Subway traffic operation decision adjusting system and method based on large visual screen

Abstract

The invention relates to the technical field of intelligent traffic, in particular to a subway traffic operation decision adjustment system and method based on a large visual screen, and the system comprises a perception data module, a data processing center, a large visual screen interaction platform, an intelligent instruction decision engine, and a dynamic adjustment unit. Wherein the sensing data module collects train positioning data, platform monitoring video streams, gate passing data and environment sensor data in real time; integrating and analyzing the data by the data processing center, and mining potential modes, association relationships and abnormal conditions in the data; the visual large-screen interaction platform presents a line network congestion thermodynamic diagram, equipment fault positioning and passenger flow prediction trend in real time; the intelligent instruction decision engine is used for dynamically generating a train shift adjustment strategy and an emergency dispersion scheme; and the dynamic adjusting unit dynamically adjusts the electronic guide screen and the platform broadcast, and dynamically dispatches the train. Therefore, the problems of lagging abnormal response, unreasonable resource allocation, low efficiency and the like in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a subway traffic operation decision adjustment system and method based on a visual large screen. BACKGROUND

[0002] Under the background of accelerating global urbanization and continuous expansion of rail transit network scale, as a large-capacity public transport tool, the subway plays a core role in urban traffic hubs. With the increasing diversification of passenger travel demand and the significant increase in train operation density, the subway operation system faces multiple challenges in balancing safety, efficiency and service quality. Under this situation, it is particularly important to build an intelligent operation decision adjustment system based on a visual large screen. This system can monitor the subway line network operation state in real time and conduct multi-dimensional data analysis, and rely on intelligent algorithms to realize rapid response to abnormal events and dynamic scheduling optimization, to meet the core needs of improving subway operation efficiency and reducing operation cost.

[0003] However, the current traditional subway operation decision mainly relies on manual experience or single data source analysis, which not only leads to delayed response to abnormal events due to low data processing efficiency, and cannot adapt to complex scenarios such as peak passenger flow mutation or equipment sudden failure, but also causes unreasonable allocation of operation resources and difficulty in guaranteeing train punctuality. At the same time, the traditional decision system is limited by the significant "data island" phenomenon, and it is difficult to link and integrate multi-source heterogeneous data such as train operation state, passenger flow distribution, equipment parameters and scheduling instruction execution time sequence, and conduct in-depth correlation analysis. In addition, the simplification of decision logic makes the system fall into a decision blind area in scenarios such as large passenger flow congestion, interval equipment failure or bad weather, resulting in low efficiency of delay event handling, and negative impact on passenger travel experience, operation safety and economic benefits. SUMMARY

[0004] The present application provides a subway traffic operation decision adjustment system and method based on a visual large screen to solve the problems of delayed abnormal response, unreasonable resource allocation and low efficiency in the prior art.

[0005] The first aspect embodiment of the application provides a subway traffic operation decision adjustment system based on a visual large screen, comprising: a perception data module, a data smelting hub, a visual large screen interactive platform, an intelligent instruction decision engine, and a dynamic adjustment unit; wherein the perception data module is configured to collect train positioning data, platform monitoring video stream, gate passage data, and environmental sensor data in real time; the data smelting hub is configured to integrate and analyze the collected data, and mine potential patterns, correlation relationships, and abnormal conditions in the data; the visual large screen interactive platform is configured to present a line network congestion heat map, device fault positioning, and passenger flow prediction trend in real time, and switch interfaces and highlight information according to different operation scenarios and needs; the intelligent instruction decision engine is configured to dynamically generate train schedule adjustment strategies and emergency evacuation schemes; and the dynamic adjustment unit is configured to dynamically adjust electronic guide screens and platform broadcasts according to decision instructions, and dynamically schedule trains.

[0006] Preferably, the perception data module comprises a sensor cluster and an abnormal data filtering unit, wherein the sensor cluster is configured to collect train position, passenger flow density, human body temperature, and obstacle distance data; and the abnormal data filtering unit is configured to use a sliding window outlier detection algorithm to remove invalid data caused by sensor drift, shielding, or communication interruption in real time.

[0007] Preferably, the data smelting hub comprises a streaming computing pipeline, a graph neural network analysis module, and a graph reasoning engine, wherein the streaming computing pipeline is configured to aggregate and convert video streams, sensor data, and operation logs in real time; the graph neural network analysis module is configured to construct a dynamic relationship topology and mine potential congestion transmission paths; and the graph reasoning engine is configured to fuse historical fault libraries and real-time working condition data, automatically generate a congestion cause reasoning tree and a risk probability matrix.

[0008] Preferably, the visual large screen interactive platform comprises a multi-level alarm projection module, an AR perspective rendering engine, and a scene adaptive engine, wherein the multi-level alarm projection module is configured to map device faults to a line network three-dimensional sand table according to influence ranges, and mark core node faults in red; the AR perspective rendering engine is configured to generate a virtual perspective view of real-time positioning of trains in tunnels, and superimpose a full load rate heat sign; and the scene adaptive engine is configured to automatically switch interface layouts of a commuting peak mode, an emergency evacuation mode, and a night maintenance mode through an operation scenario classifier.

[0009] Preferably, the intelligent instruction decision engine comprises a road network prediction model, a resource constraint solver and a contingency optimizer, wherein the road network prediction model is configured to fuse LSTM neural network and cellular automata algorithm to deduce the diffusion range and duration of a delay event in the road network; the resource constraint solver is configured to generate an optimal additional operation / stop operation scheme under multiple restrictions of train turnaround time, number of backup vehicles and crew configuration; and the contingency optimizer is configured to fuse an expert rule base to generate an emergency broadcast script and a diversion path of a hierarchical response.

[0010] Preferably, the dynamic adjustment unit comprises an interlocking controller, an optical flow guide matrix and a dispatching instruction compiler, wherein the interlocking controller is configured to trigger multi-language electronic voice and AR navigation arrows; the optical flow guide matrix is configured to generate an evacuation path light belt through programmable LED floor tiles; and the dispatching instruction compiler encodes the strategy into an executable instruction set of an ATO train automatic dispatching unit to dynamically adjust the ATO driving curve.

[0011] The second aspect embodiment of the present application provides a subway traffic operation decision adjustment method based on a visual large screen, comprising: acquiring train positioning data, platform monitoring video stream, gate passage data and environmental sensor data; constructing a real-time line network state model according to the train positioning data, platform monitoring video stream, gate passage data and environmental sensor data; based on the real-time line network state model, fusing LSTM neural network and cellular automata algorithm to identify abnormal events, and simultaneously dynamically rendering a line network congestion heat map, a device fault positioning marker and a passenger flow prediction trend curve on a visual large screen; according to the abnormal events and passenger flow prediction trend, combining a slime mold algorithm to dynamically generate a train schedule adjustment strategy and an emergency evacuation scheme; according to the adjustment strategy and the evacuation scheme, dynamically adjusting an ATO train automatic dispatching curve, simultaneously performing multi-language broadcast and updating electronic guide screen content.

[0012] The third aspect embodiment of the present application provides an electronic device, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a subway traffic operation decision adjustment method based on a visual large screen as described in the above embodiments.

[0013] The fourth aspect embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement a subway traffic operation decision adjustment method based on a visual large screen as described in the above embodiments.

[0014] The fifth aspect embodiment of the present application provides a computer program product comprising a computer program or instructions for implementing a subway traffic operation decision adjustment method based on a visual large screen as described in the above embodiments.

