Train slip fault prediction method and device

Abstract

The invention provides a train slip fault prediction method and device. The method comprises the following steps: sending a user query request to a terminal; a query result sent by the terminal is received, the query result comprises ATP slip alarm data, and the query result is visually displayed; sending a user fault analysis request to the terminal, wherein the fault analysis request comprises a query result; receiving a fault analysis report sent by the terminal, wherein the fault analysis report comprises slip fault prediction results of the plurality of trains; wherein the terminal is used for inputting the ATP slip alarm data into a pre-constructed fault prediction model to obtain slip fault prediction results, output by the fault prediction model, of a plurality of trains; the fault prediction model is obtained by training based on historical ATP slip alarm data. According to the train slip fault prediction method provided by the invention, the train slip fault prediction efficiency, accuracy and interactivity are improved, and the user experience is improved.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to a method and apparatus for predicting train slippage faults. Background Technology

[0002] Ensuring train operation safety and improving train control efficiency are core issues in the rail transit field. As a critical system for ensuring train operation safety, one of the core functions of the Automatic Train Protection (ATP) system is to monitor and protect against train slippage and coasting in real time. How to efficiently predict train malfunctions based on ATP alarm data is a crucial problem that urgently needs to be solved. However, traditional methods for predicting train slippage malfunctions rely on manual intervention, resulting in low efficiency, insufficient accuracy, poor interactivity, and a negative impact on user experience. Summary of the Invention

[0003] This invention provides a method and apparatus for predicting train slippage faults, which addresses the shortcomings of existing train slippage fault prediction methods, such as low efficiency, insufficient accuracy, poor interactivity, and negative impact on user experience.

[0004] This invention provides a method for predicting train slippage faults, applied to the user end, including: Send the user's query request to the terminal; Receive query results sent by the terminal, the query results including ATP slippage alarm data, and visualize the query results; The user fault analysis request is sent to the terminal, and the fault analysis request includes the query results; Receive a fault analysis report sent by the terminal, the fault analysis report including the slippage fault prediction results of multiple trains; The terminal is used to: input the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0005] In some embodiments, the ATP slippage alarm data includes: the type, alarm information, location, and speed of multiple trains; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer, and a fault prediction layer. Correspondingly, the step of inputting the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model includes: The types and alarm information of the multiple trains are input into the risk assessment layer to obtain the risk level of the multiple trains output by the risk assessment layer; The positions of the multiple trains are input into the position prediction layer to obtain the predicted positions of the multiple trains output by the position prediction layer. The speeds of the multiple trains are input into the speed prediction layer to obtain the predicted speeds of the multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of the multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of the multiple trains output by the fault prediction layer.

[0006] In some embodiments, before sending the user query request to the terminal, the method further includes: Obtain the query condition parameters and query instructions input by the user. The query condition parameters include time range parameters and / or train parameters to be queried. The train parameters to be queried include at least one of the following: train type, train position, train speed, alarm time, ATP slippage fault code, and alarm type. The query condition parameters are validated; If the query condition parameters are verified to be valid, the user query request is generated based on the query condition parameters and the query instruction.

[0007] In some embodiments, visualizing the query results includes: The query results are categorized and statistically analyzed to generate multiple charts; the multiple charts include: a chart showing the number of alarms for different trains, a chart showing the number of alarms for different location sections, a chart showing the number of alarms for different speed ranges, and a chart showing the trend of the number of alarms over time; The multiple charts are then visualized.

[0008] This invention also provides a method for predicting train slippage faults, applied to a terminal, comprising: The system receives user query requests sent by the user terminal; the user terminal is used to execute any of the train slippage fault prediction methods described above. Based on the user's query request, a query is performed in the database to obtain query results, which include ATP slippage alarm data; The query results are sent to the user's client. Receive a user fault analysis request sent by the user terminal, the fault analysis request including the query results; Based on the user's fault analysis request, a fault analysis report is generated, which includes the predicted slippage fault results for multiple trains. The fault analysis report is sent to the user terminal.

