A Distributed Estimation Method for the Running States of Multi-Train Cooperative Operation

Abstract

The present invention discloses a distributed estimation method for the running states of multi-train collaborative operation. The method of the present invention considers the situation of in-train communication existing in high-speed trains under collaborative operation. Based on the actual configuration of on-board sensors of high-speed trains and on the basis of the actual train operation dynamics model, a continuous-time state space model for multi-train collaborative operation is constructed and its observability is analyzed. Combining the Luenberger estimation method and the leader-follower consensus coordination method, and using the measurement information of local sensors of each train and the information interacted with neighboring trains through the in-train communication network, a distributed observer for the running states of multi-train collaborative operation is constructed to ensure that each train can perform real-time and effective estimation on the running states of all trains under collaborative operation. The present invention realizes the global estimation of the running states of trains under collaborative operation based on local measurements and local communication, and provides guarantee for the multi-train collaborative safe operation in a complex, rapidly changing, information-interactive and strongly real-time disturbed environment.

Description

Technical Field

[0001] The present invention relates to the field of multi - train cooperative operation optimization control, and particularly relates to a distributed estimation method for the running states of multi - train cooperative operation. Background Art

[0002] High - speed trains have been widely regarded and vigorously developed in recent years due to their characteristics such as high speed, low pollution, large passenger capacity, and good economic benefits. They have become a priority direction for the development of green transportation in China and are one of the outstanding representatives reflecting the level of China's industrialization development. With the continuous increase in train running speed and the continuous increase in train departure density, multi - train cooperative operation control and its related technologies with the goals of energy conservation, punctuality, reducing passenger waiting time, reducing the peak value of the traction power supply network, and automatically adjusting the operation diagram with optimization objectives have become the research focus in this field. Train state estimation is a key link in multi - train cooperative operation control. Accurately obtaining the train running state is crucial for strengthening conflict management and improving safety assurance. However, due to the influence of factors such as vehicle characteristics, line characteristics, weather conditions, and passenger capacity during the operation of high - speed trains, the operation environment has strong nonlinearity and strong interference. The method of obtaining the estimated value of the train running state only relying on single - sensor measurement technology has problems such as low reliability and poor estimation accuracy, and can no longer meet the requirements of multi - train cooperative operation optimization control. Currently, in the field of train running state estimation, domestic and foreign scholars have paid attention to the train running state estimation technology based on multi - sensor data fusion. However, most of the existing estimation methods based on multi - sensor fusion belong to centralized estimation algorithms, that is, after a center collects the measurement information of all train on - vehicle sensors, a centralized estimator is used for unified calculation to obtain the estimated values of the running states of all trains in cooperative operation. This design lacks robustness. Once the center where the centralized estimation method runs fails, it will lead to the paralysis of the entire train monitoring system. Moreover, since the collection and processing tasks of all train sensor measurement information are completed by the center, this poses high requirements on the data storage capacity, computing capacity, and communication capacity of the center, which is not conducive to the application of the algorithm in actual scenarios. Therefore, there is an urgent need to study a distributed estimation method for the running states of multi - train cooperative operation that can ensure real - time and effective monitoring of the running states of high - speed trains in cooperative operation in a high - speed train operation environment with strong nonlinearity and strong interference and is easy to be applied in practice. Summary of the Invention

[0003] Aiming at the problems existing in the above - mentioned background art, the purpose of the present invention is to propose a distributed estimation method for the running states of multi - train cooperative operation, which can ensure that each train obtains an effective estimation of the running states of all trains in cooperative operation in a running environment with strong nonlinearity and strong interference.

