A dynamic map adjustment test method and device based on scene combination

Abstract

The application discloses a dynamic adjustment automation test method and device based on scene combination, first, the basic test scene, the basic adjustment strategy and the influence condition of the test scene are defined through a configuration file, second, the automatic test scene is generated based on the basic test scene, the basic adjustment strategy and the influence condition of the test scene, and the corresponding adjustment strategy is provided for each test scene, the test scene and the adjustment strategy are taken as the input file of the dynamic adjustment of the running graph, and finally, the ATS log is parsed based on the input file, the adjustment condition of the running graph is analyzed, the adjustment strategy under the corresponding scene is compared, and the test conclusion is output. Not only can the most complete scene of the dynamic adjustment of the running graph and the adjustment strategy under the corresponding scene be output, the automation test of the dynamic adjustment can be completed, and the completeness and the test accuracy of the test scene can be ensured.

Description

Technical Field

[0001] This application relates to the field of automation control technology, and more specifically, to a dynamic mapping automation testing method and apparatus based on scene combination. Background Technology

[0002] Currently, the compilation and adjustment of railway or urban rail transit timetables mainly rely on manual inspection. The process involves creating corresponding scenarios based on test cases and then checking whether the adjusted timetable matches expectations. However, in practice, this manual inspection method suffers from problems such as tedious detailed checks, complex emergency handling, and difficulty in guaranteeing the comprehensiveness and accuracy of testing.

[0003] Intelligent dispatching systems can dynamically adjust train schedules based on various scenarios. The main task of engineering testing is to verify whether the adjusted schedules meet system design requirements, operational needs, and schedule design principles. To ensure system reliability and effectiveness, comprehensive and accurate testing is necessary. However, when intelligent dispatching is applied during operation, testing in real-world scenarios is crucial, especially when faults or emergency situations occur. This requires laboratory testing to run train schedules according to operational scenarios, creating various faults and emergency situations. This necessitates test personnel coordinating the operation of all trains on the line, requiring significant effort.

[0004] Therefore, in order to solve the above problems, it is urgent to invent a dynamic adjustment test scheme. Summary of the Invention

[0005] The purpose of this application is to overcome the shortcomings of existing technologies and provide a dynamic graph adjustment automation testing method and device based on scenario combination. It can not only combine test scenarios according to basic scenarios and output graph adjustment strategies under combined scenarios to ensure the comprehensiveness of the test scope, but also verify whether the graph adjustment is correct and reasonable according to the graph adjustment strategy under the corresponding scenario.

[0006] The objective of this application is achieved through the following technical solution:

[0007] In a first aspect, this application proposes a dynamic image adjustment automation testing method based on scene combination, the method being applied in a dynamic image adjustment automation testing device, the method comprising:

[0008] Define the basic test scenarios, basic graph adjustment strategies, and the influencing conditions of the test scenarios through configuration files;

[0009] Based on the basic test scenarios, basic graph adjustment strategies, and the influencing conditions of the test scenarios, test scenarios are automatically generated, and corresponding graph adjustment strategies are provided for each test scenario.

[0010] The test scenario and graph adjustment strategy are used as input files for dynamic adjustment of the runtime graph;

[0011] Based on the input file, the ATS logs are parsed to analyze the runtime graph adjustments and compare them with the graph adjustment strategies in the corresponding scenarios to output test conclusions.

[0012] In one possible implementation, the steps of parsing ATS logs based on the input file, analyzing the runtime graph adjustments, comparing them with the graph adjustment strategies in the corresponding scenarios, and outputting test conclusions include:

[0013] The location of the train and changes in the train's route were determined by analyzing the ATS logs.

[0014] Based on the train's location and changes in train number and route, determine whether the timetable adjustment meets expectations and record the actual and expected routes for each train.

[0015] If the test results meet expectations, a test conclusion report will be generated; otherwise, a difference report will be provided.

[0016] In one possible implementation, the basic map adjustment strategy includes interrupting map adjustment, slowing down map adjustment, and returning to the original map.

[0017] Interruption timetable adjustment: When a turnout fails at a different location, the input of the train running on a small route within the corresponding operating area at the corresponding location is used as the configuration file;

[0018] Slow-down timetable adjustment: The train slow-down time is used as the input of the configuration file, and the timetable is adjusted according to the slow-down time when the train passes through the faulty switch area;

[0019] Restore the original running chart: Ensure that the online running chart is restored to its original state.

