Train operation and maintenance management method and system, medium and product

Abstract

The invention discloses a train operation and maintenance management method and system, a medium and a product. According to the method, a vehicle-mounted sensor is adopted to comprehensively collect system operation data of train power, suspension and the like, and a fault risk prediction model is input to obtain high, medium and low fault risk levels; generating a maintenance train priority list in combination with a next-day train scheduling plan, and determining a maintenance sequence; according to the priority and the maintenance resources, making a maintenance plan covering task allocation, team arrangement, a bill of materials and estimated time; and if the actual maintenance is overtime, dynamically adjusting the plan. According to the method, accurate prediction and evaluation of the train fault risk are realized, the train maintenance plan is constructed and dynamically adjusted in combination with scientific distribution of maintenance resources, the accuracy and efficiency of train operation and maintenance are effectively improved, and a powerful guarantee is provided for safe and efficient operation of the train.

Description

Technical Field

[0001] This application relates to the field of rail transit, and in particular to a train operation and maintenance management method, system, medium and product. Background Technology

[0002] With the acceleration of global urbanization, urban rail transit has become an important mode of public transportation. Efficient operation and maintenance of trains are crucial to ensuring the safety and punctuality of rail transit. During high-intensity operation, components such as the power system, suspension system, communication system, and door system are prone to wear and tear or malfunction due to prolonged use or external environmental factors. Failure to detect and repair these problems in a timely manner can seriously affect the safety and efficiency of train operation. Therefore, effectively monitoring the operating status of trains and developing reasonable maintenance plans have become important issues in the field of rail transit.

[0003] In related technologies, train maintenance typically employs a periodic approach. For example, key train components (such as the power system and suspension system) are manually inspected and maintained at pre-set time or mileage intervals. Alternatively, sensors are installed on train equipment to collect operational data, such as parameters like temperature, vibration, and pressure, and this data is uploaded to a backend system. Maintenance personnel then use this data to monitor the train equipment and develop maintenance plans.

[0004] However, in practical use, the relevant technical solutions have certain limitations. Regular maintenance, due to its fixed schedule, may lead to the scheduling of even well-maintained train equipment, resulting in wasted resources. The ability of the relevant technologies to dynamically adjust the task sequence when combining train maintenance needs with scheduling plans needs improvement. When train maintenance time exceeds the expected time, it can easily lead to unreasonable allocation of maintenance resources, thereby affecting overall maintenance efficiency. Summary of the Invention

[0005] In view of the above-mentioned technical problems and defects, the purpose of this application is to provide a train operation and maintenance management method, system, medium and product that can reasonably arrange train maintenance time and solve the problem of low train maintenance efficiency.

[0006] To achieve the above objectives, firstly, this application provides a train operation and maintenance management method, including: The system acquires operational data collected by onboard sensors for each train during operation. This data includes operational data from the train's power system, suspension system, communication system, and door system. This data is then input into a fault risk prediction model to obtain a fault risk level for each train, categorized as high, medium, or low risk. This fault risk level characterizes the train's health status. Based on the next day's train scheduling plan and the fault risk level, a priority list of trains requiring maintenance that evening is generated. This priority list prioritizes the trains requiring maintenance that evening. Based on this priority list and train maintenance resources, a train maintenance plan is established. This plan includes the allocation of maintenance tasks for each train, the corresponding maintenance team, a list of required parts and tools, and the estimated maintenance time. If the actual maintenance time exceeds the estimated maintenance time, the train maintenance plan is dynamically adjusted.

[0007] By adopting the above technical solution, operational data from multiple train systems is first acquired, providing a rich information foundation for fault risk prediction. This operational data is then input into a fault risk prediction model, and the model's analytical capabilities are used to determine the fault risk level. Different risk levels provide a basis for subsequent maintenance decisions. A priority list is generated by combining the next day's train scheduling plan, enabling the rational arrangement of maintenance sequences. A maintenance plan is established based on the priority list and maintenance resources, ensuring the orderly conduct of maintenance work. When actual maintenance time exceeds the estimate, the plan is dynamically adjusted to ensure efficient maintenance. Overall, this approach achieves precise control over train fault risks and scientific management of maintenance work, improving the accuracy and efficiency of train operation and maintenance.

[0008] Optionally, in some embodiments, the training process of the fault risk prediction model is as follows: acquiring historical fault data, historical operating data, and historical environmental data of the train, wherein the historical fault data includes equipment fault operating parameters, fault records, and maintenance records, the historical operating data includes operating data of the train's power system, suspension system, communication system, and door system, and the historical environmental data includes external temperature, humidity, and track condition data during train operation; labeling the historical operating data and historical environmental data with fault data based on the historical fault data; constructing a historical data training dataset based on the results of the fault data labeling; and training the fault risk prediction model based on the historical data training dataset.

[0009] By adopting the above technical solution, historical train fault, operational, and environmental data are acquired, providing comprehensive data support for model training. Fault data is labeled on the historical operational and environmental data to associate data with faults, constructing a training dataset. Based on this training dataset, the fault risk prediction model is trained, allowing the model to learn the relationship between data and fault risk. In practical applications, the model can accurately predict fault risk levels based on real-time operational data, providing a reliable basis for subsequently generating maintenance priority lists and formulating maintenance plans. This enables train maintenance personnel to anticipate fault risks in advance, take maintenance measures proactively, reduce the probability of train operational failures, and ensure the safety and stability of train operation.

[0010] Optionally, in some embodiments, if the actual maintenance time exceeds the estimated maintenance time, the train maintenance plan is dynamically adjusted. This includes: during the maintenance process, by monitoring the actual maintenance progress of each train in real time, obtaining the actual maintenance time and estimated remaining time of the train maintenance task; if it is detected that the actual maintenance time of a train exceeds the estimated maintenance time, dynamically adjusting the priority of the train maintenance task; postponing the low-priority train maintenance task to the next maintenance time window; prioritizing the processing of the high-priority train maintenance task within the estimated remaining time; recalculating the estimated completion time of the train maintenance task based on the dynamically adjusted priority; and generating a new train maintenance plan based on the estimated completion time and the remaining maintenance resources.

[0011] By adopting the above technical solution, the maintenance progress is monitored in real time, obtaining the actual maintenance time and the estimated remaining time, providing real-time data for adjusting the plan. When the actual maintenance time exceeds the estimate, the priority of maintenance tasks is dynamically adjusted, postponing low-priority tasks and prioritizing high-priority tasks, thus rationally allocating maintenance resources. The estimated completion time is recalculated, and a new maintenance plan for the train is generated based on the remaining maintenance resources, ensuring that maintenance work is carried out according to the new and reasonable arrangement. This avoids the impact of individual train maintenance delays on the overall maintenance progress and subsequent train operation plans, improving the flexibility and adaptability of the maintenance plan, and ensuring the efficient completion of train maintenance work and the normal scheduling and operation of trains.

[0012] Optionally, in some embodiments, a train maintenance plan is established based on the priority list and in conjunction with train maintenance resources. Specifically, this includes: calculating the estimated maintenance time for each train based on the priority list and historical maintenance data; obtaining the allocation of train maintenance resources, including the number of maintenance teams, spare parts inventory, and availability of maintenance tools; and constructing a train maintenance plan based on the allocation of train maintenance resources and the estimated maintenance time for each train.

[0013] By adopting the above technical solutions, the estimated maintenance time is calculated based on the priority list and historical maintenance data, providing a time reference for plan formulation. The allocation of maintenance resources is obtained, clarifying their availability. Based on this, a train maintenance plan is constructed, enabling the rational allocation of maintenance teams, parts, and tools, ensuring sufficient and efficient use of resources required for maintenance work. This makes the train maintenance plan more aligned with actual conditions, avoiding resource waste and shortages, improving the efficiency of maintenance resource utilization, ensuring timely and high-quality completion of maintenance work for each train, and enhancing the overall efficiency of train operation and maintenance.

[0014] Optionally, in some embodiments, a priority list of trains requiring maintenance that evening is generated based on the train scheduling plan for the following day and the fault risk level, specifically including: Based on the next day's train scheduling plan and the fault risk level, the priority of trains requiring maintenance that evening is determined. The next day's train scheduling plan includes the train's operational needs for the following day, and the priority is categorized as high priority, medium priority, and low priority. If the fault risk level is high and a train needs to operate the following day, then the priority is determined as high priority. If the fault risk level is medium risk, or a train needs to operate the following day but is not operating during peak hours, then the priority is determined as medium priority. If the fault risk level is low risk, or a train does not need to operate the following day, then the priority is determined as low priority. Based on this priority, the trains requiring maintenance that evening are sorted to obtain a priority list of trains requiring maintenance that evening.