[0009] In some embodiments, generating a fault analysis report based on the user fault analysis request includes: The user fault analysis request is parsed to obtain the ATP slippage alarm data; The ATP slippage alarm data is input into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0010] In some embodiments, the ATP slippage alarm data includes: the type, alarm information, location, and speed of multiple trains; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer, and a fault prediction layer. Correspondingly, the step of inputting the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model includes: The types of the multiple trains and their alarm information are input into the risk assessment layer to obtain the risk level of the multiple trains output by the risk assessment layer. The positions of the multiple trains are input into the position prediction layer to obtain the predicted positions of the multiple trains output by the position prediction layer. The speeds of the multiple trains are input into the speed prediction layer to obtain the predicted speeds of the multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of the multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of the multiple trains output by the fault prediction layer.

[0011] In some embodiments, the step of querying the database based on the user query request to obtain query results includes: The user query request is parsed to obtain the parsed user query request; Based on the parsed user query request, a structured query statement is generated; Based on the structured query statement and the pre-built database index, a query is performed in the database to obtain the query results; the database index is built based on preset query condition parameters. The preset query condition parameters include: train type, train location, train speed, alarm time, ATP slippage fault code, and alarm type.

[0012] This invention also provides a train slippage fault prediction device, applied at the user end, comprising: The first sending unit is used to send user query requests to the terminal; The first receiving unit is used to receive the query results sent by the terminal, the query results including ATP slippage alarm data, and to visualize the query results; The second sending unit is used to send a user fault analysis request to the terminal, the fault analysis request including the query result; The second receiving unit is used to receive the fault analysis report sent by the terminal, the fault analysis report including the slippage fault prediction results of multiple trains; The terminal is used to: input the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0013] The present invention also provides a train slippage fault prediction device, applied to a terminal, comprising: The third receiving unit is used to receive user query requests sent by the user terminal; the user terminal is used to execute the train slippage fault prediction method as described above. The query unit is used to perform a query in the database based on the user's query request and obtain query results, including ATP slippage alarm data. The third sending unit is used to send the query result to the user terminal; The fourth receiving unit is used to receive a user fault analysis request sent by the user terminal, wherein the fault analysis request includes the query results; The first generation unit is used to generate a fault analysis report based on the user's fault analysis request. The fault analysis report includes the predicted slippage faults of multiple trains. The fourth sending unit is used to send the fault analysis report to the user terminal.

[0014] The train slippage fault prediction method and apparatus provided by this invention involves sending a user query request to a terminal; receiving the query results sent by the terminal and visually displaying the query results; sending a user fault analysis request to the terminal, the fault analysis request including the query results; and receiving a fault analysis report sent by the terminal, the fault analysis report including slippage fault prediction results for multiple trains. The slippage fault prediction results are obtained based on a fault prediction model, which improves the efficiency, accuracy, and interactivity of train slippage fault prediction and enhances the user experience. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is one of the flowcharts illustrating the train slippage fault prediction method provided in this embodiment of the invention.

[0017] Figure 2 This is a flowchart illustrating the training process of the fault prediction model provided in this embodiment of the invention.

[0018] Figure 3 This is the second flowchart of the train slippage fault prediction method provided in the embodiments of the present invention.

[0019] Figure 4 This is one of the structural schematic diagrams of the train slippage fault prediction device provided in the embodiments of the present invention.

[0020] Figure 5 This is the second schematic diagram of the train slippage fault prediction device provided in the embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, the first object can be one or more. Furthermore, in this invention, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0023] Figure 1 This is one of the flowcharts illustrating the train slippage fault prediction method provided in an embodiment of the present invention. Figure 1As shown, a method for predicting train slippage faults is provided and applied to the user end, including the following steps: step 110, step 120, step 130, and step 140. This method's steps are merely one possible implementation of the invention.

[0024] Step 110: Send the user query request to the terminal.

[0025] Optionally, user input is obtained, processed to obtain processed user input, and a user query request is generated based on the preprocessed user input.

[0026] For example, user input can be completed to obtain the completed user input, and a user query request can be generated based on the completed user input.

