Heavy haul railway power supply equipment operation and maintenance scheme adjustment method, device and equipment

Abstract

The application relates to a heavy-load railway power supply device operation and maintenance scheme adjustment method, device and equipment. The method comprises the following steps: acquiring a simulation operation and maintenance scene of an operation and maintenance scene, and a simulation power supply device of the power supply device arranged in the operation and maintenance scene; the power supply device is a power supply device for supplying power to a heavy-load railway; the simulation power supply device is operated in the simulation operation and maintenance scene to obtain a simulation operation result of the simulation power supply device; an initial operation and maintenance scheme of the power supply device is determined according to the simulation operation result; the initial operation and maintenance scheme is adjusted according to an operation state deviation between the power supply device and the simulation power supply device to obtain a target operation and maintenance scheme; wherein the target operation and maintenance scheme is used for operation and maintenance processing of the power supply device. The method can accurately adjust the operation and maintenance scheme.

Description

Technical Field

[0001] This application relates to the field of computer equipment technology, and in particular to a method, apparatus and equipment for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment. Background Technology

[0002] As the scale of heavy-haul railway power supply networks continues to expand and operation and maintenance scenarios become increasingly complex, power supply systems are placing higher demands on the coordination, real-time performance, predictability, and decision-making accuracy of operation and maintenance. Intelligent operation and maintenance technologies, centered on digital twins and intelligent agents, are gradually replacing traditional manual inspections and decentralized operation and maintenance models, becoming crucial technical means for ensuring power supply safety, managing equipment status, and efficiently maintaining heavy-haul railways, thanks to their advantages such as full-element mapping, multi-scenario collaboration, and dynamic simulation optimization.

[0003] In traditional technologies, the operation and maintenance decisions for power supply in heavy-haul railways often follow this approach: collecting monitoring data from local equipment, relying on a single decision model to determine the status and generate maintenance recommendations, and then manually compiling these into an operation and maintenance plan. However, current traditional operation and maintenance methods suffer from incomplete data coverage, focusing only on local equipment or single scenarios. They lack a holistic mapping and unified data integration of all elements of the power supply system, resulting in incomplete basis for operation and maintenance decisions and further affecting the accuracy of adjustments to the operation and maintenance plan. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, and equipment for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment, which can improve the accuracy of scheme adjustment, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for adjusting the operation and maintenance scheme of power supply equipment for heavy-haul railways, including:

[0006] The simulation of the operation and maintenance scenario is obtained, as well as the simulation of the power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways.

[0007] Run the simulated power supply equipment in a simulated operation and maintenance scenario to obtain the simulation operation results of the simulated power supply equipment;

[0008] Based on the simulation results, determine the initial operation and maintenance plan for the power supply equipment;

[0009] Based on the deviation in operating status between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain the target operation and maintenance plan;

[0010] The target operation and maintenance plan is used to perform operation and maintenance on power supply equipment.

[0011] In one embodiment, the initial operation and maintenance plan includes at least one operation and maintenance schedule; based on the operational status deviation between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain the target operation and maintenance plan, including:

[0012] For each iteration, at least one unselected operation and maintenance plan is sequentially selected from the initial operation and maintenance plan as the target operation and maintenance plan;

[0013] Run each target operation and maintenance plan in a simulated operation and maintenance scenario to obtain the operation and maintenance simulation results of the simulated power supply equipment running the target operation and maintenance plan; and,

[0014] Acquire operational status data of power supply equipment after maintenance based on the target operation and maintenance plan;

[0015] Based on the differences between the operational status data and the operation and maintenance simulation results, adjust the operation and maintenance plans that were not selected in the initial operation and maintenance plan until the differences meet the iteration stop condition, and obtain the target operation and maintenance plan for the power supply equipment.

[0016] In one embodiment, before adjusting the maintenance plans that were not selected in the initial maintenance plan based on the differences between the operational status data and the maintenance simulation results, the method further includes:

[0017] Based on the collection time of the operational status data and the generation time of the operation and maintenance simulation results, the operational status data and operation and maintenance simulation results at the same time are associated, and the unassociated operational status data and operation and maintenance simulation results are deleted.

[0018] In one embodiment, based on the simulation results, an initial operation and maintenance plan for the power supply equipment is determined, including:

[0019] Based on the simulation results, determine the indicator data of the simulated power supply equipment under at least one state evaluation index;

[0020] For each status assessment indicator, a reference operation and maintenance plan is determined based on the indicator data.

[0021] Based on the reference operation and maintenance plans corresponding to all status assessment indicators, determine the initial operation and maintenance plan for the power supply equipment.

[0022] In one embodiment, the state assessment indicators include: operational stability indicators, fault risk indicators, and performance degradation indicators; based on the simulation results, the indicator data of the simulated power supply equipment under at least one state assessment indicator are determined, including:

[0023] Based on the parameter fluctuation range, continuous operating time, and normal operating frequency of the simulated power supply equipment in the simulation results, the indicator data of the simulated power supply equipment under the operational stability index are determined; and,

[0024] Based on a preset risk assessment function, and according to the frequency of abnormal data and performance parameter offsets of the simulated power supply equipment in the simulation results, the indicator data of the simulated power supply equipment under the operational stability index are determined; and,

[0025] Linear fitting is performed on the parameter change rate and loss increment of the simulated power supply equipment in the equipment operation results to determine the index data of the simulated power supply equipment under the operation stability index.

[0026] In one embodiment, the simulated operation and maintenance scenario is obtained, along with the simulated power supply equipment for the power supply devices configured in the operation and maintenance scenario, including:

[0027] Obtain scenario description data for the operation and maintenance scenario and device description data for the power supply equipment;

[0028] Based on the scenario description data, generate a simulated operation and maintenance scenario; and,

[0029] Based on the device description data, generate a simulated power supply device for the power supply equipment.

[0030] Secondly, this application also provides a device for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment, including:

[0031] The acquisition module is used to acquire the simulated operation and maintenance scenario, as well as the simulated power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways.

[0032] The simulation module is used to run simulated power supply equipment in simulated operation and maintenance scenarios and obtain the simulation operation results of the simulated power supply equipment;

[0033] The solution module is used to determine the initial operation and maintenance plan for the power supply equipment based on the simulation results.

[0034] The adjustment module is used to adjust the initial operation and maintenance plan based on the deviation between the operating status of the power supply equipment and the simulated power supply equipment, so as to obtain the target operation and maintenance plan;

[0035] The target operation and maintenance plan is used to perform operation and maintenance on power supply equipment.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] The simulation of the operation and maintenance scenario is obtained, as well as the simulation of the power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways.

