Data configuration method of rail transit signal control system

Abstract

The application provides a data configuration method of a rail transit signal control system, comprising the following steps: defining a graph element, drawing a line graph, searching line data, verifying line data and generating configuration data, first defining the required graph element, abstracting key elements such as signals, switches and tracks in the rail transit signal control system as 'nodes' and 'edges' in graph theory, quickly and accurately establishing a rail transit line model by dragging and dropping these predefined graph elements into a canvas, then traversing the line graph by a line data search algorithm to obtain the required line data, using the B method for formal verification to ensure the correctness and consistency of the line data, and generating a configuration data file according to the format requirements of the configuration data file after the verification is passed. The application realizes the automatic generation and verification of line configuration data by introducing graph theory and formal methods.

Description

Technical Field

[0001] This invention is applicable to the generation and verification of various configuration data in the field of rail transit, and can help rail transit systems adapt to various scenarios and needs, providing great flexibility and scalability. Background Technology

[0002] The rail transit signal control system is the core of the entire rail transit control system, responsible for managing train operation, station operations, and the control of signaling equipment. Data configuration plays a crucial role in this system, specifically in the following aspects:

[0003] 1. Ensuring System Safety: Rail transit signal control systems need to process a large amount of real-time data, including train position, speed, destination, signal light status, and track usage. The accuracy of this data directly affects the safe operation of the system. If the system misreads the train's position or speed due to incorrect data configuration, it may cause serious accidents such as train collisions. Therefore, accurate data configuration is the foundation for ensuring the safe operation of the rail transit signal system.

[0004] 2. Supporting the normal operation of the system: Data configuration is the foundation for the normal operation of the rail transit signal control system. All operations, such as vehicle dispatching, track switching, and signal light display, depend on correct data configuration. Only when the data configuration is correct can trains arrive at their destinations on time and provide stable and reliable service.

[0005] 3. Facilitates system management and maintenance: Correct data configuration generates clear data records and reports, which are crucial for system management and maintenance. When a system malfunctions, the data configuration records allow for quick problem location, significantly improving troubleshooting efficiency. Furthermore, the data configuration records can provide a reference for equipment maintenance and upgrades.

[0006] Traditional rail transit signal control systems rely heavily on manual data configuration, which has several drawbacks:

[0007] 1. High error rate: The data configuration process is complex and meticulous, and relying on manual operation makes it prone to errors. Once the configuration is incorrect, it may cause the system to malfunction, and in severe cases, it may affect the safe operation of rail transit.

[0008] 2. Large workload: Traditional manual data configuration is a huge workload, time-consuming and labor-intensive. Not only is the work efficiency low, but in large-scale rail transit signaling systems, the workload of manual data configuration will increase exponentially, making it difficult to meet the needs of rapidly developing rail transit construction.

[0009] 3. Difficult to update and maintain: Whenever the hardware or software of the rail transit signal control system is updated or changed, the data configuration needs to be modified and adjusted accordingly, making such maintenance and update work tedious and difficult.

[0010] 4. Lack of flexibility: Traditional manual data configuration methods are usually rigid and not flexible enough to respond to dynamic changes and temporary adjustments in the rail transit system, which to some extent restricts the efficiency of rail transit operation.

[0011] Therefore, data configuration technology that can automatically and accurately perform large-scale data configuration, improve data configuration efficiency and reliability, and perform security verification is very important. Summary of the Invention

[0012] This invention addresses the problems of complex and labor-intensive data configuration, high error rate of manual configuration, difficulty in ensuring the correctness and consistency of configuration data, and insufficient flexibility and scalability of configuration methods in the traditional data configuration process of rail transit signal control systems. It adopts a data configuration and verification method for rail transit signal control systems based on graph theory and formal methods.

[0013] This invention provides a data configuration method for a rail transit signal control system, the method comprising the following steps: defining graphic elements, drawing a route diagram, searching route data, verifying route data, and generating configuration data.

