CN-121504102-B - Intelligent auxiliary decision-making method and system applied to urban rail system emergency command

CN121504102BCN 121504102 BCN121504102 BCN 121504102BCN-121504102-B

Abstract

The invention provides an intelligent auxiliary decision-making method and system applied to urban rail system emergency command, which belong to the technical field of urban rail traffic, and comprise the steps of firstly constructing an urban rail system full-element digital twin body covering the scenes of trains, tracks, stations, power supply and emergency resources, dismantling each scene into independent twin units, synchronizing the states and positions of physical entities, capturing abnormal trigger signals by means of the digital twin bodies, constructing an event deconstructing chain, calling urban rail emergency resource block chain storage certificate data to generate a resource combination scheme, generating triggering rules and execution logic of intelligent contracts based on the resource combination scheme and the event deconstructing chain, deploying the intelligent contracts to urban rail scheduling block chain nodes, automatically generating scheduling instructions and synchronizing the scheduling instructions to a control terminal when the triggering rules are abnormally met by the simulation optimization parameters of the digital twin bodies, realizing quick and accurate emergency response, and effectively guaranteeing the safe operation of the urban rail system.

Inventors

LI DESHENG
CHEN ZHEYU
HUANG XUEJUN
Gao Zishang
LIU HETIAN
ZHANG RUI
ZHONG QIZHI
TIAN LIYONG

Assignees

上海长合信息技术股份有限公司
宁波市市域铁路投资发展有限公司

Dates

Publication Date: 20260505
Application Date: 20260113

Claims (8)

