CN106741020A - Subway conflict early warning method - Google Patents

Abstract

The invention relates to a method for early warning of subway conflicts, which comprises the following steps: firstly, according to the planned operation parameters of each train, a topological structure diagram of a rail transit network is generated; then, based on the topological structure diagram, the controllability and sensitivity of the train flow are analyzed; According to the planned operation parameters of each train, generate multi-train conflict-free running trajectories; then at each sampling time, based on the current running state of the train and the historical position observation sequence, predict the traveling position of the train at a certain time in the future, and then establish the train The observer of continuous dynamics to discrete conflict logic maps continuous dynamics to conflict states expressed by discrete observations; when the system may violate traffic control rules, it monitors the mixed dynamic behavior of the subway traffic mixed system, providing control centers with Warning message. The invention rolls and predicts the track of the subway train in real time, effectively warns the train conflict, and improves the safety of the subway traffic.

Description

Early warning method of subway conflict

The application number of this application is: 201510150113.4, and the name of the invention is "A Kind of Pre-warning Police for Subway Train Conflicts" Law, the filing date is: the divisional application of the invention patent application on March 31, 2015.

technical field

The invention relates to a subway train conflict early warning method, in particular to a subway train conflict early warning method based on a robust strategy.

Background technique

With the increasing scale of large and medium-sized cities in our country, the urban traffic system is facing more and more pressure, and vigorously developing the rail transit system has become an important means to solve urban traffic congestion. The national "Eleventh Five-Year Plan" outline pointed out that large cities and urban agglomerations where conditions permit should take rail transit as a priority area of development. my country is experiencing an unprecedented peak period of rail transit development. Some cities have shifted from line construction to network construction, and urban rail transit networks have gradually formed. In complex areas with dense rail transit network and train flow, the method of still adopting train operation plan combined with train interval allocation based on subjective experience gradually shows its backwardness. Due to the influence of various random factors, it is easy to cause congestion in traffic flow tactical management and reduce the safety of traffic system operation; (2) The train scheduling work focuses on maintaining the safe interval between individual trains, and has not yet risen to the strategic management of train flow. At the macro level; (3) The train deployment process mostly depends on the subjective experience of the front-line dispatchers, and the timing of the deployment is relatively random, lacking scientific theoretical support; (4) The deployment methods used by the dispatchers seldom take into account external interference factors The impact of the train allocation scheme is poor in robustness and availability. In order to ensure the safe operation of subway traffic, the implementation of effective conflict early warning has become the focus of subway traffic control. The implementation of effective early warning of subway conflicts has become the focus of subway traffic control.

The discussion objects of the existing literature are mostly for long-distance railway transportation, but there is still a lack of systematic design for the scientific regulation and control scheme of the urban subway transportation system under the conditions of large flow, high density and small interval operation. The train coordination control scheme under the complex road network operating conditions needs to calculate and optimize the operating status of a single train on the regional traffic network at the strategic level, and implement collaborative planning for the traffic flow composed of multiple trains; at the pre-tactical level At present, the congestion problem can be solved by adjusting the key operating parameters in some areas of the transportation network through an effective monitoring mechanism. However, there is currently no more accurate solution for the prediction of the subway train trajectory and the early warning of train conflicts.

Contents of the invention

The technical problem to be solved by the present invention is to provide a subway conflict early warning method with better robustness and usability, the method has higher prediction accuracy for subway trains, and the accuracy and timeliness of subway train conflict early warning are better.

The technical scheme that realizes the object of the present invention is to provide a kind of subway conflict early warning method, comprises the steps:

Step A, according to the planned operation parameters of each train, generate the topology structure diagram of the rail transit network;

Step B, based on the topology diagram of the rail transit network built in step A, analyze the controllability and sensitivity of the train flow;

Step C, according to the planned operation parameters of each train, on the basis of constructing the train dynamics model, establish a train operation conflict pre-allocation model according to the train operation conflict coupling points, and generate multiple train conflict-free running trajectories;

Step D. At each sampling time t, based on the current running state of the train and the historical position observation sequence, predict the traveling position of the train at a certain moment in the future; the specific process is as follows:

