Simulation debugging method and system for single-vehicle centralized control function of centralized power motor train unit

Abstract

The invention relates to the technical field of motor trains, in particular to a single-vehicle centralized control function simulation debugging method and system for a centralized power motor train unit, and the method comprises the following steps: analyzing the simulation station task queue length and task instruction execution time delay based on a motor train unit centralized control system, standardizing data, and judging the task priority in combination with the equipment response rate; and after sorting is completed, allocating to a proper station, identifying abnormal equipment, setting a flow jump node through real-time current data comparison, and adjusting and debugging a flow path chain. Abnormal unit positioning and accurate judgment of risk links are achieved by dynamically comparing abnormal fluctuation and normal working conditions, the step-by-step execution sequence of debugging tasks is dynamically adjusted according to data feedback, a flow path responds to various emergencies in the debugging process in a self-adaptive mode, debugging task assignment and node jumping are driven by data signals, and the debugging efficiency is improved. The flow adjustment is timely and accurate, the debugging resource utilization rate and the flow response efficiency are improved, and fault misjudgment and resource waste are reduced.

Description

Technical Field

[0001] This invention relates to the field of high-speed train technology, and in particular to a method and system for simulating and debugging the centralized control function of a single train in a centralized power high-speed train. Background Technology

[0002] The field of high-speed train technology involves the power distribution and operation control in high-speed railway transportation systems, including the train's power system, traction and braking control, signal response, onboard control unit, and integrated control system. It mainly covers the overall technical system of train sets in terms of formation, traction power configuration, energy distribution, fault detection, and operational safety assurance, forming technical support for efficient, stable, and safe train operation management. Among these methods, the traditional simulation and debugging method for the centralized control function of a single car in a centralized power train refers to the step-by-step simulation and debugging of the centralized control function of each car by manually setting onboard operating terminals, adjusting switch states, loading simulated signals, detecting relevant circuit responses, and directly connecting relevant lines with testing instruments for breakpoint testing or functional verification.

[0003] In traditional methods, parameter acquisition during centralized control function simulation and debugging is limited to a single stage, task priority allocation lacks dynamic sorting support, equipment status cannot be continuously tracked, workstation load fluctuations and risk node changes are difficult to detect in a timely manner, process adjustments rely entirely on pre-set sequences, and task scheduling congestion and error jumps often occur when encountering a surge in tasks or abnormal parameters, resulting in delayed risk warnings, insufficient linkage between debugging stages, and debugging resources that are difficult to adapt to complex scenario changes. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for simulating and debugging the centralized control function of a single-car EMU (Electric Multiple Unit). The technical solution is as follows:

[0005] On the one hand, a method for simulating and debugging the centralized control function of a single-car EMU with centralized power is provided, including the following steps:

[0006] S1: Based on the EMU centralized control system, analyze the length of the simulated workstation task queue, collect the execution delay data of each task instruction, standardize the delay, add the standardized delay to the response rate of the workstation equipment, determine the task priority according to the calculation result, and obtain the scheduling priority task sequence.

[0007] S2: Based on the scheduling priority task sequence, determine the load status of the simulated workstation equipment, collect load-related data, compare the load status with the allocation boundary, filter workstations that meet the allocation criteria, and allocate priority tasks to the workstations in sequence to obtain a mapping allocation relationship set.

[0008] S3: Based on the mapping and allocation relationship set, calculate the temperature change range of the main control unit, collect continuous temperature monitoring data, analyze the difference between the temperature change and the average temperature of the same period, perform standard deviation analysis, screen out devices with deviation clusters, determine the source of anomalies, and obtain information on devices with anomaly risks.

[0009] S4: Based on the abnormal risk equipment information, analyze the current change amplitude of the instruction response unit in the debugging steps, compare it with the debugging standard reference group, determine the data items that are lower than the standard, set their debugging steps as jump nodes, and obtain the jump trigger signal group.

[0010] On the other hand, the scheduling priority task sequence includes priority parameters, task queue information, and response rate tags; the mapping allocation relationship set includes a task allocation table, workstation distribution information, and task workstation assignment index; the abnormal risk equipment information includes equipment identifier, risk level, and abnormal parameter index; and the jump trigger signal group includes jump node sequence number, signal type, and trigger rule.

[0011] On the other hand, the specific steps for obtaining the scheduling priority task sequence are as follows:

[0012] S101: Based on the EMU centralized control system, analyze the waiting and response links experienced by each debugging task from the issuance of the instruction to the completion of the workstation action, calculate the queuing order of each task in the queue and the corresponding workstation resource occupancy, filter the waiting and occupancy distribution of tasks in the queue, and obtain the queue waiting occupancy coefficient.

[0013] S102: Based on the queue waiting occupancy coefficient, compare the original execution steps of each task, optimize the standard response performance of each task in different workstations, analyze the standard response and the equipment processing capacity of the workstation, establish the superposition relationship of workstation processing capacity between tasks, and obtain the aggregated processing pressure.

[0014] S103: Determine the performance of tasks in the aggregated amount of processing pressure, adjust the execution order of tasks to be scheduled, and sort the tasks in order of workstation processing capacity to obtain a priority task sequence.

[0015] On the other hand, the steps for obtaining the mapping allocation relationship set are as follows:

[0016] S201: Based on the scheduling priority task sequence, analyze the allocation order of each task, combine the current operating status of the simulated workstation, compare the number of tasks with the resource response performance of the workstation, determine the current load pressure distribution of each workstation, and obtain the resource occupancy density.

[0017] S202: Based on the resource occupancy density, filter the resource usage of workstations, compare the load pressure of each workstation with the allocation boundary, identify workstations whose resource pressure does not exceed the allocation boundary, and assign priority tasks to the filtered workstations one by one to obtain the task workstation distribution sequence.

[0018] S203: Based on the task workstation distribution sequence, adjust the task-workstation correspondence records, optimize the workstation information assignment content when allocating each task, improve the corresponding set of task number, workstation label and allocation link, and obtain the mapping allocation relationship set.

[0019] On the other hand, the specific steps for obtaining the abnormal risk equipment information are as follows:

[0020] S301: Based on the mapping and allocation relationship set, analyze the associated main control unit, monitor the continuous temperature change of each unit during task scheduling, compare the fluctuation of temperature data at each monitoring time point, determine the fluctuation characteristics of each time period, and obtain the temperature change range parameter.

