407 results about "Urban rail" patented technology

The embodiment of the invention provides an urban rail signal system fault diagnosis method based on a knowledge graph and a large model, and the method comprises the steps: collecting system operation logs, state parameters and fault information in real time, and carrying out the data preprocessing and standardization; based on historical fault data, a fault classification and prediction model is constructed through feature extraction and mode recognition, and automatic fault recognition and risk early warning are achieved; constructing a fault diagnosis knowledge graph, and establishing an association relationship among entities such as equipment, faults, reasons, maintenance schemes and the like; a pre-training large language model and a LoRA technology are adopted for efficient fine tuning, a fault diagnosis reasoning model is trained, and end-to-end generation from fault description to diagnosis and maintenance suggestions is achieved; the knowledge graph and large model output are fused, real-time and historical data are combined, multi-path fault analysis and comprehensive diagnosis are carried out, and a diagnosis report is generated and displayed. The intelligent level, accuracy and efficiency of fault diagnosis can be improved, and technical support is provided for intelligent operation and maintenance management.

The invention relates to the technical field of power monitoring, in particular to an urban rail power supply system fault early warning method based on multi-source data fusion, which comprises the following steps: acquiring a thermogram, extracting a temperature drift trajectory, marking a thermal abnormal point, identifying abnormal data by combining voltage sampling, analyzing the overlapping property of abnormal time and direction, and positioning a fault overlapping point. And identifying the linkage early warning area through spatial statistics. According to the method, the continuous drift trajectory of the temperature gravity center in the traction transformer thermogram is recognized, the thermal abnormal points are extracted, the voltage sudden change features are analyzed in combination with the voltage sampling sequences in the same time period, the composite recognition mode of time synchronization and trend direction comparison is achieved, the fault symptom positioning accuracy is effectively improved, and the fault symptom positioning accuracy is improved. By aggregating the fault coincidence points in the plurality of power supply sections and executing the spatio-temporal clustering operation, the capability of sensing common anomalies in the region can be significantly improved, and a potential linkage fault region can be clearly identified based on joint calculation of a spatial adjacent structure and a statistical aggregation degree.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention discloses a railway bogie bearing fault diagnosis method and equipment based on a feature extraction network, and relates to the field of intelligent diagnosis and maintenance guarantee of urban rail trains. The method comprises the following steps: data acquisition: acquiring vibration signals of a bogie axle box bearing under the same rotation speed and load combination; performing data preprocessing: performing frequency spectrum adaptive decomposition on each section of signal based on improved empirical wavelet transform, introducing a Fisher score and frequency spectrum entropy joint scoring mechanism, screening out key modal signals, and then performing image mapping to reconstruct a diagnosis sequence; model construction: introducing a topology perception attention mechanism module on the basis of the lightweight convolutional neural network, and constructing a fault diagnosis model; model training: training the fault diagnosis model to obtain an optimized fault diagnosis model; and diagnosis result output: using the optimized fault diagnosis model to diagnose the fault signal of the bogie axle box bearing, and outputting the diagnosis result. The method can improve the accuracy of fault diagnosis.

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 relates to an oil-proof emulsification control method and system for an air compressor of an urban rail vehicle, and solves the problem of lubricating oil emulsification caused by insufficient working efficiency of the air compressor and environment humidity change by constructing a comprehensive control system comprising the compressor, an oil-proof emulsification device and a controller. The system monitors lubricating oil temperature and environment humidity in real time through a temperature sensor and a humidity sensor; the working rate of the air compressor is accurately calculated, wherein the vehicle operation time t2 takes the operation speed larger than 10 km / h as the calculation starting point; the controller comprehensively judges whether the oil-proof emulsification device is started or not based on temperature, humidity and working rate data, and the control strategy comprises the step of dynamically adjusting the working rate judgment node according to the real-time temperature and humidity. According to the method, accurate and reliable control over the oil-proof emulsification process is achieved, emulsification of the lubricating oil is effectively prevented, and energy consumption and maintenance cost can be reduced.