[0004] The object of the present invention is achieved by the following technical solutions: A distributed estimation method for the running states of multi-train collaborative operation, the method specifically includes the following steps:

[0005] Step 1: Considering the sensing and measurement conditions of on-vehicle sensors of high-speed trains, a multi-train collaborative operation workshop output coupling relationship graph is established by combining graph theory knowledge. Based on the actual train operation dynamics model, a multi-train collaborative operation continuous-time state space model is constructed;

[0006] Step 2: Conduct an observability analysis on the multi-train collaborative operation continuous-time state space model;

[0007] Step 3: Combining the topology relationship of the workshop communication network, the multi-train collaborative operation workshop output coupling relationship graph is processed to be directed acyclic, and a directed acyclic multi-train collaborative operation workshop output coupling relationship graph is obtained;

[0008] Step 4: Combining the observability analysis results, each train constructs a Luenberger-like observer according to the specified local measurement information in the directed acyclic multi-train collaborative operation workshop output coupling relationship graph, and obtains the estimated value of the running state of this train;

[0009] Step 5: Combining the observability analysis results, according to the topology relationship of the workshop communication network, each train uses the local communication information with the neighboring trains in the communication network to construct a consensus observer based on the leader-follower consensus coordination method, and obtains the estimated value of the running state of other collaborative trains; The Luenberger-like observer and the consensus observer together constitute a distributed observer for the running states of multi-train collaborative operation.

[0010] Further, in the above Step 1, the sensing and measurement conditions of on-vehicle sensors of high-speed trains mean that each train in collaborative operation can obtain the relative measurement information including relative position and relative speed with other collaborative trains, and some trains can obtain the absolute measurement information of this train including absolute position and absolute speed.

[0011] Further, in the above Step 1, the actual operation dynamics model of train i is as follows:

[0012]

[0013] Among them, and respectively represent the position, speed and control input of train i; the corresponding state space model of train i is expressed as follows:

[0014]

[0015] Among them, represents the state of train i,

[0016] Further, in the above step 1, the measurement information sensed by the on-vehicle sensors of train i is expressed as follows:

[0017]

[0018] Wherein, and respectively represent the measurement information of the on-vehicle sensors of train i, the measurement matrix, and the output coupling matrix between train i and train j. q i is the dimension of the measurement information observable by train i; Figure represents the diagram of the output coupling relationship between trains in a multi-train collaborative operation system composed of m trains, represents the set of neighboring trains of train i in the diagram of the output coupling relationship between trains . Each point in the point set represents each train, and each edge in the edge set represents the existing output coupling relationship between different trains. Specifically, (i, j) ∈ ε o if and only if C ij ≠ 0.

[0019] Further, in the above step 1, based on the actual operation dynamics model of the high-speed train and in combination with the measurement situation sensed by the on-vehicle sensors of the high-speed train, a continuous-time state space model for multi-train collaborative operation is constructed, and its form is as follows:

[0020]

[0021] y = Cs

[0022] Wherein, m is the total number of trains operating collaboratively, is the identity matrix, is the Kronecker product; y i is the measurement information of the on-vehicle sensors of train i,

[0023] Further, in the above step 2, an observability analysis is performed on the continuous-time state space model for multi-train collaborative operation, and the result is as follows: The multi-train collaborative operation system corresponding to (A, C) is observable if and only if each train in the multi-train collaborative operation system can obtain at least one piece of measurement information and at least one train has absolute measurement information; this observability analysis result is equivalently described using graph theory knowledge, that is, the multi-train collaborative operation system corresponding to (A, C) is observable if and only if the graph is an undirected connected graph, wherein, is a directed graph The undirected graph after removing directions is a new graph defined based on the output coupling relationship graph of multi - train collaborative operation workshops Specifically Let O represent the coordinate origin, and the edge set ε a Each edge in it represents the output coupling relationship between the train with absolute measurement information and the origin O, (O, i) ∈ ε a Indicates that train i has absolute measurement information; the multi - train collaborative operation system corresponding to (A, C) is observable, which means that under the condition of multi - train collaborative operation, the driving states of all trains in collaborative operation can be estimated using the sensor measurement information of all trains in collaborative operation

[0024] Furthermore, in step 3, a strongly - connected directed graph is used to describe the inter - vehicle communication network of the multi - train collaborative operation system composed of m trains. Among them, the edge set The pair of points (i, j) ∈ ε c Indicates that train i can transmit information to train j; combined with the topological relationship of the inter - vehicle communication network The specific process of acyclic - directed processing of the output coupling relationship graph of multi - train collaborative operation workshops is as follows

[0025] 1) Each train randomly selects a positive integer in the set of positive integers as its own ID

