3277 results about "Rail transit" patented technology

The invention provides a multi-modal fusion and semantic enhancement train positioning method and system, and belongs to the technical field of rail transit, and the method comprises the steps: carrying out the time-space alignment of data, obtaining a dense point cloud, constructing a dense semantic point cloud, and dynamically estimating the confidence coefficient weight of each type of sensors; a set residual error and a Manhattan structure constraint residual error of a plane are constructed, laser radar point cloud parameters are obtained, and visual projection constraints are constructed at the same time; constructing a comprehensive degradation scoring function to carry out degradation judgment on the current environment; when the degradation result is yes, introducing a structure and motion information independent of an external environment, maintaining trajectory estimation, and constructing a compensation constraint; introducing a prior semantic constraint and a large model semantic factor constraint; and constructing a global optimization objective function, dynamically adjusting the weight of each modal factor, obtaining an optimal estimation state, and outputting a high-precision train positioning result. According to the method, high-precision and robust track estimation in an extreme scene is realized, so that the continuity, safety and intelligence of train positioning are guaranteed.

The invention relates to the technical field of rail transit intelligence, and discloses a rail transit intelligent scheduling management method and system, and the method comprises the steps: collecting the entering and exiting data of passengers, the number of people in a waiting area, the train load factor and the platform congestion degree through an automatic fare collection system, a video monitor and a sensor of each station of rail transit; the collected passenger flow data are preprocessed, a box plot about passenger flow distribution is constructed, and sudden passenger flow fluctuation areas are identified in different time windows; a K-means clustering algorithm is adopted to classify passenger flow modes in peak periods, and the distribution type of passenger flow fluctuation is analyzed; a time sequence prediction model is constructed by adopting a Transform model in combination with weather, holidays and festivals and emergencies, the future short-term and medium-and-long-term passenger flow trend is predicted, and the train departure interval is optimized; and based on the predicted passenger flow distribution, a scheduling optimization objective function is constructed, and a reinforcement learning algorithm is combined. The method has the advantage of improving the passenger flow prediction precision in the peak period.

The invention relates to a multi-modal data-driven rail transit construction potential safety hazard identification method and system, and solves the problem that the accuracy and real-time performance of potential hazard identification are insufficient due to the fact that multi-source data cannot realize deep collaboration, and the method comprises the following steps: obtaining multi-modal data containing a BIM + GIS model, an unmanned aerial vehicle image stream and Internet of Things data; according to an edge end-cloud architecture, by taking BIM + GIS coordinates as a reference, the edge end associates and binds data and cleans and transmits the data to the cloud, and the cloud corrects and generates a standardized data set; and the comprehensive risk index is calculated by using an entropy weight method, and a report is generated and pushed to a construction management terminal. The method has the following effects: the multi-modal data barrier is broken, the integrated accurate identification of the hidden hidden danger of the rail transit construction is realized, and the accuracy and efficiency of the construction safety management and control are fundamentally improved.

The invention discloses an operation optimization method, system and equipment for cooperative carbon reduction of an intelligent rail line network, and relates to the technical field of energy-saving scheduling and low-carbon operation of urban public transport. A refined section-level energy consumption and carbon emission quantitative model is constructed based on dynamic characteristics of intelligent rail vehicles and carbon emission factors of a power grid; a deep learning network is used for fusing multi-source data to carry out cross-line short-time passenger flow prediction, real-time passenger flow demands, energy consumption and carbon emission indexes and intersection signal phase time window constraints are uniformly incorporated into a multi-target collaborative optimization framework, and solving is carried out through mixed integer linear programming in a rolling vision field. According to the method, combined optimization and dynamic closed-loop control of departure intervals, section operation speeds and road right strategies are realized, so that the total operation energy consumption and carbon emission of the intelligent rail line network are remarkably reduced on the premise of ensuring passenger flow transportation requirements and service quality, and the line network level energy-saving and carbon-reducing operation targets are achieved.