[0015] By adopting the above technical solution, maintenance priorities are determined based on the next day's train scheduling plan and fault risk level. Trains with high risk and operational needs the following day are classified as high priority and prioritized for maintenance to ensure safe operation the next day; trains with medium risk or off-peak operational needs are classified as medium priority, balancing maintenance and operational needs; and trains with low risk or no operational needs are classified as low priority, with maintenance time allocated accordingly. A list is generated by prioritizing trains, ensuring a clear order for maintenance work, prioritizing critical train maintenance tasks, and rationally allocating maintenance resources. This optimizes maintenance resource allocation and improves the overall efficiency of train operation and maintenance while ensuring train operational safety and efficiency.

[0016] Optionally, in some embodiments, acquiring operational data collected by onboard sensors during the operation of each train specifically includes: dividing train equipment into high-priority components, medium-priority components, and low-priority components. The high-priority components include the train power system and communication system, the medium-priority components include the suspension system, and the low-priority components include the door system; acquiring operational data collected by real-time monitoring sensors of the high-priority components in real time; and periodically acquiring operational data collected by sampling monitoring sensors of the medium-priority and low-priority components, wherein the sampling frequency of the sampling monitoring sensors is set according to the component priority.

[0017] By adopting the above technical solution, train equipment is categorized, and different data acquisition methods are set for components of different priorities. High-priority components acquire data in real time, enabling timely detection of potential faults; medium-priority and low-priority components are sampled periodically, ensuring data validity while reducing data acquisition costs and processing pressure. The sampling frequency is set according to component priority, making data acquisition more targeted and rational. The acquired operational data provides accurate information for fault risk prediction, ensuring that the model can predict fault risks based on effective data, improving the accuracy and timeliness of train fault warnings, and ensuring train operation safety.

[0018] Optionally, in some embodiments, before postponing the low-priority train maintenance task to the next maintenance time window, the method further includes: Based on the operational needs of the next day's train scheduling plan, the maintenance time window is dynamically allocated. Dedicated maintenance time windows are allocated to high-priority trains to meet their maintenance requirements; non-fixed maintenance time windows are allocated to low-priority trains. If the actual maintenance time for a train exceeds the preset time, the maintenance time window for the low-priority train's maintenance task is compressed and reassigned to the high-priority train's maintenance task. If the low-priority train's maintenance task cannot be completed within the current window due to time compression, it is postponed to the next maintenance time window.

[0019] By adopting the above technical solution, maintenance time windows are dynamically allocated according to the next day's train scheduling plan, ensuring that the allocation of time windows matches the train operation needs. Dedicated windows are allocated to high-priority trains to ensure their maintenance needs are met; low-priority trains are allocated non-fixed windows for flexible maintenance scheduling. When the actual maintenance time for a train exceeds the preset time, the window for a low-priority train is compressed and allocated to a high-priority train, ensuring the maintenance progress of critical trains. Low-priority tasks are postponed to avoid maintenance tasks being unable to be completed due to time conflicts. This effectively improves the utilization efficiency of maintenance time resources, ensures the maintenance quality and operational safety of high-priority trains, and ensures that train maintenance work is coordinated with the operation plan.

[0020] In a second aspect, embodiments of this application provide a train operation and maintenance management system, including: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the train operation and maintenance management system to perform the methods described in the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on the train operation and maintenance management system, cause the train operation and maintenance management system to perform the method described in the first aspect or the second aspect, and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product containing instructions that, when the computer program product is run on the train operation and maintenance management system, causes the train operation and maintenance management system to perform the method described in the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0023] Understandably, the train operation and maintenance management method system provided in the second aspect, the storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the method provided in this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By employing multi-level operational data collection, fault risk prediction model analysis, priority list generation combined with scheduling plan, and dynamic adjustment of maintenance plan, the technology effectively solves the technical problems of wasteful allocation of train operation and maintenance resources and inflexibility in response to changes in related technologies. This achieves the technical effects of accurately controlling train fault risks, scientifically managing maintenance work, improving the accuracy and efficiency of train operation and maintenance, and ensuring safe and efficient train operation. 2. By adopting technical means such as real-time monitoring of maintenance progress, dynamic adjustment of maintenance task priorities, recalculation of estimated completion time, and generation of new plans in combination with remaining resources, the technical problems of lack of flexibility in maintenance plans and susceptibility to the impact of individual task delays on overall progress and train scheduling in related technologies have been effectively solved. This has resulted in improved adaptability of maintenance plans, efficient completion of maintenance work, and normal train scheduling and operation, ensuring the orderly progress of train operation and maintenance work. 3. By adopting technical means such as dynamically allocating time windows based on train scheduling plans, setting up dedicated windows for trains of different priorities, and flexibly adjusting window resources, the technical problems of unreasonable maintenance time allocation, inability to guarantee the maintenance needs of critical trains, and low resource utilization efficiency in related technologies have been effectively solved. This has resulted in improving the efficiency of maintenance time resource utilization, ensuring the maintenance quality and operational safety of high-priority trains, ensuring the coordination and consistency between maintenance and operation plans, and optimizing the allocation of train operation and maintenance resources. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 This is a schematic diagram illustrating a train operation and maintenance management method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a train operation and maintenance management method provided in an embodiment of this application; Figure 3 This is another schematic diagram of a train operation and maintenance management method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the physical device structure of a train operation and maintenance management system in the embodiments of this application. Detailed Implementation

[0026] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0027] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0028] It should also be noted that, unless otherwise explicitly specified and limited, the terms "setting" and "connection" in this application embodiment should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components; it can be a wired communication connection or a wireless communication connection. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The embodiments of this application are described in detail below.

[0029] Please refer to Figure 1This is a schematic diagram of a train operation and maintenance management method provided in an embodiment of this application.

[0030] Traditionally, train maintenance primarily involves periodic maintenance. This method involves comprehensive manual inspection and servicing of critical train components, including the power system, suspension system, braking system, and bogies, based on pre-set time cycles (e.g., monthly, quarterly) or mileage intervals (e.g., every 50,000 km, 100,000 km). Maintenance personnel follow established procedures, checking the wear, tightness, and operating parameters of each component. Severely worn parts are replaced, loose parts are tightened, and systems with abnormal operating parameters are adjusted.

[0031] However, this traditional method of scheduled maintenance has shortcomings. Because it relies on fixed times or mileage, it cannot accurately reflect the actual health condition of various train components. Even if some train equipment is in good health and does not require maintenance, it may still be scheduled for inspection and maintenance due to the arrival of the scheduled time or mileage period, undoubtedly wasting maintenance resources such as manpower, materials, and time. Furthermore, when train maintenance time exceeds expectations, the traditional method cannot reallocate maintenance resources in a timely and reasonable manner. This may force the postponement of maintenance work on other trains, creating a situation where maintenance resources are both idle and strained, severely reducing overall maintenance efficiency, and even potentially creating safety hazards due to untimely maintenance.

[0032] Therefore, embodiments of this application provide a train operation and maintenance management method. Figure 1 In the scenario shown, onboard sensors in train 101 collect real-time operational data from multiple systems, including the train's power system, suspension system, and communication system. These onboard sensors include a door system sensor 102, a power system sensor 103, and a suspension system sensor 104. A fault risk prediction model is used to assess the fault risk level of each train. Based on the risk level and the next day's scheduling plan, a maintenance priority list is generated, and maintenance is scheduled as needed to avoid resource waste. Regarding task scheduling, the maintenance sequence is flexibly determined according to the priority list and scheduling plan, ensuring that maintenance work closely aligns with train operation schedules. When actual maintenance time exceeds expectations, the solution can dynamically adjust maintenance task priorities and re-plan based on remaining maintenance resources, ensuring efficient progress of overall maintenance work and effectively addressing the shortcomings of traditional methods. This solution resolves the problems of traditional train maintenance methods and significantly improves the efficiency and quality of train operation and maintenance.

[0033] like Figure 2 The diagram shown is a flowchart illustrating a train operation and maintenance management method provided in an embodiment of this application. The method is described in detail below: 201. Obtain the operating data collected by the on-board sensors of each train during operation. The operating data includes the operating data of the train's power system, suspension system, communication system, and door system.

