Sequence control test system based on Internet of Things

Abstract

The invention relates to the technical field of sequence control, in particular to a sequence control test system based on the Internet of Things, which comprises a task decomposition module for analyzing equipment load, decomposing tasks according to equipment operation capability, acquiring task requirements and equipment parameters, judging operation conditions of equipment stations, refining task dependency relationships and performing task scheduling on the basis of a system task sequence; and associating the tasks with the equipment parameters to generate an equipment task sequence table. According to the method, efficient coordination of multiple devices under different loads is ensured by adopting a distributed coordination algorithm, task scheduling is more flexible and efficient by analyzing task dependency and state changes of the devices, optimizing the task execution sequence and arranging the execution time and stations of the devices, and the task scheduling efficiency is improved by judging synchronous execution conditions and obtaining time nodes. The synchronous execution sequence of the tasks is coordinated, repeated occupation or idling of equipment resources is avoided, and the stability and task processing capacity of a sequence control system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sequential control, and in particular to a sequential control test system based on the Internet of Things. Background Art

[0002] Sequential control technology: Sequential control technology is mainly used to control a series of operations, events or states in an orderly manner, ensuring that each operation is performed in a predetermined order, thereby achieving an automated and efficient workflow. The focus is on making the system run smoothly according to the set steps through programming and logical control.

[0003] The purpose of the sequential control test system based on the Internet of Things is to utilize the remote communication and data acquisition capabilities of the Internet of Things to monitor, control and test the equipment and processes in the system in real time to ensure the reliability and consistency of sequential operations. By combining the Internet of Things with sequential control technology, sequential tasks can be automatically executed in complex production environments, and status feedback can be provided to achieve intelligent management and fault detection.

[0004] Traditional sequential control systems are characterized by insufficient processing capabilities for task dependencies and equipment loads, the use of fixed task scheduling modes, and a lack of the ability to respond to real-time status changes of equipment, which results in the inability to effectively handle task concurrency issues between multiple devices. They rely on static configurations and are unable to adapt to dynamic changes in equipment loads, resulting in waste of resources or delayed task execution, leading to poor system response speed and overall coordination. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a sequential control test system based on the Internet of Things.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A sequential control test system based on the Internet of Things includes: Task decomposition module: Based on the system task sequence, it conducts equipment load analysis, decomposes tasks according to equipment operating capabilities, collects task requirements and equipment execution parameters, and determines the operation status of each equipment station, subdivides task dependencies, associates tasks with equipment parameters, and generates an equipment task sequence table; Sequential processing module: Based on the equipment task sequence table, a distributed coordination algorithm is used to compare equipment load and task dependency, analyze equipment status changes, confirm whether task dependencies are met, judge and select non-dependent tasks for priority execution, adjust task sequence and complete dependency update, and generate a priority task scheduling sequence; Equipment scheduling module: Based on the priority task scheduling sequence, check the equipment availability status, match the equipment operating conditions according to the task requirements, confirm whether the equipment meets the task execution requirements, judge the equipment status changes and task matching, allocate task execution time and execution station, and generate a list of equipment execution tasks; Resource management module: Based on the task list executed by the device, through the current resource allocation situation, analyze the device resource occupancy rate, determine whether the device resources meet the task requirements, select spare resources or devices for matching, allocate resources and adjust resource occupancy, and generate system resource allocation status; Task synchronization module: based on the system resource allocation status, judge the conditions for device synchronization execution, obtain the time node of task synchronization, adjust the execution order of synchronization tasks, set the time difference between tasks, confirm the completion of synchronization execution conditions, update synchronization execution conditions, and generate task synchronization execution diagram; Status monitoring module: based on the task synchronization execution diagram, monitors the status of device task execution through device status changes, records device running time, monitors progress changes in task execution, determines the working status of the device and task completion, completes the recording of device deviations, and generates task execution status records; Execution adjustment module: Based on the task execution status record, check the task execution deviation, reallocate the task station and equipment according to the current status of the equipment, adjust the equipment operating conditions, update the task execution status, confirm the matching of the equipment task after correction, adjust the task sequence, and generate a task adjustment implementation plan.

[0007] As a further solution of the present invention, the equipment task sequence table includes task number, equipment type, equipment working cycle and workstation allocation, the priority task scheduling sequence is specifically the task execution order, the task execution order includes start time, end time, and dependency between tasks, the equipment execution task list includes equipment name, task allocation time, task execution workstation and equipment availability status, the system resource allocation status includes resource type, current occupancy status, remaining resource amount and resource allocation priority, the task synchronization execution graph includes synchronization task list, equipment synchronization node, time interval between tasks and equipment synchronization status, the task execution status record includes current progress of task, real-time status of equipment, task delay time and equipment load status, and the task adjustment implementation plan includes adjusted equipment allocation, workstation change, task execution order and equipment backup plan.