[0015] Thus, the present application includes the following advantages:

[0016] The embodiment of the present application collects train position and passenger flow density key data in real time through the perception data module, removes invalid information in cooperation with the abnormal data filtering unit, and ensures the accuracy and reliability of the data. The data processing hub performs real-time aggregation, deep mining and intelligent reasoning on massive data by means of the stream computing pipeline, the graph neural network analysis module and the graph reasoning engine, accurately locates potential congestion paths and fault causes, and the visual large-screen interactive platform converts complex operation data into intuitive three-dimensional sand table and virtual perspective view by using multi-level alarm projection, AR perspective rendering and scene adaptive engine, and automatically switches the interface layout according to different scenes to help operation personnel quickly grasp the overall situation. The intelligent instruction decision engine combines LSTM neural network and cellular automata algorithm to deduce the diffusion influence of late events and generate the optimal train scheduling scheme and hierarchical emergency response plan under resource constraints. The dynamic adjustment unit quickly converts the decision instruction into multi-language broadcast, AR navigation and LED floor tile guidance, dynamically adjusts the ATO train scheduling curve, improves the subway operation efficiency and emergency disposal capacity, and reduces the operation risk and cost. Thus, the problems of existing technology such as abnormal response lag, unreasonable resource allocation and low efficiency are solved.

[0017] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0019] Figure 1 A structural schematic diagram of a subway traffic operation decision adjustment system based on a visual large screen provided according to an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a perception data module provided according to an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a subway morning peak operation scene provided according to an embodiment of the present application;

[0022] Figure 4 A schematic diagram of a data processing hub provided according to an embodiment of the present application;

[0023] Figure 5 A schematic diagram of a visual large-screen interactive platform provided according to an embodiment of the present application;

[0024] Figure 6A schematic diagram of a weekday subway evening peak period according to an embodiment of the present application;

[0025] Figure 7 A schematic diagram of an intelligent instruction decision engine according to an embodiment of the present application;

[0026] Figure 8 A schematic diagram of a dynamic adjustment unit according to an embodiment of the present application;

[0027] Figure 9 A schematic diagram of a subway traffic operation decision adjustment system based on a visual large screen according to an embodiment of the present application;

[0028] Figure 10 A flowchart of a subway traffic operation decision adjustment method based on a visual large screen according to an embodiment of the present application;

[0029] Figure 11 A schematic diagram of a weekday subway morning peak period in a certain city according to an embodiment of the present application;

[0030] Figure 12 A schematic diagram of a subway traffic operation decision adjustment method based on a visual large screen according to an embodiment of the present application;

[0031] Figure 13 A structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0033] With reference to the accompanying drawings, an embodiment of the present application is described below. A subway traffic operation decision adjustment system based on a visual large screen is provided. In view of the problem of abnormal response lag mentioned in the background art, the present application provides a subway traffic operation decision adjustment system based on a visual large screen. In the system, the perception data module collects train position and passenger flow density key data in real time, and cooperates with the abnormal data filtering unit to remove invalid information, so as to ensure the accuracy and reliability of the data. The data processing hub uses a stream computing pipeline, a graph neural network analysis module and a graph reasoning engine to perform real-time aggregation, deep mining and intelligent reasoning on massive data, accurately locates potential congestion paths and fault causes, and uses a multi-level alarm projection, AR perspective rendering and scene adaptive engine to convert complex operation data into intuitive three-dimensional sand table and virtual perspective view, and automatically switches the interface layout according to different scenes to help operation personnel quickly grasp the overall situation. The intelligent instruction decision engine combines LSTM neural network and cellular automata algorithm to deduce the influence of late events and generate an optimal train scheduling scheme and a hierarchical emergency response plan under resource constraints. The dynamic adjustment unit quickly converts the decision instruction into multi-language broadcast, AR navigation and LED floor tile guidance, dynamically adjusts the ATO train scheduling curve, improves the subway operation efficiency and emergency disposal capacity, and reduces the operation risk and cost. Thus, the problems of abnormal response lag, unreasonable resource allocation and low efficiency in the prior art are solved.

[0034] Figure 1 A structure diagram of a subway traffic operation decision adjustment system based on a visual large screen provided by an embodiment of the present application.

[0035] An embodiment of the present application provides a subway traffic operation decision adjustment system based on a visual large screen. The system 10 comprises:

[0036] A perception data module 100, a data processing hub 200, a visual large screen interactive platform 300, an intelligent instruction decision engine 400 and a dynamic adjustment unit 500.

[0037] The perception data module 100 is configured to collect train positioning data, platform monitoring video stream, gate passage data and environmental sensor data in real time. The data processing hub 200 is configured to integrate and analyze the collected data, mine potential patterns, correlation and abnormal conditions in the data. The visual large screen interactive platform 300 is configured to present a line network congestion heat map, device fault positioning and passenger flow prediction trend in real time, and switch interfaces and highlight information according to different operation scenes and needs. The intelligent instruction decision engine 400 is configured to dynamically generate train schedule adjustment strategies and emergency relief schemes. The dynamic adjustment unit 500 is configured to dynamically adjust electronic guide screens and platform broadcasts according to decision instructions, and dynamically schedules trains.

[0038] It can be understood that, in the embodiments of the present application, the train position, passenger flow density key data are collected in real time by the perception data module, invalid information is removed by the abnormal data filtering unit to ensure the accuracy and reliability of the data; the data processing hub uses the streaming computing pipeline, the graph neural network analysis module and the graph reasoning engine to perform real-time aggregation, deep mining and intelligent reasoning on massive data, accurately locates potential congestion paths and fault causes; the visual large screen interaction platform uses multi-level alarm projection, AR perspective rendering and scene adaptive engine to convert complex operation data into intuitive three-dimensional sand table and virtual perspective view, and automatically switches the interface layout according to different scenes to help operation personnel quickly grasp the overall situation; the intelligent instruction decision engine combines LSTM neural network and cellular automata algorithm to deduce the influence of late events and generate the optimal train scheduling scheme and hierarchical emergency response plan under resource constraints; the dynamic adjustment unit quickly converts the decision instruction into multi-language broadcast, AR navigation and LED floor tile guidance, dynamically adjusts the ATO train scheduling curve, improves the subway operation efficiency and emergency disposal capacity, and reduces the operation risk and cost. Thus, the problems of abnormal response lag, unreasonable resource allocation and low efficiency in the prior art are solved.

[0039] In the embodiments of the present application, the perception data module 100 further comprises: as shown in Figure 2 The sensor cluster, the abnormal data filtering unit.

[0040] The sensor cluster is used to collect train position, passenger flow density, human body temperature and obstacle distance data; the abnormal data filtering unit uses a sliding window outlier detection algorithm to remove invalid data caused by sensor drift, shielding or communication interruption in real time.

[0041] It can be understood that, in the embodiments of the present application, the sensor cluster collects train position and passenger flow density data to provide rich raw information for subway operation decision and comprehensively perceive the operation environment; the abnormal data filtering unit uses a sliding window outlier detection algorithm to identify and remove invalid data caused by sensor drift, shielding or communication interruption in time, improve the accuracy and reliability of the data, avoid invalid data interference in subsequent analysis and decision, and improve the stability and decision accuracy of subway traffic operation.

[0042] It should be noted that the sliding window outlier detection algorithm is a real-time data anomaly recognition algorithm based on a sliding data window of a set size, which calculates the statistical quantity in the window and determines whether the current data is an outlier according to the statistical quantity. The formula of the sliding window outlier detection algorithm is:

[0043] S t ={x t-w+1 ,x t-w+2 ,…,x t}

[0044]

[0045] wherein S t is the sliding window data set at time t; w is the sliding window size; x t is the data value at time t; μ t is the mean of the data within the window; σ t is the standard deviation of the data within the sliding window data set S t at time t; x i is the i-th observation in the time series.