Step D1, train track data preprocessing, take the stop position of the train at the starting station as the origin of coordinates, and at each sampling time, according to the acquired original discrete two-dimensional position sequence of the train x=[x ₁ ,x ₂ ,.. .,x _n ] and y=[y ₁ ,y ₂ ,...,y _n ], use the first-order difference method to process them to obtain a new train discrete position sequence △x=[△x ₁ ,△x ₂ ,...,△x _n-1 ] and △y=[△y ₁ ,△y ₂ ,...,△y _n-1 ], where △x _i = _{xi +1} -xi ,△y _i =y _i+1 -y _i (i=1,2,...,n-1);

Step D2, clustering the train track data, clustering the new discrete two-dimensional position sequence △x and △y of the train after processing by setting the number of clusters M', using the K-means clustering algorithm to cluster them respectively ;

Step D3. Use the Hidden Markov Model to perform parameter training on the clustered train trajectory data. By treating the processed train trajectory data △x and △y as the obvious observations of the hidden Markov process, by setting The number of hidden states N' and the parameter update period τ', based on the latest T' position observations and using the BW algorithm to obtain the latest hidden Markov model parameter λ'; is dynamically changing. In order to track the state changes of the train trajectory in real time, it is necessary to readjust it on the basis of the initial trajectory hidden Markov model parameters λ′=(π,A,B), so as to more accurately speculate that the train is at The position at a certain moment in the future; every interval τ', according to the latest T′ observations (o ₁ ,o ₂ ,...,o _T ′) for the trajectory hidden Markov model parameter λ′=(π, A, B) re-estimate;

Step D4, according to the Hidden Markov Model parameters, use the Viterbi algorithm to obtain the hidden state q corresponding to the observed value at the current moment;

Step D5, every time period According to the latest hidden Markov model parameters λ′=(π,A,B) and the latest H historical observations (o ₁ ,o ₂ ,...,o _H ), based on the hidden state q of the train at the current moment , at time t, by setting the prediction time domain h′, the predicted position value O of the train in the future period is obtained;

Step E, establish an observer from the continuous dynamics of the train to the discrete conflict logic, and map the continuous dynamics of the subway traffic system to the conflict state expressed by the discrete observation values; when the system may violate the traffic control rules, the mixed system of the subway traffic The mixed dynamic behavior is monitored to provide timely alarm information for the subway traffic control center.

Further, the specific process of step A is as follows:

Step A1, from the database of the subway traffic control center, extract the site information that each train stops during operation;

Step A2, classify the station information where each train stops according to the positive and negative running directions, and merge the same stations in the same running direction;

Step A3, according to the site merging result, connect multiple sites before and after with straight lines according to the spatial layout of the sites.

Further, the specific process of step B is as follows:

Step B1, constructing a traffic flow control model on a single subsection; its specific process is as follows:

Step Bl.1. Introduce state variable Ψ, input variable u and output variable Ω, where Ψ represents the number of trains that exist at a certain moment on the connected section between stations, and it includes two types of single section and multi-section, and u indicates rail transit dispatcher The dispatching measures implemented for a certain road section, such as adjusting the speed of the train or changing the time of the train at the station, etc., Ω represents the number of trains leaving on the road section in a certain period of time;

Step B1.2, by discretizing the time, establish the form such as Ψ(t+△t)=A ₁ Ψ(t)+B ₁ u(t) and Ω(t)=C ₁ Ψ(t)+D ₁ u A discrete-time traffic flow control model on a single subsection of (t), where △t represents the sampling interval, Ψ(t) represents the state vector at time t, and A ₁ , B ₁ , C ₁ and D ₁ represent the State transition matrix, input matrix, output measurement matrix and direct transfer matrix;

Step B2, constructing a traffic flow control model on multiple sub-sections; the specific process is as follows:

Step B2.1, according to the spatial layout form of the line and the historical statistical data of the train flow, obtain the traffic ratio parameter β on each sub-section of the crossing line;