[0034] The train operation and maintenance management system (hereinafter referred to as the system for convenience) needs to establish a complete and efficient data acquisition mechanism when acquiring operational data collected by onboard sensors during the operation of each train. First, it is essential to ensure the normal operation and proper layout of the onboard sensors. Numerous high-precision sensors need to be installed in various key systems of the train, such as the power system, suspension system, communication system, and door system.

[0035] Taking the powertrain system as an example, key components such as the engine and transmission are equipped with various sensors. Temperature sensors monitor the engine's operating temperature in real time; excessively high temperatures may indicate overheating issues or excessive internal friction, suggesting potential malfunctions. Pressure sensors monitor the pressure in the fuel and hydraulic systems; abnormal pressure could indicate blocked lines or a faulty fuel pump. Speed ​​sensors accurately measure engine speed, crucial for determining the engine's operating status. For instance, a sudden abnormal fluctuation in engine speed during normal train operation could indicate a powertrain malfunction.

[0036] Sensors in the suspension system are equally indispensable. Accelerometers detect vibrations during train operation, and by analyzing the frequency and amplitude of these vibrations, they can determine the effectiveness of the suspension system's damping. Displacement sensors measure changes in the displacement of suspension components; when the displacement exceeds the normal range, it may indicate damage to the springs or shock absorbers in the suspension system. For example, if the suspension system sensors show abnormal displacement after the train has traveled over a bumpy section of road, the suspension system needs to be inspected promptly.

[0037] Sensors in a communication system are primarily used to monitor indicators such as signal strength, stability, and transmission rate. During train operation, the communication system needs to maintain stable communication with the ground control center and other trains. A sudden weakening of signal strength or a drop in transmission rate could affect train operation safety. For example, when a train enters a tunnel, the communication signal may be interfered with. In such cases, sensors can promptly detect signal changes and transmit the data to the operation and maintenance management system.

[0038] The sensors in the door system monitor the door's open / closed status, lock status, and the seal between the door and the vehicle body. Position sensors accurately determine whether the door is fully closed, while pressure sensors detect whether the lock pressure is normal. If a door is not fully closed or the lock becomes loose during train operation, the sensors will immediately issue an alarm and transmit the relevant data to the operation and maintenance management system.

[0039] To ensure data accuracy and timeliness, the train operation and maintenance management system needs to establish a real-time data transmission channel with onboard sensors. Wireless communication technology can be used to quickly and stably transmit the data collected by the sensors to the ground-based operation and maintenance management center. Simultaneously, the system also needs to monitor and verify the transmitted data in real time. If any data anomalies are detected, such as data loss or errors, they must be processed and corrected promptly.

[0040] 202. Input the operating data into the fault risk prediction model to obtain the fault risk level of each train. The fault risk level includes high risk, medium risk and low risk. The fault risk level is used to characterize the health status of the train.

[0041] Fault risk prediction models play a crucial role in train operation and maintenance management. They accurately predict the fault risk level of a train based on its operational data, characterizing the train's health status. These fault risk levels are categorized as high, medium, and low, corresponding to poor, good, and healthy train health statuses, respectively. A high-risk train indicates a serious potential fault in its critical systems or components, with significantly degraded equipment performance, requiring emergency maintenance. A medium-risk train signifies some performance degradation or abnormalities in certain systems or components, necessitating scheduled inspection and maintenance. A low-risk train operates well overall, with stable performance of all systems and components, exhibiting only minor issues that do not affect normal operation; routine inspections and maintenance, along with continuous monitoring of its status, are sufficient.

[0042] Before using the model, it needs to be thoroughly trained. First, historical fault data, historical operating data, and historical environmental data of the train must be acquired. Historical fault data is a valuable resource, containing equipment fault operating parameters, fault records, and maintenance records. For example, past fault records may reveal that a particular model of train is prone to failure in a specific component of its power system under certain operating conditions. Analyzing these fault records can reveal the causes, frequency, and scope of the faults. Maintenance records provide the fault repair process and methods, offering significant reference value for subsequent fault handling.

[0043] Historical operational data encompasses the train's power system, suspension system, communication system, and door system. This data reflects the train's operating status under different conditions. For example, power system operational data may include parameters such as engine power, torque, and speed. Analyzing these parameters reveals the engine's efficiency and health status.

[0044] Historical environmental data includes external temperature, humidity, and track condition data during train operation. Environmental factors have a significant impact on train operation. In high-temperature environments, the train's power system and train operation and maintenance management system may experience overheating failures; in humid environments, the electrical system is prone to short-circuit failures. Track condition data reflects information such as track smoothness and gradient; poor track conditions may lead to accelerated wear on the train's suspension system and wheels.

[0045] Based on historical fault data, fault data is labeled on historical operating data and historical environmental data. For example, if a fault is caused by overheating of a component in the power system, then the corresponding historical operating data needs to be labeled with the temperature changes of that component before and after the fault occurred. In this way, historical data can be correlated with fault information, providing valuable samples for subsequent model training.

[0046] Based on the labeled fault data, a historical data training dataset was constructed. This dataset contains a large number of samples, each including train operation data, environmental data, and a corresponding fault label. This dataset was divided into a training set and a test set. The training set was used to train the fault risk prediction model, and the test set was used to evaluate the model's performance.

[0047] When training a fault risk prediction model, machine learning or deep learning methods can be employed. For example, decision tree algorithms can be used to classify train fault risks based on different features; neural network algorithms can automatically learn complex patterns and rules in the data. By continuously adjusting the model's parameters, the accuracy and recall on the training set can be optimized.

[0048] The acquired train operation data is input into a trained fault risk prediction model. Based on the characteristics and patterns of the data, the model calculates the fault risk level for each train, categorized as high, medium, or low risk. This fault risk level characterizes the train's health status. For example, if the power system operation data of a train shows excessively high engine temperature and reduced power, while the communication system signal strength is unstable, the model might classify the train as high-risk. When a train is at a high-risk level, its health status is poor.

[0049] 203. Based on the train scheduling plan for the next day and the aforementioned fault risk level, generate a priority list of trains that need to be maintained that night, wherein the priority list includes the priority order of the trains that need to be maintained that night.

[0050] When generating a priority list of trains requiring maintenance that evening, the train operation and maintenance management system needs to comprehensively consider the train scheduling plan for the following day and the train's fault risk level. The train scheduling plan for the following day specifies the operational tasks for each train, including information such as operating time, route, and stops.

[0051] Trains with a high-risk fault level require priority maintenance. These trains have significant potential for malfunctions, and failure to maintain them promptly could lead to breakdowns the following day, disrupting normal train operations and even endangering passenger safety. For example, if a train's power system exhibits a serious potential fault, such as engine oil leaks or abnormal transmission noises, maintenance should be prioritized even if the train's scheduling load is light the following day.

[0052] For trains with a medium-risk fault level, a comprehensive assessment is required based on the next day's scheduling plan. If the train's operational tasks the following day are significant, such as handling peak-hour transport or running on critical lines, then maintenance can be prioritized. Conversely, if the train's operational tasks the following day are less demanding, the maintenance time can be appropriately postponed. For example, if a train's door system has a potential fault, such as occasional door lock loosening, and the train's operational tasks the following day involve running on a less busy line during off-peak hours, then the maintenance priority for that train can be appropriately reduced.

[0053] For trains with a low-risk fault level, scheduling can generally be based on available time in the dispatching plan. If the dispatching plan for the next day is relatively relaxed, with ample free time, preventative maintenance can be performed on these low-risk trains to reduce the probability of future faults. For example, if the sensors in a train's suspension system show slight vibration abnormalities that do not affect the train's normal operation, and the train has a long period of idle time the next day, then its suspension system can be scheduled for inspection and maintenance.

[0054] When generating the priority list, the limitations of maintenance resources must also be considered. Maintenance resources include maintenance personnel, equipment, and materials. If maintenance resources are limited, priority should be given to ensuring the maintenance needs of high-risk trains. For example, if only a limited number of maintenance personnel and equipment are available that night, these resources should be concentrated on the maintenance of high-risk trains to ensure their safe operation the following day.

[0055] Meanwhile, the train operation and maintenance management system can also optimize the priority list by combining historical maintenance data and repair experience. For example, if a train has frequently experienced the same type of fault in the past, its maintenance priority can be appropriately increased even if the current fault risk level is medium risk.