[0020] In one possible implementation, the influencing conditions of the test scenario include the turnout's concentration area and turnout number, turnout type, and the train's location when the turnout malfunctions.

[0021] In one possible implementation, the basic test scenarios include turnout failure, signaling system failure, train failure, track failure, and power system failure.

[0022] Secondly, this application proposes an automated testing device for dynamic image adjustment based on scene combination, the device comprising:

[0023] The definition module is used to define basic test scenarios, basic graph adjustment strategies, and the influencing conditions of test scenarios through configuration files.

[0024] The generation module is used to automatically generate test scenarios based on basic test scenarios, basic graph adjustment strategies, and the influencing conditions of test scenarios, and to provide corresponding graph adjustment strategies for each test scenario.

[0025] The adjustment module is used to take the test scenario and graph adjustment strategy as input files for dynamic adjustment of the runtime graph;

[0026] The analysis module is used to parse ATS logs based on input files, analyze the runtime graph adjustments, compare them with the graph adjustment strategies in the corresponding scenarios, and output test conclusions.

[0027] In one possible implementation, the defined module is also used for:

[0028] The location of the train and changes in the train's route were determined by analyzing the ATS logs.

[0029] Based on the train's location and changes in train number and route, determine whether the timetable adjustment meets expectations and record the actual and expected routes for each train.

[0030] If the test results meet expectations, a test conclusion report will be generated; otherwise, a difference report will be provided.

[0031] In one possible implementation, the basic map adjustment strategy includes interrupting map adjustment, slowing down map adjustment, and returning to the original map.

[0032] Interruption timetable adjustment: When a turnout fails at a different location, the input of the train running on a small route within the corresponding operating area at the corresponding location is used as the configuration file;

[0033] Slow-down timetable adjustment: The train slow-down time is used as the input of the configuration file, and the timetable is adjusted according to the slow-down time when the train passes through the faulty switch area;

[0034] Restore the original running chart: Ensure that the online running chart is restored to its original state.

[0035] In one possible implementation, the influencing conditions of the test scenario include the turnout's concentration area and turnout number, turnout type, and the train's location when the turnout malfunctions.

[0036] In one possible implementation, the basic test scenarios include turnout failure, signaling system failure, train failure, track failure, and power system failure.

[0037] The main solution and its various further alternatives described above can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application; furthermore, the (non-conflicting alternatives) can also be freely combined with each other and with other alternatives. Those skilled in the art, after understanding the solution of this application, will realize from the prior art and common general knowledge that there are many combinations, all of which are technical solutions to be protected by this application, and will not be exhaustively listed here.

[0038] This application discloses an automated testing method and apparatus for dynamic graph adjustment based on scenario combinations. First, basic test scenarios, basic graph adjustment strategies, and influencing conditions of the test scenarios are defined through a configuration file. Second, automatically generated test scenarios are generated based on the basic test scenarios, basic graph adjustment strategies, and influencing conditions of the test scenarios, and a corresponding graph adjustment strategy is provided for each test scenario. The test scenarios and graph adjustment strategies are used as input files for dynamic graph adjustment. Finally, the ATS logs are parsed based on the input files to analyze the graph adjustment status, compare it with the graph adjustment strategies under the corresponding scenarios, and output test conclusions. This method can not only output the most complete scenarios for dynamic graph adjustment and the corresponding graph adjustment strategies under each scenario, but also complete the automated testing of dynamic graph adjustment, ensuring the completeness of the test scenarios and the accuracy of the tests. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This paper illustrates an automated testing method for dynamic image adjustment based on scene combination, as proposed in an embodiment of this application.

[0041] Figure 2 The complete test scenario and corresponding graph adjustment strategy generation process proposed in the embodiments of this application are shown. Detailed Implementation

[0042] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0043] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] In existing technologies, intelligent dispatching systems can dynamically adjust operating schedules based on various scenarios. The main task of engineering testing is to verify whether the adjusted operating schedule meets the system design requirements, operational needs, and operating schedule design principles. To ensure the reliability and effectiveness of the system, comprehensive and accurate testing is required. However, when intelligent dispatching is applied during operation, failures or emergency scenarios may occur. Therefore, testing in real-world scenarios is particularly important. This requires laboratory testing to run train schedules according to operational scenarios, creating various failure and emergency scenarios. This requires testers to coordinate the operation of all trains on the line and also expends a great deal of effort.