[0046] For example, as Figure 3 shown, in the subway operation scenario, taking the morning peak period of a busy station as an example, during 8:00-8:30, the sensor cluster collected 360 groups of passenger flow density data. The anomaly data filtering unit uses the sliding window outlier detection algorithm, and sets the sliding window size to 30 groups of data (i.e. covering the data of the previous 150 seconds). As time progresses, the window is constantly updated. At 8:10, the algorithm calculates that the mean passenger flow density in the sliding window at this time is 5 people per square meter, and the standard deviation is 0.8 people. At this time, the newly collected data shows that the passenger flow density is 9 people per square meter, which deviates from the mean far beyond the set threshold (usually set to mean ± 2 times standard deviation), and the algorithm quickly determines that this data is an outlier, i.e. invalid data caused by temporary obstruction or failure of the sensor, and it is excluded. According to statistics, during this morning peak period, the algorithm accurately excluded about 5% of abnormal data in this way, ensuring the accuracy of subsequent passenger flow analysis, providing a solid data foundation for operation decision-making, and helping the subway to operate efficiently and stably.

[0047] In the embodiments of the present application, the data processing hub 200 includes: as Figure 4 shown, a streaming computing pipeline, a graph neural network analysis module, and a graph reasoning engine.

[0048] The streaming computing pipeline is used to aggregate and convert video streams, sensor data, and operation logs in real time; the graph neural network analysis module is used to construct a dynamic relationship topology and mine potential congestion transmission paths; and the graph reasoning engine is used to fuse historical fault libraries and real-time working condition data, automatically generate a congestion cause reasoning tree and a risk probability matrix.

[0049] It can be understood that the embodiments of the application aggregate and convert video streams, sensor data and operation logs in real time through a streaming computing pipeline, quickly integrate multi-source heterogeneous data, and provide a unified and standardized data format; the graph neural network analysis module deeply mines the potential relationship between stations, trains and passenger flow by constructing a dynamic relationship topology, accurately predicts the congestion transmission path, and identifies operation risks in advance; the graph reasoning engine fuses historical fault libraries with real-time working condition data, automatically generates a congestion cause reasoning tree and a risk probability matrix, clearly presents the fault logical relationship and risk possibility, improves the processing efficiency and analysis depth of subway operation data, and assists operation personnel in quickly locating the root cause of the problem, predicting risks, and enhancing the safety and stability of subway operation.

[0050] It should be noted that real-time aggregation and conversion of video streams, sensor data and operation logs first access video streams through the RTSP protocol, collect sensor data using the MQTT protocol, pull operation logs using Flume, clean the data using a sliding window outlier detection algorithm, and convert them into a PROTOBUF / JSON format, complete standardization, and then correlate and aggregate the data based on space-time dimensions, and convert the aggregated data into structured data that can be directly used for analysis.

[0051] The RTSP protocol is an application layer protocol used to control the real-time transmission of audio and video streams; the MQTT protocol is a lightweight instant messaging protocol; Flume is a distributed log collection system used to collect, aggregate and transport log data from multiple data sources to a storage system.

[0052] To build a dynamic relationship topology and mine potential congestion transmission paths, first, the subway system is abstracted into a basic graph structure composed of stations (nodes) and lines (edges), and real-time attributes of passenger flow and train operation status are assigned to nodes and edges; then, through a space-time correlation algorithm, real-time data in different time windows are mapped into the graph structure to form a dynamic graph that evolves over time; then, through graph embedding, high-dimensional features of nodes and edges are compressed into low-dimensional vectors to capture the inherent correlation; the dynamic graph is iteratively updated and reasoned to identify hidden dependency relationships between nodes, and a relationship topology that reflects dynamic characteristics such as passenger flow propagation and fault diffusion is constructed.

[0053] The space-time correlation algorithm formula is:

[0054]

[0055] wherein, is the correlation weight between station i and station j at time t; α and β are weight coefficients; Spatial(i, j) is a spatial correlation term that measures the correlation between nodes i and j in space; Temporal(i, j, t) is a temporal correlation term that reflects the correlation between nodes i and j in the time dimension at time t.

[0056] The fusion history fault library and real-time working condition data are used to automatically generate a congestion cause reasoning tree and a risk probability matrix. The history fault library and real-time working condition data are fused to construct a knowledge graph containing entities and relationships. Then, a cause-effect chain reasoning rule is used to perform forward and backward reasoning from an abnormal event. The reasoning tree is generated by combining the evidence weight calculation. The risk probability matrix is formed by quantifying the occurrence probability of each cause and influence according to historical statistics and real-time data.

[0057] The cause-effect chain reasoning rule is a logical deduction criterion based on the event cause-effect relationship. The rule defines the correlation rule of “if a certain condition is established, then the subsequent result is triggered”. The possible influence and root cause of the initial abnormal point are derived to construct the reasoning logic of the fault propagation chain.

[0058] The evidence weight calculation formula is as follows:

[0059]

[0060] Wherein, Weight(E|H) is the weight of evidence E to hypothesis H; p(H) is the probability that hypothesis H is true; p(E|H) is the probability that evidence E appears when hypothesis H is true; p(E|H) is the probability that evidence E appears when hypothesis H is not true; and log is a logarithmic function. The risk probability matrix formula is as follows:

[0061] Ost=P1×P2×(ω1·Cth+ω2·Dyr+ω3·Eil)

[0062] Wherein, P1 is a historical use weight ratio; P2 is an environmental influence weight ratio; Cth, Dyr, and Eil are animal / plant / human garbage influence coefficients; Ost is a comprehensive risk index; ω1, ω2, and ω3 are sub-item weight coefficients.

[0063]

[0064] ​​For example, in the subway operation scenario, when there is a passenger flow congestion at a certain transfer station during the morning peak, the graph reasoning engine quickly retrieves the historical fault library and matches the records of the station that once caused congestion due to escalator failure and insufficient security channel. At the same time, real-time working condition data is fused, including the current security pass rate, escalator operation status, and the increase in the rate of incoming passenger flow. Through correlation analysis, a congestion cause reasoning tree is automatically generated, with the root cause pointing to the superimposed effect of the increase in commuter passenger flow of surrounding office buildings and the diversion of passenger flow at B station. The first branch includes direct factors such as reduced security efficiency and decreased transfer channel capacity, and the second branch is further refined to specific equipment failures and construction information. At the same time, the engine outputs a risk probability matrix. It is shown that within the next 15 minutes, the risk probability of the A station platform congestion reaching 50% overstaffing is 80%; the risk probability of causing gate failure due to passenger backlog is 30%; and the risk probability of causing a chain congestion at surrounding stations (such as C station) is 60%. Operation personnel immediately start the emergency plan based on these accurate data, deploy 5 standby security personnel to A station, open a temporary security channel, and coordinate with the construction party to suspend the transfer channel construction for 2 hours to restore the capacity, successfully alleviating the congestion risk.

[0065] In the embodiment of the present application, the visual large-screen interactive platform 300 includes, as shown in Figure 5 a multi-level alarm projection module, an AR perspective rendering engine, and a scene adaptive engine.

[0066] The multi-level alarm projection module is used to map device failures to the line network three-dimensional sand table according to the influence range, with core node failures marked in red.

[0067] It can be understood that, in the embodiment of the present application, the multi-level alarm projection module classifies device failures according to the influence range, with core node failures marked in red, intuitively presenting the influence degree of the fault on the subway line network, facilitating the operation and maintenance personnel to quickly lock the key problems; the AR perspective rendering engine generates a virtual perspective view of the real-time positioning of the train in the tunnel, with the full load rate of the carriages superimposed, helping the dispatchers to accurately grasp the train operation and passenger distribution, and reasonably arrange the transport capacity; the scene adaptive engine automatically switches the adaptive interface layout according to different modes such as commuter peak, emergency evacuation, and night maintenance through the operation scene classifier, highlights the key information, and improves the operation efficiency and decision accuracy of the staff in various scenarios. The visual degree, response speed, and intelligent level of subway operation management are enhanced, and the safe and efficient operation of the subway is ensured.