[0021] S302: Based on the temperature change range parameters, calculate the temperature data for each time period, analyze the degree of deviation between the temperature of each monitoring point and the average value of the period under the same cycle, summarize the offset performance of the equipment unit in the cycle, and obtain the offset degree distribution.

[0022] S303: Based on the aforementioned offset distribution, determine the concentration trend of the offset phenomenon within a single device unit, identify device units with continuous offset phenomena, establish a set of associated device numbers and corresponding abnormal information, and obtain abnormal risk device information.

[0023] On the other hand, the specific steps for obtaining the jump trigger signal group are as follows:

[0024] S401: Based on the abnormal risk equipment information, analyze the debugging steps involved, collect the real-time current signal of the command response unit in each debugging stage, compare the fluctuation range of the current signal in each debugging period, determine the change amplitude of each response unit in the task process, and obtain the response current fluctuation amount.

[0025] S402: Based on the response current fluctuation, compare the current fluctuation data of each unit with the reference range of the debugging standard reference group, identify the data content that is lower than the standard lower limit, summarize the debugging step number and associated position corresponding to the data, and obtain the low current event index.

[0026] S403: Based on the low current event index, determine its corresponding position in the original debugging step sequence, mark this part of the task as a jumpable node, collect the jump number and the corresponding trigger condition type, and obtain the jump trigger signal group.

[0027] On the other hand, the method also includes:

[0028] S5: Based on the jump trigger signal group, adjust the execution sequence of the debugging steps, read the jump node steps, adjust the original path order, skip or insert compensation operations for the marked steps, correct the process structure, and obtain the process execution path chain.

[0029] The process execution path chain includes the step node sorting, path structure information, and process change records.

[0030] On the other hand, the specific steps for obtaining the process execution path chain are as follows:

[0031] S501: Based on the jump trigger signal group, analyze the status of each node in the debugging step sequence, determine the order of each step marked as a jump node in the original process path, adjust the connection order between steps, optimize the associated paths before and after nodes, and obtain the node path adjustment factor.

[0032] S502: Based on the node path adjustment factor, filter the marked jump nodes, determine whether the step is skipped or a compensation action is inserted, adjust the execution link of the debugging step, establish the updated complete process information, and obtain the process execution path chain.

[0033] On the other hand, the EMU centralized control system refers to an information platform or hardware and software system used for centralized management and monitoring of the operating status, data acquisition, task scheduling and automatic control of various equipment of a single EMU. The simulation workstation task queue length refers to the number of debugging tasks waiting to be processed at each workstation used for simulation testing on the simulation debugging platform.

[0034] On the other hand, a simulation and debugging system for the centralized control function of a single-car EMU with centralized power is provided. This system is applied to the simulation and debugging method for the centralized control function of a single-car EMU with centralized power, including:

[0035] The priority sorting module is based on the EMU centralized control system. It analyzes the length of the simulated workstation task queue, collects the execution delay data of each task instruction, standardizes the execution delay, adds the standardized delay to the response rate of the workstation equipment according to the task, judges the task priority based on the addition result, sorts the scheduling order of all tasks, and obtains the scheduling priority task sequence.

[0036] Based on the scheduling priority task sequence, the intelligent allocation module determines the load status of the simulated workstation equipment, collects workstation load-related data, compares the load status with the allocation boundary, filters workstations that meet the allocation criteria, allocates the priority tasks to the filtered workstations in sequence, corrects the allocation mapping relationship of each task, and obtains a mapping allocation relationship set.

[0037] Based on the mapping and allocation relationship set, the anomaly identification module calculates the temperature change range of the main control unit, collects continuous temperature monitoring data, calculates the difference between the temperature change and the average temperature in the same period, performs standard deviation analysis on the deviation data of the equipment unit, filters out the situation where the deviation is clustered in the equipment unit, determines the unit of the anomaly source, and obtains the information of the equipment with anomaly risk.

[0038] Based on the abnormal risk equipment information, the jump determination module analyzes the current change amplitude of the command response unit in the corresponding debugging step, collects the real-time current data of the command response unit, compares the collected data with the debugging standard reference group, determines the data items that are lower than the reference group, sets the debugging step corresponding to each data item that is lower than the standard as a jump node, and summarizes all nodes to obtain the jump trigger signal group.

[0039] Based on the jump trigger signal group, the path adjustment module adjusts the execution sequence of the debugging steps, reads the debugging steps marked as jump nodes, adjusts the original step path order, performs skip or insertion compensation operations on the marked steps, corrects the complete process structure, and obtains the process execution path chain.

[0040] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0041] Based on real-time collection and weighting of multiple parameters, workstation resource allocation and task priority are automatically linked. By dynamically comparing abnormal fluctuations with normal working conditions, the system can accurately locate abnormal units and identify risky links. The step-by-step execution order of debugging tasks is dynamically adjusted based on data feedback. The process path adaptively responds to various emergencies during the debugging process. Debugging task assignment and node jumps are all driven by data signals, ensuring timely and accurate process adjustments, improving the utilization rate of debugging resources and the efficiency of process response, and reducing fault misjudgment and resource waste. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of the main steps of the present invention;

[0044] Figure 2 This is a flowchart of steps S1 of the present invention;

[0045] Figure 3 This is a flowchart of steps S2 of the present invention;

[0046] Figure 4This is a flowchart of steps S3 of the present invention;

[0047] Figure 5 This is a flowchart of step S4 of the present invention;

[0048] Figure 6 This is a flowchart of steps S5 of the present invention;

[0049] Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0051] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0052] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0053] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0054] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0055] This invention provides a method for simulating and debugging the centralized control function of a single car in a centralized power EMU, such as... Figure 1 As shown, it includes the following steps:

[0056] S1: Based on the EMU centralized control system, analyze the length of the simulated workstation task queue, collect the execution delay data of each task instruction, standardize the execution delay, add the standardized delay to the response rate of the workstation equipment according to the task, determine the task priority based on the addition result, sort all task scheduling order, and obtain the scheduling priority task sequence.

[0057] S2: Based on the priority task sequence, determine the load status of the simulated workstation equipment, collect workstation load correlation data, compare the load status with the allocation boundary, filter workstations that meet the allocation criteria, allocate priority tasks to the filtered workstations in sequence, correct the allocation mapping relationship of each task, and obtain the mapping allocation relationship set.