[0014] First, define the required primitives. Abstract the key elements in the rail transit signal control system, such as signals, switches, and tracks, into "nodes" and "edges" in graph theory. By dragging and dropping these predefined primitives onto the canvas, a rail transit line model can be quickly and accurately built.

[0015] Then, the required line data is obtained by traversing the line map using a line data search algorithm.

[0016] To ensure the correctness and consistency of the line data, a B-model translator is used to translate the line data and corresponding data specifications and constraints into a B-model, and the B-method is used for formal verification.

[0017] After successful verification, the configuration data generator is invoked to generate the configuration data file according to the required format.

[0018] This invention, by introducing graph theory and formal methods, achieves automated generation and verification of line configuration data. This invention simplifies and speeds up the configuration and adjustment of signal control systems, making them safer and more reliable. It also simplifies system maintenance and upgrades, reducing manual operation time and errors, thereby saving costs. Attached Figure Description

[0019] Figure 1 A flowchart illustrating the data configuration process of the method of this invention;

[0020] Figure 2 This is the definition of the signal device elements in this invention;

[0021] Figure 3 The signal anchor point adsorption of the present invention;

[0022] Figure 4 This is the definition of the turnout elements in this invention;

[0023] Figure 5 This is a single-turnout section route diagram of the present invention;

[0024] Figure 6 This is the graph structure in NetworkX for the single-turnout section route diagram of the present invention;

[0025] Figure 7 This is a flowchart of the algorithm for searching all paths in this invention;

[0026] Figure 8 This is the single-turnout section track configuration data for the present invention;

[0027] Figure 9 This is the B-model corresponding to the single-turnout section track data of the present invention;

[0028] Figure 10 This is the verification result of the B-model corresponding to the single turnout section line data of the present invention;

[0029] Figure 11 This is a route diagram of a single crossover turnout area in an embodiment of the present invention;

[0030] Figure 12 This is the graph structure in NetworkX for the single-crossover turnout area route diagram in this embodiment of the invention;

[0031] Figure 13 This refers to the B-model corresponding to the single-crossover turnout line data in this embodiment of the invention.

[0032] Figure 14 This is the B-model verification result corresponding to the single crossover turnout area line data in this embodiment of the invention;

[0033] Figure 15 The result of the verifyRoute operation in the OPERATIONS block of the B model corresponding to the single crossover turnout area line data in this embodiment of the invention.

[0034] Figure 16 This refers to the B model corresponding to the modified single-crossover turnout line data in this embodiment of the invention.

[0035] Figure 17 The B-model verification results are for the modified single-crossover turnout line data in this embodiment of the invention.

[0036] Figure 18 This refers to the line configuration data for the single crossover turnout area in this embodiment of the invention. Detailed Implementation

[0037] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that the existing situations referred to in this specification do not represent publicly disclosed prior art, but are merely for the purpose of elucidating the key technologies and innovations of the present invention.

[0038] The concept of this invention is to combine graph theory and formal methods to achieve the automated generation of line configuration data and ensure the correctness and consistency of the data.

[0039] Graph theory, which studies the structure and various properties of graphs, such as connectivity and paths, is a valuable method for describing and calculating rail transit lines. For example, sections can be defined as "edges" in graph theory, while stops, insulated joints, signals, and switches can be defined as "nodes." A node has a "degree" attribute, which refers to the number of edges connected to that node. For instance, a stop has a degree of 1, an insulated joint or signal has a degree of 2, and a switch has a degree of 3.

[0040] Formal methods are a way to describe and verify the behavior and attributes of objects through mathematical and logical reasoning. There are many types of formal methods, and the B-method is one of them. A B-model is a model created when using the B-method for modeling. In a B-model, line data and corresponding data specifications, data constraints, etc., can be formally described, allowing for formal verification to ensure the model's correctness. The B-method has the advantages of strong specification capabilities and flexibility. This invention uses the B-method to describe and verify line data to ensure its correctness. Specifically, this invention includes the following:

[0041] 1. Graph Theory-Based Route Traversal: By introducing graph theory principles, this invention can abstract key elements in the rail transit signal control system, such as tracks, signals, and switches, into "nodes" and "edges" in a graph. Then, by traversing the route graph, the required data can be obtained quickly and accurately, and route data can be generated.