1. An intelligent auxiliary decision-making method applied to urban rail system emergency command is characterized by comprising the following steps: Constructing a city rail system full-element digital twin body, wherein the city rail system full-element digital twin body covers scenes of trains, tracks, stations, power supply and emergency resources, each scene is disassembled into independent twin units, and each independent twin unit synchronizes the running state and the space position of a physical entity through a real-time sensing channel to realize mapping of a physical world and a digital space; Capturing an abnormal trigger signal according to the dynamic sensing capability of the digital twin body, tracking the propagation track of the abnormal trigger signal among the independent twin units, and constructing an event deconstructing chain comprising abnormal source positioning, affected independent twin unit layers and fault conduction intensity, wherein the fault conduction intensity reflects the difference of the influence degree of the abnormality on different independent twin units; Invoking urban rail emergency resource block chain evidence storage data, dynamically coupling the event deconstructing chain with an emergency resource independent twin unit, and generating a resource combination scheme covering resource adaptation characteristics, response efficiency and deployment sequence, wherein the urban rail emergency resource block chain evidence storage data comprises attribute characteristics, storage positions, maintenance records and available states of emergency resources; Based on the resource combination scheme and the conduction characteristics of the event deconstructing chain, triggering rules and execution logic of an intelligent contract are generated, wherein the intelligent contract is used for definitely scheduling the automatic execution flow of instructions, the authority boundary of resource use and the time sequence cooperation requirement of an emergency link, and the action connection nodes and interaction modes of all the participants are standardized through the time sequence cooperation requirement; Deploying the intelligent contracts to urban rail scheduling block chain nodes, building simulation environments through digital twin bodies to simulate contract execution processes and optimize parameters, and automatically generating scheduling instructions and synchronizing the scheduling instructions to control terminals of corresponding physical entities when abnormal states meet trigger rules; The capturing of the abnormal trigger signal by depending on the dynamic sensing capability of the digital twin body comprises the following steps: generating differential abnormality determination criteria for each independent twin unit; Continuously analyzing real-time monitoring data of each independent twin unit through a perception data processing module of the digital twin body, and generating an abnormal trigger signal after the real existence of the abnormality is confirmed through comparison of associated independent twin unit data and historical abnormal data when a plurality of continuous synchronous periods of the real-time monitoring data exceed an abnormality judgment standard, wherein the content of the abnormal trigger signal comprises an abnormality occurrence time, a corresponding independent twin unit identifier and specific abnormal performance; the method for tracking the propagation track of the abnormal trigger signal among the independent twin units, constructing an event deconstructing chain comprising abnormal source positioning, affected independent twin unit level and fault conduction intensity, comprises the following steps: The method comprises the steps of taking an independent twin unit triggering an abnormality as a tracing starting point, searching the independent twin unit which has direct physical association or function dependence with the tracing starting point, marking the independent twin unit as a first-stage affected independent twin unit, and recording association types and an affected transfer mode; Trend analysis is carried out on the monitoring data of the first-stage affected independent twin units, if the data shows obvious abnormal change characteristics and accords with a conduction rule, the associated independent twin units are continuously traversed and marked as second-stage affected independent twin units, the logic is expanded layer by layer until a traversing program does not search for new affected independent twin units; Calculating fault conduction intensity for each affected independent twin unit, wherein the fault conduction intensity is comprehensively generated based on the association tightness degree of the independent twin units and an abnormal starting point, the physical distance and the function dependency level, the association tightness degree is quantified through an entity connection relation, the physical distance is calculated through space coordinates, and the function dependency level is determined through system architecture analysis; According to the sequence of abnormal conduction, arranging an abnormal starting point and each stage of affected independent twin units into a hierarchical sequence, and correspondingly marking fault conduction intensity, abnormal change characteristics and conduction paths for each independent twin unit to form a structured affected independent twin unit hierarchical table; positioning and marking an abnormal starting point, wherein the marking content comprises an abnormal type, a specific position coordinate, an initial influence range and a development trend pre-judgment, and integrating an abnormal source mark, an affected independent twin unit level table and fault conduction intensity data to form an event deconstructing chain; and (3) re-evaluating the abnormal state and fault conduction intensity of each affected independent twin unit after each twin body synchronization period, and performing addition and deletion adjustment on the hierarchy table of the affected independent twin units.
2. The intelligent auxiliary decision-making method applied to urban rail system emergency command according to claim 1, wherein the construction of the urban rail system full-element digital twin body comprises the following steps: acquiring full-dimensional data of physical entities of a urban rail system, and constructing a high-precision basic twin model of each entity based on the full-dimensional data, wherein the full-dimensional data comprises geometric structure data, material attribute data, operation parameter data and association relation data, and the geometric structure data comprises train body contours, track section forms, site building layout and power supply line trends; configuring a multi-source perception interface for each basic twin model, and accessing real-time monitoring data of a corresponding physical entity; Performing time stamp alignment and space coordinate calibration on state data of each independent twin unit, establishing real-time binding between the train independent twin unit and the track independent twin unit through track mileage coordinates, realizing association between the station independent twin unit and the emergency resource independent twin unit through station area grid coordinates, forming a linkage relation between the power supply independent twin unit and other independent twin units through power supply network topology, and dynamically adjusting the association relation along with the running state of the entity; And displaying the running state of each independent twin unit by adopting layered rendering and dynamic highlighting technology.