[0026] 2) Each train determines its own level according to the layering mechanism. The layering principle is that trains that can obtain absolute measurement information are automatically divided into level 0, which is the lowest level. Trains that can obtain relative measurement information with trains in level 0 are automatically divided into level 1, and trains that can obtain relative measurement information with trains in level 1 are automatically divided into level 2, and so on

[0027] 3) It is stipulated that the relative measurement information between two trains in different levels can only be used by the train in the higher level, and the relative measurement information between two trains in the same level can only be used by the train with a larger ID

[0028] 4) The transmission of the relative measurement information involved in process 3) is guaranteed by the inter - vehicle communication network to ensure

[0029] According to the above acyclic - directed processing, the output coupling relationship graph of multi - train collaborative operation workshops is transformed into a directed acyclic graph

[0030] Furthermore, in step 4, when the multi - train collaborative operation system corresponding to (A, C) is observable and the output coupling relationship of multi - train collaborative operation workshops is transformed into a directed acyclic graph In the case of, the state - space model of train i corresponding to (J, C ii ) is observable. On this basis, each train uses the local measurement information specified in Figure to construct a Luenberger - like observer to obtain an estimate of its own driving state; the form of the Luenberger - like observer of train i is as follows:

[0031]

[0032] where, represents the estimate of train i's own driving state obtained by using the Luenberger - like observer, represents the estimate of train l's driving state by train i using the consensus observer, represents the set of neighbor trains of train i in the directed acyclic graph of inter - vehicle output coupling relationship F i represents the observation gain, which can be calculated locally by train i, and the condition it needs to satisfy is to ensure that all eigenvalues of the matrix J - F i C ii have negative real parts.

[0033] Furthermore, in step 5, when the multi - train cooperative operation system corresponding to (A, C) is observable, each train constructs a consensus observer based on the leader - follower consensus coordination method to obtain an estimate of the driving states of all trains in cooperative operation; the consensus observer of train i consists of two parts, and the specific form is as follows:

[0034]

[0035]

[0036] where, represents the estimate of train i's own driving state obtained by using the consensus observer, represents the estimate of train j's driving state by train i using the consensus observer, represents the set of neighbor trains of train i in the communication network , and represent the consensus weights, which are related to the topological relationship of the communication network . Specifically, if there is an edge between two nodes in the communication network , the corresponding consensus weight is set to 1, otherwise it is set to 0; μ is the coupling gain, and the condition it needs to satisfy is to ensure that all eigenvalues of the matrix have negative real parts, where is the consensus weight matrix.

[0037] Furthermore, the Luenberger-like observer and the consensus observer based on the leader-follower consensus coordination method together constitute a distributed observer for the running states of multi-train cooperative operation; during the actual operation process, Step 4 and Step 5 are executed synchronously to jointly obtain the estimation of the running states of all trains in cooperative operation by each train.

[0038] The beneficial effects of the present invention are as follows: The present invention proposes a distributed estimation method for the running states of multi-train cooperative operation. Considering the situation of communication among high-speed trains in cooperative operation and combining the actual configuration of on-vehicle sensors of high-speed trains, a continuous-time state space model for multi-train cooperative operation is constructed based on the actual train operation dynamics model, and its observability is analyzed. Combining the Luenberger estimation method and the leader-follower consensus coordination method, using the measurement information of local sensors of each train and the information interacted with neighboring trains through the train-to-train communication network, it is ensured that each train can perform real-time and effective estimation of the running states of all trains in cooperative operation. The present invention realizes the global estimation of the running states of cooperative trains based on local measurements and local communication, has strong robustness, low communication cost and low computational cost, and provides guarantee for the cooperative and safe operation of multi-trains in a complex, rapidly changing, information-interactive and strongly disturbed real-time environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flowchart of the implementation of the method for estimating the running states of multi-train cooperative operation provided by the present invention;

[0040] Figure 2 is an error graph of the estimated running states of multi-train cooperative operation shown in an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] Figure 1 is a flowchart of the implementation of the method for estimating the running states of multi-train cooperative operation provided by an embodiment of the present invention. As Figure 1 shown, starting from the continuous-time state space model of multi-train cooperative operation, the observability of this model is analyzed, and the directed acyclic processing is performed on the graph of the train-to-train output coupling relationship included in the model. On this basis, a distributed observer for the running states of multi-train cooperative operation is constructed. This observer consists of two parts: a Luenberger-like observer and a consensus observer based on the leader-follower consensus coordination method. This observer uses local information including the measurement information of on-vehicle sensors of each train and the information interacted through the train-to-train communication network to obtain the estimation of the global state of the multi-train cooperative operation system.