[0027] For example, if the user input contains ambiguous terms, natural language processing can be performed on the user input to obtain the user intent, and a user query request can be generated based on the user intent.

[0028] In some embodiments, before sending a user query request to the terminal, the method further includes: Obtain the query condition parameters and query instructions input by the user. The query condition parameters include time range parameters and / or train parameters to be queried. The train parameters to be queried include at least one of the following: train type, train position, train speed, alarm time, ATP slippage fault code, and alarm type. Validate the query condition parameters; If the query condition parameters are validated and passed, a user query request is generated based on the query condition parameters and query instructions.

[0029] Optionally, the time range parameter includes a start date and an end date.

[0030] It should be noted that the client provides a date picker, allowing users to select the start and end dates; the client has a default time range, such as the most recent week or month; the client also has minimum and maximum time ranges to avoid excessive data query volume.

[0031] Optionally, the user terminal provides a drop-down menu that supports multi-dimensional and multi-level parameter selection. Users can select parameters such as train type, train position, train speed, alarm time, ATP slippage fault code, and alarm type through the drop-down menu.

[0032] Step 120: Receive the query results sent by the terminal. The query results include ATP slippage alarm data. Visualize the query results. Among them, the ATP slippage alarm data is used to accurately determine the state under which the train has experienced idle / slippage or coasting.

[0033] Optionally, the ATP slippage alarm data shall include at least: alarm information, train ID, train type, train speed, train position, train operation mode, train direction, track conditions, and weather information; the alarm information shall include at least the alarm type, alarm level, alarm time, ATP slippage fault code, and alarm status.

[0034] In some embodiments, the query results are visualized, including: The query results are categorized and statistically analyzed to generate multiple charts, including: a chart showing the number of alarms for different trains, a chart showing the number of alarms for different location sections, a chart showing the number of alarms for different speed ranges, and a chart showing the trend of alarm numbers over time. Visualize multiple charts.

[0035] The charts include, but are not limited to: bar charts, line charts, curve charts, scatter plots, and pie charts.

[0036] Optionally, the number of alarms in the query results can be categorized and statistically analyzed according to alarm type, train ID / type, and interval (such as time, location, speed interval) to obtain statistical information.

[0037] Optionally, based on the train ID / type, the ATP slippage alarm data can be statistically analyzed to calculate the number of alarms for different trains, resulting in an alarm count display chart for different trains. This chart can show the ranking and percentage of alarm counts for different trains, such as displaying the top ten or top five trains.

[0038] Optionally, based on intervals (such as train position and speed intervals), statistical analysis of ATP slippage alarm data can be performed to obtain alarm quantity display charts for different position segments (such as trend curves, with the horizontal axis representing the time dimension and the vertical axis representing the number of alarms in the segment, and multiple segments corresponding to multiple curves) and alarm quantity display charts for different speed intervals (such as scatter plots), showing the ranking and proportion of alarm quantity in different segments.

[0039] Optionally, based on the time dimension (such as by day, week or month), the ATP slippage alarm data can be statistically analyzed to obtain a trend graph (such as a line graph or curve graph) showing the change in the number of alarms over time, with the horizontal axis representing the time dimension and the vertical axis representing the number of alarms.

[0040] Step 130: Send the user's fault analysis request to the terminal. The fault analysis request includes the query results.

[0041] Optionally, before sending the user fault analysis request to the terminal, the method further includes: Obtain the report generation instructions from the user input; Based on the query results and report generation instructions, generate a user fault analysis request.

[0042] Optionally, a user fault analysis request can be generated based on the statistical information and multiple charts corresponding to the query results, as well as the report generation instructions.

[0043] Step 140: Receive the fault analysis report sent by the terminal. The fault analysis report includes the predicted slippage fault results of multiple trains.

[0044] The terminal is used to: input ATP slippage alarm data into a pre-built fault prediction model to obtain slippage fault prediction results for multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0045] The fault analysis report includes, but is not limited to: the analysis results of slippage faults of multiple trains in historical time periods, and the prediction results of slippage faults in future time periods.

[0046] The prediction results for slippage faults of multiple trains include, but are not limited to: slippage fault risk level, slippage location, slippage speed, fault type, and recommended measures.