[0042] 2. Automated Generation of Configuration Data: Thanks to the graph theory traversal described above, this invention can automatically generate route data and configuration data. This feature not only significantly reduces the workload of configuration but also minimizes errors that may occur during manual configuration. The generated configuration data can be directly used for configuring the rail transit signal control system, avoiding additional processing steps.

[0043] 3. Formal Verification Using Formal Methods: To ensure the correctness and consistency of configuration data, this invention introduces the B method for formal verification. By formally describing and proving the generated line data based on the B method, the security and reliability of the data can be ensured during the configuration phase.

[0044] In summary, this invention provides an innovative method for verifying and generating line configuration data for rail transit signal control systems. By combining graph theory and the B-method, not only is the automated generation of line data achieved, but the accuracy and consistency of the data are also ensured.

[0045] The data configuration method of the present invention includes: defining graphic elements, drawing route diagrams, searching route data, verifying route data, and generating configuration data, such as... Figure 1 As shown.

[0046] First, the required graphical primitives are defined. Key elements in the rail transit signal control system, such as signals, switches, and tracks, are abstracted as "nodes" and "edges" in graph theory. By dragging and dropping these predefined primitives onto the canvas, a rail transit line model can be quickly and accurately built. Then, a line data search algorithm is used to traverse the line map to obtain the required line data. To ensure the correctness and consistency of the line data, a B-model translator is used to translate the line data and corresponding data specifications and constraints into a B-model, and formal verification is performed using the B-method. After successful verification, a configuration data generator is finally called to generate the configuration data file according to the required format. This invention significantly reduces the configuration workload, improves the efficiency and accuracy of data configuration, reduces labor costs, and enhances the reliability, security, flexibility, and scalability of the system, contributing to the automation and intelligentization of rail transit.

[0047] The process of obtaining configuration data for rail transit signal control systems using this technology is as follows: Figure 1 As shown, the detailed implementation steps are as follows:

[0048] I. Defining Graphic Elements: Before drawing the track, it is necessary to define the elements required for drawing, such as various signals, switches, tracks, axle counters, and other graphic elements. Figure 2 The image shown is the graphic element definition interface provided by this invention. In this interface, various basic graphics provided on the left can be used to draw track-related graphic elements, and various attributes can be added and defined for these graphic elements on the right.

[0049] Figure 2 The interface shown displays a predefined signal. Two anchor points are defined at the base of the column to automatically attach to the track when drawing the signal path, as shown below. Figure 3 The image shows the state of a signal anchor point attached to the track. This element is defined as a "node." Since there can only be two track segments on either side of the signal, according to graph theory, the "degree" of this "node" should be set to 2. After setting this, two columns of dots (two dots per column) will appear below the "degree" setting, indicating the track direction passing through this signal "node." Generally, tracks next to signals allow bidirectional traffic, so connections are made between points 1 and 2, and between points 2 and 1. Defining elements also requires adding attributes to define these attribute values ​​during drawing. For example, a track often has multiple types of signals, such as guide signals and shunting signals, so types need to be added. Multiple signals of the same type need to be added for differentiation. Signals also have different directions, so direction attributes also need to be added, such as... Figure 2 As shown on the right.

[0050] like Figure 4 The image shows a predefined turnout. Since the turnout connects three track segments, three anchor points are defined, and the "degree" of this "node" should be set to 3. Below the "degree" setting, two columns of dots (three in each column) appear to indicate the track direction passing through this turnout "node." Turnouts generally only allow passage between points 1 and 2, or between points 1 and 3; passage between points 2 and 3 is not allowed. Therefore, the two columns of dots can only connect between points 1 and 2, or between points 1 and 3, but not between points 2 and 3. A name attribute is also added to the turnout.