[0038] Run the simulated power supply equipment in a simulated operation and maintenance scenario to obtain the simulation operation results of the simulated power supply equipment;

[0039] Based on the simulation results, determine the initial operation and maintenance plan for the power supply equipment;

[0040] Based on the deviation in operating status between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain the target operation and maintenance plan;

[0041] The target operation and maintenance plan is used to perform operation and maintenance on power supply equipment.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0043] The simulation of the operation and maintenance scenario is obtained, as well as the simulation of the power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways.

[0044] Run the simulated power supply equipment in a simulated operation and maintenance scenario to obtain the simulation operation results of the simulated power supply equipment;

[0045] Based on the simulation results, determine the initial operation and maintenance plan for the power supply equipment;

[0046] Based on the deviation in operating status between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain the target operation and maintenance plan;

[0047] The target operation and maintenance plan is used to perform operation and maintenance on power supply equipment.

[0048] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0049] The simulation of the operation and maintenance scenario is obtained, as well as the simulation of the power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways.

[0050] Run the simulated power supply equipment in a simulated operation and maintenance scenario to obtain the simulation operation results of the simulated power supply equipment;

[0051] Based on the simulation results, determine the initial operation and maintenance plan for the power supply equipment;

[0052] Based on the deviation in operating status between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain the target operation and maintenance plan;

[0053] The target operation and maintenance plan is used to perform operation and maintenance on power supply equipment.

[0054] The aforementioned method, apparatus, and equipment for adjusting the operation and maintenance (O&M) scheme of heavy-haul railway power supply equipment constructs a simulation environment for the O&M scenario and corresponding simulated power supply equipment. By running the simulation in this environment and obtaining simulation results, the operating characteristics of the power supply equipment can be pre-assessed and an initial O&M scheme can be generated without actual shutdown or disturbance to the real system. Based on this, the initial O&M scheme is dynamically adjusted by comparing the operating state deviations between the real and simulated power supply equipment. This ensures that the final target O&M scheme effectively adapts to the current actual operating state and changes in working conditions of the real equipment. This scheme overcomes the limitations of traditional O&M decisions, which rely on local data, have fixed schemes, and cannot respond to state deviations in real time. It significantly improves the pertinence, adaptive adjustment capability, and decision reliability of the O&M scheme, providing more scientific, precise, and dynamically optimizable O&M support for heavy-haul railway power supply equipment. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This embodiment provides an application environment diagram for a method of adjusting the operation and maintenance scheme of power supply equipment for heavy-haul railways.

[0057] Figure 2 A flowchart illustrating the adjustment method for the first operation and maintenance scheme of heavy-haul railway power supply equipment provided in this embodiment;

[0058] Figure 3 This is a flowchart illustrating the adjustment steps of an operation and maintenance solution provided in this embodiment;

[0059] Figure 4 This is a flowchart illustrating the steps for determining an initial operation and maintenance plan in this embodiment.

[0060] Figure 5 This is a flowchart illustrating a simulation model acquisition step provided in this embodiment;

[0061] Figure 6 This embodiment provides a structural block diagram of an operation and maintenance scheme adjustment device for heavy-haul railway power supply equipment.

[0062] Figure 7 This is an internal structural diagram of a computer device provided in this embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] The operation and maintenance adjustment method for heavy-haul railway power supply equipment provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. The computer equipment acquires a simulated operation and maintenance scenario, and a simulated power supply device for the power supply equipment set in the operation and maintenance scenario; the power supply equipment is used to supply power to heavy-haul railways; the simulated power supply equipment is run in the simulated operation and maintenance scenario to obtain the simulated operation results; based on the simulation results, an initial operation and maintenance plan for the power supply equipment is determined; based on the deviation in operating status between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain a target operation and maintenance plan; the target operation and maintenance plan is used to perform operation and maintenance processing on the power supply equipment. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0065] In one exemplary embodiment, such as Figure 2 As shown, a method for adjusting the operation and maintenance scheme of power supply equipment for heavy-haul railways is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps S201 to S204. Wherein:

[0066] S201 obtains the simulated operation and maintenance scenario of the operation and maintenance scenario, as well as the simulated power supply equipment of the power supply equipment set in the operation and maintenance scenario.

[0067] In this context, "O&M scenarios" refer to specific business situations or environmental conditions that may arise in real-world heavy-haul railway power supply systems, requiring O&M decisions. Examples include normal operation scenarios, equipment anomaly scenarios, sudden fault scenarios, emergency response scenarios, extreme environment scenarios (such as high temperatures, thunderstorms, snow, etc.), or routine maintenance scenarios. O&M scenarios encompass equipment operating status, environmental parameters, operating procedures, and potential risk factors, serving as a crucial basis for developing O&M plans.

[0068] Among them, simulated operation and maintenance scenarios refer to virtual operating environments generated through digital twin or simulation modeling technologies based on descriptive data of operation and maintenance scenarios (such as scenario type, equipment scope, simulation objectives, etc.). This simulation environment can simulate equipment status changes, parameter fluctuations, environmental impacts, and process execution effects in real operation and maintenance scenarios. It is used to reproduce or rehearse various operation and maintenance scenarios in virtual space, thereby supporting the simulation and deduction of the operating status of power supply equipment and the verification and optimization of operation and maintenance strategies.

[0069] Power supply equipment refers to all kinds of physical equipment actually deployed in the power supply system of heavy-haul railways to provide power supply for the railways. This includes, but is not limited to: substation equipment, distribution station equipment, overhead contact line equipment, transformers, circuit breakers, disconnect switches, surge arresters, and power lines. Power supply equipment is the direct target of operation and maintenance plans, and its operating status data is collected in real time through monitoring terminals. Power supply equipment is specifically designed to supply power to heavy-haul railways.

[0070] Among them, simulated power supply equipment refers to a virtual equipment model generated using digital twin or simulation modeling technology based on the equipment description data (such as physical parameters, installation location information, topology connection relationships, historical maintenance records, technical specifications, etc.) of real power supply equipment. This simulated equipment highly corresponds to the real power supply equipment in terms of structural parameters, operating logic, and state change patterns. It can run in simulated operation and maintenance scenarios and generate simulation operation results, which can be used to replace real equipment for virtual testing, state assessment, and pre-verification of operation and maintenance plans.

[0071] In some embodiments, this embodiment can directly obtain a pre-established simulated operation and maintenance scenario, as well as a simulated power supply device for the power supply equipment set in the operation and maintenance scenario.

[0072] S202 runs the simulated power supply equipment in a simulated operation and maintenance scenario and obtains the simulation operation results of the simulated power supply equipment.

[0073] The simulation operation results refer to a set of data obtained after placing the simulated power supply equipment in a simulated operation and maintenance scenario, which reflects the operating status, performance, and change patterns of the simulated power supply equipment in a virtual environment. The simulation operation results typically include, but are not limited to: equipment operating parameters, status change trajectories, anomaly triggering information, performance parameter changes, and environmental response data.