[0047] In this embodiment of the invention, by sending a user query request to a terminal; receiving the query results sent by the terminal and visually displaying the query results; sending a user fault analysis request to the terminal, the fault analysis request including the query results; and receiving a fault analysis report sent by the terminal, the efficiency, accuracy, and interactivity of train slippage fault prediction are improved, thereby enhancing the user experience.

[0048] In some embodiments, the ATP slippage alarm data includes: the type of multiple trains, alarm information, location and speed; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer and a fault prediction layer; Correspondingly, the ATP slippage alarm data is input into a pre-built fault prediction model to obtain slippage fault prediction results for multiple trains output by the fault prediction model, including: Input the types and alarm information of multiple trains into the risk assessment layer to obtain the risk level of multiple trains output by the risk assessment layer; The positions of multiple trains are input into the position prediction layer to obtain the predicted positions of multiple trains output by the position prediction layer. The speeds of multiple trains are input into the speed prediction layer to obtain the predicted speeds of multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of multiple trains output by the fault prediction layer.

[0049] It should be noted that different vehicle models have different traction / braking characteristics, weight distribution, axle load, etc., which are important prior knowledge for risk assessment.

[0050] The risk level can be low, medium, high, or emergency.

[0051] Optionally, based on the train's position, speed, and acceleration in the current time period, and taking into account geographical information such as the speed limit, gradient, and curves of the line, the train's position and speed in the future time period can be predicted.

[0052] Optionally, predictive environmental data can be obtained, including weather data and route status for future time periods.

[0053] Optionally, the predicted environmental data, as well as the risk level, predicted location, and predicted speed of multiple trains, are input into the fault prediction layer to obtain the slippage fault prediction results of multiple trains output by the fault prediction layer.

[0054] Optionally, the predicted environmental data, as well as the risk level, predicted location, and predicted speed of multiple trains, are fused to obtain fused information. Based on the fused information, the slippage fault prediction results of multiple trains are obtained.

[0055] In this embodiment of the invention, by inputting the types and alarm information of multiple trains into the risk assessment layer, the risk levels of multiple trains are obtained; by inputting the positions of multiple trains into the position prediction layer, the predicted positions of multiple trains are obtained; by inputting the speeds of multiple trains into the speed prediction layer, the predicted speeds of multiple trains are obtained; and by inputting the risk levels, predicted positions, and predicted speeds of multiple trains into the fault prediction layer, the slippage fault prediction results of multiple trains are obtained, thereby improving the efficiency, accuracy, and interpretability of fault prediction.

[0056] Figure 2 This is a flowchart illustrating the training process of a fault prediction model provided in an embodiment of the present invention. In some embodiments, the training process of the fault prediction model includes: Step 210: Obtain historical ATP slippage alarm data; Step 220: Determine the true labels of slippage faults for multiple historical trains; Step 230: Input the historical ATP slippage alarm data into the initial fault prediction model to obtain the slippage fault prediction results of multiple historical trains output by the initial fault prediction model. Step 240: Calculate the loss function value based on the slippage fault prediction results and actual slippage fault labels of multiple historical trains; Step 250: Based on the loss function value, iteratively optimize the parameters of the initial fault prediction model to obtain the fault prediction model.

[0057] Optionally, historical ATP slippage alarm data includes: the type of multiple historical trains, historical alarm information, historical location and historical speed; the fault prediction model includes an initial risk assessment layer, an initial location prediction layer, an initial speed prediction layer and an initial fault prediction layer.

[0058] Optionally, historical ATP slippage alarm data is input into the initial fault prediction model to obtain slippage fault prediction results for multiple historical trains output by the initial fault prediction model, including: Input the types and historical alarm information of multiple historical trains into the initial risk assessment layer to obtain the risk levels of multiple historical trains output by the initial risk assessment layer; The historical positions of multiple historical trains are input into the initial position prediction layer to obtain the predicted positions of multiple historical trains output by the initial position prediction layer. The historical speeds of multiple historical trains are input into the initial speed prediction layer to obtain the predicted speeds of multiple historical trains output by the initial speed prediction layer. The risk level, predicted location, and predicted speed of multiple historical trains are input into the initial fault prediction layer to obtain the slippage fault prediction results of multiple historical trains output by the fault prediction layer.