[0045] To address the aforementioned technical issues, this application proposes a dynamic graph adjustment automated testing method and apparatus based on scenario combinations. According to test cases, test scenarios are set, scenario configuration files are created, and the expected adjustment results of the runtime graph under each scenario are clearly defined. Through log analysis, the differences between the runtime graph before and after adjustment are compared to analyze whether the runtime graph adjustment strategy meets expectations, and the final test results are obtained. A detailed explanation follows.

[0046] Please refer to Figure 1 , Figure 1 This application illustrates an automated testing method for dynamic image adjustment based on scene combination, which is applied in an automated testing device for dynamic image adjustment. The method includes the following steps:

[0047] Step S1: Define the basic test scenario, basic graph adjustment strategy, and the influencing conditions of the test scenario through the configuration file.

[0048] The basic test scenarios include turnout failures, signaling system failures, train failures, track failures, and power system failures. The signaling system failure scenario simulates various signaling system malfunctions, such as traffic light failures and signal malfunctions. The train failure scenario simulates various train malfunctions, such as braking system failures and power system failures. The track failure scenario simulates various track-related faults, such as track breaks and foreign object intrusion. The power system failure scenario simulates various power supply system malfunctions, such as substation failures and cable failures. The communication system failure scenario simulates various communication system malfunctions, such as wireless communication interruptions and wired communication interruptions.

[0049] The influencing conditions of the test scenario include the turnout's designated area and number, turnout type, and the train's location when the turnout malfunctions. The turnout's designated area refers to the geographical or administrative region where the turnout is located, and the turnout number refers to the unique identifier for each turnout, used to precisely identify its specific location. Turnout types are divided into single-action turnouts and crossovers.

[0050] When a turnout malfunctions, the train's location must be checked to determine if there are any trains running on the turnout's route. If so, the troubleshooting becomes more complex. Additionally, it's crucial to consider whether a train is about to enter the turnout's route. If so, the dispatcher needs to make advance decisions to change the train's path to avoid potential risks.

[0051] In one possible implementation, a specific test scenario is generated, for example: the turnout belongs to area 1, the turnout number is D101, the turnout type is a single-action turnout, and the train is located in a situation where no train is running on the turnout's route. In this case, the fault can be handled relatively simply because no train is running in the affected area. Another specific test scenario is generated: the turnout belongs to area 2, the turnout number is D102, the turnout type is a crossover, and the train is located in a situation where a train is running on the turnout's route. In this case, handling the fault is more complex because it is necessary to consider how to safely guide the train already on the turnout to another path.

[0052] Basic map adjustment strategies include interrupting map adjustments, slowing down map adjustments, and returning to the original map.

[0053] Interruption timetable adjustment: When a turnout fails at a different location, the input of the train running on a small route within the corresponding operating area at the corresponding location is used as the configuration file;

[0054] Slow-down timetable adjustment: The train slow-down time is used as the input of the configuration file, and the timetable is adjusted according to the slow-down time when the train passes through the faulty switch area;

[0055] Restore the original running chart: Ensure that the online running chart is restored to its original state.

[0056] Timetable adjustments are measures taken to ensure train operation safety and avoid traffic accidents caused by turnout failures. First, the specific turnout that failed is identified. Based on the location of the failed turnout, train routes within the affected area are replanned. For example, if turnout number 1 at station A fails, trains between stations B and C need to turn back at station C and run on the short route of B and C; while trains at station CA need to wait at the platform until the fault is resolved or a new route is arranged. This adjusted route information is recorded in a configuration file.

[0057] Slow-down timetable adjustments are implemented when a normal timetable cannot be immediately restored. This involves reducing train speeds to ensure safe passage through the affected area while minimizing the impact on overall operations. Based on the fault situation and safety requirements, a reasonable slow-down time is set for trains passing through the affected switch area (i.e., the train's speed in this area must be lower than normal). This slow-down time parameter is then input into the system, automatically adjusting the entire timetable so that all affected trains are adjusted according to the slow-down time. The results of this stage of operation also need to be configured in a corresponding configuration file for subsequent execution and verification.

[0058] The purpose of reverting to the original schedule is to restore normal operations as quickly as possible once the fault is repaired or another solution is found. First, it must be ensured that the fault has been completely resolved and there are no safety hazards. Using pre-saved original train schedule data, the current actual operating conditions are gradually adjusted back to the original planned state. Throughout the reverting process, the system's response and the actual operating status of the trains are closely monitored.