[0058] S3: Based on the mapping and allocation relationship set, calculate the temperature change range of the main control unit, collect its continuous temperature monitoring data, calculate the difference between the temperature change and the average temperature in the same period, use standard deviation analysis on the deviation data of the equipment unit, screen the situation where the deviation is clustered in the equipment unit, determine the abnormal source unit, and obtain abnormal risk equipment information.

[0059] S4: Based on the information of abnormal risk equipment, analyze the current change amplitude of the command response unit in the corresponding debugging step, collect the real-time current data of the command response unit, compare the collected data with the debugging standard reference group, determine the data items that are lower than the reference group, set the debugging step corresponding to each data item that is lower than the standard as a jump node, and summarize all nodes to obtain the jump trigger signal group.

[0060] S5: Based on the jump trigger signal group, adjust the execution sequence of the debugging steps, read the debugging steps that have been marked as jump nodes, adjust the original step path order, perform skip or insertion compensation operations on the marked steps, correct the complete process structure, and obtain the process execution path chain.

[0061] The scheduling priority task sequence includes priority parameters, task queue information, and response rate tags. The mapping and allocation relationship set includes a task allocation table, workstation distribution information, and task workstation assignment index. The abnormal risk equipment information includes equipment identifier, risk level, and abnormal parameter index. The jump trigger signal group includes jump node sequence number, signal type, and trigger rule. The process execution path chain includes step node sorting, path structure information, and process change record.

[0062] In S1, the EMU centralized control system refers to a comprehensive information platform or hardware and software system used for centralized management and monitoring of the operating status, data acquisition, task scheduling, and automatic control of various equipment on a single EMU train; the simulation workstation task queue length refers to the number of debugging tasks waiting to be processed at each workstation used for simulation testing on the simulation debugging platform, reflecting the current load of the workstation; the instruction execution delay data refers to the time interval between the issuance of the task instruction and the response and completion of the action by the workstation equipment after each debugging task is assigned to the simulation workstation, reflecting the workstation processing efficiency; standardization processing refers to transforming the collected raw delay data to the same dimension, interval, or distribution (such as normalization) so that data from different workstations can be directly compared or used in subsequent mathematical operations; the workstation equipment response rate refers to the average response capability of the equipment used at the workstation for debugging tasks, reflecting the efficiency of the workstation in processing tasks per unit time; task priority judgment refers to comprehensively comparing and calculating all tasks to be scheduled based on parameters such as delay and response rate to determine the order in which each task is executed first.

[0063] In S2, the load status of the simulated workstation equipment refers to the workload of the equipment at each simulated workstation, mainly reflected in the number of currently running tasks and resource utilization. Workstation load-related data refers to data directly related to the current load status of each workstation, such as task queue length, current equipment operating status, and historical average load. Allocation limits refer to the boundary conditions preset by the scheduling system regarding the tasks that can be allocated to a workstation, such as the maximum number of tasks a single workstation can handle or resource usage warning lines. Workstations that meet the allocation criteria refer to workstations whose load status is within an acceptable range, allowing for the allocation of new tasks. Allocation mapping relationship refers to the correspondence formed by pairing the tasks with higher priority with the selected workstations, clarifying which workstation each task is specifically assigned to.

[0064] In S3, the temperature change range of the main control unit refers to the temperature fluctuation range of the main control unit (such as the controller, motherboard, etc.) within a certain period, which is an important indicator for monitoring the safety of hardware operation; the difference from the average temperature within the same period refers to the difference between the current temperature data of the main control unit and the average temperature of the historical temperature within the same period, which is used to detect abnormal temperature rise or fall; the equipment unit deviation data refers to the deviation of various operating parameters (temperature, etc.) of the equipment unit from the average or normal operating conditions, which is used to determine abnormal behavior; standard deviation analysis refers to the statistical standard deviation calculation of the deviation data of the equipment unit to measure the degree of dispersion of the deviation, which is used to detect abnormal data distribution; clustering refers to the statistically concentrated occurrence of abnormal deviation data of certain equipment units, indicating that there is a systematic anomaly in the unit; the anomaly source unit refers to the hardware unit that is determined to have the most concentrated or most prominent abnormal signal through the aforementioned data analysis, which is the key target for subsequent fault injection and simulation debugging.

[0065] In S4, the debugging steps refer to the debugging process being broken down into several sequential operation units, each corresponding to a specific hardware check, signal acquisition, or command issuance action; the command response unit refers to the hardware unit that executes the debugging command and collects the corresponding signal feedback, such as the drive module, actuator, and response module; the current change amplitude refers to the maximum change range of the current signal of the monitored unit during the debugging process, used to determine the equipment response status and energy consumption characteristics; the debugging standard reference group refers to the qualified range of each parameter in the debugging process set according to experience or specifications, used to compare whether the currently collected data is abnormal; data items below the reference group refer to the situation where the actual collected data is less than the lower limit of the reference standard, indicating that the relevant equipment has an abnormal response; setting a jump node means that when the key parameters of a certain debugging step do not meet the standard, the step is marked in the process as a node that needs to be skipped or requires additional compensation operations.

[0066] In S5, the debugging step execution sequence refers to the actual execution order of all operation steps in the entire debugging process; skipping or inserting compensation operations refers to skipping certain invalid or risky steps in a targeted manner based on the jump signal, or automatically inserting compensation checks, secondary executions, and other operations; the complete process structure refers to the logical chain and actual arrangement of all debugging operation steps after the correction, ensuring that all process actions are based on evidence and the link is closed.

[0067] like Figure 2 As shown, the specific steps for obtaining the scheduling priority task sequence are as follows:

[0068] S101: Based on the EMU centralized control system, analyze the waiting and response links experienced by each debugging task from the issuance of the instruction to the completion of the workstation action, calculate the queuing order of each task in the queue and the corresponding workstation resource occupancy, filter the waiting and occupancy distribution of tasks in the queue, and obtain the queue waiting occupancy coefficient.