The invention relates to the technical field of urban rail train safety detection, and discloses a mechanical rail train detection method and system with a real-time detection function, and the method comprises the steps: collecting the states of obstacles on the two sides of a bogie and normally open and normally closed contacts of derailment detection travel switches; calculating a trigger state code; an emergency or non-emergency braking signal is output and an alarm is given; and when the state returns to normal and a reset instruction is received, the alarm is released and the initial state is recovered. In the prior art, obstacle or derailment detection is realized by depending on a multi-stage sensor and an electronic control unit, and especially in complex operation environments of high electromagnetic interference, high humidity dust and the like, stable and low-fault-rate real-time detection cannot be realized. Due to the fact that a redundancy judgment mechanism combining a pure mechanical structure and the vehicle programmable logic control unit LCU is adopted, high-reliability real-time detection of the obstacle and derailment state is achieved, and the safety and maintainability of a train system are improved.

The invention discloses a medium-low traffic volume train operation plan optimization method based on adaptive large neighborhood search, and relates to the technical field of medium-low traffic volume urban rail transit transportation organizations, and the method comprises the steps: obtaining rail operation basic data information based on a selected target urban rail line; a train operation scheme optimization mechanism is used for processing, and a train candidate set is generated; according to the train candidate set and the pre-processed integrated optimization decision variable, pre-configuring a train operation optimization target and a train scheduling constraint condition, and constructing a train collaborative optimization model; the train collaborative optimization model sequentially executes corresponding processing operations on a train candidate set, a pre-configured train scheduling constraint condition and the preprocessed neighborhood operator to obtain an adaptive large neighborhood search algorithm; and analyzing by using the train collaborative optimization model and the adaptive large neighborhood search algorithm to obtain an optimal train operation plan scheme. According to the invention, the flexibility of medium-and-low-traffic-volume urban rail driving and the passenger flow space-time distribution coupling are improved.

The invention relates to the technical field of electrical fault positioning, in particular to an urban rail train relay state online monitoring method and system. The specific implementation process comprises the following steps: collecting coil current, contact voltage, load current and environment vibration signals by using an electrical sensor group; when the coil current is subjected to step change, intercepting an electromechanical characteristic data set; arc energy and contact resistance fluctuation characteristics are calculated, correlation analysis is carried out in combination with environment vibration signals, and passive fluctuation and active fluctuation are distinguished; and establishing a multi-factor health degree evaluation model, dynamically adjusting the arc energy and the weight coefficient of the contact resistance according to the magnitude of the load current and the vibration intensity, monitoring the health state of the relay in real time, and predicting the residual life. Through multi-dimensional data synchronous acquisition and vibration decoupling, the interference of mechanical vibration on electrical performance monitoring is effectively eliminated, the problem that a dynamic working condition cannot be reproduced in an off-line test is improved, and the accuracy of relay state evaluation and service life prediction is remarkably improved.

The invention provides an urban rail system emergency scheduling method and system combined with big data analysis, and relates to the technical field of urban rail transit, and the method comprises the steps: firstly, converging dynamic monitoring data of an urban rail system, including train real-time working conditions, station region people flow distribution, rail structure vibration, power supply network load and external environment interference data; secondly, constructing an abnormal evolution topology based on the data, taking data fluctuation characteristics as nodes, connecting the nodes into a network by reflecting evolution association strength of a mutual induction relationship, and then identifying key conduction nodes and diffusion boundaries from the abnormal evolution topology; then, key conduction nodes, diffusion boundaries and emergency resource layout data are combined to generate a collaborative scheduling matrix which comprises multiple sequences such as resource pre-scheduling, train path reconstruction, station load evacuation and system function compensation and is linked through time sequence correlation parameters; and finally, converting the cooperative scheduling matrix into executable scheduling instruction sets, and pushing the executable scheduling instruction sets to corresponding control terminals according to execution subject classification, thereby realizing efficient emergency scheduling.