[0008] As a further solution of the present invention, the task decomposition module includes a task load analysis submodule, a workstation determination submodule, and a task dependency subdivision submodule, wherein: Task load analysis submodule: Based on the system task sequence, perform equipment load analysis, extract the working capacity of the equipment, evaluate its current working status, decompose the tasks according to the load capacity, associate the tasks with the operating parameters of the equipment, determine the task execution status of each equipment, and generate the equipment load capacity table; Workstation determination submodule: Based on the equipment load capacity table, the equipment station is determined, the working conditions of the equipment's current station are analyzed, whether it meets the task requirements, and the equipment's station allocation is adjusted in combination with the station's resource allocation to obtain the current station allocation and generate a station allocation result table; Task dependency subdivision submodule: Based on the workstation allocation result table, analyze the interdependencies between tasks, determine the execution order of each task, subdivide the dependency relationship of each task, confirm whether the dependency conditions of each task are completed, generate a matching relationship between tasks and workstations, and generate a task dependency subdivision table.

[0009] As a further solution of the present invention, the sequence processing module includes a task dependency submodule, a task selection submodule, and a sequence adjustment submodule, wherein: Task dependency submodule: Based on the device task sequence table, analyze the dependency of tasks one by one, screen out tasks with strong dependencies by judging the correlation between tasks, determine their execution order, classify and sort out the dependency of tasks in combination with the running conditions of tasks, and generate a task dependency chain; Task selection submodule: based on the task dependency chain, screen out tasks with no or low dependency, and gradually arrange the priority execution order of tasks with no dependency, combine the availability of equipment and current load, confirm executable tasks, optimize the execution order of tasks, and generate a priority task list; Sequence adjustment submodule: Based on the priority task list, a distributed coordination algorithm is used to re-adjust the task order according to the task execution time and the current status of the device, arrange the execution order of the tasks, ensure that the tasks run synchronously according to the dependency relationship and device status, and generate a priority task scheduling sequence.

[0010] As a further solution of the present invention, the distributed coordination algorithm is according to the formula:

[0011] in: For each task's priority, For the task The estimated execution time, For equipment The current available status, For the task Dependencies on other tasks, For the task The degree of resource utilization required, For equipment The switching cost, For the task The weight coefficient of .

[0012] As a further solution of the present invention, the device scheduling module includes a device status checking submodule, a task matching submodule, and a task allocation submodule, wherein: Equipment status check submodule: Based on the priority task scheduling sequence, the equipment current status data is collected to analyze the equipment operation status, determine whether the equipment is in an available state, and check the equipment operation time and maintenance cycle in combination with the equipment's working cycle and load conditions to generate an equipment availability status table; Task matching submodule: Based on the equipment available status table, by parsing the task execution requirements, analyzing the performance parameters of the equipment required for the task, comparing the load capacity of the equipment with the task requirements, combining the execution time of the task with the current available load of the equipment, determining the matching degree between the equipment and the task, and generating the task equipment matching result; Task allocation submodule: Based on the task-device matching results, tasks are allocated to each device. By combining the workstation conditions of the equipment with the execution order of tasks, the execution time and workstation location of each task are allocated to ensure the reasonable arrangement of tasks, complete the scheduling of equipment, and generate a list of equipment execution tasks.

[0013] As a further solution of the present invention, the resource management module includes a resource occupancy analysis submodule, a backup resource selection submodule, and a resource allocation adjustment submodule, wherein: Resource occupancy analysis submodule: Based on the task list executed by the device, by counting the current resource usage of the device, analyzing the resource occupancy rate of the device, determining whether the resource load of the device meets the task requirements, combining the types of resources required for task execution with the current available resources of the device, and generating a device resource occupancy table; Backup resource selection submodule: based on the equipment resource occupancy table, compare the current resource usage, analyze the resource type required for the task and the availability of backup resources, select suitable backup resources or equipment, confirm the degree of adaptation between the backup resources and the task execution, and generate backup resource matching results; Resource allocation adjustment submodule: Based on the backup resource matching results, adjust the allocation order of current resources, reallocate resource usage time periods according to the task execution priority and the resource requirements of the device, and optimize the resource allocation plan in combination with the resource load of the device to generate the system resource allocation status.

[0014] As a further solution of the present invention, the task synchronization module includes a synchronization condition determination submodule, a synchronization time node acquisition submodule, and a synchronization task adjustment submodule, wherein: Synchronization condition determination submodule: based on the system resource allocation status, read the current resources of the device, analyze the available resources of the device, determine whether the synchronization execution conditions are met, confirm that the resource status matches the requirements of the synchronization conditions, and generate the device synchronization condition status; Synchronization time node acquisition submodule: based on the synchronization condition status of the device, read the device operation log, extract the historical execution time of the task, set the time difference of each task, obtain the specific time node of synchronization by comparing the execution time data between tasks, and generate the task synchronization time node; Synchronous task adjustment submodule: based on the task synchronization time nodes, the synchronous tasks are sorted by priority, the task execution order is adjusted, the synchronization conditions between tasks are updated, the task dependencies are confirmed, the execution order of the synchronous tasks is generated according to the new order, and the task synchronization execution graph is generated.

[0015] As a further solution of the present invention, the state monitoring module includes a device state change monitoring submodule, a task execution progress monitoring submodule, and a device deviation recording submodule, wherein: Equipment status change monitoring submodule: Based on the task synchronization execution diagram, monitor the task execution status of the equipment, analyze the status changes of the equipment in each operation stage, record the start and end time of each task of the equipment, and generate equipment status change records; Task execution progress monitoring submodule: based on the equipment status change record, track the time of each stage in the task execution, analyze the completion progress and current progress of the task, judge the completion status of the task and the operation status of the equipment, and generate the task execution progress status; Equipment deviation recording submodule: Based on the task execution progress status, compare the expected and actual task completion status, record the deviations in the equipment task execution process, analyze the abnormal conditions of equipment operation, generate equipment operation deviation data, and generate task execution status records.