[0059] In this embodiment of the invention, an initial fault prediction model is trained based on historical ATP slippage alarm data, and a fault prediction model is obtained after training, which improves the generalization and accuracy of the fault prediction model.

[0060] Figure 3 This is a second schematic flowchart of the train slippage fault prediction method provided in an embodiment of the present invention. Figure 3 As shown, a method for predicting train slippage faults is provided and applied to a terminal, including the following steps: steps 310 to 360. This method's steps are merely one possible implementation of the invention.

[0061] Step 310: Receive user query requests sent by the user terminal; the user terminal is used to execute any of the train slippage fault prediction methods described above.

[0062] Optionally, the client is used for: Obtain the query condition parameters and query instructions input by the user. The query condition parameters include time range parameters and / or train parameters to be queried. The train parameters to be queried include at least one of the following: train type, train position, train speed, alarm time, ATP slippage fault code, and alarm type. Validate the query condition parameters; If the query condition parameters are validated and passed, a user query request is generated based on the query condition parameters and query instructions.

[0063] Step 320: Based on the user's query request, perform a query in the database to obtain the query results, which include ATP slippage alarm data.

[0064] Optionally, the ATP slippage alarm data shall include at least: alarm information (alarm type, alarm level, alarm time, ATP slippage fault code, alarm status), train ID, train type, train speed, train position, train operation mode, train direction, track conditions, and weather information.

[0065] In some embodiments, based on a user query request, a query is performed in the database to obtain query results, including: The user query request is parsed to obtain the parsed user query request; Based on the parsed user query request, a structured query statement is generated; Based on structured query statements and pre-built database indexes, queries are performed in the database to obtain query results; the database indexes are built based on preset query condition parameters. The preset query parameters include: train type, train location, train speed, alarm time, ATP slippage fault code, and alarm type.

[0066] Optionally, a MySQL database can be pre-created, and the structure of the ATP slippage alarm data table can be designed; indexes can be defined to speed up data retrieval; and scheduled tasks or data import scripts can be written to store real-time alarm data in the database.

[0067] Step 330: Send the query results to the user's client.

[0068] Step 340: Receive the user fault analysis request sent by the user terminal. The fault analysis request includes the query results.

[0069] Step 350: Based on the user's fault analysis request, generate a fault analysis report, which includes the predicted slippage faults of multiple trains.

[0070] Optionally, a fault analysis report can be generated based on a preset report template.

[0071] In some embodiments, a fault analysis report is generated based on a user fault analysis request, including: The user's fault analysis request is parsed to obtain ATP slippage alarm data; The ATP slippage alarm data is input into a pre-built fault prediction model to obtain the slippage fault prediction results of multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0072] Step 360: Send the fault analysis report to the user's terminal.

[0073] In this embodiment of the invention, by receiving a user query request sent by a user terminal; performing a query in the database based on the user query request to obtain query results; sending the query results to the user terminal; receiving a user fault analysis request sent by the user terminal, the fault analysis request including the query results; generating a fault analysis report based on the user fault analysis request; and sending the fault analysis report to the user terminal, the efficiency, accuracy, and interactivity of fault prediction are improved, thereby enhancing the user experience.

[0074] In some embodiments, the ATP slippage alarm data includes: the type of multiple trains, alarm information, location and speed; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer and a fault prediction layer; Correspondingly, the ATP slippage alarm data is input into a pre-built fault prediction model to obtain slippage fault prediction results for multiple trains output by the fault prediction model, including: Input the types of multiple trains and their alarm information into the risk assessment layer to obtain the risk level of the multiple trains output by the risk assessment layer. The positions of multiple trains are input into the position prediction layer to obtain the predicted positions of multiple trains output by the position prediction layer. The speeds of multiple trains are input into the speed prediction layer to obtain the predicted speeds of multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of multiple trains output by the fault prediction layer.

