152 results about "Transims" patented technology

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention relates to the technical field of agricultural equipment, in particular to a hybrid tractor whole machine dynamic property evaluation method fusing multiple working condition parameters, which comprises the following steps: acquiring an environment working condition parameter set, an operation task parameter set and an energy flow dynamic parameter set in real time; a dynamic coupling relation mathematical model is constructed based on the parameter set, the whole machine dynamic property characteristic values of the steady state working point and the transient working point are calculated respectively, and a whole machine dynamic property evaluation index set is generated; inputting the evaluation index set into a hardware-in-the-loop simulation platform, and verifying the effectiveness of the evaluation indexes through multi-modal working condition simulation; and constructing a three-dimensional dynamic property evaluation map based on the verified evaluation indexes, dividing dynamic property partitions, outputting the dynamic property partitions to the vehicle-mounted intelligent terminal in real time, and generating a self-adaptive control instruction set when entering a failure area. According to the invention, multi-source information fusion modeling, all-working-condition dynamic response evaluation and visual map feedback can be realized, and the dynamic adaptability and regulation and control intelligence of the hybrid tractor in a complex operation scene are improved.

The invention relates to the technical field of automobile material temperature simulation analysis, in particular to an automobile cabin temperature field simulation analysis method and system based on multi-physics field coupling. According to the method, high-precision and high-efficiency simulation is realized through thermal-solid-fluid-electric coupling modeling in combination with the genetic algorithm, the neural network and reinforcement learning AI dynamic optimization, and the problems that the multi-physical field coupling effect cannot be comprehensively reflected and the parameter optimization efficiency is low in the prior art are solved. Time sequence analysis and dynamic boundary condition adjustment are introduced, transient working condition simulation is supported, and temperature field changes in actual driving can be better predicted. Through simultaneous solution of the Maxwell equation and the heat conduction equation, the temperature rise risk of the electronic component is accurately predicted, and the method is suitable for heat management optimization of key components such as a battery pack and a motor controller of a new energy automobile. Cloud high-performance calculation and edge end lightweight model cooperation are adopted, efficient simulation and real-time early warning are achieved, and rapid analysis and design optimization of extreme working conditions are supported.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention relates to a self-adaptive longitudinal vehicle speed estimation method, which comprises the following steps of: establishing detailed vehicle dynamic models, including a vehicle longitudinal dynamic model and a wheel speed dynamic model; a Sage-Husa self-adaptive unscented Kalman filtering algorithm is combined with a vehicle dynamics model, a state equation and an observation equation are designed, the longitudinal vehicle speed of a vehicle serves as a state variable, and observation vectors are constructed through data collected by a wheel speed sensor, an acceleration sensor and the like; divergence calculation is introduced for detecting and correcting divergence phenomena in the filtering process; and on the basis of a self-adaptive mechanism of a Sage-Husa algorithm, the filtering gain is adjusted in real time, and the vehicle speed estimation process is optimized. The improved Sage-Husa adaptive filter can dynamically adjust the filter parameters according to the change of the system state and the difference of noise, detects transient disturbance in combination with divergence calculation, reduces the influence of the transient disturbance on longitudinal vehicle speed estimation, and effectively improves the estimation precision.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that improve efficiency and flexibility of implementing computer devices by providing efficient user interfaces to provider devices that include optimal digital routes selected based on dynamic route efficiency metrics. In particular, the disclosed systems can identify and surface an optimal alternative route from a pickup location to a drop-off location associated with a transportation match based on a variety of dynamic non-temporal factors. The disclosed systems can utilize a variety of computer implemented models to determine non-temporal factors, such as distance efficiency metrics, route-segment access efficiency metrics, congestion efficiency metrics, risk efficiency metrics, and match-based efficiency metrics. The disclosed systems can then combine these non-temporal factors utilizing a common efficiency framework to determine and surface an optimal alternative route.