[0068] It should be noted that equipment failures are mapped onto the 3D network sandbox by impact level. Level 1 alerts (emergency, marked in red) address core node failures or large-scale outages, such as a main substation trip. Level 2 alerts (serious, marked in orange) address critical equipment anomalies affecting localized operations, such as the outage of critical equipment at a single station. Level 3 alerts (information, marked in yellow) focus on equipment failures or warnings with less significant operational impact, such as non-critical equipment anomalies. Level 4 alerts (information, marked in blue / gray) record auxiliary information such as equipment status changes. Fault information is mapped onto the 3D network sandbox using color and hierarchy, helping operations and maintenance personnel quickly identify high-priority faults and efficiently handle emergencies.

[0069] For example, Figure 6 As shown in the figure, during the evening rush hour on a weekday, a core transfer station on Metro Line 5 experienced a sudden outage. The multi-level alarm projection module swiftly activated, and through rapid analysis of the fault data, it determined the fault to be a Level 1 alarm. On the 3D sandbox of the subway network, the node representing this transfer station was prominently marked in red, and the surrounding affected lines were also displayed in a semi-transparent red color. Statistics show that the fault caused train delays on Line 5 within a 10-kilometer section before and after the fault, with an average delay of 20 minutes. Fifteen trains were affected, carrying approximately 3,000 passengers. Simultaneously, three adjacent stations experienced passenger backlogs, with each station experiencing a backlog of approximately 200-300 passengers, marking them as Level 2 alarm areas. Based on the different levels of alarm displayed on the 3D sandbox, relevant personnel quickly prioritized repairs at the core transfer station. They also deployed personnel to the backlogged stations to help passengers, minimizing the impact of the fault on subway operations.

[0070] In the embodiment of the present application, the intelligent instruction decision engine 400 includes: Figure 7 As shown, the road network prediction model, resource constraint solver and plan optimizer.

[0071] Among them, the road network prediction model is used to integrate the LSTM neural network and the cellular automaton algorithm to deduce the diffusion range and duration of delay events in the road network; the resource constraint solver is used to generate the optimal additional / suspension plan under multiple constraints such as train turnaround time, number of spare cars and crew configuration; the plan optimizer is used to integrate the expert rule base to generate hierarchical response emergency broadcast language and diversion paths.

[0072] It can be understood that the embodiments of the application fuse LSTM neural network and cellular automata algorithm through a road network prediction model, accurately deduce the diffusion range and duration of the late event in the road network, and master the potential influence area in advance; the resource constraint solver comprehensively considers multiple restriction conditions such as train turnaround time, number of standby vehicles and crew configuration, quickly generates the optimal start-up / shut-down scheme, reasonably allocates resources, and reduces the loss of delay; the pre-plan optimizer generates the emergency broadcast language and diversion path of hierarchical response combined with the expert rule base, ensures accurate information transmission and efficient guidance. The decision efficiency and scientificity of the subway in response to the sudden situation of delay are improved, the operation order is ensured, and the passenger travel experience is enhanced.

[0073] It should be noted that the LSTM neural network formula is:

[0074] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0075] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0076]

[0077] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0078] h t =o t ⊙tanh(C t )

[0079] Wherein, f t is the forget gate output; t is the time step; x t is the input vector; h t-1 is the hidden state at the last time; W f is the forget gate weight matrix; b f is the forget gate bias vector; sigma is the Sigmoid activation function; i t is the input gate output; W i is the input gate weight matrix; b i is the input gate bias vector; is the candidate cell state; tanh is the hyperbolic tangent activation function; W C is the candidate state weight matrix; b Cis a candidate state bias vector; C t is a cell state; C t-1 is a cell state of the previous time; o t is an output gate output; W o is an output gate weight matrix; b o is an output gate bias vector; h t is a hidden state.

[0080] The formula of the cellular automaton algorithm is:

[0081] {s c (t+1)} c∈C =CA-LSTM({s c (t)} c∈C ,Rule fusion )

[0082] where s c (t+1) is the state of the cell c at time t+1; c is each basic unit in the road network; C is the set of all cells; s c (t) is the state of the cell c at time t; CA-LSTM is a joint calculation function combining the cellular automaton and the long short-term memory network; Rule fusion is a fusion rule; {s c (t)} c∈C is the current state set; {s c (t+1)} c∈C is the updated state set.

[0083] For example, during the morning peak on a working day, the first train of the subway line 3 is delayed by 10 minutes due to signal equipment failure. The road network prediction model is immediately started, and the LSTM neural network is used to analyze similar fault data in the past 30 days, and it is found that similar delay events in history will spread to 5 adjacent stations within 40 minutes; at the same time, the cellular automaton algorithm divides the line into 20 road section cells, and according to the connection relationship and operation rules of each road section, simulates that the delay will spread along the upward direction at a speed of 2 cells per 10 minutes. According to the model deduction, it is predicted that the affected range will cover the whole line of the line 3 and the transfer station with the line 5 after 1 hour, causing about 8000 person-times of delay. The operation department adjusts the subsequent train departure plan in advance, adds 3 standby trains in the affected area, and guides passengers to staggered travel through broadcasting, effectively reducing the influence range and duration of the delay event.

[0084] In the embodiments of the present application, the dynamic adjustment unit 500 includes, as shown in Figure 8 , a broadcast linkage controller, an optical flow guide matrix, and a scheduling instruction compiler.

[0085] The broadcast linkage controller is used for triggering the multilingual electronic voice and the AR navigation arrow; the light flow guide matrix is used for generating an evacuation path light band through programmable LED floor tiles; and the dispatch instruction compiler encodes a strategy into an executable instruction set of an ATO train automatic dispatch unit, and dynamically adjusts an ATO driving curve.

[0086] It can be understood that the embodiments of the present application trigger the multilingual electronic voice and the AR navigation arrow through the broadcast linkage controller to provide clear and multilingual guidance for passengers; the light flow guide matrix generates an evacuation path light band through programmable LED floor tiles to intuitively guide passenger flow evacuation; and the dispatch instruction compiler encodes a strategy into an executable instruction set of an ATO train automatic dispatch unit to dynamically adjust a driving curve. From passenger guidance to train dispatch coordination, the efficiency of emergency response is improved, the passenger evacuation experience is optimized, the train operation is adapted to real-time strategies, and the dynamic regulation and service level of metro operation are enhanced.

[0087] For example, during the morning peak of the subway, a passenger flow congestion occurs at a transfer station due to equipment failure, and the light flow guide matrix responds quickly. According to the evacuation strategy output by the intelligent instruction decision engine, the programmable LED floor tiles in the station drive to generate clear evacuation path light bands in the station hall and platform area. From the transfer channel to the temporary security check area, the light band guides the orderly shunting of more than 2000 passengers in a continuous flowing light, and cooperates with the multilingual voice and AR navigation arrow of the broadcast linkage controller to increase the congestion area passenger flow dissipation rate to 80% within 5 minutes, effectively relieving the passenger flow pressure and ensuring the safety and order of passenger traffic.