[0074] Among them, the equipment operating parameters are the time-series changes of key indicators such as voltage, current, temperature, power, and frequency during the simulation process; the state change trajectory is the operating status (normal, abnormal, warning, fault, etc.) of the simulated power supply equipment at different time points or under different operating conditions and its switching records; the abnormal trigger information is the relevant data of abnormal events such as parameter deviation, over-limit alarm, and fault trigger that occur during the simulation process, including the time of occurrence of the abnormality, the type of abnormality, and the degree of abnormality; the performance parameter changes are data reflecting the health status of the equipment, such as equipment efficiency, loss increment, parameter change rate, and performance degradation trend; and the environmental response data is the response results of the equipment to changes in external environmental parameters (such as temperature, humidity, and meteorological conditions) in the simulated operation and maintenance scenario.

[0075] In some embodiments, the simulated power supply equipment is placed in a simulated operation and maintenance scenario, and the simulated operation and maintenance scenario containing the simulated power supply equipment is run; the simulation operation results of the simulated power supply equipment are obtained.

[0076] For example, in this embodiment, simulation parameters can be transmitted to a state assessment agent. The state assessment agent sets the initial state of the digital twin based on the parameters and initiates the simulation. The simulation process covers various operation and maintenance scenarios, including normal operation, equipment maintenance, fault occurrence, and emergency response. It simulates equipment state changes, parameter fluctuations, environmental impacts, and process execution effects, recording operational data, state transitions, and anomaly trigger information at fixed time intervals. The dynamic optimization agent receives simulation data in real time, identifies equipment anomalies, operational bottlenecks, and risk points, and forms analytical conclusions. The system integrates simulation process data, state change trajectories, and risk warning information to generate operation and maintenance scenario simulation results that include scenario operation processes, equipment state curves, hidden danger distribution, and bottleneck analysis, providing simulation basis for subsequent state assessment and decision-making.

[0077] For example, after receiving parameters, the state assessment agent sets the initial state of the digital twin based on the scenario simulation parameters, restoring the initial operating parameters, environmental conditions, and topology connection status of the equipment in the corresponding scenario to ensure that the initial state is consistent with the actual operation and maintenance scenario. Then, scenario simulation is initiated to simulate changes in the operating state of the equipment under different operation and maintenance scenarios. Fault scenarios simulate abnormal fluctuations and fault evolution processes; routine maintenance scenarios simulate the impact of normal equipment operation and maintenance operations; and emergency response scenarios simulate equipment state adjustments during fault handling. During the simulation, equipment operating parameters, state changes, and environmental data are recorded at preset sampling intervals. All simulation records are integrated to generate state simulation data that is time-series clear, continuous, and reflects changes in equipment state, providing data support for subsequent simulation analysis.

[0078] Furthermore, the system dynamically optimizes the integrated state simulation data and simulation analysis report of the intelligent agent, generating simulation results of the operation and maintenance scenario that include the scenario operation trajectory, equipment state change curves and operation and maintenance risk points, and synchronously transmits them to the multi-agent collaborative platform for storage.

[0079] Based on the simulation results, S203 determines the initial operation and maintenance plan for the power supply equipment.

[0080] The initial operation and maintenance (O&M) plan refers to a set of plans, strategies, or operational steps for the initial O&M of real power supply equipment, determined based on the simulation results obtained from the operation of simulated power supply equipment in a simulated O&M scenario. Specifically, the initial O&M plan typically includes the following: a set of O&M plans, i.e., one or more O&M plans, each corresponding to a specific O&M task, such as equipment maintenance, component replacement, parameter adjustment, status monitoring, and fault handling; O&M objects and scope, i.e., clearly defining the specific power supply equipment requiring O&M and the related equipment parts and line sections; O&M timing and sequence, i.e., initially arranging the execution time, priority, and execution sequence of various O&M tasks; O&M resource requirements, i.e., initially matching the required O&M personnel, tools, spare parts, consumables, and other resources; and O&M operation specifications, i.e., initially establishing operating procedures, process requirements, and safety precautions. The initial O&M plan is a preliminary plan generated based on simulation results and has not yet been corrected by feedback from actual operating status. Its purpose is to provide a basic framework and starting point for subsequent iterative adjustments. By comparing and analyzing the differences between the initial operation and maintenance plan and the actual operating status data of the power supply equipment after maintenance round by round, the unexecuted parts of the plan can be continuously optimized, and finally a target operation and maintenance plan that is more in line with the actual working conditions can be formed.

[0081] In some embodiments, based on the scheme generation model and the simulation results, an initial operation and maintenance scheme for the power supply equipment is determined.

[0082] S204 adjusts the initial operation and maintenance plan based on the deviation between the operating status of the power supply equipment and the simulated power supply equipment to obtain the target operation and maintenance plan.

[0083] Operational status deviation refers to the difference between the operational status data generated by the actual power supply equipment after operation or maintenance and the simulation results obtained by running the same operation and maintenance plan in a virtual environment. Specifically, operational status deviation is usually reflected in the following aspects: parameter deviation, i.e., the numerical difference between the actual equipment's operating parameters such as voltage, current, and temperature and the corresponding parameters in the simulation results; state deviation, i.e., the inconsistency between the actual equipment's operating state (normal, warning, abnormal, fault, etc.) and the expected state in the simulation results; trend deviation, i.e., the deviation between the actual equipment's parameter change trend over time (such as decay rate, fluctuation amplitude) and the predicted trend in the simulation results; and response deviation, i.e., the difference between the actual equipment's response effect under the same operation and maintenance operation or changes in operating conditions and the expected response in the simulation results. Operational status deviation is the core basis for dynamically adjusting the initial operation and maintenance plan. The target operation and maintenance plan is used to perform operation and maintenance on the power supply equipment.

[0084] In some embodiments, the operational state deviation between the power supply equipment and the simulated power supply equipment is determined; based on the scheme adjustment model, the initial operation and maintenance scheme is adjusted according to the operational state deviation to obtain the target operation and maintenance scheme.

[0085] The aforementioned method for adjusting the operation and maintenance (O&M) scheme for heavy-haul railway power supply equipment constructs a simulation environment for the O&M scenario and corresponding simulated power supply equipment. By running the simulation within this environment and obtaining simulation results, the operating characteristics of the power supply equipment can be pre-assessed and an initial O&M scheme generated without actual shutdown or disturbance to the real system. Based on this, the initial O&M scheme is dynamically adjusted by comparing the operating state deviations between the real and simulated power supply equipment. This ensures that the final target O&M scheme effectively adapts to the current actual operating state and changes in working conditions of the real equipment. This scheme overcomes the limitations of traditional O&M decisions, which rely on local data, have rigid schemes, and cannot respond to state deviations in real time. It significantly improves the pertinence, adaptive adjustment capability, and decision reliability of the O&M scheme, providing more scientific, precise, and dynamically optimizable O&M support for heavy-haul railway power supply equipment.