[0051] After defining all the required primitives, a primitive library is formed, such as... Figure 5 As shown on the left. These predefined primitives are used to quickly and accurately build a rail transit line model.

[0052] II. Drawing the route diagram: Select graphic elements such as signals, turnouts, and sections, drag and drop them onto the canvas, and connect them with anchor points to complete the route drawing. Figure 5 The diagram shown is a single-turnout section of the railway line. This section includes 3 signals, 3 sections, and 1 turnout.

[0053] III. Route Data Search: This invention utilizes the NetworkX graph theory library, supporting the creation of complex graph structures. It provides tools for creating, manipulating, and studying the structure, dynamics, and functionality of graphs, including many classic graph theory algorithms such as shortest path search, maximum flow search, and minimum spanning tree search. It is suitable for searching and analyzing the topology of rail transit routes. After the route map is drawn, the graph structure within NetworkX can be viewed using the drawing functions. Figure 5 The diagram structure of the branch line shown in NetworkX is as follows: Figure 6 As shown in the figure, this graph illustrates how a railway line is represented in graph theory. For example, a turnout is a node with a degree of 3, and a signal is a node with a degree of 2. Sections are edges.

[0054] Since NetworkX already provides powerful graph theory algorithms, it is very convenient for users to define search algorithms that meet their needs to obtain the required line data. For example... Figure 7 The diagram shows a flowchart of an algorithm for searching all routes on a rail transit line. Based on this algorithm... Figure 6 Searching and printing the results yields two paths: Route1 (containing Track1 and Track2) and Route2 (containing Track1 and Track3).

[0055] IV. Verifying Line Data: This invention employs a formal method to verify the generated line data. This method is a technique that uses data proof to verify whether the line data meets relevant requirements. Compared to traditional testing methods, it provides complete coverage and offers higher reliability and accuracy. The formal method used in this invention is Method B, which uses first-order predicate logic, set theory, relational algebra, and other methods to express the data and related requirements. This method has strong expressive power, can meet more verification needs, and is adaptable to more application scenarios.

[0056] like Figure 4 As shown, a clear constraint on the track data is that routes via turnouts cannot go from turnout 2 to turnout 3 or from turnout 3 to turnout 2. Therefore, it is necessary to verify whether the track data meets this constraint. Figure 1 As shown, during verification, the line data, data specifications, data constraints, and other data requirements need to be converted into a B-model. After converting the above line data and data constraints into a B-model, as shown... Figure 9 As shown.

[0057] The SETS section contains a set of all objective entities on the track, such as sections, signals, and switches.

[0058] TRACKS={Track1,Track2,Track3}

[0059] This represents the set of all sections of the line.

[0060] ABSTRACT_CONSTANTS and PROPERTIES, through relational algebra, jointly express the relationships between sections, signals, switches, and routes. ABSTRACT_CONSTANTS declares what relationships exist, while PROPERTIES formally expresses the relationships declared within ABSTRACT_CONSTANTS. For example:

[0061]

[0062] This indicates that the first segment within route ROUTE1 is Track1, the second segment is Track2, and so on.

[0063] The above three parts are the formal representation of the line data in the B model. INVARIANT is used to represent data constraints, for example:

[0064]

[0065] This expression finds the intersection of all segments within all routes with any two segments from all turnouts that cannot simultaneously pass through turnouts 2 and 3. It then checks if the intersection size is less than 2. If it is, the route has not passed through any turnouts 2 and 3, thus satisfying the condition that all routes cannot simultaneously pass through any of these two turnouts. Figure 4 The data constraints for turnouts 2 and 3 shown are not satisfied unless they are met.

[0066] Running the model using the ProB model validator can determine whether the data constraints expressed by the INVARIANT are satisfied, such as... Figure 10 The results of ProB's verification of the model are shown, indicating that the line data meets the data constraints and the verification is successful.

[0067] V. Generating Line Data: After the line data verification is passed, it proves that the line data meets the requirements. At this point, the configuration data generator can be called to generate the configuration data file according to the format requirements of the configuration data file. For example, Figure 8 The following is a summary: Figure 6 The route shown, and through Figure 7 The algorithm shown generates an XML-formatted configuration data file that expresses the relationships between routes, signals, switches, and sections by searching for route and other line data.

