1245 results about "Urban rail transit" patented technology

The invention relates to an MCP-based urban rail transit intelligent operation and maintenance service construction method. The method comprises the steps of obtaining an input question of a user; inputting the input question into the large language model for processing and extracting key information; the big language model judges data required for solving the problem according to the key information; acquiring data required for solving the problem from different MCP Clients through an MCP protocol; and outputting a final result by combining the big language model with data required for solving the problem. According to the method, the MCP protocol is introduced into the field of urban rail transit intelligent operation and maintenance for the first time, and a unified model context communication interface is constructed to realize structured interaction between a large model and multi-source operation and maintenance data, so that connection between various operation and maintenance services and intelligent agents is more efficient and standardized, and the operation and maintenance efficiency is improved. The technical problems that in a traditional system, interfaces are heterogeneous, data are closed, and a model is difficult to call external services are solved, and infrastructure support is provided for modular deployment and cross-system integration of an intelligent operation and maintenance platform.

The invention relates to the technical field of rail transit, and discloses an intelligent diagnosis method and system for the health degree of urban rail equipment based on a cloud platform and a medium, the intelligent diagnosis method for the health degree of the urban rail equipment based on the cloud platform comprises the following steps: S1, multi-source data collection and edge preprocessing: collecting operation data of the urban rail equipment through a sensor, performing time sequence alignment and abnormal value filtering on the data to generate preprocessed data; s2, triggering type data uploading: based on the preprocessed data, judging whether an uploading condition is met or not through a statistical rule or a model prediction result, and uploading the data meeting the condition to a cloud platform; and S3, cloud model training and health degree prediction: constructing an equipment health state prediction model through a transfer learning algorithm by using the uploaded data. According to the invention, through deep learning modeling, edge calculation deployment and a multi-factor early warning mechanism, efficient evaluation, stable early warning and low-delay response of the equipment state are realized.

The invention relates to the technical field of big data, and discloses an urban rail transit emergency processing method and system based on a visual large model, and the method comprises the steps: collecting visual data, sound data and equipment operation data at a platform and a carriage of urban rail transit, and carrying out the preprocessing and timestamp synchronization; performing dynamic frame difference processing on continuous video frames of the visual data to generate a two-dimensional motion heat map, and performing space-time aggregation; constructing a three-dimensional digital model based on engineering drawings of the platform and the carriage, and establishing a mapping relation between the two-dimensional motion heat map and the three-dimensional digital model; performing abnormal behavior detection on a mapping result, performing multi-modal fusion analysis in combination with sound data and equipment operation data, and determining the severity of an abnormal event; emergency response strategies are formulated according to severity levels of the abnormal events, a control instruction is sent to the rail transit equipment through an industrial protocol interface, and abnormal event information is pushed to a control center, inspection personnel and passenger terminals.

The invention provides a state monitoring and fault early warning method for a maglev vehicle system, relates to the technical field of urban rail transit data processing, and solves the problem of how to effectively combine an empirical threshold mode and a machine learning technology and give full play to a combination effect. The method comprises the steps of collecting operation data of a magnetic levitation vehicle system, performing corresponding data preprocessing operation, performing analysis and calculation in a mode of constructing a mathematical model, and evaluating the health state of the magnetic levitation vehicle system; constructing and training a fault prediction model, and inputting the evaluation data into the trained fault prediction model to obtain potential fault data of the magnetic levitation vehicle system; and generating early warning information based on the potential fault data, retrieving an expert experience library by using the early warning information, generating an intelligent decision result, and feeding back the intelligent decision result to the operation and maintenance personnel. According to the method, machine learning and expert experience are effectively combined, so that the real-time performance and the accuracy of fault early warning are improved.