[0059] Step S2: Based on the basic test scenario, basic graph adjustment strategy, and the influencing conditions of the test scenario, automatically generate test scenarios and provide corresponding graph adjustment strategies for each test scenario.

[0060] First, input the basic test scenario (including the conditions for causing a turnout failure), the basic timetable adjustment strategy (the specific steps for interrupting timetable adjustment, slowing down timetable adjustment, and returning to the original timetable), and the test scenario influencing conditions (the turnout's concentration area and turnout number, turnout type, and the train's location when causing a turnout failure). The output content consists of different combinations of the above input conditions and the specific timetable adjustment measures to be taken under each test scenario.

[0061] In one possible implementation, a specific test scenario is as follows:

[0062] The basic test scenario involves a malfunction of single-action turnout No. 1 at station A. The affected conditions are that there are no trains in the turnout's designated route and a train is about to enter the turnout's designated route. The first step of the timetable adjustment strategy is to interrupt the timetable adjustment. Trains running between stations B and C need to turn back at station C and run according to the short route of B and C. Trains at station CA need to wait on the platform until the malfunction is resolved or a new route is arranged. The second step is a slow-down timetable adjustment, where all trains resume their original long-distance routes. When passing turnout No. 1, train travel times are adjusted (e.g., speed is reduced to ensure safe passage). The third step is to restore the timetable, where all trains on the line resume their original routes. This ensures the entire system returns to normal operation.

[0063] Basic test scenarios, basic graph adjustment strategies, and test scenario influencing conditions are written into the configuration file. Based on the information in the configuration file, a variety of different test scenarios are automatically generated. Each scenario is a combination of specific conditions. For each generated test scenario, the corresponding graph adjustment strategy is automatically calculated and output according to the preset basic graph adjustment strategy.

[0064] Step S3: Use the test scenario and graph adjustment strategy as input files for dynamic adjustment of the runtime graph.

[0065] The basic test scenarios define specific turnout failure situations. The basic timetable adjustment strategies include the specific steps for interrupted timetable adjustment, delayed timetable adjustment, and timetable revert. An automated device generates a large number of test scenarios based on the input configuration file. Each scenario is a combination of specific conditions. For each generated test scenario, the device automatically calculates and outputs the corresponding timetable adjustment strategy. For example, if the No. 1 single-action turnout at station A fails, trains between stations B and C need to turn back at station C and run according to the BC short-route; trains at station CA need to wait at the platform. The generated test scenarios and their corresponding timetable adjustment strategies are saved as input files for subsequent automated testing.

[0066] Step S4: Based on the input file, parse the ATS logs, analyze the runtime graph adjustment, compare it with the graph adjustment strategy in the corresponding scenario, and output the test conclusion.

[0067] The generated test scenarios and corresponding timetable adjustment strategies are input into the automated testing device as configuration files. By parsing the logs generated by the ATS system, key data such as train location information, train number, and running path are obtained. Specifically, the current location of the train, the train number, and changes in the actual running path can be extracted from the logs. Based on the parsed log information, the actual timetable adjustment is determined, for example, checking whether train number 1001 has been changed from BA to BC.

[0068] The actual timetable adjustments are compared with the expected timetable adjustment strategy in the configuration file. The actual adjustments are then verified to ensure they conform to the expected strategy. In one possible implementation, if the expectation is that trains between stations B and C will complete a turnaround at station C and operate on the B-C short route, then the actual logs should reflect this change. If the actual timetable adjustments match the expected strategy, the test passes. If they do not match, the specific differences are recorded, and a test failure report is generated, indicating where the expectations were not met.

[0069] Figure 2This document illustrates the complete test scenario and corresponding timetable adjustment strategy generation process proposed in this application's embodiments. The basic test scenario represents the handling strategy when a fault is encountered under this scenario. When a fault occurs, the influencing conditions to be considered include: the turnout's concentration area, the turnout number, and the train's location. After considering these conditions, operations such as interrupting timetable adjustment, slowing down timetable adjustment, or reverting to the original timetable may be performed. Different timetable adjustment strategies exist for different turnout fault scenarios, including: the timetable adjustment strategy for turnout fault scenario one, the timetable adjustment strategy for turnout fault scenario two, and so on, up to the timetable adjustment strategy for turnout fault scenario N. After handling the turnout fault scenario, the system returns to the basic timetable adjustment strategy.