[0088] The subway traffic operation decision adjustment system based on the visual large screen proposed in the embodiments of the present application collects train position and passenger flow density key data in real time through a perception data module, and cooperates with an abnormal data filtering unit to remove invalid information, ensuring the accuracy and reliability of the data. The data processing hub uses a streaming computing pipeline, a graph neural network analysis module, and a graph reasoning engine to perform real-time aggregation, deep mining, and intelligent reasoning on massive data, accurately locating potential congestion paths and failure causes. The visual large screen interactive platform uses multi-level alarm projection, AR perspective rendering, and scene adaptive engine to convert complex operation data into intuitive and visual three-dimensional sand tables and virtual perspective views, and automatically switches the interface layout according to different scenes to help operation personnel quickly grasp the overall situation. The intelligent instruction decision engine integrates LSTM neural networks and cellular automata algorithms to deduce the diffusion influence of a late event and generate an optimal train dispatching scheme and a hierarchical emergency response plan under resource constraints. The dynamic adjustment unit quickly converts the decision instructions into multilingual broadcasts, AR navigation, and LED floor tile guidance, dynamically adjusts the ATO train dispatching curve, improves the efficiency and emergency response capability of subway operation, and reduces operation risks and costs. Thus, the problems of existing technologies such as delayed abnormal response, unreasonable resource allocation, and low efficiency are solved.

[0089] A subway traffic operation decision adjustment system based on a visual large screen will be described below through a specific embodiment, as shown in the accompanying drawings, comprising: Figure 9

[0090] Deployed in a certain subway operation control center, covering 5 lines and 80 stations of the subway line network. The sensor cluster of the perception data module (including 2000 train positioning sensors, 1500 platform monitoring cameras, 3000 gate sensors and 800 sets of environmental sensors) is distributed in the line, station and train. The data processing hub relies on 10 high-performance servers to build a streaming computing and graph neural network analysis environment. The visual large screen uses a 12m x 4m ultra-high-definition splicing screen. The intelligent instruction decision engine and dynamic adjustment unit are interconnected with each station and train subsystem through the subway special communication network.

[0091] The train positioning sensor collects data once every second, with an accuracy of 0.5 meters; the platform monitoring camera collects video stream at 25 frames per second; the gate sensor records each passage data (including passenger ID, passage time); the environmental sensor collects temperature (accuracy ±0.3℃), obstacle distance (accuracy ±0.1 meters) and other data every 5 minutes. The abnormal data filtering unit uses the sliding window outlier detection algorithm S t = {x t-w+1 , x t-w+2 , …, x t}, The window size is set to 10 seconds. For example, in the train positioning data, if the positioning drift exceeds 2 meters (beyond the normal error range) in the last 3 windows, it is determined to be invalid data and is excluded. On a certain working day during the morning peak, the filtering unit excluded more than 300 invalid gate data within 1 hour due to sensor communication interruption, ensuring data quality.

[0092] The streaming computing pipeline aggregates and converts video stream, sensor data and operation logs by seconds. For example, by integrating train positioning and gate passage data, real-time passenger flow change curves of each station are generated, data correlation is completed within 1 second, the graph neural network analysis module constructs a dynamic relationship topology, taking stations as nodes and lines as edges, and mines potential congestion transmission paths. Through analysis, it is found that during the morning peak, passenger flow congestion at station A is easy to be transmitted to stations B and C through transfer stations, with a transmission delay of about 15 minutes, providing a basis for pre-plan making. The graph reasoning engine integrates historical fault library (including 2000 fault records in the past 5 years) and real-time working condition data to automatically generate congestion cause reasoning tree and risk probability matrix. When passenger flow congestion occurs at station D, the reasoning engine finds out the causes such as equipment failure (such as gate failure) and large passenger flow (such as large event dispersal), with corresponding risk probabilities of 30% and 70% respectively, assisting operation decision making.

[0093] ​The multi-level alarm projection module classifies device faults according to the influence range (core node fault, general node fault) and maps them to a line network three-dimensional sand table. On a certain day, the signal device of station E fails (core node fault), and the large screen is marked in red, while displaying 3 lines and 5 stations affected by the fault, which facilitates the rapid positioning of the operation personnel. The AR perspective rendering engine generates a virtual perspective view of the real-time positioning of the train in the tunnel, and superimposes the full load rate thermal identifier (green for full load rate <60%, yellow for 60%-80%, and red for >80%). The operation personnel can directly see the train position and the degree of compartment congestion, and the full load rate of a certain train compartment reaches 90% (red mark), which triggers the passenger flow diversion instruction in time. The scene adaptive engine automatically switches the mode through the operation scene classifier (trained based on passenger flow, time period, etc.). During the morning peak (7:00-9:00), it switches to the commuter peak mode, highlighting the passenger flow heat map and train punctuality rate; during emergency evacuation, it switches to the emergency mode, focusing on the evacuation path and emergency resource distribution; during night maintenance (23:00-5:00), it switches to the maintenance mode, displaying the device maintenance progress and skylight operation area.

[0094] The road network prediction model fuses LSTM neural network t = σ(W f · [h t-1 , x t ] + b f ), i t = σ(W i · [h t-1 , x t ] + b i ), o t = σ(W o · [h t-1 , x t ] + b o ), h t = o t ⊙ tanh(C t ) and cellular automaton algorithm {s c (t+1)} c∈C = CA-LSTM({s c (t)} c∈C , Rule fusion), and deduces the spread of the late event. The simulation shows that the train is 5 minutes late at station F, and after 10 minutes, it spreads to the adjacent 2 stations, and continues to affect for about 20 minutes, providing time prediction for train scheduling. The resource constraint solver generates the optimal additional / stop operation scheme under the constraints of train turnaround time (minimum 12 minutes), number of standby trains (10) and crew configuration (20 groups available). During the morning peak, the passenger flow of a certain line surges, and the solver calculates that 2 standby trains are added, and 3 crew shifts are adjusted, so that the capacity of the line is increased by 20%. The contingency optimizer integrates the expert rule base (including 100 emergency handling rules) to generate the emergency broadcast script and diversion path of the hierarchical response. At station G, a large passenger flow occurs, and the multi-language broadcast script such as "please go to the transfer channel, passengers transferring to line 2 from exit B" and the diversion path along the right side of the platform to the transfer channel of line 2 are generated, effectively guiding the passenger flow.

[0095] The broadcast linkage controller triggers multi-language electronic voice and AR navigation arrows. At station H, during a large passenger flow, multi-language broadcast guidance in Chinese, English and Japanese is played synchronously, and AR navigation arrows are projected on the ground and wall of the platform to guide passengers to the transfer channel, and 500 passenger times are guided within 10 minutes. The light flow guide matrix generates a light band of the evacuation path through programmable LED floor tiles. At station I, due to equipment failure, the LED floor tiles are lit within 3 seconds to form a green light band from the failure area to the safe exit, guiding passengers to evacuate, and the evacuation efficiency is improved by 30%. The dispatching instruction compiler encodes the strategy into an executable instruction set of the ATO train automatic dispatching unit to dynamically adjust the ATO driving curve. According to the passenger flow change, the instruction compiler adjusts the train running speed in the interval, and a certain train originally takes 8 minutes on the driving curve, and after adjustment, it takes 7 minutes, which compresses the running time and improves the line capacity.

[0096] In summary, the embodiments of the present application ensure data reliability through high-frequency and high-precision acquisition and abnormal filtering of the perception data module, and lay the foundation for decision-making. The data processing hub aggregates multi-source data at a second level and mines potential risks through graph neural network and graph reasoning, such as predicting congestion transmission path 15 minutes in advance and accurately locating the cause of 70% probability of large passenger flow congestion. The visual large screen realizes hierarchical alarm through three-dimensional sand table, AR thermal perspective and scene adaptive switching, which shortens the fault positioning time by 30% and improves the passenger flow state perception efficiency by 40%. The intelligent instruction decision engine integrates algorithms and rules to generate the optimal scheme, such as late spread prediction error ≤5 minutes and additional train to increase the capacity by 20%. The dynamic adjustment unit realizes the 10-minute evacuation of 500 passenger times during large passenger flow and the 30% improvement of evacuation efficiency through multi-language broadcast, light flow guidance and train driving curve optimization. The early morning peak punctuality rate of the line network is improved, the large passenger flow evacuation time is shortened, and the fault handling response speed is improved.