The invention belongs to the field of distributed system control, and particularly relates to a vehicle system distributed control method, system and device based on reinforcement learning. Cooperative and competitive dynamic characteristics of a multi-vehicle system are described through a second-order nonlinear dynamic model in combination with a signed directed graph, unknown nonlinear dynamic and external disturbance existing in the vehicle system are considered, and the whole system is modeled as a second-order strict feedback nonlinear system. In order to get rid of dependence of convergence time on initial conditions and optimize transient performance, a preset time performance function is introduced, and a three-part neural network architecture is constructed; and designing a control law cooperating with a preset time parameter, and ensuring the actual preset time boundaries of the multi-vehicle system under the unknown dynamic and external disturbance influence. According to the method, the convergence time can be accurately set, the calculation complexity of a control algorithm is remarkably reduced, and the transient performance and robustness of a vehicle system are improved.

The invention relates to the technical field of communication network maintenance, and discloses a communication network operation quality monitoring and closed-loop operation and maintenance scheduling management system, which comprises an acquisition unit for acquiring network element telemetry data; the observation module extracts a flow fingerprint, converts a link into a transfer function model, and generates a consistency gradient vector according to singular point offset; the echo prediction module converts the scheduling instruction into an expected gradient offset vector; the module calculates the scheduling residual error of the actually measured offset and the expected offset; the decision-making unit generates a path reconstruction instruction, and the scheduling residual error is utilized to check the execution efficiency and update the operation reference; according to the invention, the flow conduction consistency constraint is anchored, the link degradation characteristic and random fluctuation decoupling is realized, and the transient interference generated by the scheduling action is eliminated.

Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and / or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and / or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.

A system for controlling lateral positioning in a host vehicle includes a controller having a processor and tangible, non-transitory memory. The host vehicle is located on a host lane defined by a host lane center. The controller is adapted to determine if a first and second enabling condition are met. The first enabling condition is met when the host vehicle is travelling on a non-divided road having no physical barrier separating respective traffic lanes travelling in opposing directions. The second enabling condition is met when the host lane is an inner most lane adjacent to an opposing traffic lane. When the first and second enabling conditions are both met, the controller is adapted to determine a non-divided road offset relative to the host lane center in real-time. Operation of the vehicle is controlled based in part on a magnitude, and a direction of the non-divided road offset.

The invention relates to the technical field of immersed roadbed simulation, in particular to an immersed roadbed simulation test system and method for repeated seepage based on load action, and the system comprises a dynamic water pressure gradient generation subsystem, a stress seepage coupling response cavity subsystem and a seepage stability loss early warning network subsystem. The method comprises the following steps: forming a time-space alternating water pressure gradient field on a permeable medium substrate by a wave front of programmable pressure waves; introducing the water pressure gradient field into a three-way independent loading frame; asymmetrical cyclic stress waves are applied to the roadbed sample through the piezoelectric lattice array, and meanwhile the transient hydraulic impedance spectrum of pores in the sample is captured in real time; inputting the local strain energy density distribution and the pore dynamic water pressure pulsation intensity into a chaotic feature extractor, and outputting a structure degradation energy entropy value; and when the structure degradation energy entropy value breaks through the phase change threshold value, three-stage early warning is triggered. Controllable experimental conditions and quantitative evaluation means are provided for studying the progressive failure mechanism of the immersed roadbed under the action of repeated seepage-load coupling.

The invention relates to the technical field of e-commerce, in particular to an e-commerce intelligent prediction management method and system based on big data, and the method comprises the following steps: obtaining a real-time message in a buffer area, generating a throughput rate sequence based on a sliding window, carrying out the difference and mapping of throughput rates of adjacent periods, and constructing a gradient slope sequence; and extracting a transient abrupt change ratio by means of a moving average model to generate a pre-trigger level identifier, extracting a set boundary extreme value, carrying out quantitative evaluation to generate discrete parameters, correcting a transfer matrix, and executing steady-state iterative solution to obtain prediction confidence to construct an elastic control instruction. According to the method, the sudden change trend of the business time domain dimension is accurately captured and static reference deviation interference is eliminated, so that deep analysis and real-time correction of the dynamic transfer characteristics of the core transaction link are realized, and the dilemma of research and judgment distortion and scheduling lag existing in a traditional rule when facing burst traffic is thoroughly avoided; and the full-link prediction precision and the elastic resource management and control efficiency in a complex transaction scene are remarkably improved.