[0056] The final priority list should clearly indicate the priority order of the trains that need maintenance that night, so that maintenance personnel can carry out maintenance work in sequence, improve maintenance efficiency, and ensure the normal operation of trains the next day.

[0057] 204. Based on the priority list and in conjunction with train maintenance resources, establish a train maintenance plan, which includes the allocation of maintenance tasks for each train, the corresponding maintenance team, the list of required parts and tools, and the estimated maintenance time.

[0058] The train operation and maintenance management system is based on a priority list and fully integrates train maintenance resources to construct a train maintenance plan. The train maintenance plan covers key information such as the allocation of maintenance tasks for each train, the corresponding maintenance team, the list of required parts and tools, and the estimated maintenance time.

[0059] Maintenance Task Allocation: The system allocates maintenance tasks based on the train's fault risk level and specific fault details in the priority list. For high-risk trains, faults can directly impact operational safety, so maintenance tasks are typically complex and critical. For example, if a high-risk train's engine experiences a serious malfunction, maintenance tasks might include complete engine disassembly for inspection, repair, or replacement of damaged parts. For medium-risk trains, tasks may be simpler, such as signal debugging of the communication system or checking the door lock sealing. Maintenance tasks for low-risk trains are mostly preventative checks, such as checking for loose bolts in the suspension system or testing sensor accuracy.

[0060] Corresponding maintenance teams: The system assigns maintenance teams based on the difficulty and professional requirements of the maintenance task. For high-tech and complex maintenance tasks such as those involving the power system, experienced and highly skilled power system maintenance teams are assigned. These team members undergo specialized training and are familiar with the maintenance procedures and technical requirements of power components such as engines and transmissions. For communication system maintenance, specialized teams familiar with communication technologies and equipment are assigned. For example, in one maintenance operation, a train's communication system experienced a signal interruption. The communication maintenance team quickly arrived on-site and, leveraging their knowledge of the communication equipment and specialized tools, quickly located and resolved the fault. Simultaneously, the system also considers the workload and current working status of team members, rationally allocating tasks to prevent any team from becoming overworked.

[0061] Required Parts and Tools List: The system generates a list of required parts and tools for each train's maintenance task. For high-risk trains undergoing power system maintenance, components such as engine pistons, crankshafts, and spark plugs may be needed, along with specialized disassembly tools and torque wrenches. The system checks the inventory status of these parts through the inventory management module; if inventory is insufficient, it triggers the procurement process promptly. For medium- and low-risk train maintenance tasks, fewer parts and tools are required. For example, door system maintenance may only require some sealing strips, door lock parts, and simple screwdrivers. When generating the list, the system ensures its accuracy and completeness to avoid delays in maintenance progress due to missing parts or tools.

[0062] Estimated Maintenance Time: The system estimates the estimated maintenance time based on the complexity of the maintenance task, the experience and skill level of the maintenance team, and the availability of necessary parts and tools. For major overhauls of the power system of high-risk trains, due to the extensive disassembly, inspection, and installation work involved, the estimated maintenance time may be long, potentially several hours or even days. For preventative checks on low-risk trains, the estimated maintenance time may be short, possibly only tens of minutes to several hours. The system references historical maintenance data and considers the current situation to estimate the maintenance time as accurately as possible. For example, if a train's power system failure is similar to a previous similar failure, the system will estimate the estimated maintenance time based on the previous maintenance time, taking into account possible changing factors.

[0063] 205. Determine whether the actual maintenance time exceeds the estimated maintenance time.

[0064] 206. If the actual maintenance time exceeds the estimated maintenance time, the train maintenance plan shall be dynamically adjusted.

[0065] During train maintenance, it is possible for the actual maintenance time to exceed the estimated maintenance time. The train operation and maintenance management system needs to have the ability to dynamically adjust the train maintenance plan to ensure that maintenance work can be carried out smoothly and without affecting the normal operation of trains the next day.

[0066] When actual maintenance time exceeds the estimated maintenance time, the system will first analyze the reasons for the delay. There are many possible reasons, such as the actual fault being more complex than anticipated. For example, during the maintenance of a train's power system, it was initially expected that only a small component of the engine was damaged and could be replaced to solve the problem. However, during actual disassembly, multiple related components were found to be worn and damaged, requiring further repair and replacement work. It could also be due to delays in parts supply. The required parts were expected to arrive on time, but due to supplier issues or logistical delays, the parts failed to arrive on time, thus affecting the maintenance schedule. Additionally, technical difficulties or insufficient personnel within the maintenance team can also lead to extended maintenance time.

[0067] The system will assess the impact of the actual maintenance time exceeding the permitted limit on subsequent maintenance tasks and the train scheduling plan for the following day. If only the maintenance time of a low-risk train exceeds the limit, and the subsequent maintenance tasks and the train scheduling plan for the following day are relatively relaxed, then the overall impact may be small. However, if the maintenance time of a high-risk train exceeds the permitted limit, and there are other important train maintenance tasks to be performed later, or the train scheduling plan for the following day is very tight, then it may have a significant impact on the normal operation of trains.

[0068] Based on the analyzed causes and assessed impacts, the system dynamically adjusts train maintenance plans. If the issue is a delay in parts supply, the system promptly communicates with the supplier to understand the estimated arrival time of the parts and reschedules the maintenance tasks accordingly. If the problem stems from technical difficulties faced by the maintenance team, the system arranges for technical experts to provide remote guidance or on-site assistance. Simultaneously, the system considers whether personnel can be drawn from other maintenance teams to expedite the maintenance process. For subsequent maintenance tasks, the system readjusts the maintenance sequence and schedule based on remaining maintenance resources and time. For example, if the maintenance time for a particular train significantly exceeds the allotted time, potentially jeopardizing the maintenance of a subsequent low-risk train, the system considers postponing the maintenance of that low-risk train to a more suitable time or performing a simple inspection and repair to ensure its safe operation the following day.

[0069] Meanwhile, during the dynamic adjustment of train maintenance plans, the system promptly communicates and coordinates with relevant maintenance teams, dispatching departments, and other stakeholders. The system notifies the maintenance team of the adjusted maintenance plan, ensuring they understand the new task assignments and time requirements. Simultaneously, the system informs the dispatching department of the maintenance plan adjustments, enabling them to adjust the train dispatching schedule for the following day accordingly. For example, the system might inform the dispatching department that the maintenance time for a certain train has been extended, potentially preventing it from being put into operation on time the following day. Based on this information, the dispatching department can reschedule other trains to undertake the corresponding transportation tasks, ensuring the normal order of train operations. Through effective communication and coordination, the system can minimize the impact of actual maintenance time exceeding the scheduled maintenance time on train operations.

[0070] 207. If the actual maintenance time of a train is detected to exceed the estimated maintenance time, the maintenance resources of the current train will be reallocated.

[0071] The system will continue to monitor the maintenance progress of the train in real time to ensure that the maintenance work can be completed smoothly as planned. The system will continuously collect various data at the maintenance site, analyze the work efficiency of the maintenance team and the completion status of maintenance tasks, so as to promptly identify potential problems and take corresponding measures.

[0072] During maintenance, the system will optimize and adjust subsequent maintenance operations based on the actual progress. When the train operation and maintenance management system detects that the actual maintenance time of a train does not exceed the estimated maintenance time, the system will assess the current maintenance resources and the status of other trains awaiting maintenance to determine whether the pre-released maintenance resources can be allocated to other trains in need to improve overall maintenance efficiency.

[0073] The train operation and maintenance management method provided in the above embodiments achieves efficient management of train operation and maintenance through multi-step collaboration. First, onboard sensors comprehensively collect operational data from key systems such as train power and suspension, providing a precise information foundation for subsequent analysis. Next, a fault risk prediction model trained on historical data, combined with current operational data, accurately determines the fault risk level and provides early warnings of potential problems. Then, by integrating the next day's scheduling plan with the fault risk level, a maintenance priority list is generated, and maintenance resources are scientifically allocated. Based on the priority list and maintenance resources, a detailed maintenance plan covering tasks, teams, materials, and time is formulated to ensure the orderly progress of maintenance work. Finally, when the actual maintenance time exceeds expectations, the system dynamically adjusts the plan to respond promptly to emergencies. This effectively improves the safety and reliability of train operation, reduces safety accidents caused by faults, optimizes maintenance resource allocation, avoids resource waste, ensures stable train transportation order, improves operation and maintenance efficiency, reduces operating costs, and realizes intelligent and refined train operation and maintenance management, providing a solid guarantee for the efficient and safe operation of railway transportation.