The invention discloses a bolt color marking line detection method based on image segmentation and classification fusion, and belongs to the field of track maintenance. According to the method, a color mark line area of a bolt head is accurately segmented by collecting a track bolt image and combining image preprocessing and mask extraction. On the basis, through deep semantic feature extraction and fusion, pixel-level masks and global context information are integrated and sent to a classification network to judge whether the bolt color graticules are aligned or not. And finally, the whole network structure is optimized through a joint training mechanism, so that the accuracy and robustness of the system in a complex environment are improved. According to the method, the bolt color marking line can be accurately extracted under the complex conditions of weak light, light reflection, shielding and the like, the fine offset with the alignment deviation smaller than 15 degrees can be effectively recognized, and the method can be widely applied to the fields of subway lines, urban rail vehicle maintenance, automatic inspection robots and the like.

The present application provides a kind of urban rail vehicle electronic history data processing method, the method establishes vehicle operation configuration in combination with vehicle structure design, and vehicle configuration data structure is structured using database technology;Vehicle history data granularity refined to the smallest maintainable unit is used, the correctness and timeliness of data are guaranteed through data analysis and integration technology based on business process, the whole life cycle stage document of vehicle is stored using data storage technology, and preliminary vehicle knowledge framework is established in combination with full-text search technology, to provide basic data support for later subway vehicle procurement, technical reform, operation.In history data management, block chain technology is added to ensure the confidentiality and integrity of data flow, to prevent sensitive data from being leaked and tampered with during transmission, and to enhance the security and correctness of vehicle history data, to provide reliable data security guarantee for subsequent history sharing.

The invention discloses a public transportation network reconstruction method under urban rail networking operation, and the method comprises the steps: obtaining multivariate travel data, carrying out the data-driven calculation of passenger flow OD, and providing scientific data support for network planning; meanwhile, when the whole line network is established, a backbone network and a connection network are adopted to simplify levels, technical indexes and service standards of all the levels are defined, actual passenger flow distribution is accurately measured and calculated, and the reasonability of line planning is ensured; and meanwhile, by establishing a three-dimensional matching index system, quantifying the difference between the current situation and the ideal network, and by differentiating schemes and implementing priority ranking, lines with large difference are optimized according to priorities, so that gradual optimization of the whole network is realized, large-scale oscillation is avoided, and finally reconstruction of the public transport network is realized. A complementary situation with urban rail transit is formed, redundant waste of public traffic design is avoided, and meanwhile, residents can go out more conveniently by optimizing lines.

The invention discloses an anti-label noise urban rail train gearbox incremental fault diagnosis method, which comprises the following steps of: segmenting and standardizing collected gearbox fault data of an urban rail train bogie according to a preset length to obtain a fault sample; based on the fault sample, constructing a data set which can be used for class incremental learning; constructing a fault diagnosis model, and performing initial training on the fault diagnosis model by using the data set to obtain a fault diagnosis model after initial training; performing incremental training on the fault diagnosis model after the initial training to obtain a trained fault diagnosis model; and performing fault identification on a to-be-detected vibration signal by using the trained fault diagnosis model to obtain a diagnosis result of the gearbox. According to the method, disastrous forgetting and noise interference can be effectively handled, and the accuracy of fault diagnosis of the gearbox of the urban rail train is improved.

The invention discloses an urban rail energy consumption scheduling method based on artificial intelligence multi-objective optimization, and the method comprises the steps: effectively solving a problem of scheduling decision limitation caused by a single optimization objective in the prior art through comprehensive real-time data processing, multi-objective collaborative optimization and intelligent algorithm iteration solution; the optimal control of the overall operation energy consumption of the rail transit system is realized; the train operation energy efficiency, the regenerated energy utilization rate and the operation punctuality rate are effectively improved, meanwhile, the peak load of a traction power supply system is reduced, the overall energy utilization efficiency and the operation performance of rail transit are remarkably improved, and the dual purposes of energy conservation, consumption reduction and efficient operation of the system are achieved.