[0086] Figure 3 This is a flowchart illustrating the steps for adjusting the operation and maintenance (O&M) plan in one embodiment. In this embodiment, based on the initial O&M plan including at least one O&M schedule, the steps for adjusting the initial O&M plan according to the operational status deviation between the power supply equipment and the simulated power supply equipment, as described in the previous embodiment, are refined. This embodiment provides an optional method for adjusting the O&M plan, including the following steps:

[0087] For each iteration, S301 sequentially selects at least one unselected operation and maintenance plan from the initial operation and maintenance plan as the target operation and maintenance plan.

[0088] Among them, the target operation and maintenance plan refers to one or more operation and maintenance plans that have not yet been selected from the initial operation and maintenance plan in each round of the iteration process, which are used as specific plan units for the current round to perform simulation operation and real maintenance comparison and adjustment.

[0089] In some embodiments, based on the order of different operation and maintenance plans in the initial operation and maintenance plan, at least one operation and maintenance plan that was not selected in the initial operation and maintenance plan is selected as the target operation and maintenance plan.

[0090] S302 runs each target operation and maintenance plan in the simulated operation and maintenance scenario, and obtains the operation and maintenance simulation results of the simulated power supply equipment running the target operation and maintenance plan.

[0091] The operation and maintenance simulation results refer to the set of virtual operational performance and related data of the simulated power supply equipment during the execution of the selected target operation and maintenance plan in the simulated operation and maintenance scenario. Specifically, the operation and maintenance simulation results typically include the following: execution process data, i.e., time-series data such as state changes, parameter fluctuations, and operational responses of the simulated power supply equipment during the execution of the target operation and maintenance plan; effect evaluation data, i.e., effect indicators such as performance recovery, anomaly elimination, and stability changes of the simulated power supply equipment after the execution of the operation and maintenance plan; and expected deviation information, i.e., anomalies or deviations that may occur in the simulation results that do not conform to the initial expectations, used for subsequent comparative analysis with actual operating status data.

[0092] In some embodiments, the target operation and maintenance plan is placed in a simulated operation and maintenance scenario, and the simulated operation and maintenance scenario containing the target operation and maintenance plan is run to obtain the operation and maintenance simulation results of the simulated power supply equipment running the target operation and maintenance plan.

[0093] For example, the solution generation agent transmits the operation and maintenance plan and equipment status assessment report to the visualization interaction agent. The visualization interaction agent, based on the digital twin, realizes a 3D visualization display of the operation and maintenance plan, simultaneously generates visualization display data, and feeds it back to the multi-agent collaborative platform. After receiving the data, the visualization interaction agent calls the digital twin according to the permission allocation list. Based on the full-element model of the digital twin, it transforms the maintenance process, operating procedures, and safety precautions in the operation and maintenance plan into 3D visualization content, restoring the maintenance operation scenario, equipment layout, and operation steps, and intuitively presenting the key nodes, safety risk points, and equipment status in the maintenance process.

[0094] S303 acquires the operational status data of the power supply equipment after maintenance based on the target operation and maintenance plan.

[0095] Operational status data refers to the status information and related parameters collected by monitoring terminals after actual maintenance of the power supply equipment based on the target maintenance plan selected in the current round. This data reflects the actual operational performance of the equipment after maintenance. Specifically, operational status data typically includes the following: post-maintenance operational parameters, such as the actual measured values ​​and time-series changes of key indicators like voltage, current, temperature, power, and frequency after maintenance; status recovery status, such as whether equipment anomalies have been eliminated, performance has improved, and operation has stabilized; environmental adaptation data, such as the equipment's operational response data under actual operating conditions and external environmental conditions; and anomaly records, such as abnormal fluctuations, warning messages, and fault signals that still exist or have newly appeared after maintenance.

[0096] In some embodiments, the operating status data of the power supply equipment after maintenance based on the target operation and maintenance plan is directly obtained.

[0097] S304 adjusts the maintenance plans that were not selected in the initial maintenance plan based on the differences between the operating status data and the maintenance simulation results, until the differences meet the iteration stop condition, and obtains the target maintenance plan for the power supply equipment.

[0098] In some embodiments, the dynamic optimization agent compares the status data of the full-element digital twin and the actual power supply system in real time, captures parameter deviations, status differences and execution deviations, and adjusts the evaluation model, maintenance sequence and scheme content according to the deviation results to complete the dynamic optimization of decision results and operation and maintenance scheme.

[0099] It should be noted that in this embodiment, the operating status data and the operation and maintenance simulation results at the same time can be associated based on the collection time of the operating status data and the generation time of the operation and maintenance simulation results, and the unassociated operating status data and operation and maintenance simulation results can be deleted.

[0100] For example, the system performs association mapping between a basic static dataset and a dynamic monitoring dataset. Using fixed association mapping rules, the system traverses all data entries in both datasets, extracting the unique device identifier for each data entry. This unique device identifier distinguishes different devices, ensuring clear and unambiguous data ownership. Simultaneously, the system extracts the timestamp information for each data entry. Static data is labeled with its generation time and effective period, while dynamic data is labeled with its collection time and sampling period, clarifying the time dimension characteristics of the data. The system establishes a correspondence between static and dynamic data using the unique device identifier as the primary key and the timestamp as the secondary dimension, binding static attributes and dynamic states of the same device within the same time period. After mapping, all correspondences are organized, recording the unique device identifier, timestamp, static data index, and dynamic data index, forming a data association table with clear entries, stable correspondences, and strong traceability, providing a basis for subsequent multi-dimensional data fusion.

[0101] In the above embodiments, by introducing an iterative operation and maintenance plan selection and comparison adjustment mechanism, the operating status data of the actual power supply equipment after planned maintenance is analyzed round by round against the operation and maintenance simulation results of the same plan in the simulation environment. Based on this, the remaining operation and maintenance plans that have not yet been executed are dynamically optimized, enabling the initial operation and maintenance plan to be continuously corrected and converged based on real feedback. This scheme breaks through the limitations of traditional operation and maintenance plans that are formulated all at once and cannot be adaptively adjusted according to actual execution results. It effectively improves the incremental optimization capability and overall matching accuracy of the operation and maintenance plan, ensuring that the final target operation and maintenance plan can better fit the actual operating characteristics and working condition changes of the power supply equipment, and significantly enhances the dynamic coordination and reliability of operation and maintenance decisions for heavy-haul railway power supply.