The invention relates to the technical field of event detection and behavior specification supervision, and discloses a rail transit driver operation specification monitoring method and device based on an improved ST-GCN model, and the method comprises the steps: determining various key specification operations involved in the start and stop process of an urban rail transit train driver, key joint points corresponding to the key standard operations are calibrated in advance; collecting video data of the driver executing the various key standard operations in the internal and external environments of the vehicle, and evaluating and marking the compliance of the driver operation behaviors in the video data; constructing an ST-GCN model based on dynamic topology and a domain prior fusion layer, and training the model by using the evaluated and labeled video data; and the trained ST-GCN model based on dynamic topology and field prior fusion is utilized to monitor driver operation in real time, and a prediction result is output. According to the device and the method, the real-time performance and accuracy of supervision can be improved, and safe operation of rail transit is ensured.

The invention discloses an urban rail transit road network short-time OD passenger flow prediction method based on a space-time multi-graph neural network. The method comprises the steps of symbol definition and passenger flow data construction; a multi-relational graph structure: constructing four spatial relationships based on potential inter-station association of stations; spatial feature extraction: inputting the constructed passenger flow data, performing graph convolution operation on the constructed passenger flow data and different constructed networks, capturing global spatial features by using a Transform module, and finally performing fusion output to obtain a hidden value sequence with the spatial features and outputting the hidden value sequence; time feature extraction: using BiLSTM to respectively extract time long-distance dependence on the starting place and the destination of the OD passenger flow, fusing results, and inputting the fused results into GRU to further capture short time sequence dependence; and finally, inputting into a multi-head time convolution module, and extracting time dependence information in parallel through a plurality of convolution kernels by utilizing TCN to obtain a prediction result. The method has higher accuracy and adaptability.

The invention provides an urban rail transit energy management system considering new energy and energy storage access. The urban rail transit energy management system comprises a new energy power generation unit, an energy storage unit, a regenerative braking energy recovery unit and an energy management control platform, the new energy power generation unit and the energy storage unit are connected to a rail transit power supply system; the energy management control platform is connected with the power supply network and controls the units in a communication mode, energy flow in the rail transit power supply system is monitored, analyzed, optimized and dispatched so as to coordinate utilization of new energy power generation power, energy storage charging and discharging and regenerative braking energy, the energy utilization efficiency is improved, and the requirement for power grid outsourcing electric energy is lowered. The peak load of the system and the power grid electricity purchasing cost are effectively reduced, meanwhile, the overall energy utilization efficiency is improved, and energy conservation and consumption reduction are achieved while safe and stable operation of the rail transit system is ensured.

The invention discloses a bidder hypergraph association analysis method and system based on bid behavior recognition, and relates to the technical field of bid behavior detection. The method comprises the following steps: S1, analyzing a bidding document, constructing a bidding document structure tree according to key structure information, and carrying out document structure fingerprint similarity matching; s2, bidding behavior data are collected, bidding behavior similarity judgment is carried out, and corresponding measures are taken through threshold comparison; s3, constructing a high-order relation structure hypergraph, and performing hyperedge importance evaluation; s4, evaluating the bid enclosing probability of the bidding unit, and taking graded early warning measures; and S5, constructing a risk tracking and feedback curve according to a bid enclosing probability evaluation result, and generating an evidence report. The method mainly solves the problem that bidding behaviors cannot be effectively recognized due to the fact that bidding units share technical scheme templates and avoid detection through typesetting and camouflage in the urban rail transit design bidding process.

The invention relates to an urban rail transit passenger flow prediction method based on a multi-factor algorithm. The method comprises the following steps: collecting urban rail transit historical passenger flow data and related index factor sample data influencing passenger flow; constructing a factor analysis mathematical model, analyzing a plurality of index factors influencing the passenger flow, determining a common factor, and calculating a score; constructing a BP neural network model, taking the common factor score as input, and obtaining an optimal parameter set through a particle swarm optimization algorithm; generating a BP neural network prediction model for urban rail transit passenger flow prediction based on the optimal parameter set, and training through sample data to obtain an optimal BP neural network prediction model; and performing real-time passenger flow prediction based on the trained BP neural network prediction model. According to the method, the problems of insufficient prediction precision, low calculation efficiency and the like in the prior art can be solved, and the purpose of rapidly and accurately predicting the passenger flow of the station, the line and the line network is achieved.