[0074] In the above embodiments, the train operation and maintenance management system processes the received operational data and constructs a train maintenance plan. In practical applications, resource waste may occur during the collection of operational data from train equipment in some scenarios. Therefore, in some embodiments, during the collection of operational data from train equipment, the train equipment can be prioritized and data collected in stages according to priority. Additionally, in some scenarios, when the train operation and maintenance management system dynamically adjusts the priority of train maintenance tasks, it can use dynamically allocated maintenance time windows and generate a new train maintenance plan based on the adjustment results.

[0075] like Figure 3 The diagram shown is another flowchart illustrating a train operation and maintenance management method provided in an embodiment of this application. The method is described in detail below: 301. Train equipment is divided into high-priority components, medium-priority components and low-priority components. The high-priority components include the train power system and the communication system. The medium-priority components include the suspension system. The low-priority components include the door system.

[0076] When prioritizing train equipment, the train operation and maintenance management system needs to comprehensively consider multiple factors to ensure that resources are allocated rationally and utilized efficiently. The system first analyzes the impact of each component on train operation safety, performance, and operational efficiency.

[0077] The train's power system is the core of train operation, providing the power for forward movement. If the power system malfunctions, the train will be unable to run normally, potentially leading to service disruptions and affecting the travel of numerous passengers. For example, engine failure could cause a train to suddenly lose power during operation, creating serious safety hazards and operational chaos. The communication system is responsible for information transmission between the train and the ground control center, as well as other trains, and is crucial for train scheduling, safe operation, and emergency response. If the communication system malfunctions, the train may be unable to receive dispatch instructions in a timely manner or maintain a safe distance from other trains, increasing the risk of collisions. Therefore, the power system and communication system are classified as high-priority components.

[0078] The suspension system supports the train body, cushioning vibrations and impacts generated during operation and ensuring smooth train operation. While a suspension system malfunction won't immediately render the train inoperable, it will affect passenger comfort, and prolonged malfunctions can lead to damage to other body components, increasing maintenance costs. Therefore, the suspension system is classified as a medium-priority component.

[0079] The train door system is primarily used for passenger boarding and alighting. While its proper functioning is crucial for passenger safety and convenience, the train can still continue operating even if a malfunction occurs, although it will affect passenger boarding and alighting efficiency. Therefore, the door system is classified as a low-priority component.

[0080] After prioritizing components, the system establishes a corresponding database to record the priority information of each component. Simultaneously, the system formulates different maintenance strategies and resource allocation schemes based on component priorities to ensure that high-priority components receive priority attention and timely maintenance.

[0081] 302. Real-time acquisition of operational data collected by the real-time monitoring sensors of the high-priority components, including operational data of the train power system, suspension system, communication system, and door system.

[0082] For high-priority train power and communication systems, the train operation and maintenance management system acquires their operational data through real-time monitoring. The system establishes stable communication connections with real-time monitoring sensors installed on these components to ensure that data is transmitted to the system in a timely and accurate manner.

[0083] In terms of the powertrain, sensors monitor multiple key engine parameters in real time. Temperature sensors measure engine coolant and oil temperatures. Overheating coolant may indicate a malfunction in the engine's cooling system, such as a clogged radiator or a damaged water pump. Pressure sensors monitor fuel and oil pressure. Abnormal fuel pressure can lead to insufficient engine power or failure to start, while low oil pressure can result in inadequate lubrication of engine components, accelerating wear. Speed ​​sensors provide real-time feedback on engine speed, helping the system determine if the engine is operating normally. For example, if the engine speed suddenly fluctuates abnormally during normal train operation, the system can detect this promptly and issue an alarm.

[0084] The communication system's real-time monitoring sensors monitor parameters such as signal strength, signal stability, and data transmission rate. Signal strength directly affects the communication quality between the train and the outside world; low signal strength may lead to communication interruptions or data loss. Signal stability reflects the fluctuation of the communication signal; unstable signals may affect the accurate reception and transmission of dispatch instructions. Data transmission rate determines the efficiency of information transmission, which is particularly important for dispatch and safety information with high real-time requirements. For example, when a train enters areas prone to signal interference, such as tunnels, the system can promptly detect signal changes through real-time monitoring data and take corresponding measures, such as switching communication frequency bands or adding signal enhancement equipment.

[0085] The system analyzes and processes the acquired operational data in real time. Upon detecting any data anomalies, the system immediately issues an alarm and notifies relevant maintenance personnel for handling. Simultaneously, the system records this abnormal data for subsequent fault diagnosis and analysis. By monitoring the operational data of high-priority components in real time, the system can promptly identify potential faults and take proactive maintenance measures to ensure the safe and reliable operation of the train.

[0086] 303. Periodically acquire the operating data collected by the sampling monitoring sensors of the medium-priority components and the low-priority components, wherein the sampling frequency of the sampling monitoring sensors is set according to the component priority.

[0087] For medium-priority suspension systems and low-priority door systems, the train operation and maintenance management system acquires operational data through periodic sampling and monitoring. The system sets different sampling frequencies for the monitoring sensors based on the priority of the components.

[0088] For the suspension system, although its impact on train operation is relatively small, long-term malfunctions can lead to damage to car body components. Therefore, the system is set to a relatively moderate data collection frequency. For example, at regular time intervals (such as every few hours or a day), sampling and monitoring sensors collect vibration and displacement data from the suspension system. Vibration data reflects the shock absorption effect of the suspension system; abnormal vibrations may indicate damage or loosening of suspension components. Displacement data monitors changes in the position of suspension components to determine whether they are in normal working condition. The system analyzes the collected data, and if it finds that the data exceeds the normal range, it further assesses the severity of the malfunction and arranges maintenance work accordingly.

[0089] For the train door system, since its direct impact on train operation safety is relatively small, the system will be set to a relatively low data collection frequency. For example, data will be collected once a day or every few days. Sampling and monitoring sensors will collect data such as the opening and closing status of the doors and the locking pressure of the door locks. The opening and closing status of the doors can determine whether the doors can be opened and closed normally, while the locking pressure of the door locks can detect the security of the door locks. If any potential faults are found in the door system, such as loose door locks or difficulty in opening and closing, the system will arrange maintenance personnel to inspect and repair them at an appropriate time.

[0090] The system will dynamically adjust the sampling frequency of the monitoring sensors based on actual operating conditions and maintenance experience to ensure that the system's operating efficiency and resource utilization are improved while maintaining the monitoring effect.

[0091] The system will match the real-time assessed fault risk level with the rules in the adjustment rule base. For different fault risk levels and operational data characteristics, corresponding data collection frequency adjustment strategies are pre-defined in the rule base.

[0092] The sampling frequency adjustment strategy includes: Adjustments based on fault risk level: For medium-risk components, such as the suspension system with minor wear, the sampling frequency is increased from once a day to once an hour to balance monitoring needs and resource consumption; for low-risk components, such as the door system with aging sealing strips, the sampling frequency is reduced from once an hour to once a day to reduce system processing pressure.

[0093] Adjustments are made based on train operation scenarios: when the train is in special operating phases such as starting and braking, the power system and suspension system experience large changes in force, and the corresponding sensor acquisition frequency is increased by 50% to 100%; when the train is running in areas prone to signal interference such as tunnels and mountainous areas, or encounters severe weather such as rainstorms and sandstorms, the acquisition frequency of the communication system and environmental monitoring sensors is automatically doubled to ensure the acquisition of critical data.

[0094] Adjustments based on the sensor's own condition: If the sensor's usage time is close to its design life, or if performance degradation such as signal transmission delay or abnormal data fluctuation is detected, increase its acquisition frequency by 20% - 30% to strengthen the monitoring of the sensor's output data; after the sensor has been calibrated or repaired, gradually adjust it back to the normal acquisition frequency according to its performance recovery.

[0095] Adjustments based on historical data and experience: The system analyzes historical fault data. If a specific data change pattern is found before a certain type of fault occurs, the relevant sensor collection frequency will be automatically increased when a similar pattern reappears. In addition, the collection frequency is customized and dynamically optimized according to the operation and maintenance experience of different vehicle models and lines. For example, the collection frequency of key components of older vehicle models is appropriately increased, and the collection frequency of environmental and track monitoring sensors is increased in the early stage of operation of new lines.

