced.

The invention provides a linear traffic engineering risk zone rapid assessment method, and belongs to the field of geological disaster engineering.The method comprises the steps that data and data of a research area are collected, information amount values of disaster-inducing factors are calculated through an improved information amount model, the disaster-inducing factors with the highest correlation and the lowest contribution amount are eliminated, and the risk zone of the research area is evaluated; the method comprises the following steps: performing preliminary sample data training by adopting an HSVR model, generating a non-landslide point sample added with displacement information, recalculating an information amount value of the non-landslide point sample, inputting data into an SWO and HSVR coupled model for learning and training to obtain a weight coefficient of each disaster-inducing factor, correcting the weight coefficient, and finally realizing data visualization in a GIS (Geographic Information System). According to the method, an improved information amount model, an improved SHAP interpretable model and an SWO-HSVR model are adopted, a high-accuracy geological disaster regional prediction model and linear traffic engineering risk zone rapid evaluation are realized, the influence of geological disasters on linear traffic engineering is reduced, and engineering safety and sustainable development are guaranteed.