The invention is suitable for the technical field of automobiles, and provides an automobile passenger compartment modeling method based on neural network model order reduction, and the method comprises the steps: building a three-dimensional passenger compartment and dummy model, combining with a human body thermal comfort evaluation index to obtain data, carrying out the steady-state order reduction through a neural network, and verifying the precision through a decision coefficient and an error index. And carrying out transient order reduction by adopting a long-short-term memory network optimized by a genetic algorithm to finally obtain a one-dimensional model. According to the method, the problems that traditional three-dimensional modeling is time-consuming in simulation and the one-dimensional modeling precision is insufficient are solved, the temperature and wind speed determination coefficient of each position of the established model reaches about 0.94, the model can be coupled with an air conditioning system, the simulation operation time is shortened, and support is provided for design of an automobile thermal management control strategy. The method comprises the steps of model establishment, steady-state and transient order reduction, working condition analysis, model verification and the like, and has the advantages of precision and efficiency.

The invention relates to the technical field of flow monitoring, in particular to a data center switch abnormal flow detection method and device, equipment and a medium, and the method comprises the following steps: collecting port flow, calculating a jump ratio, identifying an abnormal path, analyzing a multi-feature behavior, tracking the attribution of flow direction oscillation aggregation equipment, and outputting a detection result. According to the method, the port abrupt change phenomenon is identified through linkage of the jump ratio and the dynamic threshold value, the discrimination logic for the network jitter initial offset is established by combining the path continuous node response delay difference, and the path-level composite feature anomaly judgment is established by fusing the multi-dimensional index intersection behavior. Potential path oscillation is identified according to flow direction alternation in a period and traced to equipment granularity to realize chain affiliation aggregation, so that the granularity and accuracy of abnormal flow identification are enhanced, closed-loop abnormality diagnosis from transient abnormality to a path level to equipment affiliation is realized, the perception capability in a complex link disturbance and multi-point abnormality cooperation scene is improved, and the reliability of abnormal flow identification is improved. And the abnormal flow detection definition of the data center switch is improved.

Along with the rapid development of urban rail transit, a station safety monitoring system puts forward higher requirements for the real-time performance and accuracy of passenger falling behavior recognition. The invention provides a station passenger falling behavior identification method fusing human-material master-slave binary information. Accurately extracting key skeleton points of a human trunk based on a Yov8-Pose model of station scene fine adjustment, and calculating a vertical direction variable quantity and a body posture inclination angle; key skeleton points of a carry-on object of a passenger are identified through target detection and an object attitude estimation network, and the vertical direction and topological structure variation of the key skeleton points are calculated; the method comprises the following steps of: mapping transient characteristics of a human body and an object into a binary quantum state by utilizing a quantum entanglement theory, calculating von Neumann entropy to quantify dynamic coupling strength between the human body and the object, and determining an entropy threshold value through a Bayesian decision boundary so as to accurately judge a tumble behavior. According to the invention, the accuracy and real-time performance of station passenger falling behavior identification can be obviously improved, and the emergency response efficiency is improved.

The present application relates to the technical field of bridge health monitoring, in particular to a bridge health monitoring system acceleration sensor online calibration method and system. The method comprises the following steps: extracting dynamic axle load time history characteristics and environmental interference correction factors according to traffic load data and environmental monitoring data; processing standard sensor data and to-be-calibrated sensor data to obtain real vibration reference characteristics and initial deviation characteristics; using the traffic load data and the real vibration reference characteristics to obtain working condition weight characteristics; combining the dynamic axle load time history characteristics, the environmental interference correction factors, the real vibration reference characteristics, the initial deviation characteristics and the working condition weight characteristics to obtain adaptive optimization calibration values. The present application can realize calibration without disassembling sensors and blocking traffic under normal operation of the bridge, evaluate transient errors through multi-data coupling, adaptively optimize calibration values, and ensure monitoring accuracy while reducing operation and maintenance costs.