[0043] The distributed estimation method for the running states of multi - train collaborative operation provided by the embodiments of the present invention specifically includes the following steps:

[0044] Step 1: Considering the sensing and measurement conditions of on - vehicle sensors of high - speed trains, a multi - train collaborative operation car - body output coupling relationship graph is established by combining graph - theory knowledge. Based on the actual running dynamics model of trains, a multi - train collaborative operation continuous - time state - space model is constructed;

[0045] 1) The sensing and measurement conditions of on - vehicle sensors of high - speed trains mean that each train in collaborative operation can obtain relative measurement information including relative position and relative speed with other collaborative - operation trains, and some trains can obtain absolute measurement information including absolute position and absolute speed of their own trains.

[0046] 2) Consider a multi - train collaborative operation system composed of m identical trains. Each train in the system is numbered, and the train numbered i is simply called train i, where i = 1, …, m. The actual running dynamics model of train i is as follows:

[0047]

[0048] Among them, and respectively represent the position, speed, and control input of train i. The corresponding state - space model of train i is expressed as follows:

[0049]

[0050] Among them, represents the state of train i,

[0051] 3) Combining the sensing and measurement conditions of on - vehicle sensors of high - speed trains in 1), the sensing and measurement information of on - vehicle sensors of train i is expressed as follows:

[0052]

[0053] Among them, and respectively represent the on - vehicle sensor measurement information of train i, the measurement matrix, and the output coupling matrix between train i and train j. q i is the dimension of the measurement information that train i can observe; the graph represents the car - body output coupling relationship graph of the multi - train collaborative operation system composed of m trains, represents the set of neighbor trains of train i in the car - body output coupling relationship graph Each point in the point set represents each train, and the edge set Each edge in represents the output coupling relationship existing between different trains. Specifically, (i,j) ∈ ε o if and only if C ij ≠ 0.

[0054] 4) Combine the sensing and measurement conditions of on-vehicle sensors of high-speed trains, and based on the actual train operation dynamics model, construct a continuous-time state space model for the collaborative operation of multiple trains, in the following form:

[0055]

[0056] y = Cs

[0057] where, m is the total number of trains operating collaboratively, is the identity matrix, is the Kronecker product; y i is the measurement information of the on-vehicle sensor of train i,

[0058] Step 2: Conduct an observability analysis on the continuous-time state space model for the collaborative operation of multiple trains. Specifically, the analysis results are as follows:

[0059] (A, C) The corresponding system (i.e., the multi-train collaborative operation system) is observable if and only if each train in the multi-train collaborative operation system can obtain at least one measurement information (both relative measurement and absolute measurement are acceptable) and at least one train has absolute measurement information. The results of this observability analysis can be equivalently described using graph theory knowledge, that is, the multi-train collaborative operation system corresponding to (A, C) is observable if and only if the graph is an undirected connected graph, where, is a directed graph is the undirected graph after removing the direction, is a new graph defined based on the output coupling relationship graph of the workshops in the multi-train collaborative operation Specifically, O represents the coordinate origin, and the edge set ε a each edge in represents the output coupling relationship between the train with absolute measurement information and the origin O, (O, i) ∈ ε a indicates that train i has absolute measurement information. The observability of the multi-train collaborative operation system corresponding to (A, C) indicates that the driving states of all trains operating collaboratively can be estimated using the measurement information of the sensors of all trains operating collaboratively under the condition of multi-train collaborative operation.