[0016] As a further solution of the present invention, the execution adjustment module includes a task deviation checking submodule, a device condition adjustment submodule, and a task sequence optimization submodule, wherein: Task deviation check submodule: based on the task execution status record, read the historical data of task execution, analyze the completion progress of the task, check the various parameters recorded during the operation of the equipment, confirm the type and deviation range of task execution deviation, compare the differences item by item for each task, and generate task execution deviation data; Equipment condition adjustment submodule: based on the task execution deviation data, check the load of the current equipment, read the current operating conditions of the equipment and the station occupancy information, reallocate the equipment and the task station, adjust the operating conditions of the equipment, correct the equipment settings during task execution, and generate equipment condition matching results; Task sequence optimization submodule: Based on the equipment condition matching results, analyze the priority of each task and the processing capacity of the equipment, rearrange the execution order of tasks, adjust the task workstation schedule, generate a complete task time schedule, and obtain the task adjustment implementation plan.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by adopting a distributed coordination algorithm, efficient collaboration of multiple devices under different loads is ensured. By analyzing the task dependencies and state changes of the devices, the task execution order is optimized, task conflicts and resource waste are avoided, and the equipment execution time and workstations are arranged to make task scheduling more flexible and efficient. By judging the synchronous execution conditions and obtaining the time nodes, the synchronous execution order of tasks is coordinated to avoid repeated occupation or idleness of equipment resources, thereby ensuring the efficiency, flexibility and intelligence of the entire system, improving the stability and task processing capability of the sequential control system, enhancing the overall optimization effect of task scheduling, and reducing delays and task conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a sequential control test system based on the Internet of Things includes: Task decomposition module: Based on the system task sequence, it conducts equipment load analysis, decomposes tasks according to equipment operating capabilities, collects task requirements and equipment execution parameters, and determines the operation status of each equipment station, subdivides task dependencies, associates tasks with equipment parameters, and generates an equipment task sequence table; Sequential processing module: Based on the equipment task sequence table, it adopts a distributed coordination algorithm to compare equipment load and task dependency, analyze equipment status changes, confirm whether task dependencies are met, judge and select non-dependent tasks for priority execution, adjust the task sequence and complete the update of dependency relationships, and generate a priority task scheduling sequence; Equipment scheduling module: Based on the priority task scheduling sequence, check the equipment availability status, match the equipment operating conditions according to the task requirements, confirm whether the equipment meets the task execution requirements, judge the equipment status changes and task matching, allocate task execution time and execution station, and generate a list of equipment execution tasks; Resource management module: Based on the device execution task list, analyze the device resource occupancy rate through the current resource allocation situation, determine whether the device resources meet the task requirements, select spare resources or devices for matching, allocate resources and adjust resource occupancy, and generate system resource allocation status; Task synchronization module: Based on the system resource allocation status, determine the conditions for device synchronization execution, obtain the time node for task synchronization, adjust the execution order of synchronization tasks, set the time difference between tasks, confirm the completion of synchronization execution conditions, update synchronization execution conditions, and generate task synchronization execution diagram; Status monitoring module: Based on the task synchronization execution diagram, it monitors the status of equipment task execution through equipment status changes, records equipment running time, monitors progress changes in task execution, determines the working status of the equipment and task completion status, completes the record of equipment deviation, and generates task execution status records; Execution adjustment module: Based on the task execution status record, check the task execution deviation, reallocate the task station and equipment according to the current status of the equipment, adjust the equipment operating conditions, update the task execution status, confirm the matching of the equipment task after correction, adjust the task sequence, and generate the task adjustment implementation plan.

[0021] The equipment task sequence table includes task number, equipment type, equipment working cycle and workstation allocation. The priority task scheduling sequence is specifically the task execution order. The task execution order includes start time, end time and dependencies between tasks. The equipment execution task list includes equipment name, task allocation time, task execution workstation and equipment availability status. The system resource allocation status includes resource type, current occupancy, remaining resource amount and resource allocation priority. The task synchronization execution diagram includes synchronization task list, equipment synchronization node, time interval between tasks and equipment synchronization status. The task execution status record includes the current progress of the task, real-time status of the equipment, task delay time and equipment load. The task adjustment implementation plan includes adjusted equipment allocation, workstation change, task execution sequence and equipment backup plan.