[0102] Figure 4 This is a flowchart illustrating the initial operation and maintenance scheme determination steps in one embodiment. In this embodiment, the steps for determining the initial operation and maintenance scheme for the power supply equipment based on simulation results, as described in the previous embodiments, are refined. This embodiment provides an optional method for determining the initial operation and maintenance scheme, including the following steps:

[0103] Based on the simulation results, S401 determines the indicator data of the simulated power supply equipment under at least one state evaluation index.

[0104] Among them, condition assessment indicators refer to various quantitative or qualitative evaluation dimensions used to measure the operational performance of simulated power supply equipment in simulated operation and maintenance scenarios. These indicators allow for a comprehensive assessment of the equipment's health status, operational quality, and potential risks from different perspectives. Condition assessment indicators include: operational stability indicators, fault risk indicators, and performance degradation indicators.

[0105] Among them, the operational stability index is used to measure the smoothness, anti-interference ability, and ability to maintain normal status of the simulated power supply equipment during operation in a simulated operation and maintenance scenario. It is calculated or evaluated comprehensively based on the parameter fluctuation range (such as voltage and current fluctuation amplitude), continuous operating time (operation time without abnormal interruptions), and normal operating frequency (the proportion of time in normal condition per unit time) of the simulated power supply equipment in the simulation results. The better the value (e.g., smaller fluctuation range, longer operating time, higher normal operating frequency), the better the equipment's operational stability.

[0106] The fault risk index measures the likelihood and severity of anomalies, deviations, or faults occurring in simulated power supply equipment during simulated operation and maintenance scenarios. It is calculated based on a preset risk assessment function, taking into account the frequency of abnormal data (the number of times anomalies occur per unit time) and performance parameter deviation (the degree of deviation between actual parameters and standard thresholds) of the simulated power supply equipment in the simulation results. A higher value indicates a greater risk of equipment failure.

[0107] The performance degradation index measures the performance decline trend or cumulative loss of simulated power supply equipment under simulated operation and maintenance scenarios as operating time or load changes. Linear fitting is performed on the parameter change rate (such as efficiency decline rate, loss increase rate) and loss increment (additional loss per unit time) of the simulated power supply equipment in the simulation results to determine the trend and rate of performance degradation. A larger value indicates faster performance degradation and a corresponding decrease in remaining lifespan or reliability.

[0108] In some embodiments, a model is determined based on preset index data, and index data of the simulated power supply equipment under at least one state evaluation index is determined based on the simulation results.

[0109] In some embodiments, the indicator data of the simulated power supply equipment under the operational stability index are determined based on the parameter fluctuation range, continuous running time, and normal operating frequency of the simulated power supply equipment in the simulation results; and, based on a preset risk assessment function, the indicator data of the simulated power supply equipment under the operational stability index are determined based on the frequency of abnormal data and performance parameter offset of the simulated power supply equipment in the simulation results; and, the indicator data of the simulated power supply equipment under the operational stability index are determined by linearly fitting the parameter change rate and loss increment of the simulated power supply equipment in the equipment operation results.

[0110] The parameter fluctuation range refers to the range between the maximum and minimum values ​​of key operating parameters (such as voltage, current, temperature, and power) of the simulated power supply equipment within a specified time period during simulation operation. This range reflects the stability of the equipment operation: the smaller the fluctuation range, the more stable the equipment operation; the larger the fluctuation range, the more likely there are disturbances, abnormalities, or unstable factors in the equipment.

[0111] The continuous runtime refers to the cumulative time that the simulated power supply equipment maintains normal operation in a simulated operation and maintenance scenario without abnormal interruptions, fault shutdowns, or state transitions. This parameter measures the equipment's ability to operate continuously under interference-free conditions; the longer the duration, the higher the equipment's operational reliability and stability.

[0112] The normal operating frequency refers to the number of times or the percentage of time that the simulated power supply equipment is in normal operating condition (i.e., without any warnings, abnormalities, or faults) within a unit of time or a specified simulation period. This parameter reflects the equipment's ability to maintain a normal state from a frequency perspective. The higher the normal operating frequency, the more stable the equipment operation, and the longer the interval or the lower the probability of abnormalities.

[0113] The preset risk assessment function refers to a pre-constructed mathematical function or calculation model used to comprehensively calculate the equipment's failure risk index based on multiple input parameters related to failure risk (such as the frequency of abnormal data, performance parameter offsets, etc.). This function typically sets the weights of each input parameter, the risk level mapping relationship, and the calculation logic, enabling the quantification of multi-dimensional risk characteristics into a unified risk index value, thereby quantitatively assessing the probability of failure in the simulated power supply equipment.

[0114] Among them, the frequency of abnormal data refers to the number of times abnormal data (such as parameter over-limit, state jump, alarm triggering, performance deviation, etc.) occurs in the simulated power supply equipment per unit time in the simulation results. This parameter reflects the frequency of abnormal events during equipment operation. The higher the frequency, the more unstable the equipment state and the greater the risk of failure.

[0115] Among them, performance parameter offset refers to the degree of deviation of the actual performance parameters (such as output voltage, operating efficiency, temperature, etc.) of the simulated power supply equipment from the preset standard threshold or rated value. The offset can be an absolute difference, a relative deviation percentage, or other quantitative form. The larger the offset, the further the equipment deviates from the normal state, and the higher the risk of failure and maintenance requirements.

[0116] Among them, the parameter change rate refers to the magnitude of change of key performance parameters (such as efficiency, output capacity, loss coefficient, etc.) of the simulated power supply equipment per unit time, usually expressed as a derivative, slope, or percentage change. This parameter is used to describe how quickly the equipment performance changes with operating time or load: the larger the change rate, the faster the equipment performance deteriorates and the more significant the degradation.

[0117] Incremental loss refers to the additional losses (such as energy loss, component wear, and material aging equivalent losses) that the simulated power supply equipment incurs per unit time or per operating cycle during simulation operation. The incremental loss can be determined by comparing the cumulative loss values ​​at different time points. A larger increment indicates a faster rate of equipment performance degradation, and a corresponding decrease in remaining lifespan or operational reliability.

[0118] For example, feature items related to equipment operation stability are extracted from the core feature dataset, including parameter fluctuation range, continuous running time and normal operation frequency. Weight coefficients are set based on the influence of each feature item, and the equipment operation stability coefficient is calculated by weighted summation.

[0119] For example, features related to fault risk are extracted, including the frequency of abnormal data, performance parameter offsets, and historical fault records. Combined with the risk level corresponding to the features, a fault risk assessment function is constructed to calculate the equipment fault risk index.

[0120] For example, feature terms related to performance degradation are extracted, including parameter change rate, loss increment and runtime. Based on the changing trend of the feature terms, a trend fitting algorithm is used to calculate the device performance degradation rate.

[0121] For each status assessment indicator, S402 determines a reference operation and maintenance plan based on the indicator data.