[0068] in:

[0069]

[0070]

[0071] This indicates that Track 1 belongs to the segment of Route 1 and Route 2;

[0072]

[0073] This indicates that Switch1 is a turnout belonging to Route1 and Route2;

[0074]

[0075] This indicates that Signal1 is the starting signal for routes Route1 and Route2;

[0076]

[0077] This indicates that the starting signal of Route 1 is Signal1, passing through Switch1, Track1, Track2, and so on. The above is the basic usage or implementation method of this invention, generally as follows: Figure 1 As shown.

[0078] Users can customize and adjust key parts of this invention according to their own needs, specifically including:

[0079] 1. Custom primitives: Used to define the basic elements required for drawing lines;

[0080] 2. Route Data Search Algorithm: Utilize graph theory algorithms provided by the NetworkX graph theory library to customize the search algorithm required by the user, for searching, acquiring, and organizing the required route data;

[0081] 3. B Model Translator: Automatically translates line data and corresponding data specifications and constraints into B model blocks such as SETS, ABSTRACT_CONSTANTS, PROPERTIES, and INVARIANT, according to user needs. This is used for formal verification of line data.

[0082] 4. Configuration Data Generator: Arranges the line data according to the data structure and format required by the user, and generates configuration data in the required format, such as configuration data in various structures of JSON, XML and other formats;

[0083] Preferred embodiment

[0084] The following example uses a single-crossover junction as an illustration of this invention. The route of the junction is drawn, route data is acquired, and combined with route data constraints to convert it into a B-model. Formal verification is then performed to identify errors. After correcting the errors and passing the verification, route configuration data is generated. The detailed steps are as follows:

[0085] I. Drawing the route diagram: Select existing graphic elements such as signals, turnouts, and sections to draw the route diagram as follows: Figure 11 The diagram shown is a single-crossover turnout section route map. This route map includes 4 signals, 5 sections, and 2 turnouts. The diagram structure in NetworkX is as follows: Figure 12 As shown.

[0086] II. Search and Generate Route Data: After running a user-defined search algorithm, the required route data can be obtained, such as... Figure 7 The algorithm shown searches all paths in this branch area and obtains 7:

[0087] 1) Route1 (including Track1 and Track2)

[0088] 2) Route 2 (including Track 1, Track 5, and Track 3)

[0089] 3) Route3 (including Track2 and Track1)

[0090] 4) Route 4 (including Track 2, Track 5, and Track 3)

[0091] 5) Route 5 (including Track 3, Track 5, and Track 2)

[0092] 6) Route 6 (including Track 3 and Track 4)

[0093] 7) Route 7 (including Track 4 and Track 3)

[0094] III. Verify the route data: After converting the above route data and data constraints into a B-model, as shown below... Figure 13 As shown. Under SETS, all objective entities such as sections, signals, and switches within the single crossover turnout area are represented. ABSTRACT_CONSTANTS and PROPERTIES, through relational algebra, jointly express the relationships between all sections, signals, switches, and routes within this turnout area. INVARIANT remains unchanged. An OPERATIONS block is added here, and a verifyRoute operation is added under OPERATIONS. Its purpose is to identify specific line data that does not meet INVARIANT when formal verification fails.

[0095] By running the B model validator ProB, we obtain the following: Figure 14The verification results for INVARIANT show that the line data does not meet the data constraints, and the verification failed. Running the verifyRoute operation at this point reveals that Route2 (containing Track1, Track5, and Track3) does not meet the INVARIANT requirements. Figure 15 As shown, the reason is that Tracks 1 and 5 of Route 2 connect to Switch 1's ports 2 and 3, which is an incorrect route. After deleting Route 2, a new B model is generated as follows. Figure 16 As shown, calling ProB to validate the B model again results in successful validation. Figure 17 As shown. At this point, highly secure and reliable configuration data can be generated, such as... Figure 18 As shown.