[0069] Time information is collected throughout the entire process from task issuance to workstation response. The time from the issuance of the instruction to the end of the queue for each task is designated as the waiting period. Then, the response time from receiving the task to its completion at the workstation is extracted. For example, task A takes 15 seconds from instruction issuance to the end of the queue, and 45 seconds from entering the execution state to completion. These two time periods are extracted and recorded. The current task queue at the workstation is then scanned to calculate the number of tasks in the queue, their order, and the estimated resource usage time for each task. For instance, if a workstation currently has 5 tasks queued, with estimated execution times of 2.0 minutes, 1.5 minutes, 1.0 minute, 1.8 minutes, and 1.2 minutes respectively, and a standard processing cycle is defined as 10 minutes, then the resource usage ratio of each task is 2:1. The tasks are numbered 1 to 5 according to their order in the queue: 0%, 15%, 10%, 18%, and 12%. Each task's queue position is multiplied by its utilization percentage to obtain a combined value, representing the overall resource burden of each task in the queue. For example, the third task, in the third position, has a utilization percentage of 10% and a combined value of 0.30. Tasks are then classified into different levels based on their combined values: values ​​less than 0.25 are low-level, values ​​between 0.25 and 0.6 are medium-level, and values ​​greater than or equal to 0.6 are high-level. In this case, the third task's combined value of 0.30 falls into the medium-level category. This process filters out the waiting utilization values ​​of all tasks and outputs a statistical table containing task number, queue position, resource utilization percentage, and combined level, preparing for determining the scheduling order.

[0070] S102: Based on the queue waiting occupancy coefficient, compare the original execution links of each task, optimize the standard response performance of each task in different workstations, analyze the standard response and the equipment processing capacity of the workstation, establish the superposition relationship of workstation processing capacity between tasks, and obtain the aggregated processing pressure.

[0071] Continuing to retrieve the actual processing performance of each task in its original workstation from the task scheduling database, the performance data is the number of feedbacks completed per unit time. For example, workstation A completes an average of 4 feedbacks per minute, workstation B 6, and workstation C 5.5. The feedback performance of the task's original workstation is compared with the feedback performance of other alternative workstations to calculate the difference. If the values ​​of workstations B and C are both higher than those of workstation A, with differences of 2 and 1.5 respectively, the substitution advantage of this task in workstations B and C is recorded. Then, combined with the resource usage data of the tasks currently assigned to each alternative workstation, the estimated execution time of this task is set to 1.5 minutes, workstation B is currently using 60% of its resources, and workstation C is using 55%. The execution time of the task is compared with the resource usage of the workstations. The system combines different workstations to calculate the resource pressure value generated by the task at each workstation. For example, if the task is placed at workstation B, the total pressure generated by the task at workstation B is 0.9, and the total pressure generated at workstation C is 0.83. The system categorizes and summarizes the load of the task at all available workstations in this way, and then aggregates the load data of multiple tasks to form the aggregated workstation pressure. For example, if there are currently three tasks at workstation B with pressure values ​​of 0.2, 0.3, and 0.4 respectively, the aggregated pressure will be 0.9. The aggregated pressure caused by the current scheduling combination at each workstation is listed in a statistical list, and the output includes the task number, the workstation it belongs to, the feedback performance, the current resource usage, the estimated time of the task, and the calculated aggregated pressure value, thus preparing the data foundation for the next step of scheduling priority ranking.

[0072] S103: Determine the performance of tasks in the processing pressure aggregation, adjust the execution order of tasks to be scheduled, sort the tasks according to the processing capacity performance of the workstation, and obtain the scheduling priority task sequence.

[0073] The aggregated pressure data from the previous step is analyzed, and the workstation pressure values ​​for each task are categorized. First, all aggregated values ​​are divided into three segments: 0 to 0.5 represents low load, 0.5 to 1.0 represents medium load, and 1.0 and above represents high load. For example, a task with an aggregated value of 0.72 at its original workstation is classified as a medium load task. Combining this with the waiting occupancy level obtained in the previous step, if the task's combined value is 0.30, it is classified as a medium level. A dual sorting strategy of "low load priority, low occupancy priority" is adopted. Tasks are first sorted from low to high aggregated pressure, and then from low to high occupancy level within the same pressure level, forming an ordered list. For example, tasks A, B, and C have pressure values ​​of 0.48, 0.72, and 1.05 respectively, with occupancy levels of low, medium, and high. Task A takes priority over B, and B takes priority over C, placing them in the 1st, 2nd, and 3rd positions of the task list. Finally, this priority task sequence list is output, which will serve as the input for subsequent task scheduling and workstation allocation.

[0074] like Figure 3As shown, the specific steps for obtaining the mapping assignment relation set are as follows:

[0075] S201: Based on the scheduling priority task sequence, analyze the allocation order of each task, combine the current running status of the simulation workstation, compare the number of tasks with the resource response performance of the workstation, determine the current load pressure distribution of each workstation, and obtain the resource occupancy density.

[0076] The scheduling number of each task is read according to its priority table. Combining the estimated execution time corresponding to the task number with the preset resource consumption ratio, the running status of all available workstations is read in the simulation workstation monitoring platform. The current number of tasks, allocated time slices, and unprocessed resource segment lengths for each workstation are obtained. The total execution time of the currently running tasks for each workstation is summarized to form the resource utilization ratio. Then, the resource utilization ratio of each workstation is uniformly converted based on the current scheduling cycle length. For example, if workstation A is currently assigned 3 tasks, occupying a total of 5 minutes, and the standard cycle is 10 minutes, then the resource utilization rate of workstation A is 50%. Simultaneously, the estimated execution time and required resource load flag value of each task in the task list are extracted, and the task parameters are compared with the remaining resources of each workstation. The system compares the current situation to determine whether each task can be completed within the remaining time at the current workstation. If a task requires 2 minutes, it determines whether workstation A has 2 minutes remaining for execution. Based on the calculation results, it determines whether the task load at that workstation is under pressure. To accurately depict the current load of different workstations, the resource occupancy rate is converted into a density quantification value. 0% to 40% is defined as low density, 41% to 80% as medium density, and above 81% as high density. The resource usage density is classified and statistically analyzed for each workstation. Finally, a structured dataset including workstation number, number of running tasks, allocated time, resource utilization rate, and occupancy density level is output as the basis for subsequent task placement. Each piece of workstation information in this structured dataset is associated and bound with real-time running data to obtain the resource occupancy density.