[0097] Secondly, a subway traffic operation decision adjustment method based on a visual large screen is described with reference to the accompanying drawings according to an embodiment of the present application.

[0098] As shown in the figure, the subway traffic operation decision adjustment method based on the visual large screen comprises the following steps: Figure 10

[0099] In step S101, train positioning data, platform monitoring video stream, gate passing data and environmental sensor data are acquired.

[0100] It can be understood that the embodiment of the present application can accurately master the train running track and position by collecting train positioning data, guaranteeing train safety and punctuality; the platform monitoring video intuitively presents the platform passenger flow distribution and abnormal conditions, facilitating the timely discovery of emergency conditions; the gate passing data quantifies the passenger entry and exit flow, providing a basis for transport capacity allocation; the environmental sensor data monitors the temperature and humidity, obstacle distance environmental parameters, and ensures the safety of the operating environment.

[0101] In step S102, a real-time line network state model is constructed according to the train positioning data, platform monitoring video stream, gate passing data and environmental sensor data.

[0102] Among them, the real-time line network state model is a dynamic digital model of the subway line network constructed by real-time data of train running track, passenger flow, equipment working condition after algorithm processing.

[0103] It can be understood that the embodiment of the present application dynamically processes the dispersed train positioning, monitoring video, gate passing and environmental sensing data using evidence reasoning algorithm, converts them into visual and analyzable digital models, accurately presents the train running track, passenger flow change and equipment working condition state in the subway line network. Let the operation personnel intuitively master the real-time dynamics of the line network, quickly identify potential problems of train delay, passenger congestion and equipment abnormality, and also provide basic data for intelligent instruction decision engine, help to develop scheduling strategy in advance, optimize transport capacity allocation, efficiently cope with emergency conditions, and improve overall operation efficiency and safety.

[0104] It should be noted that the evidence reasoning algorithm formula is:

[0105]

[0106] Among them, m(Θ) is the trust degree of the fused uncertain state; m(A) is the trust degree of the specific state A after fusion; Θ is the identification framework; A is any subset of Θ; n is the total number of data sources; w i is the weight of the i-th evidence; r i is the correlation coefficient of the i-th evidence; m i is the mass function of the i-th evidence itself; i, j are index; w j ​is the weight of the jth evidence; r j is the correlation coefficient of the jth evidence; m j is the mass function of the jth evidence itself.

[0107] In step S103, based on the real-time line network state model, the LSTM neural network and the cellular automaton algorithm are fused to identify abnormal events, and at the same time, the line network congestion heat map, the device fault positioning mark and the passenger flow prediction trend curve are dynamically rendered on the visual large screen.

[0108] The passenger flow prediction trend curve is a visual curve for showing the change trend and law of the passenger flow quantity in the future period, which is fitted by analyzing historical passenger flow data and influencing factors (such as time, activity, transportation hub dynamics, etc.) and using a mathematical model or algorithm.

[0109] It can be understood that the embodiments of the present application intuitively present the future passenger flow change trend by mining historical data and multiple influencing factors, assist the operation party to predict the peak period and passenger flow dense area in advance, flexibly allocate transport capacity, add temporary channels or adjust train schedules, and relieve congestion pressure; help the transportation hub to optimize resource allocation and reasonably arrange the number of service windows such as security check and ticket sales; at the same time, it can also provide travel reference for passengers, stagger peak planning routes, and improve travel efficiency and experience.

[0110] For example, during a large holiday, the subway operation department fits the passenger flow prediction trend curve of the next three days by using the LSTM neural network algorithm based on historical passenger flow data, combined with the holiday travel law, city transportation hub dynamics and surrounding large-scale activity arrangement. From the curve, it is found that there will be two passenger flow peaks from 10 o'clock in the morning to 2 o'clock in the afternoon and from 5 o'clock in the afternoon to 8 o'clock in the evening, and the passenger flow of the routes to the business district and scenic spots increases by more than 3 times the regular rate. The operation party accordingly increases the train departure frequency during the peak period, increases the number of guide personnel at key stations, and pushes the peak period and crowded route prompts to passengers through the official APP, effectively relieving the passenger flow pressure, improving the passenger travel experience, and ensuring the safe and efficient operation of the subway line network.

[0111] In step S104, according to the abnormal event and the passenger flow prediction trend, a train schedule adjustment strategy and an emergency relief scheme are dynamically generated by combining the slime mold algorithm.

[0112] The slime mold algorithm is an intelligent optimization algorithm that simulates the formation of an efficient foraging network by cytoplasm flow of Physarum polycephalum.

[0113] It can be understood that the embodiments of the application quickly and flexibly adjust the search strategy according to abnormal events and passenger flow prediction trends by simulating the dynamic flow characteristics of the cytoplasm of the multi-head slime bubble foraging. In the train dispatching scene, the global and local search is balanced, the calculation amount is greatly reduced, and the real-time requirement is met; at the same time, with the adaptive weight and global guide item, the complex constraints such as track capacity and safety interval are efficiently processed, in the case of sudden large passenger flow or train failure, the optimal train schedule adjustment strategy and emergency relief scheme considering the operation cost, passenger waiting time and emergency response speed are quickly generated, and the dynamic adaptability and robustness of the dispatching system are improved.

[0114] It should be noted that the Myxomyces algorithm formula is:

[0115]

[0116] wherein, is the schedule strategy; is the new generation of dispatching strategy; is the historical optimal strategy; is the external disturbance coefficient; is the data weight matrix; is a random candidate strategy; r is a random number; p is a strategy adjustment threshold; is a random exploration factor; is the current dispatching strategy.

[0117] For example, as Figure 11As shown, in the subway operation of a certain urban city, during the morning peak hours of the daily working day, the average passenger flow of several core stations in the central area of the city, such as the Financial Street Station and the International Trade Station, can reach 50-80 thousand people per hour, often causing congestion due to the concentration of commuter passenger flow. At the same time, abnormal events such as equipment failure and extreme weather occur about 20-30 times a year. To deal with these complex situations, the operation department introduces the slime mold algorithm. They take real-time passenger flow, train running speed, line carrying capacity and other key data of each station as "food source" information. When the algorithm monitors that the Financial Street Station is expected to have a passenger flow of 100,000 people in 1 hour due to sudden equipment failure, far exceeding the carrying capacity of 70,000 people per hour of the station, like slime mold sensing high concentration of food source, it quickly starts the response mechanism. According to past data and real-time information, the algorithm completes strategy generation in just 5 minutes through multiple rounds of iteration, deciding to urgently add 10 trains in 1 hour at 3 adjacent stations around the fault station to guide passengers to evacuate from these stations. At the same time, adjust the train operation plan of 15 lines, and re-plan the trains originally going to the fault station to the alternative stations 1-2 kilometers away from the fault station, effectively avoiding the fault point. Through actual verification, after using the algorithm, in similar abnormal events, the average passenger evacuation time is shortened from 30 minutes to 15 minutes, and the operation cost is reduced by about 20% when dealing with abnormal events, significantly improving the response capability and operation efficiency of the subway operation in complex dynamic environment.

[0118] In step S105, according to the adjustment strategy and the evacuation scheme, the ATO train automatic scheduling curve is dynamically adjusted, and at the same time, multilingual broadcast is broadcasted and the electronic guide screen content is updated.

[0119] Among them, the ATO train automatic scheduling curve refers to the speed-distance or time-distance curve generated by the train automatic operation system (ATO) for controlling the running state of train traction, inertia, braking, etc. according to the line parameters, train performance, timetable requirements and energy saving targets.