The invention discloses an urban rail hybrid energy storage system power distribution method based on LPF-MPC, and the method comprises the steps: employing a hybrid energy storage system (HESS) composed of a super capacitor and a battery for the frequent starting and braking of an urban rail train to generate a large amount of braking energy; on the basis that a low-pass filter (LPF) distributes power required by HESS, the rolling prediction and optimization control capability of model prediction control (MPC) is utilized to control the minimum value of the state of charge (SOC) of a super-capacitor to be close to a preset expected value, so that power distribution is optimized, and loads are reasonably distributed to the super-capacitor and a battery; according to the method, the working interval of the super-capacitor SOC is dynamically adjusted, the capacity advantage of the super-capacitor is fully played, the charging and discharging times of the battery are reduced, the energy management efficiency of the urban rail hybrid energy storage system is effectively improved, the system stability is enhanced, and the service life of the battery is prolonged.

The invention discloses an urban rail station full life cycle carbon accounting method, and relates to the technical field of urban rail transit engineering low carbon. The method comprises the following steps: constructing and regularly updating a carbon emission factor library, collecting and storing full-life-cycle activity level data and time information, determining an accounting dimension according to a scene, screening an accounting cardinal number of unit time from the data, accounting carbon emission increment of each dimension of the unit time in combination with the factor library, and finally accumulating to obtain the total amount of full-life-cycle carbon emission. According to the method, complete-cycle carbon emission accurate accounting of the urban rail station is realized, data support is provided for low-carbon decision, and the efficiency and scientificity of low-carbon management of the urban rail station are improved.

The invention provides an automatic test question generation method and device for the urban rail industry and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining question setting information input by a user, wherein the question setting information comprises knowledge point information, question setting parameters and question setting rules; obtaining a target knowledge fragment from a knowledge base according to the knowledge point information and the question setting parameters; according to the target knowledge fragment, the question setting parameters and the question setting rules, generating at least one question by adopting a question generation model; evaluating each question in at least one evaluation dimension by adopting a plurality of question evaluation models to obtain a plurality of scores and modification suggestions of each question in at least one evaluation dimension; according to the multiple scores of each question in the at least one evaluation dimension, calculating a total score and a confidence coefficient of each question; and optimizing each question by adopting a question generation model according to the total score, the confidence coefficient and the modification suggestion of each question. The test questions can be automatically generated, and the quality of the test questions is ensured.

The invention discloses an evaluation index system construction method for a construction time sequence of an urban composite channel, relates to the technical field of traffic engineering and urban planning, and solves the problems of facility layout conflict, traffic operation disturbance, low efficiency of resource allocation and the like caused by the lack of scientific time sequence overall planning of the construction time sequence of an existing urban rail-road composite channel. The construction method comprises the following steps: S11, dividing a city composite channel construction process into a plurality of evaluation stages of construction time sequence influence; s12, determining an evaluation dimension in each evaluation stage; s13, extracting an evaluation criterion based on each evaluation dimension; s14, refining a plurality of secondary evaluation indexes according to each evaluation criterion, and constructing an evaluation index system according to the secondary evaluation indexes; and S15, determining the meaning, the calculation method and the numerical value acquisition mode of each secondary evaluation index. The invention further discloses an evaluation method for the construction time sequence of the urban composite channel. According to the invention, the scientificity of traffic planning decision and the efficiency and benefit of engineering implementation can be improved.