[0096] The beneficial effects of this invention are as follows:

[0097] This invention automates the generation and verification of track data through graph theory-based automated configuration and formal verification, significantly reducing the complexity and error potential of the configuration process. It provides a new, efficient, and reliable solution for data configuration in rail transit signal control systems, offering strong protection against human error and ensuring the normal and safe operation of the system. Specifically, it includes the following beneficial effects:

[0098] Improved efficiency and accuracy: The use of graph theory-based automated data configuration technology significantly reduces the time and potential errors associated with manual configuration. This technology can quickly and accurately generate large amounts of line data, greatly improving the efficiency and accuracy of data configuration.

[0099] Reduced labor costs: Automated data configuration technology reduces reliance on manual input, which can save significant labor costs, especially in large-scale rail transit systems.

[0100] Improving system reliability and security: By using formal verification techniques, effective error checking can be performed during data configuration to ensure the correctness and consistency of the generated line data. This formal verification can significantly improve system reliability and security.

[0101] Supporting system flexibility and scalability: Graph theory-based configuration techniques can easily handle complex rail transit topologies and flexibly adapt to system changes and expansions. For example, when adding or changing rail lines, simply updating the graphical model automatically regenerates the corresponding line data.

[0102] Facilitating the Automation and Intelligence of Rail Transit: Automated data configuration technology is one of the key technologies for realizing the automation and intelligence of rail transit. By automatically generating and verifying line data, it can provide strong data support for advanced functions such as automatic driving trains and intelligent dispatching systems.

[0103] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data configuration method for a rail transit signal control system, the method comprising the following steps: defining graphic elements, drawing a route diagram, searching route data, verifying route data, and generating configuration data; First, the necessary primitives are defined. Key elements in the rail transit signal control system, such as signals, switches, and tracks, are abstracted into "nodes" and "edges" in graph theory. Combining graph theory with the description and calculation of rail transit lines, sections are defined as "edges" in graph theory, and stops, insulated joints, signals, and switches are defined as "nodes" in graph theory. "Nodes" have the attribute "degree," which refers to the number of edges connected to that node. By dragging and dropping these predefined primitives onto the canvas, a rail transit line model can be quickly and accurately built. Using the NetworkX graph theory library, after the route map is drawn, the graph structure of the drawn route map can be viewed within NetworkX through the drawing function. The graph theory algorithms provided by the NetworkX graph theory library are used to customize a search algorithm that meets the user's needs, which is used to search and analyze the topology of the rail transit route map. The route data search algorithm is used to traverse the route map to obtain and organize the required route data. To ensure the correctness and consistency of the line data, a B-model translator is used to automatically translate the line data and corresponding data specifications and constraints into the SETS, ABSTRACT_CONSTANTS, PROPERTIES, and INVARIANT blocks in the B-model according to user needs. This is used for formal verification of the line data. SETS is a set of all objective entities on the line. ABSTRACT_CONSTANTS is used to declare what relationships exist. PROPERTIES formally expresses the relationships declared in ABSTRACT_CONSTANTS. INVARIANT represents data constraints. An OPERATIONS block is also added, and a verifyRoute operation is added under OPERATIONS to analyze and identify specific line data that do not meet INVARIANT when formal verification fails. After successful verification, the configuration data generator is invoked to generate the configuration data file according to the required format.

2. The method of claim 1, wherein, The B model is a model created when using the B method for modeling. In the B model, the line data and corresponding data specifications, data constraints, etc., can be formally described, so that they can be formally verified to ensure the correctness of the model.

3. The method of claim 2, wherein, During verification, the line data, data specifications, data constraints, and other data requirements need to be converted into a B model. After converting the line data and data constraints into a B model, the model can be run through the B model verifier ProB to determine whether the expressed data constraints can be met.

4. The method of claim 1, wherein, The configuration data generator refers to arranging line data according to the data structure and format required by the user to generate configuration data in the required format, including configuration data in JSON and XML formats of various structures.

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