[0075] The risk level can be low, medium, high, or emergency.

[0076] Optionally, based on the train's position, speed, and acceleration in the current time period, and taking into account geographical information such as the speed limit, gradient, and curves of the line, the train's position and speed in the future time period can be predicted.

[0077] Optionally, predictive environmental data can be obtained, including weather data and route status for future time periods.

[0078] Optionally, the predicted environmental data, as well as the risk level, predicted location, and predicted speed of multiple trains, are input into the fault prediction layer to obtain the slippage fault prediction results of multiple trains output by the fault prediction layer.

[0079] In this embodiment of the invention, by inputting the types and alarm information of multiple trains into the risk assessment layer, the risk levels of multiple trains are obtained; by inputting the positions of multiple trains into the position prediction layer, the predicted positions of multiple trains are obtained; by inputting the speeds of multiple trains into the speed prediction layer, the predicted speeds of multiple trains are obtained; and by inputting the risk levels, predicted positions, and predicted speeds of multiple trains into the fault prediction layer, the slippage fault prediction results of multiple trains are obtained, thereby improving the efficiency, accuracy, and interpretability of fault prediction.

[0080] The train slippage fault prediction device provided in the embodiments of the present invention is described below. The train slippage fault prediction device described below can be referred to in correspondence with the train slippage fault prediction method described above.

[0081] Figure 4 This is one of the structural schematic diagrams of the train slippage fault prediction device provided in an embodiment of the present invention, such as... Figure 4 As shown, the train slippage fault prediction device 400, applied at the user end, includes: The first sending unit 410 is used to send the user query request to the terminal; The first receiving unit 420 is used to receive the query results sent by the terminal. The query results include ATP slippage alarm data and the query results are displayed visually. The second sending unit 430 is used to send a user fault analysis request to the terminal. The fault analysis request includes query results. The second receiving unit 440 is used to receive the fault analysis report sent by the terminal. The fault analysis report includes the prediction results of slippage faults of multiple trains. The terminal is used to: input ATP slippage alarm data into a pre-built fault prediction model to obtain slippage fault prediction results for multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0082] Optionally, the ATP slippage alarm data includes: the type of multiple trains, alarm information, location and speed; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer and a fault prediction layer; Correspondingly, the ATP slippage alarm data is input into a pre-built fault prediction model to obtain slippage fault prediction results for multiple trains output by the fault prediction model, including: Input the types and alarm information of multiple trains into the risk assessment layer to obtain the risk level of multiple trains output by the risk assessment layer; The positions of multiple trains are input into the position prediction layer to obtain the predicted positions of multiple trains output by the position prediction layer. The speeds of multiple trains are input into the speed prediction layer to obtain the predicted speeds of multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of multiple trains output by the fault prediction layer.

[0083] Optionally, the train slippage fault prediction device 400 also includes: The acquisition unit is used to acquire the query condition parameters and query instructions input by the user. The query condition parameters include time range parameters and / or train parameters to be queried. The train parameters to be queried include at least one of the following: train type, train position, train speed, alarm time, ATP slippage fault code, and alarm type. The validation unit is used to validate the query condition parameters; The second generation unit generates a user query request based on the query condition parameters and query instructions, provided that the query condition parameters have been verified.

[0084] Optionally, the query results can be visualized, including: The query results are categorized and statistically analyzed to generate multiple charts, including: a chart showing the number of alarms for different trains, a chart showing the number of alarms for different location sections, a chart showing the number of alarms for different speed ranges, and a chart showing the trend of alarm numbers over time. Visualize multiple charts.

[0085] Figure 5 This is a second schematic diagram of the train slippage fault prediction device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the train slippage fault prediction device 500, applied to a terminal, includes: The third receiving unit 510 is used to receive user query requests sent by the user terminal; the user terminal is used to execute any of the train slippage fault prediction methods described above. The query unit 520 is used to perform a query in the database based on the user's query request and obtain the query results, which include ATP slippage alarm data. The third sending unit 530 is used to send the query results to the user terminal; The fourth receiving unit 540 is used to receive user fault analysis requests sent by the user terminal. The fault analysis request includes query results. The first generation unit 550 is used to generate a fault analysis report based on the user's fault analysis request. The fault analysis report includes the slippage fault prediction results of multiple trains. The fourth sending unit 560 is used to send the fault analysis report to the user terminal.