The invention discloses a petroleum transportation safety supervision method and device based on artificial intelligence, and relates to the technical field of petroleum transportation, and the method comprises the following steps: S1, carrying out the quantification of a transportation chain safety factor and the construction of an intrinsic risk index; s2, obtaining a dynamic dangerous field index; s3, constructing a safety control priority index; s4, constructing a risk coupling propagation model; s5, the response intensity is quantified, and a multi-strategy response coefficient is output; s6, automatically deducing a final intervention instruction; and the security supervision device is constructed based on the steps. According to the invention, the system can identify essential risks, transient risks and cross-node linkage risks in a transportation chain in real time, can automatically screen proper regulation and control strategies according to risk propagation intensity, and finally generates an intervention instruction which can be directly executed. The upgrading of safety supervision from point-level monitoring to networked, predictive and active safety control is realized, and the overall safety level and operation reliability of a petroleum transportation system are greatly improved.

The invention relates to a multiphase flow interface flow transient parameter analysis method and system and a medium, and relates to the technical field of multiphase flow interface flow analysis. Wherein the transient parameters comprise instantaneous pressure and instantaneous speed, and the method comprises the following steps: obtaining a target area of multiphase flow interface flow transient parameter analysis, and carrying out grid division on the target area to obtain each grid unit; and for any grid unit, the initial pressure, the initial flow velocity, the unit area or volume, the surface tension and the physical strength of the fluid in the unit are obtained, and the instantaneous pressure and the instantaneous velocity are solved based on the constructed double-time-step multiphase lattice Boltzmann flux solver mesoscopic equation. Wherein in a pressure evolution equation in the constructed mesoscopic equation, a physical time item is abandoned, and only a virtual time item of the pressure is considered. Compared with the prior art, the compressibility error of the interface flow velocity is lower, so that the precision of a transient parameter analysis result can be improved.

The application belongs to the technical field of vehicle control, and provides a multi-vehicle longitudinal formation robust control method and system based on knowledge transfer, which comprises the following steps: constructing a generalized nonlinear multi-vehicle longitudinal system model of heterogeneous vehicles; mapping an original constrained tracking error to an unconstrained tracking error based on a dynamic design of a segmented performance function of the original constrained tracking error; constructing a coupled sliding mode surface based on the unconstrained tracking error, designing a multi-vehicle longitudinal formation robust controller and an uncertain parameter adaptive law; collecting input data and output data of a first following vehicle under the multi-vehicle longitudinal formation robust controller, and introducing historical brake measurements as additional input data; training a long short-term memory network and performing iterative optimization; and transferring the knowledge in the trained long short-term memory network to other following vehicle control systems. The application solves the problems of low transient response rate, high design complexity, and a large number of parameter configurations in the traditional method.

Method, system, device, and non-transitory computer-readable medium for classifying a vehicle trip. In one aspect, a computer-implemented method includes: obtaining a user classification associated with an unlabeled vehicular trip; obtaining a first set of historic device interaction data associated with work; obtaining a second set of historic device interaction data associated with personal use; training a classification model based at least in part upon the first set of historic device interaction data and the second set of historic device interaction data; obtaining a set of unlabeled device interaction data associated with the unlabeled vehicular trip; identifying and comparing a first set of baseline device interaction features, a second set of baseline device interaction features, a set of representative device interaction features; classifying the unlabeled vehicular trip; and validating the user classification based at least in part upon the user classification and the classification made using the classification model.

According to the present invention, a computer: acquires state information indicating types and locations of a plurality of mobility devices traveling on one or more roads in a predetermined area; for each of the one or more roads, calculates, on the basis of the state information and for each of the types of the plurality of mobility devices, travel comfortableness indicating how high the comfortableness is when traveling in each type of mobility device on each road among the one or more roads; and generates a database that associates, for each of the one or more roads, the type of the plurality of mobility devices with the travel comfortableness when traveling in the type of mobility device on each road.