[0022] See also Figure 2The task decomposition module includes a task load analysis submodule, a workstation determination submodule, and a task dependency submodule, among which: Task load analysis submodule: Based on the system task sequence, perform equipment load analysis, extract the working capacity of the equipment, evaluate its current working status, decompose the tasks according to the load capacity, associate the tasks with the operating parameters of the equipment, determine the task execution status of each equipment, and generate the equipment load capacity table; Workstation determination submodule: Based on the equipment load capacity table, the equipment station is determined, the working conditions of the equipment's current station are analyzed, and whether it meets the task requirements is determined. In combination with the station's resource allocation, the equipment's station allocation is adjusted to obtain the current station allocation and generate a station allocation result table; Task dependency subdivision submodule: Based on the workstation allocation result table, analyze the interdependencies between tasks, determine the execution order of each task, subdivide the dependency relationship of each task, confirm whether the dependency conditions of each task are completed, and generate the matching relationship between tasks and workstations, and generate a task dependency subdivision table; Task load analysis submodule: Based on the system task sequence, the resource constraint scheduling algorithm is used to analyze the equipment load. Specifically, the current working status of each device is evaluated, the equipment's operating time, load peak, and load balance coefficient are extracted, and the task is decomposed into multiple subtasks. The tasks are allocated according to the maximum load capacity of each device, and the operating parameters of the equipment are associated, including the equipment's load history, current load status, and expected operating time. The task execution status of each device is further verified, and the equipment load capacity table is generated by dynamically matching the subtasks with the equipment's load parameters; Workstation determination submodule: Based on the equipment load capacity table, the discrete event simulation method is used to determine the equipment station. Specifically, the current resource configuration of the station is retrieved, the resource usage, the compatibility of the station working conditions and the equipment are extracted, and the current availability of the station is determined by combining the equipment usage history and the current load of each station. By adjusting the allocation rules between equipment and stations, the location of the station where the equipment is located is reallocated, and the resource occupancy rate of each station is matched with the future available time period to obtain the optimal allocation plan for the current equipment and stations, and generate a station allocation result table; Task dependency subdivision submodule: Based on the workstation allocation result table, a topological sorting algorithm is used to analyze task dependencies. Specifically, the execution conditions and dependent tasks of each task are extracted, and a task dependency graph is constructed. The execution order of tasks is sorted using topological sorting. It is determined whether there is a circular dependency in the dependency chain, and the dependency order between tasks is subdivided. By analyzing the prerequisites for task completion, the matching status of the workstation, and the operation status of the equipment, the matching relationship between tasks and workstations is updated and confirmed to generate a task dependency subdivision table.

[0023] See also Figure 2 ,The sequence processing module includes the task dependency submodule, the task selection submodule, and the sequence adjustment submodule, where: Task dependency submodule: Based on the equipment task sequence table, analyze the dependency of tasks one by one, screen out tasks with strong dependencies by judging the correlation between tasks, determine their execution order, and classify and sort out the dependency degree of tasks based on the running conditions of tasks to generate a task dependency chain; Task selection submodule: Based on the task dependency chain, it screens out tasks with no or low dependencies, and gradually arranges the priority execution order of tasks with no dependencies, combines the availability of equipment with the current load, confirms executable tasks, optimizes the execution order of tasks, and generates a priority task list; Sequence adjustment submodule: Based on the priority task list, the distributed coordination algorithm is used to re-adjust the task order according to the task execution time and the current status of the device, arrange the execution order of the tasks, ensure that the tasks run synchronously according to the dependency relationship and device status, and generate a priority task scheduling sequence; Task dependency submodule: Based on the equipment task sequence table, the depth-first search algorithm is used to analyze the task dependencies one by one. Specifically, the predecessor tasks of each task are traced, and the task sequence is traversed using the depth-first search to extract the task dependency chain, screen out the tasks with strong dependencies, and further determine the execution order of each task. By matching the running conditions of the tasks, the dependency degree of each task is gradually classified and sorted out to generate a task dependency chain; Task selection submodule: Based on the task dependency chain, the priority queue scheduling algorithm is used to screen non-dependent or low-dependent tasks. Specifically, the priority of non-dependent tasks in the task dependency chain is set, sorted according to the priority queue, and the execution order of high-priority tasks is gradually arranged. Combined with the equipment availability data and the current equipment load, it is confirmed which tasks are eligible for execution, the task execution order is optimized, and the time nodes of the tasks are scheduled to generate a priority task list; Sequence adjustment submodule: Based on the priority task list, a distributed coordination algorithm is used to adjust the order according to the task execution time and the current status of the device. Specifically, the execution time node of each task is synchronized with the operating status of the device in real time, and the task priority is adjusted in real time through distributed coordination control. The execution order of tasks is rearranged to ensure that tasks are executed synchronously with the device status according to dependencies, and a priority task scheduling sequence is generated.

[0024] Distributed coordination algorithm, according to the formula:

[0025] in: For each task's priority, For the task The estimated execution time, For equipment The current available status, For the task Dependencies on other tasks, For the task The degree of resource utilization required, For equipment The switching cost, For the task The weight coefficient of Implementation process: First, The estimated execution time of the task, which is determined by historical data or estimation tools to ensure the rationality of the time. The current availability status of the device is confirmed based on the real-time monitoring of the device health parameters or operating status. Represents the dependencies between tasks. By analyzing the dependency graph between tasks, the number and importance of dependencies are determined. It indicates the degree of resource occupation required by a task during its execution. It is determined by calculating the ratio of CPU, memory, network bandwidth and other resources occupied during the task execution. Represents the device switching cost, that is, the energy consumption or time consumption required for the device to switch from one task to another, estimated based on the actual switching time or energy consumption data. It is a weight coefficient used to measure the priority of tasks. The weight can be set manually or automatically adjusted according to historical data based on the importance of the task and its impact on the overall performance of the system. The weight can be confirmed and dynamically adjusted through the feedback data of historical task execution. In the initial stage, the weight of all tasks can be set to 1. As the execution data accumulates, the weight is adjusted to optimize the task sorting.