[0077] S202: Based on resource occupancy density, filter the resource usage of workstations, compare the load pressure of each workstation with the allocation boundary, identify workstations whose resource pressure does not exceed the allocation boundary, and assign priority tasks to the filtered workstations one by one to obtain the task workstation distribution sequence.

[0078] The resource density level of each workstation is retrieved sequentially from the dataset. The load pressure data for each workstation is extracted item by item and compared with pre-defined allocation boundary values. The allocation boundary is set based on the performance level of the workstation equipment. For example, a medium-load workstation is allowed to continue allocating tasks up to 80% resource utilization, while a high-load workstation is only allowed up to 90%. The current utilization rate of each workstation is compared with its allocation boundary item by item. If workstation B's current occupancy rate is 65%, and its allowable upper limit is 80%, then workstation B is considered to be in an allocable state. If workstation C's occupancy rate is 88%, with an upper limit of 90%, but the time required for the task to be allocated is 3 minutes and only 1 minute of available resources remains, then workstation C is determined to be in an unallocable state. All workstations that meet the criteria are filtered out, and a list of available workstations is created. Then, tasks are read sequentially from the priority task sequence. After each task is read, the execution time required for the task is matched with the remaining resources of the available workstations. Workstations with lower resource utilization are matched first. If multiple workstations meet the criteria, the one with fewer currently running tasks is selected. In actual operation, if task D needs to be executed for 2 minutes, workstation E has a resource utilization rate of 55% and has 3 executed tasks, and workstation F has a resource utilization rate of 50% and has 1 executed task, then task D is assigned to workstation F. The task-workstation pairing operation is completed in this way to form a distribution table in which task number and workstation number correspond one-to-one, and the workstation distribution sequence of the task is output.

[0079] S203: Based on the task workstation distribution sequence, adjust the task-workstation correspondence records, optimize the workstation information assignment content when allocating each task, improve the corresponding set of task number, workstation label and allocation link, and obtain the mapping allocation relationship set;

[0080] The workstation information field in the original task scheduling record is updated, and the target workstation number corresponding to the matched task number is written into the task allocation table. The allocation field of the task record is updated at the data level, and the task scheduling record table is reconstructed according to the task number. The workstation label field is added or updated for each record. At the same time, the scheduling priority label corresponding to each task is read to form a mapping index structure between tasks and workstations. The allocation trajectory corresponding to each task also needs to be added to the scheduling link structure table. The handover path number of the task from the scheduling list to the target workstation is marked in the link field to form a chain process identifier for the workstation to receive the task. For example, if task T07 is assigned to workstation W03, the link trajectory of T07 is recorded as "L01→W03". The task number, target workstation number, workstation label and link trajectory are formed into a three-element structure according to the task dimension and written into the allocation mapping set. In the actual scenario, this mapping data is transmitted to the workstation task management unit as the core basis, which receives the scheduling instructions and prepares the execution environment to complete the landing and binding of the task allocation result of the scheduling system on the workstation side, thereby obtaining the mapping allocation relationship set.

[0081] like Figure 4 As shown, the specific steps for obtaining information on abnormal risk devices are as follows:

[0082] S301: Based on the mapping and allocation relationship set, analyze the associated main control unit, monitor the continuous temperature change of each unit during task scheduling, compare the fluctuation of temperature data at each monitoring time point, determine the fluctuation characteristics of each time period, and obtain the temperature change range parameter.

[0083] Extract the workstation number and the main control unit identifier bound to each workstation for each task. Locate the main control unit that each task depends on during execution using a mapping index. Then, retrieve the continuous temperature records of the corresponding main control unit from the environmental monitoring module. Extract the corresponding temperature change curve according to the actual time period allocated to the task. Record the temperature data at a sampling frequency of once every 5 seconds to form a monitoring sequence. Sequentially extract the temperature values ​​of continuous monitoring points and form time-temperature pairs according to the sampling time order. For example, within a 10-minute task cycle, the temperature of main control unit U01 at 0 seconds, 5 seconds, 10 seconds, 15 seconds, and 20 seconds are 42.3℃, 43.1℃, 44.0℃, 44.5℃, and 44.9℃ respectively. Record the temperature data for all time points accordingly, and then analyze the differences in temperature values ​​between adjacent time points. The system determines the temperature fluctuation range, for example, from 0 to 20 seconds, with temperature increases of 0.8℃, 0.9℃, 0.5℃, and 0.4℃ every 5 seconds. It records the temperature change range within each consecutive time period, then divides the task into several fixed time periods according to the task duration. The maximum and minimum temperature values ​​within each period are extracted as the start and end temperatures of that period, and the difference between them is used as the temperature range parameter for that period. If a period increases from 45.2℃ to 48.7℃, the temperature change for that period is recorded as 3.5℃. The fluctuation range of all temperature ranges is included in the task response temperature statistics table of the main control unit, and the fluctuation range is divided into three levels according to the magnitude of the fluctuation: less than 2℃ is low fluctuation, between 2℃ and 5℃ is medium fluctuation, and greater than 5℃ is high fluctuation. Thus, the temperature change range parameter of the main control unit during the task scheduling process is output.

[0084] S302: Based on the temperature change range parameter, calculate the temperature data for each time period, analyze the degree of deviation between the temperature of each monitoring point and the average value of the cycle under the same cycle, summarize the offset performance of the equipment unit in the cycle, and obtain the offset degree distribution.

[0085] The temperature values ​​of each main control unit are sequentially read at each sampling time point within a specific task cycle. The start and end points of the cycle are determined according to the actual duration of each task. Then, all temperature values ​​within the cycle are statistically accumulated and averaged to obtain the average temperature for that task cycle. The difference between the temperature value at each monitoring time point and the average temperature for that cycle is then processed to form a deviation value group. For example, if the temperature measured by a main control unit within a certain task cycle is 43.0℃, 44.2℃, 45.0℃, 43.8℃, 44.6℃, 45.5℃, and 44.0℃, and the calculated average temperature for the cycle is 44.4℃, then the deviation of each point from the average is -1.4℃. The deviation values ​​of ℃, -0.2℃, 0.6℃, -0.6℃, 0.2℃, 1.1℃, and -0.4℃ are statistically analyzed to distinguish between positive and negative directions, constructing the deviation performance curve of the main control unit under the task cycle. The deviation values ​​are classified according to their magnitude: deviations of 0℃ to 1℃ are considered slight deviations, 1℃ to 2℃ are considered moderate deviations, and deviations greater than 2℃ are considered severe deviations. Each monitoring point is marked according to the deviation level, and the proportion of different deviation levels in the entire cycle is statistically analyzed. For example, in the above example, slight deviations occurred 5 times, moderate deviations occurred once, and there were no severe deviations. The deviation performance of this cycle is recorded as slightly dominant, and a deviation degree distribution data table is output.