The invention provides an urban rail transit comprehensive monitoring method and system based on multi-source heterogeneous data, relates to the field of rail transit, and solves the technical problems of low fault diagnosis accuracy and difficult root cause tracing caused by splitting of physical monitoring data and operation and maintenance knowledge in an existing rail transit system. The method comprises the following steps: collecting multi-source heterogeneous data from a rail transit system, preprocessing the multi-source heterogeneous data, and constructing a rail physical topological graph and a rail operation and maintenance knowledge graph; performing feature fusion on the track physical topological graph and the track operation and maintenance knowledge graph through a cross-graph attention mechanism to obtain a fusion feature vector; and inputting the fusion feature vector into a track risk assessment model, executing joint reasoning, identifying an abnormal type and assessing a risk level. The method is used in the urban rail transit monitoring process.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention discloses an urban rail transit equipment fault prediction method based on big data analysis, and relates to the technical field of industrial big data analysis, and the method comprises the steps: building a health state index, a degradation trend index and a risk characteristic index according to the equipment type based on a standardized operation data set, and forming an index sequence; based on the index sequence, performing fault prediction calculation in a big data analysis environment to obtain a fault occurrence probability, a health state score and a residual life evaluation result; the fault occurrence probability, the health state score, the residual life evaluation result and the risk level are summarized, and fault prediction information and risk early warning information of operation and maintenance management are generated; a multi-dimensional dynamic index sequence fusing the health state, the deterioration trend and the risk characteristics is constructed, an intelligent operation and maintenance decision with clear priority and reasonable time sequence is supported and formed, and the safety, reliability and resource utilization efficiency of operation and maintenance of urban rail transit equipment are greatly improved.

The invention discloses an automatic identification and state evaluation method for rail transit unmanned aerial vehicle inspection, particularly relates to the field of rail transit unmanned aerial vehicle inspection, and is used for solving the problem that existing unmanned aerial vehicle inspection is influenced by rail attitude change and environment interference, so that rail defect identification and positioning are not comprehensive. An attitude time sequence matrix is constructed by controlling an unmanned aerial vehicle cluster to fly in the track direction, and an analysis unit is obtained by adopting space-time alignment and sliding window segmentation; based on principal component analysis, attitude cooperative oscillation characteristics are extracted, an attitude reference curved surface is constructed, and individual attitude dissimilation degree indexes are calculated; identifying an abnormal section by using a dynamic threshold value to realize track defect positioning, and controlling a cluster to execute matrix scanning and cross validation flight according to the defect distribution density; and finally, outputting a continuous track smoothness evaluation map. The method can keep stable inspection performance in a complex environment, improves the accuracy and coverage range of track anomaly recognition, and has high engineering application value.

The invention provides an urban rail transit regional passenger flow cooperative control and intelligent optimization method and device, and the method comprises the steps: describing the passenger flow evolution through a space-time state network, building a regional cooperative passenger flow control optimization model which takes the minimization of passenger waiting time as an objective function, and considers the constraints such as flow balance and capability constraints; and converting the optimization model into a deep reinforcement learning framework based on a Markov decision process, generating a reward function adaptive to passenger flow control by using a large language model LLM, and continuously optimizing a reward strategy according to the test performance of the agent through interactive training of a deep reinforcement learning agent and establishment of a reward function correction mechanism. Finally, a regional passenger flow control scheme is extracted from the trained agent model, more intelligent and efficient decision support is provided for subway managers, the operation efficiency of an urban rail transit system is improved, and the queuing waiting time of passengers is shortened.

The invention relates to a four-body coupling vehicle base vibration noise prediction optimization method and system, and relates to the technical field of rail transit operation and maintenance optimization, and the method comprises the steps: obtaining a vehicle base multi-physics field correlation monitoring data set; carrying out system level assembly in a multi-physics field co-simulation environment to generate a coupled system state parameter matrix; generating sound ray bending trajectory data; loading the vehicle operation condition time sequence data to a wave equation solver, and outputting vibration noise propagation characteristic field distribution data; building a network topology structure to form a prediction model, and executing a back propagation algorithm to train network parameters to a convergence state; designing a vehicle scheduling scheme chromosome coding structure to generate a candidate solution population; and performing selection, crossover and variation genetic operations on the primary solution set, and outputting a vehicle base operation and maintenance scheduling real-time optimization scheme. The method has the beneficial effects that the precision and generalization ability of vibration noise prediction in the future time period are greatly improved, and the fundamental transformation from passive vibration isolation treatment to active prediction optimization is realized.

