Intelligent substation sequence control method and system

Abstract

The application relates to the technical field of automation control systems, in particular to an intelligent substation sequence control method and system, which comprises the following steps: based on the equipment operation instruction of the intelligent substation, the logical sequence of equipment operation is analyzed, the state change information of the equipment is extracted, the operation analysis of the equipment state and time correlation is carried out, the dependence relationship of the equipment state is judged through the time difference of the equipment state, and an equipment operation dependence model is generated. In the application, the resource occupation of the equipment is monitored through a data flow compression algorithm, load balancing is carried out, the equipment load level is evaluated in real time, and dynamic distribution and adjustment of the load are realized, the pressure of high-load equipment is effectively relieved, an operation conflict judgment mechanism is introduced, potential conflicts of the equipment in parallel execution are identified and solved in real time, the smooth execution of parallel tasks is ensured, the delay and conflict conditions possibly occurring in the equipment execution process are effectively reduced, and the continuity and reliability of the power system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control systems, and in particular to an intelligent substation sequential control method and system. Background Art

[0002] The field of automated control system technology ensures the safety, reliability and efficiency of the power system through real-time monitoring, automated scheduling, fault handling and recovery. The automated control system uses sensors, controllers, communication networks, actuators and other equipment to achieve full-process automated control from data collection to execution feedback.

[0003] Intelligent substation sequential control methods aim to achieve coordinated control, protection action, and rapid fault recovery of substation equipment through technical means such as automated scheduling, remote control, and intelligent response, thereby improving the safety, automation, and management efficiency of power grid operation, reducing the workload of operators, increasing the response speed of substations, and ensuring a continuous and stable power supply.

[0004] Traditional substation sequential control methods rely on pre-set fixed rules or static resource allocation modes for resource allocation and task scheduling, and lack real-time load adjustment capabilities. As a result, in actual operation, some equipment is in a high-load state for a long time, unable to fully utilize the idle resources of other equipment, resulting in uneven resource utilization. The static allocation mode cannot flexibly respond to dynamic changes in equipment status, which can easily lead to performance degradation of high-load equipment and even task delays or execution failures. Traditional methods are relatively slow in handling conflicts when equipment is executed in parallel, and usually respond only after the conflict occurs, affecting the timely execution of key equipment and delaying the completion efficiency of the entire sequential control process. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent substation sequential control method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an intelligent substation sequential control method, comprising the following steps:

[0007] Step 1: Based on the equipment operation instructions of the intelligent substation, the logical sequence of equipment operations is analyzed, the equipment status change information is extracted, and the operation analysis of the equipment status and time correlation is performed. The dependency relationship between the equipment statuses is determined by the time difference, and an equipment operation dependency model is generated.

[0008] Step 2: Based on the device operation dependency model, group device operation instructions, filter instruction sets without dependencies, calculate the execution time and idle time period of each device, arrange parallel execution according to the time window, and generate a parallel operation plan;

[0009] Step 3: Based on the parallel operation plan, a data stream compression algorithm is used to monitor the resource usage of each device, analyze the device load level, select high-load devices, reallocate tasks to low-load devices, adjust the task priority and resource allocation of the devices, and generate a device load distribution table;

[0010] Step 4: Based on the device load distribution table, monitor the operation execution of the device in real time, perform operation conflict judgment, detect the parallel execution status of the device, identify possible conflicts, select different operation paths to adjust the operation sequence, and generate a device conflict resolution plan;

[0011] Step 5: Based on the device conflict handling solution, perform multi-threaded task processing for parallel operations, allocate time periods for device execution, optimize the order of task allocation, ensure that multi-threaded tasks are executed according to the time windows, and generate multi-threaded processing results;

[0012] Step 6: Based on the multi-threaded processing results, identify the critical path in the sequential control process, calculate the execution time of each device operation on the critical path, postpone the execution of non-critical tasks, optimize the execution order and time of critical devices, and generate a critical path optimization plan;

[0013] Step 7: Based on the critical path optimization solution, check the dependencies of all devices, verify the adjusted device operation process, ensure that the dependency conditions of each device meet the sequence control requirements, call the dependency chain mechanism for operation verification, and generate a device operation verification report.

[0014] As a further solution of the present invention, the specific process of generating the device operation dependency model is as follows:

[0015] Based on the equipment operation instructions of the intelligent substation, the operation records of each device are extracted, the status changes of the devices are analyzed one by one, the operation duration is matched with the status switching time, and the status changes of each device are sorted out in chronological order to generate the device status identification and time data set;

[0016] Based on the device status identification and time data set, by comparing the device status changes with the timestamps, the device status changes are associated with the corresponding operation times one by one, the time difference of each device operation is calculated, the operation sequence of each device is obtained, and a device operation sequence table is generated;

[0017] Based on the device operation sequence table, the associated operations between devices are further analyzed, the dependency chains between devices are identified one by one, the sequence of operation states is analyzed, and the dependency chains are constructed to generate a device operation dependency model.