Step B2.2. According to the traffic ratio parameter and the discrete-time traffic flow control model on a single subsection, construct a form such as Ψ(t+△t)=A ₁ Ψ(t)+B ₁ u(t) and Ω(t) = Discrete-time traffic flow control model on multi-subsections of C ₁ Ψ(t)+D ₁ u(t);

Step B3, according to the relationship between the rank of the controllable coefficient matrix [B ₁ ,A ₁ B ₁ ,...,A ₁ ^n-1 B ₁ ] of the control model and the value n, qualitatively analyze its controllability, according to the control model The sensitivity coefficient matrix [C ₁ (zI-A ₁ ) ^-1 B ₁ +D ₁ ], quantitative analysis of its input and output sensitivity, where n represents the dimension of the state vector, I represents the identity matrix, z represents the original discrete time The basic factor for the transformation of the traffic flow control model.

Further, the specific process of step C is as follows:

Step C1, train state transfer modeling, the process of the train running along the rail transit network is a dynamic switching process between stations, according to the station settings in the train operation plan, a Petri net model for a single train switching between different stations is established : E=(g, G, Pre, Post, m) is the train section transfer model, where g represents each sub-section between stations, G represents the transition point of the train speed state parameter, Pre and Post represent each sub-section and station respectively The forward and backward connection relationship between Indicates the running section of the train, where m represents the model identifier, and Z ⁺ represents a set of positive integers;

Step C2: Modeling the hybrid system of the train's full running profile, considering the running of the train between stations as a continuous process, starting from the stress situation of the train, deriving the dynamic equation of the train at different running stages according to the energy model, and combining external disturbance factors , establish a mapping function v _G = λ(T ₁ , T ₂ , H, R, α) about the speed v _G of the train in a certain running stage, where T ₁ , T ₂ , H, R and α represent the traction force of the train, Random fluctuation parameters of train braking force, train resistance, train gravity and train state;

Step C3, guessing and solving the train trajectory by means of hybrid simulation, and recursively solving the distance between the train at a certain operation stage and the initial stop position point at any time by subdividing the time and using the characteristics of continuous state change, Where J ₀ is the voyage of the train from the initial stop point at the initial moment, △τ is the value of the time window, and J(τ) is the distance between the train and the initial stop point at the time τ, from which the trajectory of a single train can be inferred;

Step C4, train station time probability distribution function modeling, for a specific operating line, by calling the train stop time data at each station, to obtain the train stop time probability distribution under the conditions of different lines and different stations;

Step C5, conflict-free robust trajectory allocation of multi-train coupling, according to the time when each train arrives at the conflict point, through the time period division, at each sampling time t, under the premise of incorporating random factors, according to the scheduling rules Robust quadratic programming for train trajectories that do not meet safety interval requirements.

Further, in step D, the value of the number of clusters M' is 4, the value of the number of hidden states N' is 3, the parameter update period τ' is 30 seconds, T' is 10, is 30 seconds, H is 10, and the prediction time domain h' is 300 seconds.

Further, the specific implementation process of step E is as follows:

Step E1, constructing a conflict hypersurface function set based on control rules: establishing a hypersurface function set to reflect the conflict situation of the system, wherein, the continuous function h _I related to a single train in the conflict hypersurface is a type I hypersurface, and The continuous function h _II related to the two trains is the type II hypersurface;

Step E2, establish an observer from the continuous state of the train to the discrete conflict state, and construct the safety rule set d _ij (t)≥d _min that the train needs to satisfy when running in the traffic network, where d _ij (t) represents the train i and The actual interval of train j at time t, d _min represents the minimum safe interval between trains;

Step E3. Based on the human-machine system theory and the hierarchical control principle of complex systems, and according to the train operation mode, build a real-time monitoring mechanism for trains with people in the loop, to ensure that the operation of the system is within the safe reachable set, and design from conflict to conflict relief The discrete monitor of the means, when the discrete observation vector of the observer indicates that the safety rule set will be violated, it will immediately send a corresponding alarm message to the subway traffic control center.

The present invention has positive effects: (1) the subway conflict early warning method of the present invention satisfies the premise of the rail traffic control safety interval, based on the real-time position information of the train, using data mining means to dynamically infer the train track; The rules provide an alarm for possible conflicts, and the early warning effect on conflicts is better. It can effectively, accurately and real-time predict the trajectory of trains and predict train conflicts, effectively improving the safety of subway traffic.