[0122] Among them, the reference operation and maintenance plan refers to a set of preliminary operation and maintenance strategies or handling suggestions independently formulated based on the indicator data of a specific status assessment indicator (such as operation stability indicator, fault risk indicator or performance degradation indicator).

[0123] In some embodiments, for each state assessment metric, a model is determined based on a preset scheme, and a reference operation and maintenance scheme is determined based on the metric data of the state assessment metric.

[0124] For example, the condition assessment agent extracts equipment operating characteristics, calculates stability coefficients, failure risk indices, and performance degradation rates, and generates an equipment condition assessment report. This report is then transmitted to the maintenance planning agent, which combines maintenance resources, safety regulations, and cost data to develop a preliminary maintenance plan.

[0125] Based on the reference maintenance plans corresponding to all status assessment indicators, S403 determines the initial maintenance plan for the power supply equipment.

[0126] In some embodiments, the reference operation and maintenance schemes corresponding to all status assessment indicators are integrated to determine the initial operation and maintenance scheme for the power supply equipment.

[0127] For example, the maintenance planning agent transmits a preliminary maintenance plan to the solution generation agent. Based on the preliminary maintenance plan and digital twin scenario data, the solution generation agent generates an operation and maintenance plan that includes maintenance procedures, operating procedures, and safety precautions. After receiving the preliminary maintenance plan, the solution generation agent, according to the permission allocation list, calls upon the digital twin scenario data, covering scenario information such as device topology, installation location, operating environment, and surrounding related devices. Based on the core content of the preliminary maintenance plan and combined with the digital twin scenario data, the system refines the maintenance process, clarifying maintenance steps, operation sequence, and operational requirements for each step to avoid conflicts; it formulates operating procedures, defining the operational standards and technical requirements for each operation to ensure compliance; and it supplements safety precautions, clarifying safety protection measures, risk control points, and emergency response procedures during the maintenance process to prevent maintenance safety risks. All refined content is integrated to generate an operation and maintenance plan that includes maintenance procedures, operating procedures, and safety precautions, ensuring the plan is implementable and executable, providing support for subsequent operation and maintenance implementation.

[0128] In the above embodiments, based on simulation results, indicator data is extracted from multiple state evaluation dimensions (such as operational stability, fault risk, performance degradation, etc.), and a corresponding reference operation and maintenance plan is independently determined for each indicator. All reference operation and maintenance plans are then integrated to generate the final initial operation and maintenance plan. This approach overcomes the limitations of traditional methods that rely solely on a single evaluation dimension or local indicators to formulate operation and maintenance strategies. It achieves a panoramic assessment and collaborative decision-making of the multi-dimensional operational status of power supply equipment, enabling the initial operation and maintenance plan to simultaneously consider multiple objectives such as equipment stability, risk control, and performance degradation. This significantly improves the scientific rigor, systematicity, and comprehensive adaptability of the operation and maintenance plan, providing more comprehensive decision support for the efficient and precise operation and maintenance of heavy-haul railway power supply equipment.

[0129] Figure 5 This is a flowchart illustrating the simulation model acquisition steps in one embodiment. In this embodiment, the steps for acquiring the simulated operation and maintenance scenario and the simulated power supply equipment set in the operation and maintenance scenario, as described in the above embodiments, are detailed. This embodiment provides an optional method for obtaining the simulation model acquisition steps, including the following steps:

[0130] S501 acquires scenario description data for operation and maintenance scenarios and device description data for power supply equipment.

[0131] Scenario description data refers to a set of information used to define and characterize the features, boundary conditions, and behavioral patterns of an operational scenario. This data serves as the fundamental input for generating simulated operational scenarios, determining the specific type of the simulation environment, its operating conditions, and the simulation objectives. Specifically, scenario description data typically includes, but is not limited to: scenario type, equipment scope, simulation objectives, environmental parameters, initial state, triggering conditions, and operating rules.

[0132] The scenario type specifies the category of the operation and maintenance scenario, such as normal operation scenario, equipment anomaly scenario, sudden fault scenario, emergency response scenario, extreme environment scenario (high temperature, thunderstorm, snow, etc.), and routine maintenance scenario. The equipment scope defines the specific power supply equipment types, models, quantities, line sections, and related equipment relationships involved in the scenario. The simulation objective clarifies the purpose to be achieved through the simulation of this scenario, such as simulating equipment state changes, predicting fault evolution paths, and verifying the effectiveness of operation and maintenance solutions. The environmental parameters describe the external environmental conditions in the scenario, such as temperature, humidity, meteorological conditions, and electromagnetic interference. The initial state sets the operating status, load level, and switch status of each device at the start of the simulation. The triggering conditions define the timing and rules for triggering anomalies, faults, or operational events in the scenario (such as triggering when a specific time point or parameter reaches a threshold). The operating rules describe the logical constraints, operating procedures, state switching conditions, and other behavioral norms of the equipment operation within the scenario.

[0133] Equipment description data refers to a set of information used to define and characterize the physical attributes, structural features, operating logic, and historical status of real power supply equipment. This data serves as the fundamental input for generating simulated power supply equipment, determining the degree of correspondence between the virtual equipment model and the real equipment, and the reliability of the simulation. Specifically, equipment description data typically includes, but is not limited to: physical parameters (geometric dimensions, material properties, rated voltage / current, power rating, factory configuration, and other intrinsic characteristic parameters of the equipment), installation location information (line section where the equipment is located, tower number, spatial orientation, connection relationships with adjacent equipment, and other location association information), topological connection relationships (connection methods, upstream and downstream dependencies, hierarchical structure, and linkage logic of the equipment in the power supply system), historical maintenance records (time of each maintenance, maintenance content, fault handling results, component replacement status, and full lifecycle operation and maintenance status of the equipment), technical specification data (design requirements, operating limits (upper / lower limits), maintenance conditions, control standards, and operating procedures of the equipment), and operating characteristics (start-up / stop characteristics, load response patterns, efficiency curves, aging patterns, and other dynamic behavioral characteristics of the equipment).

[0134] In some embodiments, scenario description data of the operation and maintenance scenario and device description data of the power supply equipment are obtained directly.

[0135] For example, in this embodiment, fixed data collection rules are used to retrieve and verify the physical parameter data corresponding to the power supply equipment within the jurisdiction, recording the equipment's structural dimensions, material properties, rated indicators, factory configurations, etc., to ensure that the physical parameters completely cover the characteristics of the equipment itself. Simultaneously, equipment installation location information is collected, clarifying the line section, tower number, spatial orientation, and corresponding relationship with adjacent equipment, forming location association information. Historical maintenance records are also collected, including maintenance time, maintenance content, fault handling results, and component replacement information, reconstructing the equipment's full lifecycle operation and maintenance status. In addition, equipment technical specification data is collected, covering design requirements, operating limits, maintenance conditions, and control standards. After collection, the formats of various static data are standardized, duplicate entries and invalid information are removed, and a unified data structure is established according to equipment category, generating a basic static dataset that is complete in content, reliable in source, and uniform in structure, providing static benchmark support for subsequent dynamic data association.