[0086] S303: Based on the distribution of offset degree, determine the concentration trend of offset phenomenon in a single equipment unit, identify equipment units with continuous offset phenomenon, establish a set of associated equipment numbers and corresponding abnormal information, and obtain abnormal risk equipment information;

[0087] The offset data of all master control units are compared horizontally to check whether a single device unit continuously exhibits the same level of offset across multiple cycles. The master control unit number corresponding to each task is read from the task scheduling sequence. The offset level for each cycle is recorded vertically, with the task cycle time period as the horizontal axis, forming an offset trend matrix. For a single master control unit, if it exhibits a medium or severe offset level in three or more consecutive task cycles, it is determined that the unit has a continuous offset phenomenon. For example, if master control unit U03 has an offset level of medium, medium, medium in five consecutive cycles... If a control unit is identified as having a concentrated offset, its number, offset trend characteristics, median offset value, and maximum offset level are recorded. Simultaneously, the task mapping table is called to look up the corresponding task execution records for that control unit. If all tasks exhibit rapid temperature rise or delayed heat dissipation, their relevant indicators are recorded in the anomaly information set to construct an identification set for anomaly control units. Each data item in the set includes the device number, number of consecutive offset cycles, offset level sequence, cumulative offset duration, task index, and anomaly tag field. This set is then output as information on anomaly risk devices.

[0088] like Figure 5As shown, the specific steps for obtaining the jump trigger signal group are as follows:

[0089] S401: Based on the information of abnormal risk equipment, analyze the debugging steps involved, collect the real-time current signal of the command response unit in each debugging stage, compare the fluctuation range of the current signal in each debugging period, determine the change amplitude of each response unit in the task process, and obtain the response current fluctuation amount.

[0090] Based on the list of abnormal equipment, the commissioning task number associated with each risky device is extracted, and the commissioning process records are searched item by item to determine the commissioning step number and the response unit involved in each task. Then, the real-time data sequence recorded by the current acquisition module of each response unit during the commissioning process is read. A complete current signal time series is formed by acquiring data once per second within the task cycle. Each commissioning step is divided into corresponding current segments by time period. The current data within each segment is extracted and extreme values ​​are compared to obtain the fluctuation range. For example, in the time period corresponding to commissioning step E12, the lowest current value of a certain response unit is 0.36A, the highest value is 0.58A, and the fluctuation range is 0.22A. The current change in step E12 for this unit is recorded as 0.22A. The same processing is applied to each step of all debugging tasks. After classifying by response unit number, the current fluctuation amplitude table for each unit at different debugging stages is output. To characterize the current change, the fluctuation amplitude is graded: fluctuation values ​​less than 0.10A are classified as weak fluctuations, 0.10A to 0.30A as moderate fluctuations, and greater than 0.30A as severe fluctuations. The corresponding level is marked in the record. For example, the 0.22A fluctuation in step E12 is classified as moderate. All processing results are summarized into a current fluctuation statistics table for the corresponding debugging steps of the response unit, and the current fluctuation amount experienced by each response unit during the task is output.

[0091] S402: Based on the response current fluctuation, compare the current fluctuation data of each unit with the reference range of the commissioning standard reference group, identify the data content that is lower than the standard lower limit, summarize the commissioning step number and associated position corresponding to the data, and obtain the low current event index.

[0092] The system retrieves the fluctuation range reference for each response unit set by the commissioning standard reference group. This standard range is established based on statistics from a large number of historical commissioning samples, and its lower limit serves as the baseline for determining anomalies. For example, if the current fluctuation range of actuator X in the standard commissioning process is 0.20A to 0.40A, then 0.20A is used as the lower limit for this unit's fluctuation. Subsequently, the actual fluctuation range collected for each step is compared. If the current fluctuation value of a response unit at a certain step is less than its corresponding lower limit, then the data is considered abnormal. For example, in step E17, the current fluctuation of the response unit is 0.14A, which is lower than the lower limit of 0.40A. 0.20A is registered as a low current event. Then, the corresponding debugging step number, task number, response unit number and timestamp are recorded together. An index structure is built for each low current event in all records. The step number of the low current situation in each debugging task is summarized according to the task dimension, and associated with its execution position and station information in the debugging flowchart. If it is found that the current fluctuation of a response unit is lower than the standard lower limit for two consecutive steps in actual execution, a continuous event identifier field is added to the index for subsequent node adjustment strategy trigger judgment. The low current event index table is output.

[0093] S403: Based on the low current event index, determine its corresponding position in the original debugging step sequence, mark this part of the task as a jumpable node, collect the jump number and the corresponding trigger condition type, and obtain the jump trigger signal group;

[0094] Based on the debugging step number recorded in each event, its position index value is searched in the original execution sequence of the debugging task to confirm the specific sequential position of the step in the overall process path. Then, based on the event type field, it is determined whether the jump trigger condition is met. If the event type is a single low current event with a value difference exceeding the standard lower limit of 0.05A, or two consecutive low current events, the debugging step is marked as a jumpable node. Subsequently, the jump node number is written into the jump number list, and the trigger condition type information of the jump point is supplemented in the process control identifier table, including classification fields such as "single low value," "consecutive low values," and "excessive low value." The process control mapping link is updated based on the workstation and step sequence of the task. All jump nodes are grouped and summarized by task to form a complete jump signal group structure. This structure consists of task number, jump step number, jump reason type, trigger condition value, and associated workstation. In the execution of the instance, for example, task T09 is judged to have a current fluctuation of only 0.09A in step E15, which is lower than the lower limit of 0.20A, with a difference of 0.11A. It is identified as an "excessive low value" type event, and E15 is set as a jump node and recorded as "Node_T09_E15". Finally, all nodes are constructed into a jump trigger signal group and output.