[0096] Once a decision is made, the system immediately sends adjustment commands to the relevant sampling and monitoring sensors, which will then collect data at the new sampling frequency. During the adjustment process, the system continuously monitors the quality of the data collected by the sensors and the system's operational status.

[0097] The effectiveness of the adjustment is verified by comparing the data characteristics and fault risk assessment results before and after the adjustment. If the adjusted data can more accurately reflect the operating status of the components and the fault risk assessment is more accurate, it indicates that the adjustment strategy is effective; otherwise, the system will re-analyze the reasons, optimize and adjust the adjustment rules, forming a closed-loop feedback mechanism to ensure that the system can continuously and stably adjust the acquisition frequency to adapt to changes in the train's operating status.

[0098] 304. Input the operating data into the fault risk prediction model to obtain the fault risk level of each train, wherein the fault risk level includes high risk, medium risk and low risk.

[0099] The train operation and maintenance management system will systematically input high, medium, and low priority component operation data collected at different frequencies into the fault risk prediction model. When the system inputs real-time and periodically collected operation data into the model, the model will conduct in-depth analysis of the data based on preset algorithms and training results.

[0100] For real-time data of high-priority components, such as abnormal high temperature and speed fluctuations of the engine in the power system, and sudden drops in signal strength of the communication system, the model will focus on capturing the abnormal features in these data and compare them with historical similar fault data.

[0101] For periodically sampled data of medium- and low-priority components, the model analyzes the trends in data changes. Taking the suspension system as an example, if several consecutive vibration data collections show that the vibration amplitude gradually increases and exceeds the normal fluctuation range, the model will incorporate this trend into the evaluation, judging that the suspension system may have potential faults such as component wear or loosening. If the door system shows unstable door lock locking pressure in multiple data collections, the model will also consider its fault risk.

[0102] Ultimately, the fault risk prediction model outputs the fault risk level for each train, categorized as high, medium, or low risk. A high-risk level indicates a serious potential fault that, if not addressed promptly, could lead to train service disruptions or even accidents. A medium-risk level indicates some degree of abnormality in train components, requiring maintenance at an appropriate time. A low-risk level indicates that the train's overall operating condition is good, but continuous monitoring is still necessary. In this way, the system can predict the train's health status in advance, providing a scientific basis for subsequent maintenance decisions.

[0103] 305. Based on the train scheduling plan for the next day and the aforementioned fault risk level, determine the priority of trains that need maintenance that night. The train scheduling plan for the next day includes the train operation requirements for the next day, and the priority includes high priority, medium priority, and low priority.

[0104] After obtaining the fault risk level of each train from the fault risk prediction model, the train operation and maintenance management system will combine it with the next day's train scheduling plan to comprehensively determine the priority of trains requiring maintenance that evening. The next day's train scheduling plan includes detailed information on the train's operational needs for the following day, such as operating time, route, and whether it undertakes peak-hour transportation tasks.

[0105] The system first performs a correlation analysis between the fault risk level of each train and the operational needs of the following day. Trains with a high fault risk level have a high maintenance urgency regardless of their operational needs the following day. However, when combined with the dispatch plan, if the train has operational needs the following day, especially if it undertakes transportation tasks on important lines or during peak hours, its maintenance priority will be further increased.

[0106] For trains with a medium-risk fault level, the system will conduct a differentiated assessment based on the operational needs of the following day. If the train has operational needs the following day, but is not undertaking peak-hour transportation tasks, or the importance of the operating line is relatively low, its maintenance priority will be determined as medium priority.

[0107] For trains with a low fault risk level, the system will also consider the operational needs of the following day. If the train has no operational needs the following day, it means there is more time for maintenance, and its maintenance priority is naturally lower; if there are operational needs the following day, but considering the low fault risk and minimal impact on operations, its maintenance priority will also be determined as low.

[0108] By combining the fault risk level with the train scheduling plan for the next day, the system can comprehensively and scientifically determine the maintenance priority of each train that night, ensuring that maintenance resources can be allocated reasonably, thus guaranteeing both train operation safety and operational efficiency.

[0109] 306. If the fault risk level is high risk and there is a train required to run the next day, then the priority is determined to be high priority.

[0110] 307. If the fault risk level is medium risk, or if there is a demand for trains on the next day but it is not peak time, then the priority is determined to be medium priority.

[0111] 308. If the fault risk level is low risk, or there are no trains required to run the next day, then the priority is determined to be low priority.

[0112] 309. Sort the trains that need maintenance that night according to the priority to obtain a priority list of the trains that need maintenance that night.

[0113] The system first groups all trains awaiting maintenance according to priority categories, ensuring that high-priority trains are grouped together, followed by medium-priority trains, and low-priority trains are placed last. Within train groups of the same priority, the system further refines the sorting rules to achieve optimal allocation of maintenance resources.

[0114] For high-priority trains, the system prioritizes the urgency of the fault risk. The system also considers historical fault data; if a train frequently experiences similar high-risk faults recently, its ranking will be appropriately increased to avoid potential risks from recurring faults.

[0115] Within medium-priority train sets, the system prioritizes trains based on the urgency of their operational needs for the following day. Furthermore, the system considers the progression of faults; if monitoring data indicates a rapidly deteriorating condition in a particular train, it will be prioritized higher.

[0116] For low-priority trains, the system will take into account both the train's idle time and the availability of maintenance resources. If a low-priority train will be out of service for an extended period the following day, while another train still has short-distance service tasks the next day, the system will prioritize the latter for maintenance to ensure that it can successfully complete its tasks for the following day.

[0117] After sorting, the system generates a priority list of trains that need maintenance that night. The list clearly marks the number, priority level, and sorting basis of each train, providing clear guidance for the formulation of subsequent maintenance plans.

[0118] 310. Calculate the estimated maintenance time for each train based on the priority list and historical maintenance data.

[0119] The allocation of train maintenance resources is obtained, including the number of maintenance teams, spare parts inventory, and availability of maintenance tools.

[0120] Based on the allocation of train maintenance resources and the estimated maintenance time for each train, a train maintenance plan is constructed. The train maintenance plan includes the allocation of maintenance tasks for each train, the corresponding maintenance team, the list of required parts and tools, and the estimated maintenance time.

[0121] After receiving the priority list, the train operation and maintenance management system first calculates the estimated maintenance time for each train based on the train information in the list and historical maintenance data. The system retrieves historical completion time data for similar faults or maintenance tasks, taking into account factors such as the complexity of the fault and the skill level of the maintenance team to make a comprehensive estimate. For example, for trains with high-risk power system faults, the system will search for historical maintenance records of similar engine faults. If the average maintenance time for similar faults in the past was 6 hours, the estimated maintenance time will be adjusted appropriately based on the specific circumstances of the current fault, such as the number of damaged parts and the difficulty of repair, possibly resulting in an estimated maintenance time of 7-8 hours.

[0122] Next, the system obtains real-time information on the allocation of train maintenance resources. Regarding maintenance teams, the system records the number of currently available teams, the area of ​​expertise for each team, and their workload. Regarding spare parts inventory, the system uses the inventory management module to query the quantity of spare parts needed for each train's maintenance. For example, if a train's power system maintenance requires a specific type of piston, and the inventory shows only 2, while the maintenance task is expected to require 3, the system will promptly mark the spare parts shortage. Regarding the availability of maintenance tools, the system confirms whether the required tools are in place and functioning properly. For example, for maintenance tasks requiring a high-precision torque wrench, the system will check whether the tool is already in use by other maintenance tasks.

[0123] Based on the above information, the system begins to construct a train maintenance plan. In assigning maintenance tasks, high-priority trains are prioritized for allocation to the most suitable maintenance teams. For example, trains with high-risk power system failures are assigned to experienced power system maintenance teams, while those with high-risk communication system failures are assigned to communication-specialized maintenance teams. After determining the maintenance teams, the system generates a list of required parts and tools. For parts with insufficient inventory, the system automatically triggers a procurement request process and coordinates with suppliers for expedited delivery. The system incorporates the estimated maintenance time for each train into the plan, clearly marking the start and estimated completion times for each train's maintenance, forming a complete train maintenance plan that includes maintenance task allocation, maintenance teams, a list of parts and tools, and estimated maintenance time, ensuring that maintenance work is carried out in an orderly and efficient manner.