[0060] Step 3: Combining the topology relationship of the workshop communication network, perform acyclic processing on the output coupling relationship diagram of the multi-train collaborative operation workshop to obtain an acyclic multi-train collaborative operation workshop output coupling relationship diagram. Specifically:

[0061] Use a strongly connected digraph to describe the workshop communication network of the multi-train collaborative operation system composed of m trains. Among them, the edge set point pair (i, j) ∈ ε c means that train i can transmit information to train j. Combining the workshop communication network topology relationship, the specific process of performing acyclic processing on the output coupling relationship diagram of the multi-train collaborative operation workshop is as follows:

[0062] 1) Each train randomly selects a positive integer in the set of positive integers as its own ID;

[0063] 2) Each train determines its own level according to the layering mechanism. The layering principle is that trains that can obtain absolute measurement information are automatically classified into layer 0, which is the lowest layer. Trains that can obtain relative measurement information with trains in layer 0 are automatically classified into layer 1. Trains that can obtain relative measurement information with trains in layer 1 are automatically classified into layer 2, and so on;

[0064] 3) It is stipulated that the relative measurement information between two trains at different levels can only be used by the train at the higher level, and the relative measurement information between two trains at the same level can only be used by the train with the larger ID;

[0065] 4) The transmission of the relative measurement information involved in process 3) is guaranteed by the workshop communication network to ensure.

[0066] According to the above acyclic processing, the output coupling relationship diagram of the multi-train collaborative operation workshop is transformed into an acyclic graph

[0067] Step 4: Combining the observability analysis results, each train constructs a Luenberger-like observer based on the local measurement information specified in the acyclic multi-train collaborative operation workshop output coupling relationship diagram to obtain an estimated value of the driving state of this train. Specifically:

[0068] When the multi-train collaborative operation system corresponding to (A, C) is observable and the output coupling relationship of the multi-train collaborative operation workshop is transformed into an acyclic graph the state space model of train i corresponding to (J, C ii ) can be obtained to be observable. On this basis, each train can use the graph Construct a Luenberger-like observer using the specified local measurement information to obtain an estimated value of the train's own driving state. The form of the Luenberger-like observer for train i is as follows:

[0069]

[0070] where, represents the estimate of train i's own driving state obtained using the Luenberger-like observer, represents the estimate of train l's driving state obtained by train i using the consensus observer, represents the set of neighbor trains of train i in the directed acyclic output coupling relationship graph of the trains F i represents the observation gain, which can be calculated locally by train i. The condition it needs to satisfy is to ensure that all eigenvalues of the matrix J - F i C ii have negative real parts.

[0071] Step 5: Combine the results of the observability analysis. According to the topological relationship of the train communication network, each train uses the local communication information with neighbor trains in the communication network to construct a consensus observer based on the leader-follower consensus coordination method to obtain the estimated value of the driving state of other co-running trains; the Luenberger-like observer and the consensus observer together constitute a distributed observer for the driving state of multi-train co-running. Specifically:

[0072] When the multi-train co-running system corresponding to (A, C) is observable, each train can obtain the estimate of the driving state of all co-running trains by constructing a consensus observer based on the leader-follower consensus coordination method. The consensus observer of train i consists of two parts, and the specific form is as follows:

[0073]

[0074]

[0075] where, represents the estimate of train i's own driving state obtained using the consensus observer, represents the estimate of train j's driving state obtained by train i using the consensus observer, represents the set of neighbor trains of train i in the communication network and and represent the consensus weights, which are related to the topological relationship of the communication network Specifically, if there is an edge between two nodes in the communication network , the corresponding consensus weight is set to 1, otherwise it is set to 0; μ is the coupling gain, and the condition it needs to satisfy is to ensure that the matrix All the eigenvalues have negative real parts, where is the consistency weight matrix, and I 4 is a 4×4 identity matrix.

[0076] The Luenberger-like observer and the consistency observer based on the leader-follower consistency coordination method together constitute a distributed observer for the driving states of multi-train cooperative operation; during the actual operation process, Step 4 and Step 5 are executed synchronously to jointly obtain the estimation of the driving states of all trains in cooperative operation by each train.