(2) Based on the controllability and sensitivity analysis results of the rail transit network topology, the present invention can provide a scientific basis for early warning of subway traffic flow and overcome the arbitrariness of conventional early warning scheme selection.

(3) The present invention is based on the constructed "people in the loop" scene monitoring mechanism, which can respond effectively in time to the frequent interaction of train internal continuous variables and external discrete events, and overcome the shortcomings of conventional open-loop off-line monitoring solutions.

(4) The present invention is based on the constructed train trajectory rolling prediction scheme, which can be timely integrated into various interference factors in the real-time operation of the train, improves the accuracy of train trajectory prediction, and overcomes the shortcomings of low accuracy of conventional off-line prediction schemes.

Description of drawings

Figure 1 is an analysis diagram of train flow operation characteristics;

Figure 2 is a conflict-free 3D robust trajectory estimation diagram;

Fig. 3 is a mixed monitoring diagram of the train running state.

detailed description

(Example 1)

A subway traffic flow optimization control system includes a line topology generation module, a data transmission module, a vehicle terminal module, a control terminal module, and a trajectory monitoring module. The trajectory monitoring module collects train status information and provides it to the control terminal module.

The control terminal module includes the following submodules:

Conflict-free trajectory generation module before train operation: According to the planned operation timetable of the train, the train dynamics model is established first, and then the train operation conflict pre-allocation model is established according to the train operation conflict coupling point, and finally the conflict-free train operation trajectory is generated.

Short-term trajectory generation module for train operation: According to the real-time status information of the train provided by the trajectory monitoring module, use the data mining model to predict the trajectory of the train in the future period.

Train operation status monitoring module: at each sampling time t, based on the train trajectory estimation results, when there may be a violation of safety rules between trains, monitor its dynamic behavior and provide alarm information to the control terminal.

Train collision avoidance trajectory optimization module: when the train operation status monitoring module sends out an alarm message, on the premise of satisfying the physical performance of the train, the regional flow capacity constraints and the rail traffic dispatching rules, the adaptive control theory method is adopted by setting the optimization index function The control terminal module performs robust two-layer planning on the train trajectory, and transmits the planning results to the vehicle terminal module through the data transmission module for execution. The train collision avoidance trajectory optimization module includes two types of planning processes: inner planning and outer planning.

The subway conflict early warning method using the above-mentioned subway traffic flow optimization control system includes the following steps:

Step A, according to the planned operation parameters of each train, generate the topology structure diagram of the rail transit network; its specific process is as follows:

Step A1, from the database of the subway traffic control center, extract the site information that each train stops during operation;

Step A2, classify the station information where each train stops according to the positive and negative running directions, and merge the same stations in the same running direction;

Step A3, according to the site merging result, connect multiple sites before and after with straight lines according to the spatial layout of the sites.

Step B, based on the topological structure diagram of the rail transit network constructed in step A, analyze the controllability and sensitivity characteristics of the train flow; the specific process is as follows:

Step B1, see Fig. 1, build the traffic flow control model on the single subsection; Its concrete process is as follows:

Step Bl.1. Introduce state variable Ψ, input variable u and output variable Ω, where Ψ represents the number of trains that exist at a certain moment on the connected section between stations, and it includes two types of single section and multi-section, and u indicates rail transit dispatcher The dispatching measures implemented for a certain road section, such as adjusting the speed of the train or changing the time of the train at the station, etc., Ω represents the number of trains leaving on the road section in a certain period of time;

Step B1.2, by discretizing the time, establish the form such as Ψ(t+△t)=A ₁ Ψ(t)+B ₁ u(t) and Ω(t)=C ₁ Ψ(t)+D ₁ u A discrete-time traffic flow control model on a single subsection of (t), where △t represents the sampling interval, Ψ(t) represents the state vector at time t, and A ₁ , B ₁ , C ₁ and D ₁ represent the State transition matrix, input matrix, output measurement matrix and direct transfer matrix;