[0086] Optionally, based on the user's fault analysis request, a fault analysis report is generated, including: The user's fault analysis request is parsed to obtain ATP slippage alarm data; The ATP slippage alarm data is input into a pre-built fault prediction model to obtain the slippage fault prediction results of multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

[0087] Optionally, the ATP slippage alarm data includes: the type of multiple trains, alarm information, location and speed; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer and a fault prediction layer; Correspondingly, the ATP slippage alarm data is input into a pre-built fault prediction model to obtain slippage fault prediction results for multiple trains output by the fault prediction model, including: Input the types of multiple trains and their alarm information into the risk assessment layer to obtain the risk level of the multiple trains output by the risk assessment layer. The positions of multiple trains are input into the position prediction layer to obtain the predicted positions of multiple trains output by the position prediction layer. The speeds of multiple trains are input into the speed prediction layer to obtain the predicted speeds of multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of multiple trains output by the fault prediction layer.

[0088] Optionally, based on the user's query request, a query is performed in the database to obtain the query results, including: The user query request is parsed to obtain the parsed user query request; Based on the parsed user query request, a structured query statement is generated; Based on structured query statements and pre-built database indexes, queries are performed in the database to obtain query results; the database indexes are built based on preset query condition parameters. The preset query parameters include: train type, train location, train speed, alarm time, ATP slippage fault code, and alarm type.

[0089] The device embodiments of the present invention have the following beneficial effects: (1) Real-time monitoring and early warning: It can monitor the ATP slippage alarm status of the vehicle in real time and provide timely reminders, which helps users take timely measures to avoid potential problems.

[0090] (2) Intuitive analysis and visualization: By displaying charts and statistical data, alarm data is presented in an intuitive way, which helps users better understand and analyze alarm situations and facilitates timely detection of abnormal situations.

[0091] (3) Rapid fault handling: Provides analysis of fault recovery time, can assess the efficiency of vehicle fault handling, optimize maintenance and repair plans, and reduce downtime and costs.

[0092] (4) Vehicle management and optimization: Vehicles can be ranked and analyzed based on indicators such as the number of alarms and the proportion, which helps to identify vehicles with frequent alarms and carry out targeted maintenance and improvement to improve vehicle reliability and operating efficiency.

[0093] (5) Flexible query and export: Supports data query based on time range and vehicle information, making it convenient to view and export alarm data as needed for further analysis, reporting and decision-making.

[0094] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0095] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting train slippage faults, characterized in that, Applied to the user end, including: Send the user's query request to the terminal; Receive query results sent by the terminal, the query results including ATP slippage alarm data, and visualize the query results; The user fault analysis request is sent to the terminal, and the fault analysis request includes the query results; Receive a fault analysis report sent by the terminal, the fault analysis report including the slippage fault prediction results of multiple trains; The terminal is used to: input the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

2. The train slippage fault prediction method according to claim 1, characterized in that, The ATP slippage alarm data includes: the type of multiple trains, alarm information, location, and speed; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer, and a fault prediction layer. Correspondingly, the step of inputting the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model includes: The types and alarm information of the multiple trains are input into the risk assessment layer to obtain the risk level of the multiple trains output by the risk assessment layer; The positions of the multiple trains are input into the position prediction layer to obtain the predicted positions of the multiple trains output by the position prediction layer. The speeds of the multiple trains are input into the speed prediction layer to obtain the predicted speeds of the multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of the multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of the multiple trains output by the fault prediction layer.

3. The train slippage fault prediction method according to claim 1, characterized in that, Before sending the user query request to the terminal, the process also includes: Obtain the query condition parameters and query instructions input by the user. The query condition parameters include time range parameters and / or train parameters to be queried. The train parameters to be queried include at least one of the following: train type, train position, train speed, alarm time, ATP slippage fault code, and alarm type. The query condition parameters are validated; If the query condition parameters are verified to be valid, the user query request is generated based on the query condition parameters and the query instruction.