3. The intelligent auxiliary decision method applied to urban rail system emergency command according to claim 1, wherein the calling urban rail emergency resource block chain certification data dynamically couples the event deconstructing chain with an emergency resource independent twin unit, comprising: Accessing distributed nodes of a urban rail emergency resource block chain, and calling emergency resource data subjected to encryption and certification; Extracting type information of an abnormal source, a distribution range and fault characteristics of an affected independent twin unit from the event deconstructing chain, and extracting resource function requirements required by emergency treatment; and (3) performing preliminary matching on the basis of the resource function requirements and the resource type attribute in the blockchain storage certificate data, screening out resources which meet the function requirements and are normal in the current available state, and forming a candidate resource set.
4. The intelligent auxiliary decision-making method applied to urban rail system emergency command according to claim 3, wherein the generating a resource combination scheme covering resource adaptation characteristics, response efficiency and deployment sequence comprises: Acquiring real-time position information of an independent twin unit of the emergency resource corresponding to the candidate resource in the candidate resource set through a digital twin body, and obtaining response efficiency of each candidate resource reaching an abnormal source and a main affected independent twin unit by combining the mobile capability characteristic of the candidate resource; calculating resource adaptation characteristics by comparing the resource technical performance parameters with the abnormal handling requirement parameters, calling a resource history maintenance database, extracting fault handling success rate data and correcting the resource adaptation characteristics; Setting an evaluation weight parameter based on the initial influence level of the abnormal source, quantifying the initial influence level through the abnormal sweep range and the hazard degree, and converting the response efficiency into a standardized response score; sequencing candidate resources according to the comprehensive adaptation scores, extracting quantity distribution data and fault conduction intensity data of the affected independent twin units, and selecting K types of resources with the front comprehensive adaptation scores to form a resource combination so as to cover all abnormal scenes and the affected independent twin units; And labeling a specific resource adaptation characteristic, a response efficiency evaluation result and a deployment sequence for each resource in the resource combination, wherein the deployment sequence is determined by the fault conduction intensity of the independent twin units corresponding to the resource in an event deconstructing chain, when the independent twin units corresponding to the fault conduction intensity are core-level function dependency level entities, the deployment sequence of the corresponding resource is set to be a first priority, when the independent twin units correspond to the circuit-level function dependency level entities, the independent twin units correspond to a second priority, when the independent twin units correspond to the facility level, the independent twin units correspond to a third priority, and the resource allocation instruction is issued according to the priorities.
5. The intelligent decision-making assisting method applied to urban rail system emergency command according to claim 1, wherein the deploying the intelligent contracts to urban rail scheduling blockchain nodes simulates the contract execution process and optimizes parameters by digital twin building simulation environment, comprising: Submitting the written intelligent contract codes to consensus nodes of a urban rail scheduling blockchain, and starting a distributed consensus verification process among the consensus nodes, wherein the distributed consensus verification process is used for performing cross verification on grammar correctness, logic integrity and authority compliance of the intelligent contract codes, writing the intelligent contract codes into a blockchain block after the verification is completed, and generating unique contract addresses by deployed intelligent contracts, wherein the contract addresses are used for subsequent calling, inquiring and state tracing operations of the intelligent contracts; extracting current running state data of the urban rail system from the digital twin, importing the running state data into a simulation environment construction module, re-carving the current running scene of the urban rail system to form a simulation environment consistent with actual system parameters, and importing triggering rules and execution logic of an intelligent contract into the simulation environment to enable the simulation environment to have basic conditions for executing the simulation contract, wherein the running state data comprises real-time parameters, space positions, association relations and external environment conditions of each independent twin unit; extracting abnormal development trend data from an event deconstructing chain, taking the abnormal development trend data as a simulation input condition, starting a simulation execution flow, simulating a complete change process of the abnormal data from an initial state to a trigger rule, and synchronously simulating trigger response and scheduling instruction generation operation of intelligent combination in the complete change process, wherein the abnormal development trend data comprises the diffusion rate of an abnormal source, the state change rule of an affected independent twin unit and the evolution characteristics of fault conduction intensity; acquiring key data in a simulation process in real time through a simulation data recording module, and sorting the key data into a structured simulation execution data set according to time sequence, wherein the key data comprises an execution result of each resource scheduling instruction, a cooperative effect of resources and an affected independent twin unit, overall process time consumption of emergency treatment, an abnormal control effect and state change data in a contract execution process; Invoking an urban rail emergency treatment standard database, extracting an emergency treatment time standard, a resource cooperative efficiency standard and an abnormal control effect standard from the urban rail emergency treatment standard database, performing multi-dimensional comparison on key data in a simulation execution data set and the emergency treatment time standard, the resource cooperative efficiency standard and the abnormal control effect standard, identifying problems in a simulation process, positioning intelligent contract parameter deviation of the problems by a problem tracing module, and generating an optimization scheme comprising a parameter adjustment direction and an adjustment amplitude; According to the optimization scheme, adjusting time sequence cooperative parameters, resource deployment sequence parameters and scheduling path planning logic parameters in the intelligent contract, reintroducing the adjusted intelligent contract parameters into a simulation environment, repeatedly executing a simulation flow, collecting new simulation execution data, comparing the new simulation execution data with emergency treatment standard again, and continuing to optimize the parameters if deviation still exists until key indexes in the simulation execution data meet the emergency treatment standard requirements, and determining final intelligent contract parameters.