Step B2, constructing a traffic flow control model on multiple sub-sections; the specific process is as follows:

Step B2.1, according to the spatial layout form of the line and the historical statistical data of the train flow, obtain the traffic ratio parameter β on each sub-section of the crossing line;

Step B2.2. According to the traffic ratio parameter and the discrete-time traffic flow control model on a single subsection, construct a form such as Ψ(t+△t)=A ₁ Ψ(t)+B ₁ u(t) and Ω(t) = Discrete-time traffic flow control model on multi-subsections of C ₁ Ψ(t)+D ₁ u(t);

Step B3, according to the relationship between the rank of the controllable coefficient matrix [B ₁ ,A ₁ B ₁ ,...,A ₁ ^n-1 B ₁ ] of the control model and the value n, qualitatively analyze its controllability, according to the control model The sensitivity coefficient matrix [C ₁ (zI-A ₁ ) ^-1 B ₁ +D ₁ ], quantitative analysis of its input and output sensitivity, where n represents the dimension of the state vector, I represents the identity matrix, z represents the original discrete time The basic factors for the transformation of the traffic flow control model;

Step C, see Figure 2, according to the planned operation parameters of each train, on the basis of constructing the train dynamics model, establish a train operation conflict pre-allocation model according to the train operation conflict coupling points, and generate multiple train conflict-free running trajectories; the specific process as follows:

Step C1, train state transfer modeling, the process of the train running along the rail transit network is a dynamic switching process between stations, according to the station settings in the train operation plan, a Petri net model for a single train switching between different stations is established : E=(g, G, Pre, Post, m) is the train section transfer model, where g represents each sub-section between stations, G represents the transition point of the train speed state parameter, Pre and Post represent each sub-section and station respectively The forward and backward connection relationship between Indicates the running section of the train, where m represents the model identifier, and Z ⁺ represents a set of positive integers;

Step C2: Modeling the hybrid system of the train's full running profile, considering the running of the train between stations as a continuous process, starting from the stress situation of the train, deriving the dynamic equation of the train at different running stages according to the energy model, and combining external disturbance factors , establish a mapping function v _G = λ(T ₁ , T ₂ , H, R, α) about the speed v _G of the train in a certain running stage, where T ₁ , T ₂ , H, R and α represent the traction force of the train, Random fluctuation parameters of train braking force, train resistance, train gravity and train state;

Step C3, guessing and solving the train trajectory by means of hybrid simulation, and recursively solving the distance between the train at a certain operation stage and the initial stop position point at any time by subdividing the time and using the characteristics of continuous state change, Where J ₀ is the voyage of the train from the initial stop point at the initial moment, △τ is the value of the time window, and J(τ) is the distance between the train and the initial stop point at the time τ, from which the trajectory of a single train can be inferred;

Step C4, train station time probability distribution function modeling, for a specific operating line, by calling the train stop time data at each station, to obtain the train stop time probability distribution under the conditions of different lines and different stations;

Step C5, conflict-free robust trajectory allocation of multi-train coupling, according to the time when each train arrives at the conflict point, through the time period division, at each sampling time t, under the premise of incorporating random factors, according to the scheduling rules Robust quadratic programming for train trajectories that do not meet safety interval requirements.

Step D. At each sampling time t, based on the current running state of the train and the historical position observation sequence, predict the traveling position of the train at a certain moment in the future; the specific process is as follows:

Step D1, train track data preprocessing, take the stop position of the train at the starting station as the origin of coordinates, and at each sampling time, according to the acquired original discrete two-dimensional position sequence of the train x=[x ₁ ,x ₂ ,.. .,x _n ] and y=[y ₁ ,y ₂ ,...,y _n ], use the first-order difference method to process them to obtain a new train discrete position sequence △x=[△x ₁ ,△x ₂ ,...,△x _n-1 ] and △y=[△y ₁ ,△y ₂ ,...,△y _n-1 ], where △x _i = _{xi +1} -xi ,△y _i =y _i+1 -y _i (i=1,2,...,n-1);