[0124] 311. During the maintenance process, the actual maintenance time and estimated remaining time of the train maintenance task are obtained by monitoring the actual maintenance progress of each train in real time.

[0125] During train maintenance, the train operation and maintenance management system monitors the maintenance progress of each train in real time through sensors, equipment terminals deployed at the maintenance site, and real-time feedback from maintenance personnel. The system starts timing at the beginning of maintenance work, recording the actual maintenance time for each train from the start of maintenance to the current moment. Simultaneously, the system dynamically calculates the estimated remaining time based on the amount of maintenance work completed, the efficiency of the maintenance team, and the difficulty of the remaining maintenance tasks.

[0126] The system displays the actual maintenance time and estimated remaining time in real time on the operation and maintenance management interface, allowing managers and maintenance teams to monitor maintenance progress at any time. For trains with delayed maintenance progress, the system will automatically issue an alert and analyze the reasons for the delay.

[0127] 312. Determine whether the actual maintenance time of the train exceeds the estimated maintenance time.

[0128] During train maintenance, the train operation and maintenance management system continuously compares the actual maintenance time with the estimated maintenance time for each train. The system accurately records the actual time consumed for each train since maintenance work began by collecting various data from the maintenance site in real time, including maintenance team work records, equipment operating status information, and parts replacement progress.

[0129] During the judgment process, the system considers the impact of various factors on maintenance time. For complex maintenance tasks, the system allows for a certain flexible time range to avoid misjudging overtime due to minor time deviations. For example, for major overhauls of the power system, considering the possibility of unforeseen faults, the system allows the actual maintenance time to fluctuate within 10% above and below the estimated time. If the actual maintenance time does not exceed this fluctuation range, it is judged as normal. Simultaneously, the system combines historical maintenance data to analyze the time fluctuation patterns of similar maintenance tasks, providing a reference for the current judgment. Through this comprehensive and detailed judgment mechanism, the system can accurately identify trains whose actual maintenance time exceeds the estimated maintenance time, providing accurate information support for subsequent response measures.

[0130] 313. If the actual maintenance time of a train is detected to exceed the estimated maintenance time, the maintenance resources of the current train will be reallocated.

[0131] Step 313 and this application Figure 2 Step 207 provided in the Chinese embodiment is similar and can be referred to the description in the relevant steps, which will not be repeated here.

[0132] 314. If the actual maintenance time of a train exceeds the estimated maintenance time, the priority of the train maintenance task shall be dynamically adjusted.

[0133] When the train operation and maintenance management system detects that the actual maintenance time of a train exceeds the estimated maintenance time, it will immediately activate the dynamic priority adjustment mechanism. The system will first conduct an in-depth analysis of the reasons for the maintenance timeout. By retrieving detailed data from the maintenance site, including the actual fault situation, parts supply status, and maintenance team operation records, it will determine whether the cause is due to the complexity of the fault exceeding expectations, delayed parts delivery, or technical problems or insufficient personnel in the maintenance team.

[0134] If the train has an important operational task the following day, and the malfunction may affect operational safety, the system will prioritize its maintenance task to the highest level.

[0135] If the train's workload is relatively light the following day, the system will adjust its priority appropriately after taking into account the situation of other trains that need maintenance.

[0136] 315. Based on the operational requirements of the train scheduling plan for the next day, dynamically allocate the maintenance time window: Dedicated maintenance time windows are allocated to the high-priority trains to meet their maintenance needs.

[0137] Non-fixed maintenance time windows are assigned to the low-priority trains.

[0138] The maintenance time window of the low-priority train maintenance task is compressed and assigned to the high-priority train maintenance task.

[0139] If a low-priority train maintenance task cannot be completed in the current window due to time compression, it will be postponed to the next maintenance time window.

[0140] The train operation and maintenance management system uses the next day's train scheduling plan as its core basis and combines it with the adjusted priority of train maintenance tasks to dynamically allocate maintenance time windows in a refined manner. The system first analyzes the operational requirements in the next day's train scheduling plan, including key information such as train departure time, operating route, frequency, and whether it undertakes peak-hour transportation. At the same time, it takes into account the current maintenance progress and remaining maintenance tasks of each train to formulate differentiated time window allocation strategies.

[0141] For high-priority trains, the system allocates dedicated maintenance time windows to meet their maintenance needs. These trains typically have serious potential for malfunctions and need to perform important transportation tasks the following day, requiring extremely high timeliness in maintenance. For example, for a train with a high-risk power system malfunction that needs to handle mainline transportation during the morning rush hour the following day, the system will allocate continuous, complete, and undisturbed maintenance periods to ensure that the maintenance team can concentrate on completing complex repair work. Assuming that the train's maintenance is expected to take 8 hours, the system will allocate a dedicated window from 22:00 to 06:00 during the nighttime maintenance period based on the availability of maintenance resources, and coordinate with other maintenance tasks to ensure that its maintenance progress is not affected.

[0142] For low-priority trains, the system allocates non-fixed maintenance time windows. These trains have a lower risk of failure or lighter operational tasks the following day, and the system will schedule their maintenance during breaks between high-priority tasks or non-critical periods. For example, if a low-priority train only undertakes short-distance transportation on suburban lines the following day, the system may schedule its maintenance between 4:00 AM and 6:00 AM after the maintenance of high-priority trains is completed, or adjust flexibly according to the actual situation. If unforeseen circumstances cause high-priority tasks to occupy more time, the maintenance window for low-priority trains will be further compressed or postponed.

[0143] When it's necessary to ensure the maintenance progress of high-priority trains, the system will compress the maintenance time window for low-priority trains and reallocate the freed-up time resources to high-priority tasks. For example, if a 3-hour maintenance window was originally allocated to a low-priority train, but the maintenance difficulty of a high-priority train exceeds expectations, the system will compress its window to 1.5 hours and allocate the saved 1.5 hours to the high-priority train's maintenance time slot. If the maintenance task of a low-priority train cannot be completed within the current window due to time compression, it will automatically be postponed to the next available maintenance time window. The system will monitor the maintenance progress and time window usage of each train in real time, automatically calculate the remaining available time using algorithms, and update the time window allocation scheme to ensure maximum efficiency in the utilization of maintenance resources.

[0144] 316. Within the estimated remaining time, prioritize the high-priority train maintenance tasks.

[0145] After dynamically adjusting maintenance time windows, the train operation and maintenance management system strictly adheres to the "highest priority, first" principle, allocating resources to prioritize high-priority train maintenance tasks within the estimated remaining maintenance time. Based on the priority list and the latest time window allocation results, the system filters out all high-priority trains and their corresponding maintenance tasks, and, combined with the real-time status of maintenance teams, parts, and tools, formulates detailed execution plans. In terms of resource allocation, the system prioritizes ensuring the manpower and material resources needed for high-priority tasks.

[0146] During the processing of high-priority tasks, the system continuously assesses their impact on subsequent maintenance plans. If a high-priority task is completed ahead of schedule, the system will adjust the maintenance plan in a timely manner based on the current resource status and the priority of other trains, allocating the freed resources to lower-priority tasks. If a task is delayed, the system will recalculate the availability of the remaining time window and assess whether it is necessary to adjust the maintenance time of lower-priority tasks again to ensure that high-priority trains can complete maintenance on time and be put into operation the next day.

[0147] 317. Based on the dynamically adjusted priority of the train maintenance task, recalculate the estimated completion time of the train maintenance task.

[0148] After adjusting maintenance task priorities and reallocating resources, the train operation and maintenance management system will recalculate the estimated completion time for each train's maintenance tasks based on the new priority order and actual maintenance status. The system first retrieves the current completed maintenance progress and remaining maintenance task list for each train, and then performs an accurate estimate by combining the adjusted maintenance team configuration, parts supply status, and time window arrangements.

[0149] 318. Based on the estimated completion time and the remaining maintenance resources, generate a new train maintenance plan.

[0150] After obtaining the estimated completion time of maintenance tasks for each train, the train operation and maintenance management system will combine the remaining maintenance resources, including the number of maintenance teams, spare parts inventory, and availability of maintenance tools, to generate a comprehensive and detailed new train maintenance plan.

[0151] The new maintenance plan will also record in detail the estimated start and end times of maintenance for each train, as well as the schedule for each key milestone. The system will generate a visual Gantt chart, clearly showing the time progress and resource usage of all train maintenance tasks, facilitating overall coordination by management. Furthermore, the system will synchronize the new plan with relevant departments and maintenance teams to ensure information consistency across all parties, and will include real-time reminders to notify relevant personnel to prepare before key milestones. By generating new train maintenance plans, the system can effectively respond to changes during the maintenance process, ensuring the orderly progress of train maintenance work and providing a solid guarantee for the safe operation of trains the following day.