6. The intelligent auxiliary decision-making method applied to urban rail system emergency command according to claim 5, wherein when the abnormal state satisfies the triggering rule, the contract automatically generates the scheduling instruction and synchronizes to the control terminal of the corresponding physical entity, comprising: Acquiring abnormal state data of each independent twin unit in real time through a sensing channel of a digital twin body, comparing the acquired abnormal state data with a triggering rule of an intelligent contract in real time, wherein the comparison process is realized through a condition judgment module built in the contract, and when a parameter value in the abnormal state data continuously reaches a threshold value set by the triggering rule, an intelligent contract is automatically called by a urban rail scheduling block chain node to generate a corresponding scheduling instruction according to execution logic; Starting a synchronous checking mechanism in the instruction transmission process, after each control terminal receives a scheduling instruction, generating a receiving confirmation signal containing an instruction content abstract, feeding back the receiving confirmation signal to an instruction distribution module to summarize the confirmation signals of all the control terminals, and retransmitting the scheduling instruction until all the corresponding control terminals feed back receiving confirmation if the control terminals which do not receive the confirmation signals exist, so that synchronous transmission of the scheduling instruction is completed; After each control terminal receives the scheduling instruction, corresponding operation is executed according to the instruction content, meanwhile, the execution state, the completion condition and abnormal feedback data of the operation are transmitted to the urban rail scheduling block chain node in real time through a sensing channel of the digital twin body, and the block chain link node records the feedback data to a non-tamperable block according to the time sequence to form a complete contract execution log.
7. The intelligent auxiliary decision-making method applied to urban rail system emergency command according to claim 1, characterized in that it further comprises: Continuously acquiring abnormal state change data of an affected independent twin unit, working state data of an emergency resource independent twin unit and operation progress data through a perception channel of a digital twin body in the whole intelligent contract execution process, classifying and integrating the data according to the acquisition time sequence, and generating a real-time monitoring data set containing abnormal recovery progress data, resource consumption data and treatment effect evaluation data; Extracting state recovery data of an affected independent twin unit from a real-time monitoring data set, and comparing the state recovery data with a state recovery standard in an abnormal recovery judging standard, wherein the abnormal recovery judging standard comprises the state recovery standard of the affected independent twin unit, an abnormal data stability requirement and a safe operation verification project, and meanwhile, invoking sensor data of a physical entity and analog data of a digital twin body to carry out multi-source cross verification; Transmitting an abnormal recovery confirmation result to the urban rail scheduling blockchain node, so that the urban rail scheduling blockchain node executes a termination flow of an intelligent contract after receiving the result, and generates an emergency treatment completion report, wherein the emergency treatment completion report comprises event basic information, resource use details, treatment flow records and recovery effect evaluation, and meanwhile, visual playback data of the whole abnormal development and treatment process is extracted from the digital twin body, and the visual playback data is added to the emergency treatment completion report; Writing the emergency treatment completion report into the urban rail dispatching block chain through an encryption certification storage interface of the block chain to form a non-tamperable treatment record, and simultaneously pushing the emergency treatment completion report to management terminals of an urban rail emergency dispatching center, a security management department and related operation units, so that the management terminals automatically generate a receiving log after receiving the report and feed the receiving log back to the block chain node; after technical performance parameters, loss data and fault hidden trouble records of the emergency resource independent twin units are extracted from the real-time monitoring data set, state evaluation results of each emergency resource are generated by comparing the resource delivery performance parameters with maintenance standards, and the state evaluation results comprise available states, loss degree and recommended maintenance items of the resource; transmitting the state evaluation result to a urban rail emergency resource block chain node, updating the certificate storage data of the corresponding emergency resource in the block chain, and if the state evaluation result shows that the resource has a fault or loss, generating a maintenance resource allocation instruction and transmitting the maintenance resource allocation instruction to the corresponding maintenance unit; Extracting blockchain log data of the emergency treatment, an emergency treatment completion report and a resource state evaluation result from a urban rail scheduling blockchain node, calculating the deviation value of an abnormal judgment standard and actual abnormal data of a digital twin body, counting the misjudgment rate of an intelligent contract triggering rule, analyzing the functional adaptation deviation data of a resource matching algorithm, and generating parameter adjustment suggestions based on the analysis result; Writing the parameter adjustment suggestion into a configuration module of the digital twin and the intelligent contract, updating an abnormality judgment threshold value of the digital twin and a triggering parameter of the intelligent contract, generating a parameter update log after updating, writing the parameter update log into a urban rail scheduling block chain for parameter tracing of a subsequent emergency scheduling process; The method comprises the steps of extracting an event deconstructing chain, a resource combination scheme, intelligent contract content, an emergency treatment completion report and parameter optimization suggestion from emergency treatment data, classifying and sorting according to an abnormal type, a treatment scene and a resource type, and inputting a standardized emergency treatment case library, wherein the standardized emergency treatment case library is used for carrying out data retrieval according to the abnormal type, the influenced independent twin unit type and the resource use type by establishing a multi-dimensional retrieval index, and automatically associating historical similar cases.
8. Be applied to intelligent auxiliary decision-making system of urban rail system emergency command, characterized by comprising: A processor; a machine-readable storage medium storing machine-executable instructions for the processor; wherein the processor is configured to perform the intelligent auxiliary decision method of any one of claims 1 to 7 applied to urban rail system emergency command via execution of the machine executable instructions.