The invention relates to the technical field of subway passenger flow prediction, and discloses a space-time perception subway passenger flow prediction method based on large model and multi-source information fusion, which comprises the following steps: constructing a data agent based on a large language model and a model context protocol; generating text description of each subway station and a corresponding spatial context embedding vector based on the peripheral interest point data of each subway station by utilizing a data agent; constructing an enhanced graph neural network according to the spatial context embedded vector, and performing spatio-temporal joint prediction based on the historical passenger flow data and the spatial context embedded vector by using the enhanced graph neural network to obtain a preliminary passenger flow prediction result; and constructing a cue word template, and performing domain knowledge calibration based on the cue word template by using the pre-trained large language model to obtain a final passenger flow prediction result. According to the method, external multi-source information is deeply fused, so that the urban rail transit passenger flow prediction task performance, robustness and interpretability are remarkably improved.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention relates to the technical field of functional materials, in particular to a polyvinyl butyral flexible material as well as a preparation method and application thereof. The molecular chain regularity is destroyed through an isobutyraldehyde branched chain, the crystallinity is reduced, and the flexibility is improved; cesium-tin co-doped tungsten oxide has high infrared rejection rate and good dispersity, and the influence on the flexibility of the material is reduced; magnesium salicylate and magnesium formate are compounded, so that the reaction activity with resin is reduced, and the bonding stability in the bending process is ensured; the flexibility and migration resistance of the material are synergistically improved through the flexible plasticizer, and the interlayer sheet is prevented from being sticky; the flexible reinforcing agent polycaprolactone micro powder is used for forming an elastic node, bending stress is absorbed, and the recovery performance is improved. The polyvinyl butyral flexible material provided by the invention has flexible bending performance, an efficient infrared barrier function and stable bonding performance, is suitable for scenes such as curved building curtain walls, special-shaped automobile sunroofs or arc-shaped rail traffic windows, and has a wide application prospect.

The invention relates to a global passenger flow direction real-time tracking method for rail transit station video monitoring, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, collecting video data and business data in a station, and carrying out the time-space synchronization and preprocessing of multi-source heterogeneous data; s2, extracting and fusing fine-grained clothing textures, carried objects and gait features of passengers in real time; s3, compressing a search space based on a station topology and a space-time reachability model, and performing global identity association by adopting a probability graph model; s4, maintaining and correcting the continuity of the track through forward-backward joint reasoning and external data verification, and finally outputting a continuous passenger track with consistent identity; s5, carrying out association and semantic annotation on the continuous passenger tracks and the business data; and S6, performing analysis based on the fused data and generating a regulation and control instruction.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

The invention relates to a rail transit engineering digital twin management and control method based on unmanned aerial vehicle honeycomb inspection, and solves the problem that a twin model is disjointed from the real-time state of an engineering field, and the method comprises the steps: firstly obtaining multi-source data containing BIM, GIS and Internet of Things data and an unmanned aerial vehicle initial image; carrying out BIM lightweight processing, carrying out registration with a GIS (Geographic Information System) and establishing an engineering entity-environment-event dynamic association network to form a digital twin reference model; based on the real-time state data of the model and external factors, a multi-dimensional evaluation algorithm is used to generate an inspection task priority; an adaptation rule is established in combination with an unmanned aerial vehicle honeycomb performance threshold, and unmanned aerial vehicle obstacle avoidance inspection is scheduled; the inspection data is preprocessed and then fused to the twin model, and the twin model is driven to dynamically iterate so as to realize real-time synchronization with the site. The method has the following effects: real-time and precise rail transit engineering management and control are realized through digital twin virtual-real mapping and unmanned aerial vehicle honeycomb intelligent inspection closed loop, and the core management and control efficiency is improved.