The invention belongs to the field of mechanical engineering, and particularly relates to a method for calculating the dynamic stiffness of a new energy reducer shell bearing seat. The method aims to solve the problem of low dynamic stiffness prediction precision caused by neglecting manufacturing residual stress and multi-physics field coupling effect in traditional simulation. A refined finite element model containing a high-density mixed grid and material nonlinear characteristics is constructed, a die-casting residual stress field based on neural network prediction is integrated, multi-source dynamic loads such as gear alternating torque, electromagnetic pulse power and road surface excitation are coupled, nonlinear transient dynamics solution is executed, and a die-casting residual stress field is obtained. And obtaining time domain response data of the bearing seat node. In addition, a process-structure-performance database and a rapid prediction model are established, lightweight design closed-loop optimization and digital twinning online monitoring are supported, simulation accuracy and engineering application efficiency are improved, and the method is suitable for dynamic performance analysis and service life management of the new energy automobile speed reducer.

This invention relates to the field of distributed streaming data processing and dynamic reconfiguration of cross-domain processing logic, and particularly to a real-time data stream analysis method and system for the international automotive market. The method includes: acquiring pure physical kinematic state data of the vehicle and pre-authorized original environmental data from a pre-defined system; calculating and generating a transient symmetric key; encrypting the original environmental data into ciphertext data and writing it into a memory buffer; inputting the kinematic data into a gradient descent optimization model, fine-tuning the feature transformation computation graph weight matrix and bias terms, and outputting a new rule domain matching operator path; reading the hardware clock cycle value and injecting it into the header of the ciphertext data tensor to generate a routing marker, guiding the data flow to the new operator path; after confirming the path switch, sending an overwrite command to replace the transient symmetric key with an all-zero physical state, blocking the decryption and restoration path of the remaining ciphertext data. This invention solves the problem of feature data leakage or computational interruption caused by differences in feature transformation standards during cross-regional driving.

The invention belongs to the technical field of vehicle control, and provides a multi-vehicle longitudinal formation robust control method and system based on knowledge migration, and the method comprises the steps: constructing a generalized nonlinear multi-vehicle longitudinal system model of heterogeneous vehicles; dynamically designing a piecewise performance function based on the originally constrained tracking error, and mapping the originally constrained tracking error into an unconstrained tracking error; constructing a coupling sliding mode surface based on an unconstrained tracking error, and designing a multi-vehicle longitudinal formation robust controller and an uncertain parameter adaptive law; collecting input data and output data of a first following vehicle under a multi-vehicle longitudinal formation robust controller, and introducing historical braking measurement as additional input data; training a long short-term memory network, and performing iterative optimization; and transferring the knowledge in the trained long and short-term memory network to other following vehicle control systems. The problems that a traditional method is low in transient response rate, high in design complexity, large in parameter configuration number and the like are solved.

The invention provides a vehicle off-road thermal management simulation calibration method, device and equipment and a storage medium, and the method comprises the steps: obtaining the road test reproduction data of a vehicle, and building a whole vehicle three-dimensional simulation model according to the first boundary data, flow field simulation and temperature field simulation are carried out on the whole vehicle three-dimensional simulation model to obtain the air speed and the air temperature of inlet air on the surface of a heat exchanger in the vehicle, one-dimensional simulation modeling is carried out on a heat management system of the vehicle to obtain a one-dimensional model, and the second boundary data, the air speed and the air temperature are input into the one-dimensional model to carry out transient simulation to obtain heat management simulation data. And according to a data gap between the thermal management simulation data and the thermal management reproduction data, carrying out iterative calibration on model parameters of the one-dimensional model until the data gap meets a preset precision threshold value, and completing vehicle off-road thermal management simulation calibration. According to the method, the wind speed and the wind temperature of the inlet air on the surface of the heat exchanger are obtained through the high-fidelity road test reproduction data and the whole vehicle three-dimensional simulation model, and vehicle off-road thermal management simulation is accurately calibrated.