The invention provides a rail transit power supply digital twin system and a construction method, and relates to the technical field of data processing, and the method comprises the steps: constructing an urban rail transit power supply system digital twin model, achieving the digital virtualization associated with a physical entity, completing the model rendering, and displaying the state of the entity through a virtual model; according to the entity state, operating parameters of a substation and key equipment of a contact network are collected through a sensor and a hardware device; the operation parameters are cached by the station-level monitoring device body through the double-ring network architecture and then scheduled to a cloud platform communication ring network node, a data transmission path is optimized based on a load balancing strategy, and the data are uploaded to a cloud platform in real time. The management level can be improved, the operation and maintenance cost can be reduced, and a foundation is laid for intelligent operation and maintenance of a rail transit power supply system.

The invention relates to a train scheduling and passenger flow guiding collaborative optimization method and device, and the method comprises the steps: obtaining the first passenger flow information of a road network in a preset time period, the road network comprises a plurality of lines, each line is provided with a plurality of stations, and the first passenger flow information indicates the number of passengers taking from a departure station to a destination station; the departure station and the destination station are respectively any one station in the road network; the first passenger flow information is input into an optimization model for train dispatching and passenger flow guiding collaborative optimization, an optimization result is obtained, and the optimization model is determined through optimization target training based on the minimum power supply energy consumption and the minimum passenger waiting time; the optimization result indicates train scheduling information and passenger flow guiding information on each line in the optimized road network. According to the train scheduling and passenger flow guiding collaborative optimization method and device provided by the embodiment of the invention, the energy consumption of a traction power supply system can be effectively reduced, and the average waiting time of passengers is shortened, so that the energy efficiency of urban rail transit is improved, and the service quality is improved.

The invention discloses an urban rail transit network cascade failure analysis method and system, and belongs to the technical field of rail transit and control science. Identifying key stations of the urban rail transit time-varying network of each time step; marking the sites attacked by the key sites as invalid sites; for the unattacked sites, whether the sites fail or not is judged by comparing whether the real-time load of each site of the urban rail transit time-varying network of each time step exceeds the self capacity of the corresponding site or not, and the sites with the real-time loads exceeding the self capacity are marked as failure sites; obtaining the residual capacity of a station adjacent to the failed station, and redistributing the real-time load of the failed station; and analyzing the cascade failure process of the urban rail transit network according to the redistribution result. According to the method, the dynamic change of the key stations of the time-varying network of the urban rail transit in the cascade failure process can be accurately grasped, so that the cascade failure process of the urban rail transit network is better analyzed.

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 discloses a method for adjusting train operation based on predicted passenger flow, which comprises the following steps of: establishing a passenger flow dynamic model by combining the predicted passenger flow condition, and constructing a train adjustment optimization model based on the predicted passenger flow by combining the train dynamic model and considering the constraint condition and the target function of the model; in consideration of the real-time performance of train operation adjustment, a train adjustment strategy searching method based on MPC (model predictive control) is designed and used for solving a train adjustment optimization model; according to the method, passenger flow prediction and train operation adjustment are combined, and the train operation plan is optimized while the travel experience of passengers is ensured, so that the train operation adjustment scheme can well adapt to the dynamic change of the passenger flow, and the service level of urban rail transit is improved.

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 a subway multi-task passenger flow prediction method fusing time-space multi-dimensional features, and the method comprises the steps: preprocessing original passenger flow data collected by an AFC system, and obtaining a passenger flow distribution matrix of all stations of a rail transit network; collecting multi-dimensional features, including external dynamic environment and external geographical environment features, station space relationships, periodic hour codes, date features and peak code features; constructing an end-to-end multi-dimensional feature integration model CompLSTM (CompGCN-Attention-LSTM), combining a multi-graph adjacency matrix, and integrating origin-destination passenger flow, a station space connection relationship, a station distance relationship and a station function relationship; and adopting Attention-LSTM (Long Short Term Memory) to mine space-time characteristics of the passenger flow matrix fused with the multi-dimensional characteristics. According to the method, multi-dimensional features are fused, an end-to-end integrated model is constructed, spatio-temporal data characteristics are excavated comprehensively, the subway network multi-station origin-destination short-time passenger flow and the in-out short-time passenger flow can be accurately predicted, line expansion planning and network optimization design are facilitated, a subway operation strategy is optimized, and intelligent management of an urban rail transit network is achieved.