[0077] As Figure 2 shown, it is the estimation error diagram of the driving states of multi-train cooperative operation of the present invention. Among them, the multi-train cooperative operation system is composed of 3 trains. In the figure, (a), (b), and (c) are respectively the estimation error diagrams of the real-time absolute position information of Trains 1, 2, and 3 by the 3 trains, and represents the estimation error of the position of Train i with respect to Train j. It can be seen that the estimation error of each train in the multi-train cooperative operation system can quickly converge to 0, which further illustrates the effectiveness of the distributed estimation method for the driving states of multi-train cooperative operation of the present invention.

[0078] The above is only the preferred embodiment of the present invention. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes, without departing from the scope of the technical solution of the present invention. Therefore, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the protection of the technical solution of the present invention.

Claims

1. A distributed estimation method for the running states of multi-train collaborative operation, characterized in that, it includes the following steps: Step 1: Considering the sensing and measurement conditions of on-vehicle sensors of high-speed trains, a multi-train collaborative operation workshop output coupling relationship graph is established by combining graph theory knowledge. Based on the actual train running dynamics model, a multi-train collaborative operation continuous-time state space model is constructed; Step 2: Conduct an observability analysis on the multi-train collaborative operation continuous-time state space model; Step 3: Combining the topology relationship of the workshop communication network, the multi-train collaborative operation workshop output coupling relationship graph is processed to be directed acyclic, and a directed acyclic multi-train collaborative operation workshop output coupling relationship graph is obtained; Step 4: Combining the results of the observability analysis, each train constructs a Luenberger-like observer according to the specified local measurement information in the directed acyclic multi-train collaborative operation workshop output coupling relationship graph to obtain the estimated value of the running state of this train; When the multi - train collaborative operation system corresponding to (A, C) is observable and the output coupling relationship between workshops in the multi - train collaborative operation is transformed into a directed acyclic graph , the state - space model of train i corresponding to (J, C ii ) is observable. On this basis, each train uses the local measurement information specified by the graph to construct a Luenberger - like observer to obtain the estimated value of its own driving state. The form of the Luenberger - like observer of train i is as follows: Among them, u i represents the control input of train i, and respectively represent the on-vehicle sensor measurement information and measurement matrix of train i, q i is the dimension of the measurement information that train i can observe, is the identity matrix, m is the total number of trains operating in coordination, is the Kronecker product, represents the output coupling matrix between train i and train j; represents the estimation of the running state of train i obtained by using a Luenberger-like observer, represents the estimation of the running state of train l by train i using a consensus observer, represents train i in the directed acyclic graph of inter-train output coupling relationship in the set of neighbor trains, F i represents the observation gain, which is calculated locally by train i, and the condition it needs to satisfy is to ensure that all the eigenvalues of the matrix J - F i C ii have negative real parts; Step 5: Combining the results of the observability analysis, according to the topology relationship of the workshop communication network, each train uses the local communication information with the neighboring trains in the communication network to construct a consensus observer based on the leader-follower consensus coordination method to obtain the estimated values of the running states of other collaborative trains; The Luenberger-like observer and the consensus observer together constitute a distributed observer for the running states of multi-train collaborative operation.

2. A distributed estimation method for the running states of multi-train collaborative operation according to claim 1, characterized in that, in the said Step 1, the sensing and measurement conditions of on-vehicle sensors of high-speed trains mean that each train in collaborative operation can obtain the relative measurement information including relative position and relative speed with other collaborative trains, and some trains can obtain the absolute measurement information including absolute position and absolute speed of this train.

3. A distributed estimation method for the running states of multi-train collaborative operation according to claim 1, characterized in that, in the said Step 1, the actual running dynamics model of train i is as follows: Among them, and represent the position, speed, and control input of train i respectively; the state space model of train i is represented as follows: Among them, represents the state of train i.

4. A distributed estimation method for the running states of multi-train collaborative operation according to claim 3, characterized in that, in the said Step 1, the on-vehicle sensor sensing and measurement information of train i is expressed as follows: Among them, the figure represents the workshop output coupling relationship diagram of a multi-train collaborative operation system composed of m trains, represents the set of neighbor trains of train i in the workshop output coupling relationship diagram , and each point in the point set represents each train, and each edge in the edge set represents the output coupling relationship existing between different trains.