[0120] It can be understood that the embodiments of the present application dynamically optimize the train running state by adjusting the real-time strategy and evacuation scheme. In the case of sudden large passenger flow, by prolonging the running time between stations and increasing the station stop time, the station congestion is relieved; in the case of equipment failure, the train running path and speed are adjusted to ensure the safety of train operation. The transportation efficiency is improved, the train delay is reduced, the energy consumption is reduced, the green operation is realized, and at the same time, the multilingual broadcast and the electronic guide screen are linked to timely transmit accurate information to passengers, improve the passenger travel experience, and ensure the safe, efficient and stable operation of the rail transit system in complex scenarios.

[0121] For example, during the international marathon event held in a certain city subway, it is expected that 30,000 return passengers will rush into the station near the finish line within 1 hour after the race. The subway dispatching system enables the ATO train automatic dispatching curve dynamic adjustment mechanism: for the trains heading to the terminal, the system adjusts the interval running curve to the "acceleration section extension + braking section advance" mode based on the passenger flow prediction data and the line parameters, increases the maximum speed from 80km / h to 85km / h on the flat line, shortens the running time between stations by 15%; at the same time, the stop time at the terminal station is extended from 30 seconds to 45 seconds, and the train stopping accuracy is controlled within ±0.5 meters through curve optimization, which facilitates passengers to quickly get on and off the train. In addition, the system dynamically adjusts the tracking curve according to the position of the subsequent train, and the departure interval is compressed from 4 minutes to 3 minutes. Finally, through the precise adjustment of the ATO curve, the line transports 28,000 passenger trips within 1 hour, the train punctuality rate reaches 99%, the transport capacity is increased by 30% compared with the conventional dispatching mode, and the energy consumption is reduced by 12%, realizing efficient operation in the large passenger flow scenario.

[0122] According to the subway traffic operation decision adjustment method based on the visual large screen provided in the embodiments of the present application, the train position and passenger flow density key data are collected in real time through the perception data module, invalid information is removed through the abnormal data filtering unit, and the accuracy and reliability of the data are ensured; the data processing hub aggregates, deeply mines and intelligently reasons the massive data in real time by means of the stream computing pipeline, the graph neural network analysis module and the graph reasoning engine, accurately locates the potential congestion path and the fault cause; the visual large screen interactive platform converts the complex operation data into intuitive three-dimensional sand table and virtual perspective view by using multi-level alarm projection, AR perspective rendering and scene adaptive engine, and automatically switches the interface layout according to different scenes to help the operation personnel quickly grasp the overall situation; the intelligent instruction decision engine fuses the LSTM neural network and the cellular automaton algorithm, deduces the diffusion influence of the late event, generates the optimal train dispatching scheme and the hierarchical emergency response plan under the resource constraint; the dynamic adjustment unit quickly converts the decision instruction into multi-language broadcast, AR navigation and LED floor tile guidance, dynamically adjusts the ATO train dispatching curve, improves the subway operation efficiency and emergency disposal capacity, and reduces the operation risk and cost. Thus, the problems of abnormal response lag, unreasonable resource allocation and low efficiency in the prior art are solved.

[0123] A subway traffic operation decision adjustment method based on a visual large screen will be described below through a specific embodiment, as shown in Figure 12 , comprising:

[0124] The Line 3 of a certain city subway, as the city's main artery of transportation, has a daily passenger flow of over 500,000 people during the morning rush hour (7:30-9:00). The line runs through multiple core business centers and large residential areas, with high overlap of commuting and shopping passenger flow, making daily operation under great pressure. At 8:00 during the morning rush hour on a certain day, the system monitoring network acutely captures abnormal fluctuations in key section data, triggering a subway traffic operation decision adjustment process based on visual large screens.

[0125] Train positioning data is collected by the vehicle-mounted GPS module and the trackside communication base station at a high frequency of 1 per second, including real-time position, running speed, and head and tail coordinates. For example, train T1 is accurately positioned at the 15th signal section of Line 3 at 8:00, with coordinates (X1, Y1) and a speed of 40 km / h. High-definition cameras on each platform record dynamic platform conditions at a resolution of 1920x1080 and a frame rate of 25 frames per second, and real-time video streams are transmitted back through a dedicated network. The gate system accurately records the entry and exit information of each passenger, including passage time, gate number, and passenger type. For example, during 8:00-8:01, gate G1 at Station A records 32 people passing through. Meanwhile, environmental sensors such as temperature, humidity, smoke, and vibration are deployed in tunnels and stations to provide real-time environmental data support for operation safety.

[0126] The acquired multi-source heterogeneous data is immediately transmitted to the data processing server cluster for cleaning, conversion, and fusion, and invalid information such as abnormal data points with GPS positioning errors exceeding the threshold is removed to ensure data quality. Subsequently, a real-time line network state model is built using the graph database Neo4j, abstracting stations, sections, switches, and signal machines in the subway line as nodes and abstracting train running paths and passenger flow directions as edges to visually present the real-time running state of the subway line network and provide a clear data model foundation for subsequent decision-making.

[0127] In the abnormal event identification link, the system combines LSTM neural network and cellular automata algorithm to deeply analyze real-time line network state model data. LSTM neural network learns from historical passenger flow and train operation time series data, mines time sequence characteristics and rules, and accurately predicts normal operation trend; cellular automata algorithm divides the subway line network into multiple cells, simulates dynamic evolution process according to train state and passenger flow density in the cell. At 8:00, the system comprehensively analyzes and finds that the train running speed in the C station to D station interval drops sharply, and the subsequent train queues, combined with the passenger gathering situation in the platform monitoring video, determines that equipment failure occurs in the interval and triggers an alarm. In the visualization display aspect, the line section passenger flow and station platform passenger flow are dynamically rendered in red (congestion), yellow (busy), and green (smooth); precise positioning of equipment failure is marked with GIS map technology, and the type and location coordinates of the faulty equipment are displayed in detail; based on historical and real-time monitoring data, the LSTM prediction results are superimposed to show the passenger flow trend of each station in the next 30 minutes, and the large screen is updated in real time to provide visual basis for operation decision-making.

[0128] When an abnormal event is identified (such as a sudden large passenger flow at a station, with passenger flow exceeding 80% of the maximum carrying capacity of the station within 15 minutes) and combined with passenger flow prediction trend (future 30 minutes passenger flow will continue to rise), slime mold algorithm is used to generate adjustment strategy and evacuation scheme. Set the slime mold algorithm parameters, population size is 50, maximum iteration number is 100, decay coefficient is 0.5, take train departure frequency and marshalling number of each period as decision variables, construct the objective function of minimizing operation cost (weight 0.4), passenger average waiting time (weight 0.3) and average delay time (weight 0.3); through core formula iteration, finally generate adjustment strategy, add 8 trains within 30 minutes at 3 adjacent stations around the affected station, adjust 5 related line departure intervals from 5 minutes to 3 minutes, and develop emergency evacuation scheme, set temporary guide signs, multi-language broadcast to guide passengers to adjacent stations, and update delay and alternative route information in real time on electronic guide screen.

[0129] In the execution adjustment stage, the train schedule adjustment strategy is quickly transmitted to the train automatic operation system (ATO), and the ATO dynamically adjusts the train automatic scheduling curve accordingly. The running path and speed curve of the added section train are re-planned to ensure the safety interval with the existing train; the train is adjusted according to the departure interval, and the traction, braking time and speed threshold of each section are finely modified, such as reducing the train inter-station running speed by 10% when the departure interval is extended, to ensure the running safety and punctuality. In the information release link, the control center sends multilingual (Chinese, English, Japanese, etc.) broadcast instructions to each station through the broadcast system, and broadcasts the equipment failure, train adjustment and transfer suggestion in detail; at the same time, the content of the electronic guide screen of each station is remotely updated, such as the electronic guide screen of B station displays in real time that "the equipment in C-D section is faulty, and it is recommended to take X bus to D station", to ensure that passengers can obtain accurate information in time.