The invention relates to the technical field of urban rail transit, in particular to an urban rail transit passenger monitoring data processing and analyzing system and method, and the system comprises a data collection layer which is used for collecting video streams, gate passing records, passenger positioning data and environment parameters of temperature, humidity and illumination in real time; the spatio-temporal feature fusion layer is used for converting the video image data into analyzable feature vectors and integrating card swiping records and position information to form spatio-temporal trajectory data of passengers; the three-level anomaly detection layer comprises an individual layer detection unit, a group layer detection unit and a system layer prediction unit; the dynamic decision-making layer is used for calculating an abnormal score based on a dynamic threshold value, and dynamically adjusting a behavior coefficient according to a historical disposal effect through a PPO reinforcement learning algorithm; and the execution layer comprises an edge computing node and a cloud analysis platform. Therefore, the problems of single data acquisition, lack of comprehensive anomaly detection means, fixed and lagged decision response, ineffective utilization of resources and the like in the prior art are solved.

The invention discloses an ATS interface autonomous regulation and control method, and belongs to the technical field of urban rail transit information processing. Monitoring elements in an ATS interface are classified by constructing an ATS interface composition system, and then the monitoring elements are grouped and numbered according to the relative relation between the monitoring elements; the method comprises the following steps: monitoring a monitoring element in an ATS interface, monitoring the display characteristics of the monitoring element in real time, judging the state of the monitoring element according to the display characteristics of the monitoring element, and if a fault occurs, matching a corresponding solution from a monitoring database according to the fault type and the fault level. Therefore, the whole software does not need to be subjected to integrated testing, the labor cost is reduced, meanwhile, automatic monitoring on the train cannot be interrupted during regulation and control, and the operation efficiency of the ATS is guaranteed.

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.

Disclosed are a coordinated control method for urban rail transit passenger flow, an electronic device, and a storage medium. The method includes: predicting passenger flow in peak hours of urban rail lines using a simulation deduction function in a rail simulation system, including station entry ID, station exit ID, station entry time period, and passenger number data; obtaining passenger flow of passengers arriving at stations s and preparing to board during time periods t by dividing according to the different time periods t and the stations s; counting passenger flow in each direction of historical passenger flow, and calculating a proportion of the passenger flow in each direction of the historical passenger flow; constructing a mixed integer programming model of multi-station passenger flow coordinated control; and obtaining an optimal station entry passenger flow scheme by solving the mixed integer programming model of multi-station passenger flow coordinated control.

The invention relates to the technical field of carbon emission accounting, in particular to a city railway traffic full-link carbon emission accounting system and method, and the system comprises a data collection and processing unit which is used for obtaining the activity data of a raw material stage, a construction stage, an operation and maintenance stage, and a scrap recovery stage; the carbon emission factor tracking unit is used for acquiring a material life cycle carbon factor, a power grid carbon intensity factor and a recycling benefit carbon factor; the emission allocation calculation unit is used for carrying out carbon emission accounting on activity data of a raw material stage, a construction stage, an operation maintenance stage and a scrap recovery stage based on a material life cycle carbon factor, a power grid carbon strength factor and a recycling benefit carbon factor, so as to obtain a total carbon emission amount of a whole link; according to the accounting method, the response capability of the new energy fluctuation to the carbon intensity is enhanced, and data support is provided for sensitivity analysis and dynamic optimization of the carbon emission influence factors.

The invention discloses an urban rail transit pull-in passenger flow prediction method fusing multi-source spatio-temporal data. The method comprises the following steps: collecting multi-source spatio-temporal data of a target station and a corresponding associated station; kernel density estimation is carried out on the surrounding POI density corresponding to each site, the spatial thermal characteristics of each site are generated, one-hot coding is carried out on the real-time weather index corresponding to each site, the weather influence characteristics of each site are generated, segmented coding is carried out on the date type identifier corresponding to each site, and the periodic effect characteristics of each site are generated. Performing differential stabilization on the historical pull-in passenger flow sequence of each station to generate a time sequence fluctuation characteristic of each station; tensor splicing is carried out on the space thermal characteristics, the weather influence characteristics, the periodic effect characteristics and the time sequence fluctuation characteristics of all the stations, a space-time fusion characteristic tensor is constructed and input into a space-time self-adaptive prediction model, and an inbound passenger flow prediction value of the target station is generated. The accuracy and timeliness of urban rail transit pull-in passenger flow prediction can be improved.