5. A distributed estimation method for the running states of multi-train collaborative operation according to claim 4, characterized in that, in the said Step 1, combining the sensing and measurement conditions of on-vehicle sensors of high-speed trains, a multi-train collaborative operation continuous-time state space model is constructed based on the actual train running dynamics model, and the form is as follows: y = Cs Among them, 6. A distributed estimation method for the running states of multi-train collaborative operation according to claim 5, characterized in that, In step 2, the observability analysis of the continuous-time state space model of multi-train cooperative operation is carried out, and the results are as follows: The multi-train cooperative operation system corresponding to (A, C) is observable if and only if each train in the multi-train cooperative operation system can obtain at least one measurement information and at least one train has absolute measurement information; The results of this observability analysis are equivalently described using graph theory knowledge, that is, the multi-train cooperative operation system corresponding to (A, C) is observable if and only if the graph is an undirected connected graph, where is a directed graph is the undirected graph after removing the direction, is a new graph defined based on the output coupling relationship graph of multi-train cooperative operation Specifically, O represents the coordinate origin, and each edge in the edge set ε a represents the output coupling relationship between the train with absolute measurement information and the origin O, (O, i) ∈ ε a means that train i has absolute measurement information; The observability of the multi-train cooperative operation system corresponding to (A, C) indicates that the driving states of all trains in cooperative operation can be estimated by using the sensor measurement information of all trains in cooperative operation under the condition of multi-train cooperative operation.

7. A distributed estimation method for the running states of multi-train collaborative operation according to claim 6, characterized in that, In step 3, a strongly connected directed graph is used to describe the in-vehicle communication network of the multi-train collaborative operation system composed of m trains. Among them, the edge set point pair (i, j) ∈ ε c means that train i can transmit information to train j; combined with the in-vehicle communication network topological relationship, the specific process of performing acyclic processing on the multi-train collaborative operation in-vehicle output coupling relationship diagram is as follows: 1) Each train randomly selects a positive integer from the set of positive integers as its own ID; 2) Each train determines its own level according to the hierarchical mechanism. The hierarchical principle is that trains that can obtain absolute measurement information are automatically classified into level 0, which is the lowest level. Trains that can obtain relative measurement information with trains in level 0 are automatically classified into level 1. Trains that can obtain relative measurement information with trains in level 1 are automatically classified into level 2, and so on; 3) It is stipulated that the relative measurement information between two trains in different levels can only be used by the train in the higher level, and the relative measurement information between two trains in the same level can only be used by the train with a larger ID; 4) The transfer of the relative measurement information involved in process 3) is ensured by the workshop communication network to ensure; According to the above directed acyclic processing, the output coupling relationship diagram of multi-train collaborative operation workshops is transformed into a directed acyclic graph 8. A distributed estimation method for the running state of multi-train collaborative operation as described in claim 7, characterized in that, in step 5, when the multi-train collaborative operation system corresponding to (A, C) is observable, each train obtains an estimate of the running states of all trains in the collaborative operation by constructing a consensus observer based on the leader-follower consensus coordination method; the consensus observer of train i consists of two parts, and the specific form is as follows: Among them, represents the estimate of the driving state of train i obtained by using the consensus observer, represents the estimate of the driving state of train j obtained by train i using the consensus observer, represents the set of neighbor trains of train i in the communication network ; and represent the consensus weights, which are related to the topological relationship of the communication network . Specifically, if there is an edge between two nodes in the communication network , the corresponding consensus weight is set to 1, otherwise it is set to 0; μ is the coupling gain, and the condition to be satisfied is to ensure that all eigenvalues of the matrix have negative real parts. Here, is the consensus weight matrix.

9. A distributed estimation method for the running state of multi-train collaborative operation as described in claim 1, characterized in that, the Luenberger-like observer and the consensus observer based on the leader-follower consensus coordination method together constitute a distributed observer for the running state of multi-train collaborative operation; during the actual operation process, step 4 and step 5 are executed synchronously to jointly obtain an estimate of the running states of all trains in the collaborative operation by each train.

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