[0026] See also Figure 2 ,The equipment scheduling module includes the equipment status checking submodule, the task matching submodule, and the task allocation submodule, where: Equipment status check submodule: Based on the priority task scheduling sequence, the module collects data on the current status of the equipment, analyzes the equipment's operating status, determines whether the equipment is in an available state, checks the equipment's operating time and maintenance cycle based on the equipment's working cycle and load conditions, and generates an equipment availability status table; Task matching submodule: Based on the equipment availability status table, by parsing the task execution requirements, analyzing the performance parameters of the equipment required for the task, comparing the load capacity of the equipment with the task requirements, combining the task execution time with the current available load of the equipment, determining the matching degree between the equipment and the task, and generating the task equipment matching result; Task allocation submodule: Based on the task-equipment matching results, tasks are allocated to each device. By combining the equipment's workstation conditions with the execution order of tasks, the execution time and workstation location of each task are allocated to ensure the reasonable arrangement of tasks, complete the equipment scheduling, and generate a list of equipment execution tasks; Equipment status check submodule: Based on the priority task scheduling sequence, the status monitoring algorithm is used to collect data on the current status of the equipment. Specifically, the operation data is extracted from each device, including the current load, remaining operating time, working cycle, etc. of the equipment. By analyzing the equipment operating parameters, the load status of the equipment is compared with the execution requirements of the current task. Combined with the maintenance cycle and accumulated working time of the equipment, it is determined whether the equipment is in an available state and a table of equipment available status is generated; Task matching submodule: Based on the equipment available status table, the performance matching algorithm is used to analyze the task execution requirements. Specifically, the key performance parameters of the equipment required for each task, such as load capacity, processing speed, etc., are extracted, and the resources required for the task are compared with the current load status of the equipment. Combined with the execution time of the task, the maximum load value of the equipment and the current load level, the matching degree between the task requirements and the equipment performance is calculated to determine the optimal matching combination of equipment and tasks, and generate the task equipment matching result; Task allocation submodule: Based on the task-equipment matching results, the workstation scheduling algorithm is used to allocate tasks to each device. Specifically, tasks are allocated according to the equipment's workstation conditions and task execution order, and the equipment's workstation location, available time window and task priority are extracted. By combining the use of workstation resources with the task execution time, the execution time node and workstation location of each task are gradually allocated to complete the equipment scheduling and generate a list of equipment execution tasks.

[0027] See also Figure 2 The resource management module includes a resource occupancy analysis submodule, a backup resource selection submodule, and a resource allocation adjustment submodule, wherein: Resource occupancy analysis submodule: Based on the device execution task list, by counting the current resource usage of the device, analyzing the resource occupancy rate of the device, determining whether the resource load of the device meets the task requirements, and combining the types of resources required for task execution with the current available resources of the device, generating a device resource occupancy table; Backup resource selection submodule: Based on the equipment resource occupancy table, compare the current resource usage, analyze the resource types required for the task and the availability of backup resources, select suitable backup resources or equipment, confirm the degree of adaptation between backup resources and task execution, and generate backup resource matching results; Resource allocation adjustment submodule: Based on the backup resource matching results, adjust the current resource allocation order, reallocate resource usage periods according to the task execution priority and the device's resource requirements, and optimize the resource allocation plan based on the device's resource load to generate the system resource allocation status; Resource occupancy analysis submodule: Based on the device execution task list, the resource occupancy rate calculation method is used to count the current resource usage of the device. Specifically, the current resource data of the device is extracted, including memory occupancy, CPU load, storage usage, etc. By calculating the occupancy rate of each resource of the device, the resources required for task execution are compared with the current resource availability of the device, and the device resources are analyzed to see whether they have reached a load-balanced state. Combined with the specific resource types required for task execution, it is determined whether the resource load of the device meets the task requirements, and a device resource occupancy table is generated.

[0028] Backup resource selection submodule: Based on the equipment resource occupancy table, a resource matching algorithm is used to compare the current resource usage. Specifically, the resource type required for the task is extracted with the available resource parameters in the backup resource pool, and the available resource status of the equipment is matched with the task requirements one by one. The availability of backup resources is analyzed, and the matching degree between the load of the backup equipment and the task requirements is calculated. Suitable backup resources or equipment are screened, and their compatibility with the task execution is confirmed to generate backup resource matching results.

[0029] Resource allocation adjustment submodule: Based on the backup resource matching results, a dynamic resource allocation algorithm is used to adjust the current resource allocation order. Specifically, according to the priority of task execution and the resource requirements of the equipment, the execution time window and resource usage period of each task are extracted. By rearranging the resource usage order, the resource occupancy period and equipment load balance are calculated, and resources are allocated to priority tasks. The resource usage period and allocation rules are optimized to generate the system resource allocation status.