[0136] Based on the scenario description data, S502 generates a simulated operation and maintenance scenario.

[0137] In some embodiments, this embodiment can generate a simulated operation and maintenance scenario based on a preset simulation model and scenario description data.

[0138] For example, a multi-agent collaborative platform drives a full-element digital twin to conduct scenario-based simulations. A visual interactive agent receives the operation and maintenance scenario requirements, analyzes the scenario type, equipment scope, and simulation objectives, and generates scenario simulation instructions. These instructions are transmitted to the solution generation agent via the collaborative platform. The solution generation agent calls upon the parameter data and topology data of the corresponding devices in the digital twin panoramic dataset to generate scenario simulation parameters. The simulation parameters are transmitted to the state evaluation agent, which sets the initial state of the digital twin based on the parameters and initiates the simulation.

[0139] For example, a visual interactive intelligent agent receives user-inputted operation and maintenance (O&M) scenario requirements via a dedicated interactive interface. These requirements cover O&M-related needs such as troubleshooting, routine maintenance, and emergency response. The visual interactive intelligent agent uses fixed requirement parsing rules to break down the text of the O&M scenario requirements sentence by sentence, removing irrelevant expressions, extracting the scenario type, and clarifying the corresponding O&M scenario category; extracting the equipment scope, defining the power supply equipment models, line sections, number of devices, and associated equipment involved in the requirement; and extracting the simulation objectives, clarifying the user's desired goals through simulation, including equipment status simulation, fault evolution prediction, and O&M effect verification. After parsing, the scenario type, equipment scope, and simulation objectives are integrated, packaged in a fixed format, and annotated with the requirement reception time, corresponding equipment identifier, and simulation objective description. This generates clear, parameter-complete, and requirement-aligned scenario simulation instructions, providing instruction support for subsequent scenario simulation parameter generation.

[0140] It should be noted that in this embodiment, the intelligent agent that generates the scheme can transmit the scenario simulation parameters to the state evaluation intelligent agent. The state evaluation intelligent agent sets the initial state of the digital twin based on the simulation parameters, simulates the changes in the operating state of the equipment under different operation and maintenance scenarios, and generates state simulation data.

[0141] S503 generates a simulated power supply device based on the device description data.

[0142] In some embodiments, this embodiment can generate a simulated power supply device based on a preset simulation model and device description data.

[0143] For example, a real-time data acquisition network covering the entire area is constructed by deploying multi-source monitoring terminals within the verification jurisdiction. The terminals are distributed at key locations on the power supply equipment according to a preset layout rule, continuously carrying out real-time data acquisition. The acquired data includes power supply equipment operating parameters, recording equipment operating status, electrical indicators, load changes, and operational stability data. Environmental parameters are also collected simultaneously, including external information affecting equipment operation such as regional temperature, humidity, meteorological conditions, and surrounding environmental disturbances. Fault warning data is also collected concurrently, capturing information related to abnormal equipment fluctuations, status deviations, and potential hazard triggers, identifying potential fault signs. The acquisition process is continuously executed at fixed time intervals to ensure data continuity and timeliness. The collected dynamic data undergoes preliminary screening, eliminating abnormal fluctuations and invalid sampling points, and is organized and stored according to time series, generating a dynamic monitoring dataset with strong real-time performance, comprehensive coverage, and complete status reflection, providing dynamic basis for subsequent data fusion.

[0144] For example, a full-element digital twin is built based on a standardized fusion dataset. The system utilizes digital twin modeling tools to load static parameters, location information, operational data, environmental data, and fault information from the standardized fusion dataset. Combined with verification of the actual topology of the power supply system in the jurisdiction, it reconstructs line connection relationships, equipment hierarchical relationships, power transmission paths, and regional distribution patterns, constructing a full-element digital twin that completely corresponds to the real physical system. After the twin is built, the system assigns status labels to all equipment within the model, reflecting different states such as normal operation, abnormal warning, fault occurrence, and maintenance. The update frequency of various data types is simultaneously labeled, distinguishing between static data update cycles and dynamic data refresh intervals. Simultaneously, the topological relationships between equipment are analyzed, recording upstream and downstream dependencies, linkage logic, and control relationships. Finally, the model status, data rules, relationships, and panoramic information are integrated to generate a digital twin panoramic dataset containing equipment status labels, data update frequencies, and topological relationships, providing visualization and data support for collaborative decision-making by the operation and maintenance intelligent agent.

[0145] In the above embodiments, by acquiring scenario description data of the operation and maintenance scenario and device description data of the power supply equipment respectively, and generating simulated operation and maintenance scenarios based on the scenario description data and simulated power supply equipment based on the device description data, the decoupled construction and flexible configuration of the simulation environment and simulation objects are realized. This scheme enables the simulated operation and maintenance scenario and simulated power supply equipment to be quickly and accurately customized according to actual operation and maintenance needs and data descriptions, avoiding the limitations of traditional methods such as tight coupling between scenarios and equipment, poor reusability, and difficulty in adapting to different operation and maintenance tasks. It significantly improves the flexibility, scalability, and scenario coverage of simulation modeling, providing efficient and reliable basic support for the simulation verification and optimization decision-making of operation and maintenance strategies for heavy-haul railway power supply equipment under different operating conditions and environments.

[0146] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0147] Based on the same inventive concept, this application also provides an apparatus for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment to implement the above-mentioned method for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment. The solution provided by this apparatus is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the apparatus for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment provided below can be found in the limitations of the method for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment described above, and will not be repeated here.

[0148] In one exemplary embodiment, such as Figure 6 As shown, a device for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment is provided, including: an acquisition module 601, a simulation module 602, a scheme module 603, and an adjustment module 604, wherein:

[0149] The acquisition module 601 is used to acquire the simulated operation and maintenance scenario of the operation and maintenance scenario, and the simulated power supply equipment of the power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to the heavy-haul railway.

[0150] The simulation module 602 is used to run the simulated power supply equipment in a simulated operation and maintenance scenario and obtain the simulation operation results of the simulated power supply equipment.

[0151] Solution module 603 is used to determine the initial operation and maintenance plan for the power supply equipment based on the simulation results;

[0152] The adjustment module 604 is used to adjust the initial operation and maintenance plan based on the deviation between the operating status of the power supply equipment and the simulated power supply equipment to obtain the target operation and maintenance plan; wherein, the target operation and maintenance plan is used to perform operation and maintenance on the power supply equipment.