The invention relates to the technical field of collision damage prediction and structure optimization of guardrails, and discloses a collision damage degree simulation prediction-based guardrail structure optimization method, which comprises the following steps of: based on a design drawing of a guardrail, extracting parameters of geometric dimensions, establishing a three-dimensional geometric model by using parameterized modeling software, and establishing a three-dimensional model; then, a material constitutive model is constructed, a strain rate item is calculated, simulation collision processes of different working conditions are solved based on an explicit dynamics analysis method, transient response data in the collision processes are calculated, the transient response data are input into a damage degree model, a dynamic camber value of a vehicle is quantitatively evaluated, and the dynamic camber value of the vehicle is calculated. The maximum transverse dynamic deformation of the guardrail and the dynamic camber value of the vehicle are used as constraints, and the optimal solution of the geometric dimension of the guardrail is searched by using an optimization algorithm, so that the effects of reducing the cost and difficulty of guardrail structure optimization and improving the efficiency of structure optimization are achieved.

The application discloses a kind of bridge crane stress history acquisition method and system, the method includes obtaining the characteristic data of crane actual working state under preset time;The hoisting load data of preset time is expanded by Latin hypercube sampling technique, obtain the hoisting load data of crane inspection cycle, the hoisting load data of inspection cycle is input into the pre-trained tentacle-least squares support vector machine prediction model, and the predicted working cycle number is obtained;The hoisting load data of inspection cycle and the predicted working cycle number are converted into the trolley wheel pressure of equal sample size;The finite element model of crane girder is established, and the trolley is simulated with random load at rated speed from the side span of girder to the other side span, after traversing all trolley wheel pressure load, the stress history of crane girder at any position in inspection cycle is obtained.The application provides high-precision stress characteristic data for crane structure fatigue life assessment.

The application discloses a simulation analysis method for an aircraft landing gear tire under heavy landing impact load, and relates to the technical field of aeronautical engineering and computational mechanics. First, in the tread and sidewall rubber area, the micro-heterogeneity of the rubber material in the tire is equivalent to the super-elastic mechanical behavior of the macro-continuous medium based on the homogenization theory. This process avoids the extremely dense mesh and complex constitutive relationship required for depicting the micro-features such as filler distribution and interface effect in the traditional model. Then, the rebar element representing force transmission under impact load is created in the pre-constructed rubber matrix grid, and the rebar element is coupled with the rubber matrix. The simplified coupled model can accurately capture the anisotropic stiffness, main force transmission path and reinforcement effect of the cord-rubber composite structure, so as to finally stably and accurately simulate the transient impact process of the tire under heavy landing impact load, which includes the triple nonlinearity of geometry, material and contact.

The present invention provides a walking evaluation apparatus (10) including: a position information acquisition unit (12) that acquires position information indicating a current position of a user during a movement by walking; a traffic rule information acquisition unit (13) that acquires, from a storage unit (17) that stores traffic rule information indicating a traffic rule for each position, the traffic rule information indicating the traffic rule around the current position of the user; a determination material acquisition unit (14) that acquires behavior information indicating a behavior content of the user; a determination unit (15) that determines whether the traffic rule around the current position of the user is observed, based on the behavior information; and an evaluation unit (16) that computes an evaluation value of a way of walking by the user, based on the determination result.

The invention discloses a bridge damage identification method and system, and relates to the technical field of bridge damage identification, and the method comprises the steps: constructing a load-environment scene matching mechanism, enabling health reference modes to form a reference cluster according to scene classification, and carrying out the adaptive adjustment of a dynamic tolerance threshold value based on the discrete degree of the cluster, natural fluctuation of bridge health data in different scenes is accurately adapted, the problem that normal fluctuation is easily misjudged as damage due to poor adaptability of a traditional fixed threshold value is solved, and the accuracy of damage identification is improved; according to the method, manual intervention is not needed in the whole process from transient event screening, short-term verification, long-term tracking to damage confirmation, and automatic closed loop of data acquisition, feature extraction, trend fitting and logic confirmation is achieved. Compared with traditional manual detection, traffic interruption is not needed, normalized bridge health monitoring can be achieved, monitoring efficiency is improved, manpower and time cost is reduced, and real-time and continuous guarantee is provided for bridge structure safety.