[0070] The ATS system operates, generating system logs. Relevant information is retrieved and parsed from these logs. Differences before and after timetable adjustments are analyzed. Based on the test scenario and train location, a timetable adjustment strategy is determined. The determined timetable adjustment strategy is compared with the corresponding timetable adjustment strategy for the given scenario, and the final test results are output.

[0071] Step S4 includes:

[0072] The location of the train and changes in the train's route were determined by analyzing the ATS logs.

[0073] Based on the train's location and changes in train number and route, determine whether the timetable adjustment meets expectations and record the actual and expected routes for each train.

[0074] If the test results meet expectations, a test conclusion report will be generated; otherwise, a difference report will be provided.

[0075] Relevant log information is extracted from the ATS system and parsed to obtain key data such as train ID, current location, and direction of travel. Based on the parsed data, the exact location of each train at a given moment can be determined. Comparing log entries at different time points allows identification of whether trains have deviated from their planned routes or whether any anomalies have occurred. The pre-set expected operating plan (i.e., the theoretically correct path the trains should follow) is compared with the actual path derived from the ATS logs to analyze the differences and determine whether the actual adjustments met the expected goals.

[0076] For each monitored train, its actual route and the originally planned route are recorded. Any deviations are meticulously documented. When all tests show that the actual operation matches expectations, the relevant information is compiled into a formal test conclusion report. If there are discrepancies, in addition to a summary, the specific location of the problem must be clearly identified, and a possible cause analysis must be provided as part of the discrepancy report.

[0077] Compared with the prior art, the embodiments of this application have the following beneficial effects:

[0078] First, test scenarios are combined based on the basic scenarios, and the image adjustment strategies under the combined scenarios are output to ensure the comprehensiveness of the test scope.

[0079] Second, based on the chart adjustment strategy for the corresponding scenario, verify whether the chart adjustment is correct and reasonable.

[0080] The following is a possible implementation of a scene-based dynamic mapping automated testing device, which is used to execute the various execution steps and corresponding technical effects of the dynamic mapping automated testing method shown in the above embodiments and possible implementations. The device includes:

[0081] The definition module is used to define basic test scenarios, basic graph adjustment strategies, and the influencing conditions of test scenarios through configuration files.

[0082] The generation module is used to automatically generate test scenarios based on basic test scenarios, basic graph adjustment strategies, and the influencing conditions of test scenarios, and to provide corresponding graph adjustment strategies for each test scenario.

[0083] The adjustment module is used to take the test scenario and graph adjustment strategy as input files for dynamic adjustment of the runtime graph;

[0084] The analysis module is used to parse ATS logs based on input files, analyze the runtime graph adjustments, compare them with the graph adjustment strategies in the corresponding scenarios, and output test conclusions.

[0085] In one possible implementation, the defined module is also used for:

[0086] The location of the train and changes in the train's route were determined by analyzing the ATS logs.

[0087] Based on the train's location and changes in train number and route, determine whether the timetable adjustment meets expectations and record the actual and expected routes for each train.

[0088] If the test results meet expectations, a test conclusion report will be generated; otherwise, a difference report will be provided.

[0089] In one possible implementation, the basic map adjustment strategy includes interrupting map adjustment, slowing down map adjustment, and returning to the original map.

[0090] Interruption timetable adjustment: When a turnout fails at a different location, the input of the train running on a small route within the corresponding operating area at the corresponding location is used as the configuration file;

[0091] Slow-down timetable adjustment: The train slow-down time is used as the input of the configuration file, and the timetable is adjusted according to the slow-down time when the train passes through the faulty switch area;

[0092] Restore the original running chart: Ensure that the online running chart is restored to its original state.

[0093] In one possible implementation, the influencing conditions of the test scenario include the turnout's concentration area and turnout number, turnout type, and the train's location when the turnout malfunctions.

[0094] In one possible implementation, the basic test scenarios include turnout failure, signaling system failure, train failure, track failure, and power system failure.