Step D2, clustering the train track data, clustering the new discrete two-dimensional position sequence △x and △y of the train after processing by setting the number of clusters M', using the K-means clustering algorithm to cluster them respectively ;

Step D3. Use the Hidden Markov Model to perform parameter training on the clustered train trajectory data. By treating the processed train trajectory data △x and △y as the obvious observations of the hidden Markov process, by setting The number of hidden states N' and the parameter update period τ', based on the latest T' position observations and using the BW algorithm to obtain the latest hidden Markov model parameter λ'; is dynamically changing. In order to track the state changes of the train trajectory in real time, it is necessary to readjust it on the basis of the initial trajectory hidden Markov model parameters λ′=(π,A,B), so as to more accurately speculate that the train is at The position at a certain moment in the future; every time period τ', according to the latest T′ observations (o ₁ ,o ₂ ,...,o _T′ ) for trajectory hidden Markov model parameters λ′=(π, A, B) re-estimate;

Step D4, according to the Hidden Markov Model parameters, use the Viterbi algorithm to obtain the hidden state q corresponding to the observed value at the current moment;

Step D5, every time period According to the latest hidden Markov model parameters λ′=(π,A,B) and the latest H historical observations (o ₁ ,o ₂ ,...,o _H ), based on the hidden state q of the train at the current moment , at time t, by setting the prediction time domain h′, the predicted position value O of the train in the future period is obtained;

The value of the above clustering number M' is 4, the value of the number of hidden states N' is 3, the parameter update period τ' is 30 seconds, T' is 10, is 30 seconds, H is 10, and the prediction time domain h' is 300 seconds.

Step E, as shown in Figure 3, establishes an observer from the continuous dynamics of the train to the discrete conflict logic, and maps the continuous dynamics of the subway traffic system to the conflict state expressed by discrete observations; when the system may violate traffic control rules, the subway The mixed dynamic behavior of the traffic mixed system is monitored to provide timely alarm information for the subway traffic control center;

The concrete implementation process of described step E is as follows:

Step E1, constructing a conflict hypersurface function set based on control rules: establishing a hypersurface function set to reflect the conflict situation of the system, wherein, the continuous function h _I related to a single train in the conflict hypersurface is a type I hypersurface, and The continuous function h _II related to the two trains is the type II hypersurface;

Step E2, establish an observer from the continuous state of the train to the discrete conflict state, and construct the safety rule set d _ij (t)≥d _min that the train needs to satisfy when running in the traffic network, where d _ij (t) represents the train i and The actual interval of train j at time t, d _min represents the minimum safe interval between trains;

Step E3. Based on the human-machine system theory and the hierarchical control principle of complex systems, and according to the train operation mode, build a real-time monitoring mechanism for trains with people in the loop, to ensure that the operation of the system is within the safe reachable set, and design from conflict to conflict relief The discrete monitor of the means, when the discrete observation vector of the observer indicates that the safety rule set will be violated, it will immediately send a corresponding alarm message to the subway traffic control center.

Apparently, the above-mentioned embodiments are only examples for clearly illustrating the present invention, rather than limiting the implementation of the present invention. For those of ordinary skill in the art, other changes or changes in different forms can be made on the basis of the above description. It is not necessary and impossible to exhaustively list all the implementation manners here. And these obvious changes or modifications derived from the spirit of the present invention are still within the protection scope of the present invention.

Claims (1)