The invention provides a rail transit weak current system operation and maintenance management method. The method comprises the following steps: S1, data acquisition and preprocessing; s2, fault probability and residual service life (RUL) prediction based on an LSTM / Transform + attention mechanism; s3, fault propagation analysis and root positioning based on a graph neural network (GNN); and S4, operation and maintenance decision optimization based on reinforcement learning. According to the invention, through fusion of deep learning, a graph neural network and a reinforcement learning algorithm, a full-process and closed-loop rail transit weak current system operation and maintenance management system is constructed.

The invention discloses an intelligent monitoring and predicting system for rail transit equipment, which relates to the technical field of rail transit engineering, and comprises the following steps: acquiring a historical fault operation log of a rail system, learning a fault representation independent space, constructing a dynamic knowledge graph of historical faults of the rail, identifying a high-incidence area of the rail faults, and establishing a fault predicting model of the rail system. Based on a sensor, the coupling situation of the vehicle and the track is calculated in real time, a dynamic reference model of operation characteristics in a healthy state is constructed, and the real-time drift deviation between the vehicle and the track is calculated; establishing a drift deviation-environment correlation model, comparing the reference deviation with the actual drift deviation after environment compensation, and setting a dynamic safe operation boundary; and if the drift deviation exceeds a safe operation boundary, judging that the drift deviation is abnormal, immediately triggering graded alarm, associating and pairing a track system fault prediction model, quantifying the future fault occurrence probability and risk level of the region, and realizing intelligent monitoring and prediction of the track. The beneficial effect of the invention is that the fault occurrence rate is reduced.

The invention discloses a Beidou and UWB dual-mode cooperative rail transit positioning method, and particularly relates to the technical field of fusion positioning, and the method comprises the following steps: constructing a cross-modal distortion discrimination array based on the space coverage state of two types of signals in a train path, recognizing a track mutation region, and dynamically adjusting the signal fusion weight; abrupt change points are corrected through a constraint backtracking trajectory fitting algorithm, and pseudo trajectory points are eliminated in combination with a positioning base station space topological structure; performing time sequence analysis on the signal fusion weight change, and if a frequent signal hopping trend exists, lowering the related fusion weight to the lowest tolerance threshold value to improve the track continuity and the positioning robustness; according to the method, the cross-modal distortion discrimination array is constructed, accurate identification of a track mutation region is realized, abnormal track points are effectively corrected in combination with fusion weight dynamic adjustment and a constraint backtracking track fitting algorithm, a signal jump trend is suppressed through a time sequence analysis mechanism, and the stability and positioning precision of track fusion are improved.

The invention relates to the technical field of data communication, and discloses an intelligent agent high-speed bus implementation method and system based on an FPGA and a dynamic smoothing technology. The method comprises the following steps: constructing a single handshake message through a lightweight protocol, adjusting network indexes by adopting an exponential smoothing method, constructing a pipeline processing unit in an FPGA card by utilizing a VHDL, realizing key updating and encryption processing, constructing a three-layer adaptive structure connection subsystem, and optimizing algorithm parameters and resource allocation according to a running state. And a high-performance bus communication system is formed. On the premise of ensuring the communication security and reliability, the network transmission delay is remarkably reduced, and the data throughput is improved, so that the communication system can meet the requirements of emergency linkage and large data volume transmission in a scene (such as a rail transit station-level system) with a high real-time requirement.

The invention discloses a rail transit dispatching simulation practical training and automatic examination system integrated with emergency assistance, and relates to the technical field of simulation practical training, and the system comprises a data collection module which is used for collecting basic data, operation data and historical emergency event data of a rail transit system; and the simulation environment construction module is used for constructing a high-fidelity rail transit simulation environment by applying a digital twin technology based on the data acquired by the data acquisition module, mapping a geometric field model, a physical field model, a behavior field model and a rule field model which are required by virtual equipment, constructing a four-model mapping rule base and fusing a multi-field model. According to the invention, the simulation environment construction module relies on a digital twinning technology and real acquired data, combines geometric, physical, behavioral and regular four-field models and a mapping rule base, constructs a high-fidelity simulation environment and meets diversified practical training requirements, and the emergency aid decision-making module introduces a quantum computing technology and can quickly process mass data in a complex scene.