[0130] In summary, the embodiment of the application realizes accurate perception and dynamic modeling of subway operation state through real-time data collection and deep fusion, quickly and accurately identifies abnormal events by using LSTM neural network and cellular automata algorithm, and intuitively presents the operation situation by using visual large screen, thereby providing clear basis for decision-making. On this basis, the slime mold algorithm is used to scientifically optimize the train scheduling strategy, effectively balancing the operation cost and passenger experience. In the scheme execution stage, the automatic adjustment curve and multi-channel information release ensure the efficient landing of the strategy and the timely awareness of passengers, greatly improving the response speed and disposal efficiency of the subway in the face of complex operation scenes and sudden conditions, reducing the operation risk, enhancing the safety, convenience and satisfaction of passenger travel, and significantly improving the intelligent operation level and comprehensive service ability of the urban subway transportation system.

[0131] Figure 13 The structure schematic diagram of the electronic device provided by the embodiment of the application is shown. The electronic device can include:

[0132] The memory 1301, the processor 1302, and the computer program stored in the memory 1301 and executable on the processor 1302.

[0133] The processor 1302 executes the program to implement the subway traffic operation decision adjustment method based on the visual large screen provided in the above embodiment.

[0134] Further, the electronic device further includes:

[0135] The communication interface 1303 is used for communication between the memory 1301 and the processor 1302.

[0136] The memory 1301 is used to store the computer program executable on the processor 1302.

[0137] The memory 1301 can include a high-speed RAM (Random Access Memory) memory, and can also include a nonvolatile memory such as at least one disk memory.

[0138] If the memory 1301, the processor 1302 and the communication interface 1303 are implemented independently, the communication interface 1303, the memory 1301 and the processor 1302 can be connected to each other through a bus and complete communication between each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 13 Only one thick line is used in the figure to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0139] Optionally, in a specific implementation, if the memory 1301, the processor 1302 and the communication interface 1303 are integrated on a chip, the memory 1301, the processor 1302 and the communication interface 1303 can complete communication between each other through an internal interface.

[0140] The processor 1302 can be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0141] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the above method for adjusting subway traffic operation decision based on a visual large screen.

[0142] In addition, the embodiments of the present application also provide a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed to implement the above method for adjusting subway traffic operation decision based on a visual large screen.

[0143] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms is not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, different embodiments or examples described in the present specification and the features of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0144] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0145] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing one or more steps of a method or process, including a set of executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the present application includes additional implementation involving other steps not shown or discussed, including the performance of functions in a different order than shown or discussed, and including the performance of functions in substantially simultaneous manner, or in reverse order, as will be understood by those skilled in the art.

[0146] It should be understood that parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. As in the case of hardware implementation and in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuit with logic gate circuit for implementing logic functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) and the like.

[0147] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-described embodiment method can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. The program, when executed, includes one or a combination of the steps of the method embodiment.

[0148] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary, and are not to be interpreted as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A subway traffic operation decision-making and adjustment system based on a large-scale visualization screen, characterized in that: include: Perception data module, data processing center, visualization large screen interactive platform, intelligent command decision engine, dynamic adjustment unit; among them, The perception data module is used to collect train positioning data, platform monitoring video stream, gate passage data and environmental sensor data in real time; The data processing center is used to integrate and analyze the collected data and mine potential patterns, correlations and anomalies in the data; The large-screen interactive visualization platform is used to present network congestion heat maps, equipment fault location, and passenger flow forecast trends in real time, switching interfaces and highlighting information according to different operating scenarios and needs; The intelligent instruction decision engine is used to dynamically generate train schedule adjustment strategies and emergency diversion plans; The dynamic adjustment unit is used to dynamically adjust the electronic guidance screen and platform broadcast according to the decision instructions to dynamically dispatch the train.

2. The subway traffic operation decision-making and adjustment system based on a large-scale visualization screen according to claim 1 is characterized in that: The perception data module includes a sensor cluster and an abnormal data filtering unit, wherein the sensor cluster is used to collect train position, passenger flow density, human body temperature and obstacle distance data; the abnormal data filtering unit uses a sliding window outlier detection algorithm to eliminate invalid data caused by sensor drift, occlusion or communication interruption in real time.

3. The subway traffic operation decision-making and adjustment system based on a large-scale visualization screen according to claim 1 is characterized in that: The data processing center includes a streaming computing pipeline, a graph neural network analysis module and a graph reasoning engine. The streaming computing pipeline is used to aggregate and convert video streams, sensor data and operation logs in real time; the graph neural network analysis module is used to build a dynamic relationship topology and explore potential congestion transmission paths; the graph reasoning engine is used to integrate historical fault libraries and real-time operating condition data to automatically generate congestion cause reasoning trees and risk probability matrices.

4. The subway traffic operation decision-making and adjustment system based on a large-scale visualization screen according to claim 1 is characterized in that: The large-screen visualization interactive platform includes a multi-level alarm projection module, an AR perspective rendering engine, and a scene adaptation engine. The multi-level alarm projection module is used to map equipment failures according to the scope of impact to a three-dimensional sand table of the line network, with core node failures marked in red. The AR perspective rendering engine is used to generate a virtual perspective view of the real-time positioning of trains in the tunnel, superimposing a thermal indicator of the carriage load rate. The scene adaptation engine automatically switches the interface layout of the commuting peak mode, emergency evacuation mode, and night maintenance mode through an operating scenario classifier.

5. The subway traffic operation decision-making and adjustment system based on a large-scale visualization screen according to claim 1 is characterized in that: The intelligent instruction decision engine includes a road network prediction model, a resource constraint solver and a plan optimizer. The road network prediction model is used to integrate the LSTM neural network and the cellular automaton algorithm to deduce the diffusion range and duration of delay events in the road network; the resource constraint solver is used to generate the optimal additional / suspension plan under multiple constraints such as train turnaround time, number of spare vehicles and crew configuration; the plan optimizer is used to integrate the expert rule base to generate hierarchical response emergency broadcast language and diversion paths.

6. The subway traffic operation decision-making and adjustment system based on a large-scale visualization screen according to claim 1 is characterized in that: The dynamic adjustment unit includes a broadcast linkage controller, an optical flow guidance matrix, and a dispatch instruction compiler. The broadcast linkage controller is used to trigger multilingual electronic voice and AR navigation arrows; the optical flow guidance matrix is ​​used to generate evacuation path light strips through programmable LED floor tiles; and the dispatch instruction compiler encodes the strategy into an executable instruction set for the ATO train automatic dispatch unit to dynamically adjust the ATO driving curve.

7. A subway traffic operation decision-making and adjustment method based on a large-scale visualization screen, characterized in that: include: Obtain train positioning data, platform monitoring video streams, gate passage data, and environmental sensor data; Constructing a real-time network status model based on the train positioning data, platform monitoring video stream, gate passage data, and environmental sensor data; Based on the real-time line network status model, the LSTM neural network and cellular automation algorithm are integrated to identify abnormal events. At the same time, the line network congestion heat map, equipment fault location mark and passenger flow forecast trend curve are dynamically rendered on the large visual screen. Based on the abnormal events and passenger flow forecast trends, combined with the slime mold algorithm, train schedule adjustment strategies and emergency diversion plans are dynamically generated; According to the adjustment strategy and guidance plan, the ATO train automatic dispatching curve is dynamically adjusted. At the same time, multi-language broadcasting is carried out and the content of the electronic guidance screen is updated.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a subway traffic operation decision-making and adjustment method based on a large visual screen as described in claim 7.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the method for subway traffic operation decision-making and adjustment based on a large visual screen as described in claim 7 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the method for subway traffic operation decision-making and adjustment based on a large visual screen as described in claim 7 is implemented.

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