[0030] See also Figure 2 The task synchronization module includes a synchronization condition determination submodule, a synchronization time node acquisition submodule, and a synchronization task adjustment submodule, wherein: Synchronization condition determination submodule: Based on the system resource allocation status, read the current resources of the device, analyze the available resources of the device, determine whether the synchronization execution conditions are met, confirm that the resource status matches the requirements of the synchronization conditions, and generate the device synchronization condition status; Synchronization time node acquisition submodule: Based on the device synchronization condition status, read the device operation log, extract the historical execution time of the task, set the time difference of each task, obtain the specific time node of synchronization by comparing the execution time data between tasks, and generate the task synchronization time node; Synchronous task adjustment submodule: based on the task synchronization time node, sort the synchronous tasks by priority, adjust the task execution order, update the synchronization conditions between tasks, confirm the task dependencies, generate the execution order of the synchronous tasks according to the new order, and generate the task synchronization execution graph; Synchronization condition determination submodule: Based on the system resource allocation status, a dynamic resource monitoring algorithm is used to read the current resources of the device, specifically to extract key parameters such as the device's memory occupancy rate, CPU usage rate, storage capacity, etc., analyze the available resources of each device, and compare the current resource status of the device with the predetermined synchronization execution conditions through real-time monitoring of resource utilization to confirm whether the resources meet the minimum requirements for synchronization task execution, mark the devices that do not meet the conditions, and generate the device synchronization condition status; Synchronous time node acquisition submodule: Based on the device synchronization condition status, the device operation log is read using the time series analysis method, the historical execution time and interval of each task in the device are extracted, the execution duration and start time of the task are analyzed, and the execution time difference between multiple tasks is compared, the time difference of the task is set, and the synchronous execution node of each task is determined in combination with the device's synchronous time window, and the task synchronization time node is generated; Synchronous task adjustment submodule: Based on the task synchronization time node, the priority scheduling algorithm is used to prioritize the synchronous tasks. Specifically, the execution priority, dependency conditions and task duration of each task are extracted, and the tasks are reordered according to the priority scheduling algorithm. The task execution order is adjusted, and the synchronization conditions between tasks are gradually updated. The dependency relationship and execution order between tasks are confirmed, and the execution order of the synchronous tasks is generated according to the new order, and the task synchronization execution graph is generated.

[0031] See also Figure 2 The status monitoring module includes the equipment status change monitoring submodule, the task execution progress monitoring submodule, and the equipment deviation recording submodule, among which: Equipment status change monitoring submodule: Based on the task synchronization execution diagram, monitor the task execution status of the equipment, analyze the status changes of the equipment in each operation stage, record the start and end time of each task of the equipment, and generate equipment status change records; Task execution progress monitoring submodule: Based on the equipment status change record, it tracks the time of each stage in the task execution, analyzes the completion progress and current progress of the task, determines the completion status of the task and the operation status of the equipment, and generates the task execution progress status; Equipment deviation recording submodule: Based on the task execution progress status, compare the expected and actual task completion status, record the deviations in the equipment task execution process, analyze the abnormal conditions of equipment operation, generate equipment operation deviation data, and generate task execution status records; Equipment status change monitoring submodule: Based on the task synchronization execution graph, the status monitoring algorithm is used to monitor the task execution status of the equipment in real time. Specifically, the equipment operation data is read, the start time, end time and resource usage of each task are extracted, and the status changes of each stage of the task are analyzed in combination with the equipment operation log. The execution stage of the task and the changes of the equipment status are gradually recorded to generate equipment status change records; Task execution progress monitoring submodule: Based on the equipment status change record, the progress tracking algorithm is used to track the time of each stage in the task execution. Specifically, the time node data of each stage of the task is extracted, and the execution time of the task is compared with the planned time one by one. Through the synchronous analysis of the task execution time and the current progress of the equipment, the completion status of the task and the operation status of the equipment are judged, the completion progress of the task is gradually recorded, and the task execution progress status is generated; Equipment deviation recording submodule: Based on the task execution progress status, a deviation detection algorithm is used to compare the expected and actual task completion status. Specifically, the expected execution time of the task and the actual execution data of the equipment are extracted, and the difference in task execution time and equipment operating parameters are analyzed. By comparing the execution records of each stage, the abnormal conditions and deviation values ​​of the equipment during the task execution process are gradually recorded, and the equipment operation deviation data and task execution status records are generated.

[0032] See also Figure 2 ,The execution adjustment module includes a task deviation check submodule, an equipment condition adjustment submodule, and a task sequence optimization submodule, among which: Task deviation check submodule: Based on the task execution status record, read the historical data of task execution, analyze the completion progress of the task, check the various parameters recorded during the operation of the equipment, confirm the type and deviation range of task execution deviation, compare the differences of each task item by item, and generate task execution deviation data; Equipment condition adjustment submodule: Based on the task execution deviation data, check the current equipment load, read the current equipment operating conditions and station occupancy information, reallocate equipment and task stations, adjust the equipment operating conditions, correct the equipment settings during task execution, and generate equipment condition matching results; Task sequence optimization submodule: Based on the equipment condition matching results, analyze the priority of each task and the processing capacity of the equipment, rearrange the execution order of tasks, adjust the workstation schedule of tasks, generate a complete task schedule, and obtain the task adjustment implementation plan; Task deviation check submodule: Based on the task execution status record, the difference comparison algorithm is used to read the historical data of task execution, specifically to extract the planned execution time, actual execution time, equipment operation parameters, etc. of each task, and analyze the completion progress of the task and the time difference in task execution by comparing item by item. The temperature, pressure, load and other parameters recorded by the equipment during the task execution are further checked to confirm the type and specific deviation range of each task execution deviation, and then classify and organize them to generate task execution deviation data; Equipment condition adjustment submodule: Based on the task execution deviation data, the load balancing algorithm is used to check the current equipment load. Specifically, the CPU usage rate, memory occupancy rate, station resource usage and other data of the equipment are extracted, the current operating conditions and station occupancy information of the equipment are read, and the load matching situation between the task and the equipment is analyzed through the load balancing algorithm. The equipment and the task station are reallocated, the equipment operating conditions are adjusted, the parameter settings of the equipment in the task execution are corrected, and the equipment condition matching results are generated; Task sequence optimization submodule: Based on the equipment condition matching results, the task priority scheduling algorithm is used to optimize the task sequence. Specifically, the priority, required resources and processing capacity of each task are extracted, and the execution order of tasks is rearranged through the task priority scheduling algorithm. The task workstation schedule and equipment availability are analyzed, the execution time nodes and workstation allocation of tasks are adjusted, the scheduling order of tasks is gradually optimized, a complete task time schedule is generated, and the task adjustment implementation plan is obtained.