The invention discloses an urban rail signal and platform door integrated system comprising an integrated platform door system which comprises a central control cabinet and a plurality of platform side devices; part of subsystems of the central control cabinet are connected with the signal system through Ethernet interfaces and connected with the comprehensive monitoring system and part of platform side equipment through hard wire interfaces so as to receive control signals of all parties. Part of subsystems of the central control cabinet perform logic processing on the received control signals, and the logically processed control signals are sent to platform side equipment for controlling a platform door; and the platform side equipment for controlling the platform door responds to the control signal of the highest level according to the priority sequence and executes corresponding platform door control operation. According to the invention, network security communication is adopted to replace hard wire connection, so that intermediate control links are reduced, the linkage time is greatly shortened, the linkage efficiency is improved, the number of on-line trains is reduced, and the energy consumption is reduced.

The invention provides an intelligent auxiliary decision-making method and system applied to urban rail system emergency command, and belongs to the technical field of urban rail transit. Firstly, an urban rail system total factor digital twin covering trains, rails, stations, power supply and emergency resource scenes is constructed; each scene is disassembled into independent twin units, the state and position of a physical entity are synchronized, an abnormal trigger signal is captured by means of a digital twin, an event deconstruction chain is constructed, urban rail emergency resource block chain evidence storage data is called to generate a resource combination scheme, and a trigger rule and execution logic of an intelligent contract are generated based on the resource combination scheme and the event deconstruction chain. According to the method, the intelligent contract is deployed to the urban rail dispatching block chain node, parameters are optimized through digital twinborn simulation, the dispatching instruction is automatically generated and synchronized to the control terminal when the abnormity meets the triggering rule, rapid and accurate emergency response is achieved, and safe operation of the urban rail system is effectively guaranteed.

The invention discloses a network security situation real-time evaluation method and system based on urban rail transit, and the method comprises the steps: collecting original data from a plurality of heterogeneous data sources of an urban rail transit network, carrying out the standardization processing, and generating a standardized security event; constructing a digital twin map; calculating the risk probability of nodes in the graph by using a time-space diagram neural network model, predicting an attack path and evaluating the service influence; a stream processing engine is adopted to consume a standardized security event in real time, and model reasoning, node risk probability updating, attack path prediction and service influence evaluation are performed on a local sub-graph related to the current event in the digital twin graph; and performing real-time parameter fine tuning on the space-time diagram neural network model through an online learning mechanism, and updating the space-time diagram neural network model through an incremental learning mechanism. According to the technical scheme, real-time dynamic prediction and active defense of urban rail network security threats are achieved, and the situation awareness capability and the autonomous protection level of key infrastructures are remarkably improved.

The invention discloses a large-model-driven urban railway newly-opened station flow prediction method, and relates to the technical field of urban railway traffic operation management. The technical problems of low prediction precision, poor interpretability of a traditional model and insufficient reasoning flexibility in a complex urban scene caused by lack of historical passenger flow data of a newly opened station of an urban rail are solved. The method comprises the following steps: firstly, collecting pre-processed passenger flow related multi-dimensional data, and then constructing a multi-dimensional reference system to retrieve a target station similar reference or extract a macroscopic index reference; judging a scene category through a rule engine, calling a corresponding template, and filling information to generate a structured cue word; inputting the cue word into the large language model, and outputting a content prediction value and a result of a natural language reasoning chain according to the reasoning thinking chain template; and finally, checking the result, and completing optimization of the method based on feedback. The method has a good application prospect in the technical field of urban rail transit operation management.

The invention discloses an urban rail transit short-time origin-destination passenger flow prediction method and device and a medium. The method comprises the steps of firstly obtaining a data set containing at least one origin-destination pair, determining time distribution according to rail transit passenger flow distribution, determining spatial distribution according to network information, and establishing an origin-destination pair connection relation to obtain an origin-destination topological graph; and then calling a pre-training space-time diagram convolution model containing a feature fusion module and a time and space feature processing module, and performing prediction according to time distribution and the origin-destination topological graph to obtain a passenger flow prediction result. Wherein the time module extracts time distribution features, the space module extracts space distribution features from a topological graph, the fusion module fuses the time distribution features and the space distribution features into space-time fusion features, and finally a origin-destination passenger flow prediction result is obtained. According to the embodiment of the invention, the defects of a traditional method in urban rail origin-destination passenger flow prediction are effectively overcome, and the origin-destination passenger flow prediction precision is effectively improved.