[0152] The train operation and maintenance management method provided in the above embodiments forms a complete and efficient operation and maintenance system through a series of steps, including priority classification of train equipment, differentiated data collection, fault risk prediction, maintenance priority determination, plan formulation, and dynamic adjustment. First, train equipment is divided into high, medium, and low priorities, and different data collection frequencies are set according to priority. Data from high-priority components is collected in real time, and data from other components is collected periodically to ensure the system obtains accurate and critical equipment operation information. Based on the collected data, a fault risk prediction model is used to determine the fault risk level. Combined with the next day's scheduling plan, maintenance priorities are determined, ensuring that maintenance work considers both equipment fault risks and operational needs. Maintenance trains are sorted to generate a list, and the estimated maintenance time is calculated and a maintenance plan is constructed based on historical data and resource availability, ensuring reasonable resource allocation and orderly maintenance. When deviations occur in actual maintenance, timeout situations are judged and priorities are dynamically adjusted. Maintenance time windows are dynamically allocated according to scheduling needs, prioritizing high-priority trains. High-priority tasks are processed first within the remaining time, and the estimated completion time is recalculated. A new plan is generated based on remaining resources, achieving dynamic optimization of maintenance work. The interaction of the above-mentioned technical features enables the system to accurately allocate maintenance resources, which not only significantly reduces the risk of train operation failures and ensures train operation safety, but also significantly improves operation and maintenance efficiency, reduces resource waste, and ensures the high efficiency and reliability of train operation and maintenance management.

[0153] The method provided in the above embodiments can be executed by a train operation and maintenance management system, which is composed of the train operation and maintenance management system. The train operation and maintenance management system in this application embodiment is described below from a hardware processing perspective; please refer to [link to relevant documentation]. Figure 4 This is a schematic diagram of the physical device structure of a train operation and maintenance management system in this application embodiment.

[0154] It should be noted that, Figure 4 The structure of the train operation and maintenance management system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0155] like Figure 4As shown, the train operation and maintenance management system includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage section 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The Random Access Memory (RAM) 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0156] The following components are connected to the input / output (I / O) interface 405: an input section 406 including audio input devices, push-button switches, etc.; an output section 407 including displays, audio output devices, indicator lights, etc.; a storage section 408 including hard disks, etc.; and a communication section 409 including network interface cards such as LAN (Local Area Network) cards, modems, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output (I / O) interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0157] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by the Central Processing Unit (CPU) 401, it performs the various functions defined in this application.

[0158] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0159] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0160] Specifically, the train operation and maintenance management system of this embodiment includes a processor and a memory. The memory is coupled to one or more processors and is used to store computer program code. The computer program code includes computer instructions. One or more processors call the computer instructions to cause the train operation and maintenance management system to execute the method provided in the above embodiment.

[0161] In another aspect, this application also provides a computer-readable storage medium, which may be included in the train operation and maintenance management system described in the above embodiments; or it may exist independently and not assembled into the train operation and maintenance management system. The storage medium carries one or more computer programs that, when executed by a processor of the train operation and maintenance management system, cause the train operation and maintenance management system to implement the methods provided in the above embodiments.

[0162] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application 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. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0163] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0164] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A train operation and maintenance management method, characterized in that, include: The system acquires operational data collected by onboard sensors during the operation of each train, including operational data of the train's power system, suspension system, communication system, and door system. The operational data is input into the fault risk prediction model to obtain the fault risk level of each train. The fault risk level includes high risk, medium risk and low risk, and the fault risk level is used to characterize the health status of the train. Based on the train scheduling plan for the next day and the aforementioned fault risk level, a priority list of trains that need to be maintained that night is generated, and the priority list includes the priority order of the trains that need to be maintained that night. Based on the priority list and combined with train maintenance resources, a train maintenance plan is established. The train maintenance plan includes the allocation of maintenance tasks for each train, the corresponding maintenance team, the list of required parts and tools, and the estimated maintenance time. If the actual maintenance time exceeds the estimated maintenance time, the train maintenance plan will be dynamically adjusted.

2. The method according to claim 1, characterized in that, The training process of the fault risk prediction model is as follows: The system acquires historical fault data, historical operation data, and historical environmental data of the train. The historical fault data includes equipment fault operation parameters, fault records, and maintenance records. The historical operation data includes train power system operation data, suspension system operation data, communication system operation data, and door system operation data. The historical environmental data includes external temperature, humidity, and track condition data during train operation. Based on the historical fault data, fault data annotation is performed on the historical operating data and the historical environmental data; Based on the results of the fault data annotation, a historical data training dataset is constructed; The fault risk prediction model is trained using the historical data training dataset.

3. The method according to claim 1, characterized in that, The provision that if the actual maintenance time exceeds the estimated maintenance time, the train maintenance plan will be dynamically adjusted, specifically including: During the maintenance process, the actual maintenance progress of each train is monitored in real time to obtain the actual maintenance time and estimated remaining time of the train maintenance task; If the actual maintenance time of a train exceeds the estimated maintenance time, the priority of the train maintenance task will be dynamically adjusted. The low-priority train maintenance task will be postponed to the next maintenance time window; Within the estimated remaining time, the high-priority train maintenance tasks will be prioritized. Based on the dynamically adjusted priority of the train maintenance tasks, the estimated completion time of the train maintenance tasks is recalculated. Based on the estimated completion time and the remaining maintenance resources, a new train maintenance plan is generated.

4. The method according to claim 1, characterized in that, The step of establishing a train maintenance plan based on the priority list and in conjunction with train maintenance resources specifically includes: Based on the priority list and historical maintenance data, calculate the estimated maintenance time for each train; Obtain information on the allocation of train maintenance resources, including the number of maintenance teams, spare parts inventory, and availability of maintenance tools; Based on the allocation of train maintenance resources and the estimated maintenance time for each train, a train maintenance plan is constructed.

5. The method according to claim 1, characterized in that, Based on the train scheduling plan for the following day and the aforementioned fault risk level, a priority list of trains requiring maintenance that evening is generated, specifically including: Based on the train scheduling plan for the next day and the aforementioned fault risk level, the priority of trains requiring maintenance that night is determined. The train scheduling plan for the next day includes the train operation requirements for the next day, and the priority includes high priority, medium priority, and low priority. If the fault risk level is high risk and there is a train required to run the next day, then the priority is determined to be high priority. If the fault risk level is medium risk, or if there is a demand for trains the next day but it is not peak time, then the priority is determined to be medium priority. If the fault risk level is low risk, or there are no trains required to run the next day, then the priority is determined to be low priority. The trains that need maintenance that night are sorted according to their priority to obtain a priority list of the trains that need maintenance that night.

6. The method according to claim 1, characterized in that, The acquisition of operational data collected by onboard sensors during the operation of each train specifically includes: Train equipment is divided into high-priority components, medium-priority components, and low-priority components. The high-priority components include the train power system and communication system, the medium-priority components include the suspension system, and the low-priority components include the door system. Real-time acquisition of operational data collected by the real-time monitoring sensors of the high-priority components; The system periodically acquires operational data collected by the sampling and monitoring sensors of the medium-priority and low-priority components, with the sampling frequency of the sensors set according to the component priority.

7. The method according to any one of claims 1 to 6, characterized in that, Before postponing the low-priority train maintenance task to the next maintenance time window, the method further includes: The maintenance time window is dynamically allocated based on the operational needs of the train scheduling plan for the next day. A dedicated maintenance time window is allocated to the high-priority trains to meet their maintenance needs; Non-fixed maintenance time windows are assigned to the low-priority trains; If the actual maintenance time of a train is detected to exceed the preset time, the maintenance time window of the low-priority train maintenance task is compressed and assigned to the high-priority train maintenance task. If a low-priority train maintenance task cannot be completed in the current window due to time compression, it will be postponed to the next maintenance time window.

8. A train operation and maintenance management system, characterized in that, Includes one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors calling the computer instructions to cause the train operation and maintenance management system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed on the train operation and maintenance management system, the train operation and maintenance management system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the train operation and maintenance management system, the train operation and maintenance management system performs the method as described in any one of claims 1-7.

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