4. The train slippage fault prediction method according to claim 1, characterized in that, The visualization of the query results includes: The query results are categorized and statistically analyzed to generate multiple charts; the multiple charts include: a chart showing the number of alarms for different trains, a chart showing the number of alarms for different location sections, a chart showing the number of alarms for different speed ranges, and a chart showing the trend of the number of alarms over time; The multiple charts are then visualized.

5. A method for predicting train slippage faults, characterized in that, Applied to terminals, including: The system receives user query requests sent by a user terminal; the user terminal is used to execute the train slippage fault prediction method as described in any one of claims 1-4. Based on the user's query request, a query is performed in the database to obtain query results, which include ATP slippage alarm data; The query results are sent to the user's client. Receive a user fault analysis request sent by the user terminal, the fault analysis request including the query results; Based on the user's fault analysis request, a fault analysis report is generated, which includes the predicted slippage fault results for multiple trains. The fault analysis report is sent to the user terminal.

6. The train slippage fault prediction method according to claim 5, characterized in that, The step of generating a fault analysis report based on the user's fault analysis request includes: The user fault analysis request is parsed to obtain the ATP slippage alarm data; The ATP slippage alarm data is input into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

7. The train slippage fault prediction method according to claim 6, characterized in that, The ATP slippage alarm data includes: the type of multiple trains, alarm information, location, and speed; the fault prediction model includes a risk assessment layer, a location prediction layer, a speed prediction layer, and a fault prediction layer. Correspondingly, the step of inputting the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model includes: The types of the multiple trains and their alarm information are input into the risk assessment layer to obtain the risk level of the multiple trains output by the risk assessment layer. The positions of the multiple trains are input into the position prediction layer to obtain the predicted positions of the multiple trains output by the position prediction layer. The speeds of the multiple trains are input into the speed prediction layer to obtain the predicted speeds of the multiple trains output by the speed prediction layer. The risk level, predicted location, and predicted speed of the multiple trains are input into the fault prediction layer to obtain the slippage fault prediction results of the multiple trains output by the fault prediction layer.

8. The train slippage fault prediction method according to claim 5, characterized in that, The step of querying the database based on the user's query request and obtaining the query results includes: The user query request is parsed to obtain the parsed user query request; Based on the parsed user query request, a structured query statement is generated; Based on the structured query statement and the pre-built database index, a query is performed in the database to obtain the query results; the database index is built based on preset query condition parameters. The preset query condition parameters include: train type, train location, train speed, alarm time, ATP slippage fault code, and alarm type.

9. A train slippage fault prediction device, characterized in that, Applied to the user end, including: The first sending unit is used to send user query requests to the terminal; The first receiving unit is used to receive the query results sent by the terminal, the query results including ATP slippage alarm data, and to visualize the query results; The second sending unit is used to send a user fault analysis request to the terminal, the fault analysis request including the query result; The second receiving unit is used to receive the fault analysis report sent by the terminal, the fault analysis report including the slippage fault prediction results of multiple trains; The terminal is used to: input the ATP slippage alarm data into a pre-built fault prediction model to obtain the slippage fault prediction results of the multiple trains output by the fault prediction model; the fault prediction model is trained based on historical ATP slippage alarm data.

10. A train slippage fault prediction device, characterized in that, Applied to terminals, including: The third receiving unit is used to receive user query requests sent by the user terminal; the user terminal is used to execute the train slippage fault prediction method as described in any one of claims 1-4. The query unit is used to perform a query in the database based on the user's query request and obtain query results, including ATP slippage alarm data. The third sending unit is used to send the query result to the user terminal; The fourth receiving unit is used to receive a user fault analysis request sent by the user terminal, wherein the fault analysis request includes the query results; The first generation unit is used to generate a fault analysis report based on the user's fault analysis request. The fault analysis report includes the predicted slippage faults of multiple trains. The fourth sending unit is used to send the fault analysis report to the user terminal.

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