This application provides a clearance maintenance device, method, and storage medium for urban rail trains. The device includes: a car body; a clearance detection device disposed around the car body to determine whether the car body has touched an obstacle to be detected, and when it is determined that the car body has touched an obstacle to be detected, sending a deceleration signal to the train control system; a clearance determination device to acquire an image of the obstacle to be detected and send the image of the obstacle to be detected to the train control system so that the train control system can determine whether the obstacle to be detected is an obstacle to be processed; and a clearance maintenance device to process the obstacle to be processed when it receives a signal from the train control system to process the obstacle to be processed. The technical solution of this application can realize automatic clearance maintenance, greatly saving manpower, material resources, and time costs.

The invention discloses a hot charging process method for a wheel and an axle of a rail transit vehicle, which mainly adopts a hot charging method for cooperatively controlling oven preheating and electromagnetic induction local heating, and utilizes a thermal buffer effect of matrix preheating to slow down the heat dissipation rate of a hub hole and prolong the assembly window period. According to the method, the problem of too fast cooling caused by single local heating is solved while wheel surface oxidation and material deterioration are avoided, the assembly success rate is increased, and comprehensive energy consumption is reduced. And the method is suitable for scenes such as urban rail vehicles, high-speed rails and heavy-load trains with strict requirements on surface quality and assembly precision.

The invention provides an urban rail train air conditioner dual-redundancy variable-frequency power source control method and system, and relates to the technical field of emergency protection circuit devices.The method comprises the steps that multi-compartment cooperative control optimization solution is carried out with train exterior environment time sequence data as dynamic boundary conditions, and K refrigeration distribution time sequence baselines are generated; in the differential refrigeration executing process, time sequence state tracking is carried out, and K in-vehicle environment dynamic parameter streams and K multi-mode power state data are obtained; performing power supply health trend analysis, and outputting K power redundancy margin predicted values; demand deviation identification is carried out, and K refrigeration power dynamic compensation amounts are output; and executing two-dimensional toughness control of the K dual-redundancy variable-frequency power supplies through a distributed consensus negotiation algorithm. The technical problems that in the prior art, urban rail train air conditioner power supply control usually adopts load distribution based on preset rules, dynamic change factors of carriages cannot be responded in real time, and the overall energy efficiency of an air conditioner system is affected are solved.

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.

This invention discloses a mechanized construction device for urban rail transit using the scattered laying method, belonging to the technical field of urban rail transit construction equipment. The device includes a laying vehicle assembly installed on two temporary tracks. A conveying mechanism is installed at the center of the laying vehicle assembly. A lifting mechanism and a laying mechanism are installed on one side of the laying vehicle assembly, with the lifting mechanism positioned above the laying mechanism. This invention, through the design of a pushing and feeding mechanism, allows for the orderly placement of rubber pads on the sleepers while simultaneously adjusting the spacing between multiple sleepers. Both the sleeper adjustment and rubber pad placement processes are automated, requiring no manual operation, thus saving labor costs and significantly improving overall laying efficiency.

The invention discloses an urban rail transit network level signal professional data management and interface method, and relates to the technical field of urban rail transit signal professional data management. A data resource pool platform is established between an online network signal professional communication front-end processor and an external interface; the data resource pool platform comprises a data access module, a data transmission module, a data storage module, a data subscription module and a data service module; the data access module is used for acquiring signal data from a network signal professional communication front-end processor, the data transmission module establishes labels for the signal data and performs classified storage, the data storage module performs standardized processing on the signal data and then stores the signal data, and the data subscription module manages an external interface and performs external interface subscription content configuration. And the data service module performs identity verification on the external interface and provides data. According to the method, the intelligence level and decision-making efficiency of line network operation are remarkably improved, and a core technical guarantee is provided for safe and efficient operation of urban rail transit.