Description

Intelligent auxiliary decision-making method and system applied to urban rail system emergency command Technical Field The invention relates to the technical field of urban rail transit, in particular to an intelligent auxiliary decision-making method and system applied to urban rail system emergency command. Background In the running process of the urban rail transit system, various emergency conditions such as train faults, rail damages, station personnel crowds, abnormal power supply systems and the like are faced, and the conditions seriously influence the normal running of the urban rail transit system and the safety of passengers. The traditional emergency command decision method mainly depends on manual experience and a preset fixed emergency plan, and lacks of real-time, accurate sensing and dynamic analysis on all elements of the urban rail system. When dealing with complicated and changeable abnormal events, the method is difficult to quickly and accurately locate the abnormal source, judge the fault conduction path and the influence range, and also cannot efficiently allocate emergency resources, so that emergency response is not timely, decision is not scientific, various emergency conditions cannot be effectively dealt with, and great hidden danger is brought to the safe operation of the urban rail system. In addition, the prior art has the problems of opaque information, low cooperative efficiency and the like in the aspects of emergency resource management and scheduling, and the optimal configuration and efficient utilization of emergency resources are difficult to realize. Disclosure of Invention In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides an intelligent auxiliary decision method applied to emergency command of a urban rail system, where the method includes: Constructing a city rail system full-element digital twin body, wherein the city rail system full-element digital twin body covers scenes of trains, tracks, stations, power supply and emergency resources, each scene is disassembled into independent twin units, and each independent twin unit synchronizes the running state and the space position of a physical entity through a real-time sensing channel to realize mapping of a physical world and a digital space; Capturing an abnormal trigger signal according to the dynamic sensing capability of the digital twin body, tracking the propagation track of the abnormal trigger signal among the independent twin units, and constructing an event deconstructing chain comprising abnormal source positioning, affected independent twin unit layers and fault conduction intensity, wherein the fault conduction intensity reflects the difference of the influence degree of the abnormality on different independent twin units; Invoking urban rail emergency resource block chain evidence storage data, dynamically coupling the event deconstructing chain with an emergency resource independent twin unit, and generating a resource combination scheme covering resource adaptation characteristics, response efficiency and deployment sequence, wherein the urban rail emergency resource block chain evidence storage data comprises attribute characteristics, storage positions, maintenance records and available states of emergency resources; Based on the resource combination scheme and the conduction characteristics of the event deconstructing chain, triggering rules and execution logic of an intelligent contract are generated, wherein the intelligent contract is used for definitely scheduling the automatic execution flow of instructions, the authority boundary of resource use and the time sequence cooperation requirement of an emergency link, and the action connection nodes and interaction modes of all the participants are standardized through the time sequence cooperation requirement; the intelligent contracts are deployed to the urban rail scheduling block chain nodes, the simulation environment is built through the digital twin body to simulate the contract executing process and optimize parameters, and when the abnormal state meets the triggering rule, the intelligent contracts automatically generate scheduling instructions and synchronize to the control terminals of the corresponding physical entities. In still another aspect, an embodiment of the present invention further provides an intelligent auxiliary decision-making system applied to an emergency command of a urban rail system, which is characterized by including: the system comprises a processor, a machine-readable storage medium for storing machine-executable instructions of the processor, wherein the processor is configured to execute the intelligent auxiliary decision-making method applied to urban rail system emergency command by executing the machine-executable instructions. In yet another aspect, embodiments of the present invention further provide a co