The invention relates to the technical field of traffic planning and management optimization, and provides a regional hub and urban rail transit interchange connection optimization method, which comprises the following steps of S1, adopting different models for performing short-time arrival passenger flow prediction aiming at different travel modes to obtain respective interchange passenger flow; s2, modeling is conducted on the environment layout and pedestrian behaviors of an actual station through Anylogic software, a dynamic evaluation index system is constructed on the basis of the whole hub and the basic transfer facility state, passenger flow prediction data are input into simulation software, and transfer connection efficiency is obtained; and S3, constructing a maximum comprehensive benefit function from three aspects of a comfort index, an economic index and a facility traffic capacity balance degree, and optimizing transfer service facility configuration by adopting a genetic algorithm to optimize transfer connection efficiency. According to the optimization method provided by the invention, the transfer passenger flow volume can be accurately predicted, and the resource allocation is reasonably optimized, so that the transfer efficiency is improved, and the experience feeling of passengers is improved.

The invention relates to the technical field of intelligent management of urban rail transit equipment, in particular to a resource management method for containerization deployment of urban rail equipment. Aiming at the problems of static extensive resource management and response lagging in the existing containerization deployment, dynamic optimization is realized through the following innovations: deploying a multi-service subsystem on intelligent fusion host equipment in a containerization manner; collecting container resource data with space-time labels in real time to construct a multi-source monitoring cube; a container resource dependency relationship is modeled based on a federated learning framework and a graph neural network, and a load dynamic state is predicted in combination with a time sequence attention mechanism; dynamic resource allocation is executed through the computing power unit; adopting a priority guarantee mechanism to identify burst loads and preferentially deploying resources; and evaluating the migration cost by using a deep learning model to realize smooth container migration. According to the method, the resource utilization rate and the system stability are remarkably improved, the key service interruption risk is reduced, and high-reliability intelligent resource management support is provided for urban rail equipment.

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 discloses an emergency event handling auxiliary method and system for urban rail transit driving, and the method comprises the steps: obtaining the operation monitoring information of a target station, carrying out the time alignment and space alignment of the operation monitoring information of the target station, and obtaining the operation monitoring information of the aligned target station; event feature detection and analysis are carried out based on the aligned target station operation monitoring information, and multi-modal fusion features of the target station operation monitoring information are constructed; establishing a fuzzy rule database, and performing event level analysis and event influence analysis on the current event according to the multi-modal fusion features of the operation monitoring information of the target site to obtain event level analysis information and emergency event influence analysis information; and constructing an emergency event disposal auxiliary model, and inputting the emergency event influence analysis information and the event level analysis information into the emergency event disposal auxiliary model to generate an emergency event disposal scheme for recommendation. And the real-time performance and reliability of emergency event handling are improved.

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 management system based on multi-source data fusion, and the system is characterized in that a data collection module is connected with a data preprocessing module, and is used for collecting multi-source data of a power supply system, a vehicle system and a rail signal system; the data preprocessing module is connected with the data fusion center and is used for denoising, filtering and normalizing the received original data; the data fusion center is connected with the data acquisition module, the data preprocessing module and the decision analysis module and is used for realizing fusion of multi-source data; the decision analysis module is connected with the data fusion center and the control execution module and is used for performing fault prediction and emergency processing decision; the control execution module is connected with the decision analysis module and an external execution mechanism and used for controlling the execution mechanism to guarantee operation of the rail transit system according to instructions of the decision analysis module. According to the invention, operation efficiency and resource configuration can be improved, risks are reduced, shutdown time is reduced, safety and experience are guaranteed, maintenance and operation costs are reduced, and benefits are 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.