[0153] In some embodiments, the adjustment module 604 is further configured to, for each iteration, sequentially select at least one unselected operation and maintenance plan from the initial operation and maintenance scheme as a target operation and maintenance plan; run each target operation and maintenance plan in a simulated operation and maintenance scenario to obtain the operation and maintenance simulation results of the simulated power supply equipment running the target operation and maintenance plan; and acquire the operating status data of the power supply equipment after maintenance based on the target operation and maintenance plan; and adjust the unselected operation and maintenance plan in the initial operation and maintenance scheme according to the difference between the operating status data and the operation and maintenance simulation results until the difference meets the iteration stop condition to obtain the target operation and maintenance scheme of the power supply equipment.

[0154] In some embodiments, the adjustment module 604 is further configured to associate the running status data and the operation and maintenance simulation results at the same time according to the collection time of the running status data and the generation time of the operation and maintenance simulation results, and to delete the unassociated running status data and operation and maintenance simulation results.

[0155] In some embodiments, the scheme module 603 is further configured to determine the indicator data of the simulated power supply equipment under at least one state evaluation indicator based on the simulation operation results; for each state evaluation indicator, determine a reference operation and maintenance scheme based on the indicator data of the state evaluation indicator; and determine an initial operation and maintenance scheme for the power supply equipment based on the reference operation and maintenance schemes corresponding to all state evaluation indicators.

[0156] In some embodiments, the scheme module 603 is further configured to determine the index data of the simulated power supply equipment under the operational stability index based on the parameter fluctuation range, continuous running time, and normal operating frequency of the simulated power supply equipment in the simulation operation results; and, based on a preset risk assessment function, determine the index data of the simulated power supply equipment under the operational stability index based on the frequency of abnormal data and performance parameter offset of the simulated power supply equipment in the simulation operation results; and, perform linear fitting on the parameter change rate and loss increment of the simulated power supply equipment in the equipment operation results to determine the index data of the simulated power supply equipment under the operational stability index.

[0157] In some embodiments, the acquisition module 601 is further configured to acquire scenario description data of the operation and maintenance scenario and device description data of the power supply equipment; generate a simulated operation and maintenance scenario of the operation and maintenance scenario based on the scenario description data; and generate a simulated power supply equipment of the power supply equipment based on the device description data.

[0158] The various modules in the aforementioned operation and maintenance adjustment device for heavy-haul railway power supply equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0159] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment.

[0160] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0161] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0162] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0163] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0164] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0165] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0166] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for adjusting the operation and maintenance scheme of power supply equipment for heavy-haul railways, characterized in that, The method includes: The simulation operation and maintenance scenario is obtained, as well as the simulation power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways. The simulated power supply equipment is run in the simulated operation and maintenance scenario to obtain the simulation operation results of the simulated power supply equipment. Based on the simulation results, an initial operation and maintenance plan for the power supply equipment is determined; Based on the operational status deviation between the power supply equipment and the simulated power supply equipment, the initial operation and maintenance plan is adjusted to obtain the target operation and maintenance plan; The target operation and maintenance plan is used to perform operation and maintenance on the power supply equipment.

2. The method according to claim 1, characterized in that, The initial operation and maintenance plan includes at least one operation and maintenance schedule; adjusting the initial operation and maintenance plan based on the operational status deviation between the power supply equipment and the simulated power supply equipment to obtain the target operation and maintenance plan includes: For each iteration, at least one unselected operation and maintenance plan is sequentially selected from the initial operation and maintenance plan as the target operation and maintenance plan; Run each of the target operation and maintenance plans in the simulated operation and maintenance scenario to obtain the operation and maintenance simulation results of the simulated power supply equipment running the target operation and maintenance plans; and, Obtain the operating status data of the power supply equipment after maintenance based on the target operation and maintenance plan; Based on the differences between the operational status data and the operation and maintenance simulation results, adjust the operation and maintenance plans that were not selected in the initial operation and maintenance plan until the differences meet the iteration stop condition, and obtain the target operation and maintenance plan for the power supply equipment.

3. The method according to claim 2, characterized in that, Before adjusting the unselected maintenance plans in the initial maintenance scheme based on the differences between the operational status data and the maintenance simulation results, the method further includes: Based on the collection time of the operational status data and the generation time of the operation and maintenance simulation results, the operational status data and operation and maintenance simulation results at the same time are associated, and the unassociated operational status data and operation and maintenance simulation results are deleted.

4. The method according to claim 1, characterized in that, The step of determining the initial operation and maintenance plan for the power supply equipment based on the simulation results includes: Based on the simulation results, determine the indicator data of the simulated power supply equipment under at least one state evaluation index; For each status assessment indicator, a reference operation and maintenance plan is determined based on the indicator data of the status assessment indicator. Based on the reference operation and maintenance plans corresponding to all status assessment indicators, an initial operation and maintenance plan for the power supply equipment is determined.

5. The method according to claim 4, characterized in that, The state assessment indicators include: operational stability indicators, fault risk indicators, and performance degradation indicators; determining the indicator data of the simulated power supply equipment under at least one state assessment indicator based on the simulation results includes: Based on the parameter fluctuation range, continuous operating time, and normal operating frequency of the simulated power supply equipment in the simulation results, determine the indicator data of the simulated power supply equipment under the operational stability index; and, Based on a preset risk assessment function, and according to the frequency of abnormal data and performance parameter offsets of the simulated power supply equipment in the simulation results, the indicator data of the simulated power supply equipment under the operational stability index are determined; and, Linear fitting is performed on the parameter change rate and loss increment of the simulated power supply equipment in the equipment operation results to determine the index data of the simulated power supply equipment under the operation stability index.

6. The method according to claim 1, characterized in that, The simulated operation and maintenance scenario for acquiring the operation and maintenance scenario, and the simulated power supply equipment for the power supply equipment set in the operation and maintenance scenario, include: Obtain the scenario description data of the operation and maintenance scenario and the device description data of the power supply equipment; Based on the scenario description data, a simulated operation and maintenance scenario is generated; and, Based on the device description data, a simulated power supply device for the power supply device is generated.

7. A device for adjusting the operation and maintenance scheme of heavy-haul railway power supply equipment, characterized in that, The device includes: The acquisition module is used to acquire the simulated operation and maintenance scenario of the operation and maintenance scenario, and the simulated power supply equipment of the power supply equipment set in the operation and maintenance scenario; the power supply equipment is the power supply equipment used to supply power to heavy-haul railways; The simulation module is used to run the simulated power supply equipment in the simulated operation and maintenance scenario and obtain the simulation operation results of the simulated power supply equipment. The scheme module is used to determine the initial operation and maintenance scheme for the power supply equipment based on the simulation results. The adjustment module is used to adjust the initial operation and maintenance plan based on the operating status deviation between the power supply equipment and the simulated power supply equipment to obtain the target operation and maintenance plan; The target operation and maintenance plan is used to perform operation and maintenance on the power supply equipment.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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