1. A subway conflict early warning method is characterized in that comprising the steps: Step A, according to the planned operation parameters of each train, generate the topology structure diagram of the rail transit network; Step B, based on the topology diagram of the rail transit network built in step A, analyze the controllability and sensitivity of the train flow; Step C, according to the planned operation parameters of each train, on the basis of constructing the train dynamics model, establish a train operation conflict pre-allocation model according to the train operation conflict coupling points, and generate multiple train conflict-free running trajectories; the specific process of step C is as follows: Step C1, train state transfer modeling, the process of the train running along the rail transit network is a dynamic switching process between stations, according to the station settings in the train operation plan, a Petri net model for a single train switching between different stations is established : E=(g, G, Pre, Post, m) is the train section transfer model, where g represents each sub-section between stations, G represents the transition point of the train speed state parameter, Pre and Post represent each sub-section and station respectively The forward and backward connection relationship between Indicates the running section of the train, where m represents the model identifier, and Z ⁺ represents a set of positive integers; Step C2: Modeling the hybrid system of the train's full running profile, considering the running of the train between stations as a continuous process, starting from the stress situation of the train, deriving the dynamic equation of the train at different running stages according to the energy model, and combining external disturbance factors , establish a mapping function v _G = λ(T ₁ , T ₂ , H, R, α) about the speed v _G of the train in a certain running stage, where T ₁ , T ₂ , H, R and α represent the traction force of the train, Random fluctuation parameters of train braking force, train resistance, train gravity and train state; Step C3, guessing and solving the train trajectory by means of hybrid simulation, and recursively solving the distance between the train at a certain operation stage and the initial stop position point at any time by subdividing the time and using the characteristics of continuous state change, Where J ₀ is the voyage of the train from the initial stop point at the initial moment, △τ is the value of the time window, and J(τ) is the distance between the train and the initial stop point at the time τ, from which the trajectory of a single train can be inferred; Step C4, train station time probability distribution function modeling, for a specific operating line, by calling the train stop time data at each station, to obtain the train stop time probability distribution under the conditions of different lines and different stations; Step C5, conflict-free robust trajectory allocation of multi-train coupling, according to the time when each train arrives at the conflict point, through the time period division, at each sampling time t, under the premise of incorporating random factors, according to the scheduling rules Robust quadratic programming is implemented for train trajectories that do not meet the safety interval requirements; Step D. At each sampling time t, based on the current running state of the train and the historical position observation sequence, predict the traveling position of the train at a certain moment in the future; the specific process is as follows: Step D1, train track data preprocessing, take the stop position of the train at the starting station as the origin of coordinates, and at each sampling time, according to the acquired original discrete two-dimensional position sequence of the train x=[x ₁ ,x ₂ ,.. .,x _n ] and y=[y ₁ ,y ₂ ,...,y _n ], use the first-order difference method to process them to obtain a new train discrete position sequence △x=[△x ₁ ,△x ₂ ,...,△x _n-1 ] and △y=[△y ₁ ,△y ₂ ,...,△y _n-1 ], where △x _i = _{xi +1} -xi ,△y _i =y _i+1 -y _i (i=1,2,...,n-1); Step D2, clustering the train track data, clustering the new discrete two-dimensional position sequence △x and △y of the train after processing by setting the number of clusters M', using the K-means clustering algorithm to cluster them respectively ; Step D3. Use the Hidden Markov Model to perform parameter training on the clustered train trajectory data. By treating the processed train trajectory data △x and △y as the obvious observations of the hidden Markov process, by setting The number of hidden states N' and the parameter update period τ', based on the latest T' position observations and using the BW algorithm to obtain the latest hidden Markov model parameters λ'; specifically: due to the length of the obtained train trajectory sequence data is dynamic. In order to track the state changes of the train trajectory in real time, it is necessary to readjust it on the basis of the initial trajectory hidden Markov model parameter λ'=(π,A,B), so as to more accurately speculate that the train is at The position at a certain moment in the future; every time period τ', according to the latest T' observations (o ₁ ,o ₂ ,...,o _T' ) for trajectory hidden Markov model parameters λ'=(π, A, B) re-estimate; Step D4, according to the Hidden Markov Model parameters, use the Viterbi algorithm to obtain the hidden state q corresponding to the observed value at the current moment; Step D5, every time period According to the latest hidden Markov model parameters λ'=(π,A,B) and the latest H historical observations (o ₁ ,o ₂ ,...,o _H ), based on the hidden state q of the train at the current moment , at time t, by setting the prediction time domain h', the predicted position value O of the train in the future period is obtained; Step E, establish an observer from the continuous dynamics of the train to the discrete conflict logic, and map the continuous dynamics of the subway traffic system to the conflict state expressed by the discrete observation values; when the system may violate the traffic control rules, the mixed system of the subway traffic The mixed dynamic behavior is monitored to provide timely alarm information for the subway traffic control center.