[0095] Therefore, this application discloses a dynamic graph adjustment automation testing method and apparatus based on scenario combinations. First, it defines basic test scenarios, basic graph adjustment strategies, and influencing conditions of the test scenarios through a configuration file. Second, it automatically generates test scenarios based on the basic test scenarios, basic graph adjustment strategies, and influencing conditions of the test scenarios, and provides corresponding graph adjustment strategies for each test scenario. The test scenarios and graph adjustment strategies are used as input files for dynamic graph adjustment. Finally, it parses the ATS logs based on the input files, analyzes the graph adjustment status, and compares it with the graph adjustment strategies under the corresponding scenarios to output test conclusions. This not only outputs the most complete set of dynamic graph adjustment scenarios and corresponding graph adjustment strategies, completing automated dynamic graph adjustment testing, but also ensures the completeness and accuracy of the test scenarios.

[0096] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A dynamic map-based test automation method based on scenario composition, characterized in that, The method is applied in a dynamic graph adjustment automated testing device, and the method includes: The configuration file defines the basic test scenarios, basic map adjustment strategies, and the influencing conditions of the test scenarios. The basic map adjustment strategies include interruption map adjustment, slow map adjustment, and map return. Interruption timetable adjustment: When a turnout fails at a different location, the input of the train running on a small route within the corresponding operating area at the corresponding location is used as the configuration file; Slow-down timetable adjustment: The train slow-down time is used as the input of the configuration file, and the timetable is adjusted according to the slow-down time when the train passes through the faulty switch area; Restore the original running chart: Ensure the online running chart is restored to its original state. Based on the basic test scenarios, basic graph adjustment strategies, and the influencing conditions of the test scenarios, test scenarios are automatically generated, and corresponding graph adjustment strategies are provided for each test scenario. The test scenario and graph adjustment strategy are used as input files for dynamic adjustment of the runtime graph; The steps involved in parsing ATS logs from input files, analyzing runtime graph adjustments, comparing them with graph adjustment strategies for corresponding scenarios, and outputting test conclusions include: The location of the train and changes in the train's route were determined by analyzing the ATS logs. Based on the train's location and changes in train number and route, determine whether the timetable adjustment meets expectations and record the actual and expected routes for each train; If the test results meet expectations, a test conclusion report will be generated; otherwise, a difference report will be provided.

2. The dynamic test map automated testing method of claim 1, wherein, The influencing conditions of the test scenario include the turnout's concentration area and turnout number, turnout type, and the train's location when the turnout malfunctions.

3. The dynamic test image selection method of claim 1, wherein, The basic test scenarios include turnout failure, signal system failure, train failure, track failure, and power system failure.

4. A dynamic map-based scenario composition automated testing apparatus, characterized in that, The device includes: The definition module is used to define basic test scenarios, basic map adjustment strategies, and the influencing conditions of test scenarios through configuration files. The basic map adjustment strategies include interruption map adjustment, slow map adjustment, and map return. Interruption of timetable adjustment: When a turnout fails at a different location, the input of the train running on a small route within the corresponding operating area at the corresponding location is used as the configuration file; Slow-down timetable adjustment: The train slow-down time is used as the input of the configuration file, and the timetable is adjusted according to the slow-down time when the train passes through the faulty switch area; Restore the original running chart: Ensure the online running chart is restored to its original state. The generation module is used to automatically generate test scenarios based on basic test scenarios, basic graph adjustment strategies, and the influencing conditions of test scenarios, and to provide corresponding graph adjustment strategies for each test scenario. The adjustment module is used to take the test scenario and graph adjustment strategy as input files for dynamic adjustment of the runtime graph; The analysis module is used to parse ATS logs based on input files, analyze the operation graph adjustment, compare the graph adjustment strategy with the corresponding scenario, and output test conclusions. Modules are also used for: The location of the train and changes in the train's route were determined by analyzing the ATS logs. Based on the train's location and changes in train number and route, determine whether the timetable adjustment meets expectations and record the actual and expected routes for each train; If the test results meet expectations, a test conclusion report will be generated; otherwise, a difference report will be provided.

5. The dynamic test fixture of claim 4, wherein the test fixture is configured to automatically change the test fixture to a different test fixture based on the test fixture type. 5 The influencing conditions of the test scenario include the turnout's concentration area and turnout number, turnout type, and the train's location when the turnout malfunctions.

6. The dynamic test fixture of claim 4, wherein the test fixture is configured to automatically change the test fixture to a different test fixture based on the test fixture type. The basic test scenarios include turnout failure, signal system failure, train failure, track failure, and power system failure.

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