The invention relates to the field of rail transit vehicle intelligent diagnosis, in particular to a train fault intelligent auxiliary diagnosis method fusing multi-source knowledge, which comprises the following steps of: constructing a multi-source knowledge document library, and performing text cleaning, segmented disassembly and vectorization processing to obtain a vector database; then obtaining a fault alarm list and capturing event information, and performing diagnosis according to the vector database to obtain a diagnosis suggestion; then time sequence operation parameters before and after the fault are extracted and analyzed, and a trend chart and characteristic indexes are generated to obtain an analysis result; constructing a multi-dimensional auxiliary analysis mechanism based on the diagnosis suggestion and the analysis result, and generating a maintenance suggestion in combination with historical maintenance work order information; and finally, generating a comprehensive auxiliary diagnosis report according to the maintenance suggestion, automatically generating a maintenance dispatching task list, determining a task execution sequence according to a preset priority scheduling algorithm, and executing the maintenance dispatching task list. The accuracy and timeliness of train fault diagnosis can be improved, and the safety, the operation and maintenance efficiency and the intelligent level of rail transit equipment are improved.

The invention relates to a rail transit large model retrieval enhancement generation method and system, an application and a medium, and the method comprises the steps: receiving a user query text, converting the user query text into a triad sequence through a large language model, and sorting triads based on a semantic relationship; the method comprises the following steps: obtaining embedding vectors of entities and relationships in a sequence through an embedding model, and retrieving candidate entities and relationship sets from a knowledge graph according to the embedding vectors; and calculating a triple sequence semantic distance between the sequenced sequence and the candidate sequences by adopting a triple sequence retrieval algorithm, and selecting the first K candidate sequences meeting the structural constraint. And finally, inputting the user query and the first K candidate sequences into the large language model to generate an answer, thereby avoiding knowledge fragmentation and structure mismatch, and realizing reliable and accurate response to complex multi-hop query in the urban rail transit field.

The invention provides a subway tunnel water disaster risk evaluation method under extreme rainfall based on multi-source data, and belongs to the technical field of urban rail transit disaster prevention. The method comprises the following steps: establishing a grading evaluation system on the basis of field rainstorm monitoring data, hydrogeological parameters, field geological exploration, rock-soil physical and mechanical parameters, monitoring data of a tunnel lining structure and material strength parameters of a tunnel site area in combination with a plurality of developed tests, dividing corresponding grades and determining an influence index-; and S4, respectively carrying out weight calculation on the influence index-by adopting an analytic hierarchy process and an entropy weight method, then calculating a dynamic weight of the influence index-, correcting an abnormal dynamic weight, generating a comprehensive score, dividing risk grades, matching graded prevention and control measures, and optimizing and adjusting the prevention and control measures in real time through a parameter risk grade dynamic judgment and construction method matching mechanism. According to the method, minute-level accurate interpretation of a disaster evolution mechanism and dynamic optimization adaptation of a prevention and control scheme are realized.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention provides high chloride ion permeation resistance steel fiber fine aggregate concrete and a preparation method thereof. The high chloride ion permeation resistance steel fiber fine aggregate concrete comprises the following raw materials in parts by weight: 200-300 parts of cement; 30 to 50 parts of metakaolin; 100 to 200 parts of fly ash; 50 to 100 parts of mineral powder; 1-8 parts of nano silicon dioxide; 800 to 1000 parts of sand; 800 to 1000 parts of stone; 10 to 60 parts of steel fiber; 150 to 200 parts of water; and 5-20 parts of a water reducing agent. The steel fiber fine aggregate concrete with high chloride ion permeation resistance has the advantages of improved mechanical properties and durability, extremely high chloride ion permeation resistance, excellent volume stability, low material cost, economic feasibility of large-scale application, and wide application prospect. And the strict service requirements of the urban rail transit ballast bed under the long-term dynamic load, stray current and erosion medium coupling effect are precisely met.