[0033] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A sequential control test system based on the Internet of Things, characterized in that: The system comprises: Task decomposition module: Based on the system task sequence, it conducts equipment load analysis, decomposes tasks according to equipment operating capabilities, collects task requirements and equipment execution parameters, and determines the operation status of each equipment station, subdivides task dependencies, associates tasks with equipment parameters, and generates an equipment task sequence table; Sequential processing module: Based on the equipment task sequence table, a distributed coordination algorithm is used to compare equipment load and task dependency, analyze equipment status changes, confirm whether task dependencies are met, judge and select non-dependent tasks for priority execution, adjust task sequence and complete dependency update, and generate a priority task scheduling sequence; Equipment scheduling module: Based on the priority task scheduling sequence, check the equipment availability status, match the equipment operating conditions according to the task requirements, confirm whether the equipment meets the task execution requirements, judge the equipment status changes and task matching, allocate task execution time and execution station, and generate a list of equipment execution tasks; Resource management module: Based on the task list executed by the device, through the current resource allocation situation, analyze the device resource occupancy rate, determine whether the device resources meet the task requirements, select spare resources or devices for matching, allocate resources and adjust resource occupancy, and generate system resource allocation status; Task synchronization module: based on the system resource allocation status, determine the conditions for device synchronization execution, obtain the time node for task synchronization, adjust the execution order of synchronization tasks, set the time difference between tasks, confirm the completion of synchronization execution conditions, update synchronization execution conditions, and generate a task synchronization execution graph; Status monitoring module: based on the task synchronization execution diagram, monitors the status of device task execution through device status changes, records device running time, monitors progress changes in task execution, determines the working status of the device and task completion, completes the recording of device deviations, and generates task execution status records; Execution adjustment module: Based on the task execution status record, check the task execution deviation, reallocate the task station and equipment according to the current status of the equipment, adjust the equipment operating conditions, update the task execution status, confirm the matching of the equipment task after correction, adjust the task sequence, and generate a task adjustment implementation plan.

2. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The equipment task sequence table includes task number, equipment type, equipment working cycle and workstation allocation, the priority task scheduling sequence is specifically the task execution order, the task execution order includes start time, end time, and dependency between tasks, the equipment execution task list includes equipment name, task allocation time, task execution workstation and equipment availability status, the system resource allocation status includes resource type, current occupancy, remaining resource amount and resource allocation priority, the task synchronization execution diagram includes synchronization task list, equipment synchronization node, time interval between tasks and equipment synchronization status, the task execution status record includes current task progress, real-time equipment status, task delay time and equipment load status, the task adjustment implementation plan includes adjusted equipment allocation, workstation change, task execution order and equipment backup plan.

3. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The task decomposition module includes a task load analysis submodule, a workstation determination submodule, and a task dependency submodule, wherein: Task load analysis submodule: Based on the system task sequence, perform equipment load analysis, extract the working capacity of the equipment, evaluate its current working status, decompose the tasks according to the load capacity, associate the tasks with the operating parameters of the equipment, determine the task execution status of each equipment, and generate the equipment load capacity table; Workstation determination submodule: Based on the equipment load capacity table, the equipment station is determined, the working conditions of the equipment's current station are analyzed, whether it meets the task requirements, and the equipment's station allocation is adjusted in combination with the station's resource allocation to obtain the current station allocation and generate a station allocation result table; Task dependency subdivision submodule: Based on the workstation allocation result table, analyze the interdependencies between tasks, determine the execution order of each task, subdivide the dependency relationship of each task, confirm whether the dependency conditions of each task are completed, generate a matching relationship between tasks and workstations, and generate a task dependency subdivision table.

4. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The sequence processing module includes a task dependency submodule, a task selection submodule, and a sequence adjustment submodule, wherein: Task dependency submodule: Based on the device task sequence table, analyze the dependency of tasks one by one, screen out tasks with strong dependencies by judging the correlation between tasks, determine their execution order, classify and sort out the dependency of tasks in combination with the running conditions of tasks, and generate a task dependency chain; Task selection submodule: based on the task dependency chain, screen out tasks with no or low dependency, and gradually arrange the priority execution order of tasks with no dependency, combine the availability of equipment and current load, confirm executable tasks, optimize the execution order of tasks, and generate a priority task list; Sequence adjustment submodule: Based on the priority task list, a distributed coordination algorithm is used to re-adjust the task order according to the task execution time and the current status of the device, arrange the execution order of the tasks, ensure that the tasks run synchronously according to the dependency relationship and device status, and generate a priority task scheduling sequence.

5. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The distributed coordination algorithm is based on the formula: Where: P is the priority of each task, T i is the estimated execution time of task i, S i is the current available status of device i, D i is the dependency of task i on other tasks, R i is the degree of resource occupation required by task i, C i is the switching cost of device i, W i is the weight coefficient of task i.

6. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The device scheduling module includes a device status checking submodule, a task matching submodule, and a task allocation submodule, wherein: Equipment status check submodule: Based on the priority task scheduling sequence, the equipment current status data is collected to analyze the equipment operation status, determine whether the equipment is in an available state, and check the equipment operation time and maintenance cycle in combination with the equipment's working cycle and load conditions to generate an equipment availability status table; Task matching submodule: Based on the equipment available status table, by parsing the task execution requirements, analyzing the performance parameters of the equipment required for the task, comparing the load capacity of the equipment with the task requirements, combining the execution time of the task with the current available load of the equipment, determining the matching degree between the equipment and the task, and generating the task equipment matching result; Task allocation submodule: Based on the task-device matching results, tasks are allocated to each device. By combining the workstation conditions of the equipment with the execution order of tasks, the execution time and workstation location of each task are allocated to ensure the reasonable arrangement of tasks, complete the scheduling of equipment, and generate a list of equipment execution tasks.

7. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The resource management module includes a resource occupancy analysis submodule, a backup resource selection submodule, and a resource allocation adjustment submodule, wherein: Resource occupancy analysis submodule: Based on the task list executed by the device, by counting the current resource usage of the device, analyzing the resource occupancy rate of the device, determining whether the resource load of the device meets the task requirements, combining the types of resources required for task execution with the current available resources of the device, and generating a device resource occupancy table; Backup resource selection submodule: based on the equipment resource occupancy table, compare the current resource usage, analyze the resource type required for the task and the availability of backup resources, select suitable backup resources or equipment, confirm the degree of adaptation between the backup resources and the task execution, and generate backup resource matching results; Resource allocation adjustment submodule: Based on the backup resource matching results, adjust the allocation order of current resources, reallocate resource usage time periods according to the task execution priority and the resource requirements of the device, and optimize the resource allocation plan in combination with the resource load of the device to generate the system resource allocation status.

8. The sequential control test system based on the Internet of Things according to claim 1 is characterized in that: The task synchronization module includes a synchronization condition determination submodule, a synchronization time node acquisition submodule, and a synchronization task adjustment submodule, wherein: Synchronization condition determination submodule: based on the system resource allocation status, read the current resources of the device, analyze the available resources of the device, determine whether the synchronization execution conditions are met, confirm that the resource status matches the requirements of the synchronization conditions, and generate the device synchronization condition status; Synchronization time node acquisition submodule: based on the synchronization condition status of the device, read the device operation log, extract the historical execution time of the task, set the time difference of each task, obtain the specific time node of synchronization by comparing the execution time data between tasks, and generate the task synchronization time node; Synchronous task adjustment submodule: based on the task synchronization time nodes, the synchronous tasks are sorted by priority, the task execution order is adjusted, the synchronization conditions between tasks are updated, the task dependencies are confirmed, the execution order of the synchronous tasks is generated according to the new order, and the task synchronization execution graph is generated.

9. The sequential control test system based on the Internet of Things according to claim 1, characterized in that: The state monitoring module includes a device state change monitoring submodule, a task execution progress monitoring submodule, and a device deviation recording submodule, wherein: Equipment status change monitoring submodule: Based on the task synchronization execution diagram, monitor the task execution status of the equipment, analyze the status changes of the equipment in each operation stage, record the start and end time of each task of the equipment, and generate equipment status change records; Task execution progress monitoring submodule: based on the equipment status change record, track the time of each stage in the task execution, analyze the completion progress and current progress of the task, judge the completion status of the task and the operation status of the equipment, and generate the task execution progress status; Equipment deviation recording submodule: Based on the task execution progress status, compare the expected and actual task completion status, record the deviations in the equipment task execution process, analyze the abnormal conditions of equipment operation, generate equipment operation deviation data, and generate task execution status records.

10. The sequential control test system based on the Internet of Things according to claim 1, characterized in that: The execution adjustment module includes a task deviation checking submodule, a device condition adjustment submodule, and a task sequence optimization submodule, wherein: Task deviation check submodule: based on the task execution status record, read the historical data of task execution, analyze the completion progress of the task, check the various parameters recorded during the operation of the equipment, confirm the type and deviation range of task execution deviation, compare the differences item by item for each task, and generate task execution deviation data; Equipment condition adjustment submodule: based on the task execution deviation data, check the load of the current equipment, read the current operating conditions of the equipment and the station occupancy information, reallocate the equipment and the task station, adjust the operating conditions of the equipment, correct the equipment settings during task execution, and generate equipment condition matching results; Task sequence optimization submodule: Based on the equipment condition matching results, analyze the priority of each task and the processing capacity of the equipment, rearrange the execution order of tasks, adjust the task workstation schedule, generate a complete task time schedule, and obtain the task adjustment implementation plan.

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