[0095] like Figure 6As shown, the specific steps for obtaining the process execution path chain are as follows:

[0096] S501: Based on the jump trigger signal group, analyze the status of each node in the debugging step sequence, determine the order of each step marked as a jump node in the original process path, adjust the connection order between steps, optimize the associated paths before and after nodes, and obtain the node path adjustment factor.

[0097] Analyze the debugging steps marked as jump nodes, and extract the sequence number and specific position of each step in the original debugging process. Confirm the order of jump nodes by referring to the debugging flowchart. For example, assuming steps E10, E15, and E22 are marked as jump nodes, read their original positions in the process. In the original process, E10 is in step one, E15 is in step six, and E22 is in step ten. Further analyze the connection between steps, checking for any unreasonable sequences or interruptions. If there are any discontinuities in the sequence, reorder the jump nodes. Assuming that during debugging, the execution of E15 depends on… The execution of E22 depends on the result of E10, and the execution of E22 depends on the completion of E15. Based on this logic, the order is adjusted so that E10 is executed first, followed by E15, and E22 is executed last. The steps are rearranged and the order in the task flow is re-optimized. If the connection between a certain node is not smooth or there is a risk of execution deadlock, further adjustments are made in combination with task priority and equipment availability. Finally, a new node path adjustment factor table is generated, which records the new node order, the adjustment factors, and the task numbers and workstation numbers involved, forming an updated debugging path chain. This ensures that all steps are executed in a reasonable order and resolves potential process bottlenecks, thus obtaining the node path adjustment factors.

[0098] S502: Based on the node path adjustment factor, filter the marked jump nodes, determine whether the step is skipped or the compensation action is inserted, adjust the execution link of the debugging step, establish the updated complete process information, and obtain the process execution path chain;

[0099] Filter all steps marked as jump nodes and re-examine each node in the adjusted order to determine if skipping or compensation actions are required. For each jump node, first determine if the skipping action was triggered by a low current event, abnormal feedback, or other specific conditions. If the current value is too low during the execution of a step and fails to meet the standard requirements, the step should be skipped and marked as "Skip". If the step only delays execution but does not affect the overall process, it will be marked as "Compensation". If the step requires compensation, a compensation operation is inserted based on task priority and workstation availability. For example, if current fluctuations are detected during the execution of task E12... If an error occurs and the value is below the standard lower limit, it is determined that the error should be skipped. The subsequent step E13 needs to be compensated, so E13 will be rescheduled to the task chain and the execution order will be readjusted according to the available time of the workstation. The skip or compensation operation is marked in the execution chain of each debugging step, and the scheduling path of each task is updated. Finally, the adjusted step sequence is recorded in the task execution chain. Each skip or compensation operation is assigned a corresponding number, marking its temporary position in the debugging process, and the information of the entire debugging process is updated. In this way, a complete debugging process chain including skip nodes and compensation steps is established, and the process execution path chain is obtained.

[0100] like Figure 7 As shown, a centralized control function simulation and debugging system for a single-car EMU (Electric Multiple Unit) includes:

[0101] The priority sorting module is based on the EMU centralized control system. It analyzes the length of the simulated workstation task queue, collects the execution delay data of each task instruction, standardizes the execution delay, adds the standardized delay to the response rate of the workstation equipment according to the task, judges the task priority based on the addition result, sorts the scheduling order of all tasks, and obtains the scheduling priority task sequence.

[0102] The intelligent allocation module determines the load status of the simulated workstation equipment based on the priority task sequence, collects workstation load-related data, compares the load status with the allocation boundary, filters workstations that meet the allocation criteria, allocates priority tasks to the filtered workstations in sequence, corrects the allocation mapping relationship of each task, and obtains a mapping allocation relationship set.

[0103] The anomaly identification module calculates the temperature change range of the main control unit based on the mapping and allocation relationship set, collects continuous temperature monitoring data, calculates the difference between the temperature change and the average temperature in the same period, performs standard deviation analysis on the deviation data of the equipment unit, filters out the situation where the deviation is clustered in the equipment unit, determines the unit from which the anomaly originates, and obtains information on the equipment with anomaly risk.

[0104] The jump determination module analyzes the current change amplitude of the command response unit in the corresponding debugging step based on the abnormal risk equipment information, collects the real-time current data of the command response unit, compares the collected data with the debugging standard reference group, determines the data items that are lower than the reference group, sets the debugging step corresponding to each data item that is lower than the standard as a jump node, and summarizes all nodes to obtain the jump trigger signal group.

[0105] The path adjustment module adjusts the execution sequence of debugging steps based on the jump trigger signal group, reads the debugging steps marked as jump nodes, adjusts the original step path order, performs skip or insertion compensation operations on the marked steps, corrects the complete process structure, and obtains the process execution path chain.

[0106] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A centralized power motor train unit single vehicle centralized control function simulation debugging method, characterized in that, The method comprises: S1: based on the EMU centralized control system, analyze the simulation station task queue length, collect each task instruction execution delay data, standardize the delay, add the standardized delay and the station equipment response rate, judge the task priority according to the calculation result, obtain the scheduling priority task sequence; S2: based on the scheduling priority task sequence, judge the simulation station equipment load state, collect the load correlation data, compare the load state and the distribution limit, screen the stations that meet the distribution standard, distribute the priority tasks to the stations in turn, and obtain the mapping distribution relationship set; S3: based on the mapping distribution relationship set, calculate the main control unit temperature change interval, collect the temperature continuous monitoring data, analyze the difference between the temperature change and the same cycle average temperature, perform standard deviation analysis, screen the deviation aggregation equipment, judge the abnormal source, and obtain the abnormal risk equipment information; S4: based on the abnormal risk equipment information, analyze the instruction response unit current change amplitude in the debugging step, compare with the debugging standard reference group, judge the data items lower than the standard, set the debugging step as a jump node, and obtain a jump trigger signal group.

2. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The scheduling priority task sequence comprises priority parameters, task queue information, and response rate labels, the mapping distribution relationship set comprises a task distribution table, station distribution information, and task station assignment indexes, the abnormal risk equipment information comprises equipment identification, risk level, and abnormal parameter indexes, and the jump trigger signal group comprises jump node serial numbers, signal types, and trigger rules.

3. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The obtaining step of the scheduling priority task sequence is specifically: S101: based on the EMU centralized control system, analyze the waiting and response links experienced by each debugging task from instruction issuance to station action completion, calculate the queuing order of each task in the queue and the occupation of the corresponding station resources, screen the waiting and occupation distribution corresponding to the tasks in the queue, and obtain the queue waiting occupation coefficient; S102: based on the queue waiting occupation coefficient, compare the original execution links of each task, optimize the standard response performance of each task between different stations, analyze the standard response and the device processing capacity of the station, establish the superposition relationship of the station processing capacity between tasks, and obtain the processing pressure aggregation amount; S103: judge the task performance in the processing pressure aggregation amount, adjust the execution order of the tasks to be scheduled, sort the tasks according to the station processing capacity performance in turn, and obtain the scheduling priority task sequence.

4. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The obtaining step of the mapping distribution relationship set is specifically: S201: based on the scheduling priority task sequence, analyze the distribution order of each task, compare the task quantity and the station resource response performance in combination with the current running state of the simulation station, judge the current load pressure distribution of each station, and obtain the resource occupation density; S202: based on the resource occupation density, screen the station resource usage, compare the load pressure of each station with the distribution boundary, identify the stations whose resource pressure does not exceed the distribution boundary, correspond the priority ordered tasks to the screened stations one by one, and obtain the task station distribution sequence; S203: According to the task station distribution sequence, adjust the task and station corresponding record, optimize the assignment content of the station information when each task is allocated, perfect the corresponding set of task number, station label and allocation link, and obtain the mapping allocation relationship set.

5. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The abnormal risk equipment information acquisition step is specifically: S301: Based on the mapping allocation relationship set, analyze the associated master control unit, monitor the continuous temperature change of each unit in the task scheduling process, compare the fluctuation of temperature data at each monitoring time point, judge the fluctuation characteristics of each time period, and obtain the temperature change interval parameter; S302: Based on the temperature change interval parameter, calculate the temperature data of each time period, analyze the deviation degree between the temperature of each monitoring point and the cycle average in the same cycle, summarize the offset performance of the equipment unit in the cycle, and obtain the offset degree distribution quantity; S303: Based on the offset degree distribution quantity, judge the concentration trend of the offset phenomenon in a single equipment unit, identify the equipment unit with continuous offset phenomenon, establish the set of associated equipment number and corresponding abnormal information, and obtain the abnormal risk equipment information.

6. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The jump trigger signal group acquisition step is specifically: S401: Based on the abnormal risk equipment information, analyze the debugging steps involved, collect the real-time current signal of the instruction response unit in each debugging link, compare the fluctuation range of the current signal in each debugging period, judge the change amplitude of each response unit in the task process, and obtain the response current fluctuation quantity; S402: Based on the response current fluctuation quantity, compare the current fluctuation data of each unit with the reference interval of the debugging standard reference group, identify the data content below the lower limit of the standard, summarize the debugging step number and associated position corresponding to the data, and obtain the low current event index; S403: Based on the low current event index, judge its corresponding position in the original debugging step sequence, mark this part of the task as a jumpable node, collect the jump number and corresponding trigger condition type, and obtain the jump trigger signal group.

7. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The method further comprises: S5: Based on the jump trigger signal group, adjust the debugging step execution sequence, read the jump node step, adjust the original path sequence, skip or insert compensation operation to the marked step, modify the process structure, and obtain the process execution path chain; The process execution path chain comprises step node sorting, path structure information and process change record.

8. The centralized power motor train unit single car control function simulation debugging method according to claim 7, characterized in that, The process execution path chain acquisition step is specifically: S501: Based on the jump trigger signal group, analyze the state of each node in the debugging step sequence, judge the sorting of each step marked as a jumpable node in the original process path, adjust the connection order between steps, optimize the associated path before and after the node, and obtain the node path adjustment factor; S502: Based on the node path adjustment factor, screen the jumpable nodes that have been marked, judge whether the step performs skipping or inserts compensation action, adjust the execution link of the debugging step, establish the updated complete process information, and obtain the process execution path chain.

9. The centralized power motor train unit single car control function simulation debugging method according to claim 1, characterized in that, The motor train unit centralized control system refers to an information platform or a software and hardware system for centrally managing and monitoring the operation state of each device of a motor train unit, data acquisition, task scheduling and automatic control. The simulation station task queue length refers to the number of debugging tasks queued for processing for each station for simulation testing on a simulation debugging platform.

10. A simulation debugging system for centralized power motor train unit single car control function, the system is used to realize the simulation debugging method for centralized power motor train unit single car control function as claimed in any one of claims 1-9, characterized in that, The system comprises: The prioritization module analyzes the simulation station task queue length based on the motor train unit centralized control system, acquires the execution time delay data of each task instruction, performs standardization processing on the execution time delay, adds the standardized time delay and the station device response rate according to the tasks, judges the task priority through the addition result, sorts the task scheduling sequence, and obtains the scheduling priority task sequence. The intelligent distribution module judges the simulation station device load state based on the scheduling priority task sequence, acquires the station load correlation data, compares the load state with the distribution limit, filters the stations meeting the distribution standard, distributes the prioritized tasks to the filtered stations in sequence, corrects the distribution mapping relationship of each task, and obtains the mapping distribution relationship set. The abnormality identification module calculates the main control unit temperature variation interval based on the mapping distribution relationship set, acquires the continuous monitoring data of the temperature, calculates the difference between the temperature variation and the average temperature in the same period, performs standard deviation analysis on the device unit deviation data, filters the cases where the deviation is aggregated in the device unit, judges the abnormal source unit, and obtains the abnormal risk device information. The jump determination module analyzes the current variation amplitude of the instruction response unit in the corresponding debugging step based on the abnormal risk device information, acquires the real-time current data of the instruction response unit, compares the acquired data with the debugging standard reference group, judges the data items lower than the reference group, sets each data item lower than the standard to a jump node, and obtains the jump trigger signal group by summarizing all the nodes. The path adjustment module adjusts the debugging step execution sequence based on the jump trigger signal group, reads the debugging steps marked as jump nodes, adjusts the original step path sequence, performs a skip or insertion compensation operation on the marked steps, corrects the complete flow structure, and obtains the flow execution path chain.

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