The invention provides a wind-water linkage energy-saving control system for an urban rail transit underground station. The wind-water linkage energy-saving control system comprises a management layer; the control layer is divided into three relatively independent and expandable cooperative control subsystems capable of independently acquiring information according to the type of equipment, the control layer comprises a large system cooperative control subsystem, a small system cooperative control subsystem and a water system cooperative control subsystem, and the cooperative control among the cooperative control subsystems is completed by the management layer platform; a control strategy of the urban rail transit underground station wind-water linkage energy-saving control system comprises a system calendar scheduling strategy; a system control strategy; comprising a large system air conditioner adding strategy, a large system air conditioner reducing strategy, a water system starting working condition setting strategy, a water system operation working condition switching strategy, a cooling water temperature difference optimization setting strategy, a chilled water temperature difference optimization setting strategy, a most unfavorable tail end pressure difference setting strategy, a most unfavorable tail end pressure difference control strategy and a water chilling unit adding strategy. A water chilling unit reduction strategy, a water chilling unit alternation strategy and a water chilling unit operation load balancing strategy; and the system cooperative control comprises one-key startup of the water chilling machine room, one-key shutdown of the water chilling machine room, one-key startup of the water chilling unit, one-key shutdown of the water chilling unit, automatic addition of the water chilling unit and automatic reduction of the water chilling unit.

The invention discloses a subway transport capacity matching and passenger flow collaborative management and control method based on multi-agent reinforcement learning, and relates to the technical field of urban rail transit operation management. The method comprises the following components: S1, a subway system digital twin model construction step, S2, a multi-agent reinforcement learning training environment construction step, S3, an extreme scene simulation and agent training step, S4, a virtual-real data interaction and model iteration step and S5, an actual system deployment and decision execution step. According to the method, a high-fidelity digital twin model of a subway system is constructed, extreme scenes including passenger flow retention caused by rainstorm and instantaneous large passenger flow of a large competition field are simulated in the model, and a multi-stage sampling method based on scene clustering is combined to perform training for ten millions of times; and the decision-making capability and the cooperation efficiency of the multiple agents in the face of complex and extreme conditions are obviously improved.

The invention discloses an intelligent operation and maintenance method and system for a rail transit communication machine room based on digital twinning, and belongs to the technical field of digital twinning and three-dimensional visualization of a rail transit system. An intelligent operation and maintenance server side in the system comprises a data acquisition module, a three-dimensional modeling module, a digital twinning engine module and a database module; the intelligent operation and maintenance client comprises a dynamic loading scheduling module, a real-time data binding module and a visual interaction module; according to the system, a lightweight Web3D engine is adopted to dynamically load and render a three-dimensional model of a communication machine room as required, and real-time sensing, data synchronization, fault warning and remote operation and maintenance of the operation state of communication equipment are realized in combination with a digital twinning technology. According to the method, the functions of hierarchical loading of a multi-level model, alarm driving state rendering, operation and maintenance closed-loop management and the like can be realized. Compared with the prior art, the method has the advantages of light model weight, quick response, easiness in deployment, high expandability and the like, and is widely applicable to communication infrastructure operation and maintenance scenes in the field of rail transit.

The invention discloses a rail transit continuous positioning method realized by combining Beidou and inertial navigation, and particularly relates to the technical field of rail transit localization, comprising the following steps: completing weight adjustment and error synchronization before failure by evaluating a navigation signal environment state in real time, predicting a shielding risk and identifying a signal degradation trend; the precision of the inertial navigation system is guaranteed. Constructing a trajectory prediction window to constrain an inertia trajectory during shielding, judging the stability of a recovery signal after the recovery signal is received, and supplementing a trajectory error based on a bidirectional residual error correction mechanism after the recovery is confirmed, so as to realize whole-course continuous positioning of rail transit; according to the invention, through navigation signal degradation trend pre-judgment, inertial navigation system dynamic switching preparation and a bidirectional residual error compensation mechanism, continuous track positioning of a train inside and outside a shielding area is realized, the stability and robustness of a positioning system are improved, and non-interruption and non-jump output of the whole track traffic process is ensured.