[0018] As a further solution of the present invention, the specific process of generating the parallel operation plan is:

[0019] Based on the device operation dependency model, the operation instructions of all devices are analyzed one by one, instructions without time association are screened out, the device operations are grouped, and the device instructions in each group are arranged one by one to generate an independent operation instruction set;

[0020] Based on the independent operation instruction set, the execution time window of each device is analyzed to calculate its idle time period, the idle period is calculated by comparing the operation time and state change of the device, and the idle time of each device is recorded one by one to generate a device idle time table;

[0021] Based on the device idle time table, the idle time periods of each device are arranged, the execution windows of device operations are allocated one by one, and parallel operation tasks are arranged to ensure the simultaneous operation of the devices and generate a parallel operation plan.

[0022] As a further solution of the present invention, the specific process of generating the device load distribution table is as follows:

[0023] Based on the parallel operation plan, a data stream compression algorithm is used to monitor the resource usage of each device one by one, collect the load data of the current task, analyze the resource consumption and task processing status of the device, sort the devices by resource consumption, and generate a device resource usage list;

[0024] Based on the device resource occupancy list, select high-load devices, analyze task types and execution durations, allocate some tasks of the high-load devices to low-load devices, readjust the task execution order according to task priorities, and generate a task reallocation table;

[0025] Based on the task reallocation table, the resource allocation ratio of all devices is adjusted to balance the resource utilization between devices, ensure that the load of each device is balanced when executing tasks, and generate a device load distribution table.

[0026] As a further solution of the present invention, the data stream compression algorithm is according to the formula:

[0027]

[0028] Where: R is the average resource occupancy rate of the device, T i The time taken by the i-th device to execute the task, W i is the task weight coefficient of the i-th device, C i is the total resource capacity of the i-th device, E i is the energy consumption compensation coefficient of the i-th device, and n is the number of devices.

[0029] As a further solution of the present invention, the specific process of generating the device conflict handling solution is as follows:

[0030] Based on the device load distribution table, the operation execution status of each device is monitored in real time, the parallel operation status of the devices is detected in sequence, whether there is a conflict or resource competition, and an operation status conflict record is generated;

[0031] Based on the operation status conflict records, analyze the devices and tasks that may cause operation conflicts, identify the task group where the conflict point is located, select a low-conflict path to adjust the operation sequence, rearrange the task priorities, and generate an operation sequence adjustment plan;

[0032] Based on the operation sequence adjustment plan, the execution paths and time windows of the devices are reallocated, and real-time adjustments are made to enable tasks to be executed in parallel, thereby generating a device conflict handling plan.

[0033] As a further solution of the present invention, the specific process of generating the multi-threaded processing result is:

[0034] Based on the device conflict handling solution, the parallel operation tasks of each device are assigned one by one, the execution period of the task and the conflict record of the device are analyzed, the execution time period is determined according to the load of the device, the execution order of the task is adjusted, and the device execution schedule is generated;

[0035] Based on the equipment execution schedule, the task allocation sequence is optimized, tasks are analyzed one by one according to task priority and equipment idle time, the execution sequence of equipment tasks is adjusted, the execution sequence of tasks within the time period is rearranged, and an optimized task sequence table is generated;

[0036] Based on the task optimization sequence table, parallel operation tasks are executed, and time windows for each device task are allocated to ensure that each task is executed in sequence within the time period. The operation tasks of all devices are executed through parallel tasks to generate multi-threaded processing results.

[0037] As a further solution of the present invention, the specific process of generating the critical path optimization solution is:

[0038] Based on the multi-threaded processing results, key equipment operations in the sequential control process are analyzed one by one, the task execution time of each key equipment is calculated, and the operation time of all key tasks is identified by recording the equipment operation time, thereby generating a key equipment operation schedule;

[0039] Based on the critical equipment operation schedule, non-critical tasks are identified one by one, their execution periods are analyzed for conflicts with critical tasks, the execution of non-critical tasks is delayed to reduce interference with critical tasks, the execution time of tasks is adjusted, and a non-critical task adjustment table is generated;

[0040] Based on the non-critical task adjustment table, the execution sequence of the critical equipment is optimized one by one, the operation time period of the critical task is adjusted, unnecessary delay is reduced, the time window is redistributed, the critical task is ensured to be executed in priority, and a critical path optimization scheme is generated.

[0041] As a further scheme of the present application, the specific process of generating the device operation verification report is:

[0042] Based on the critical path optimization scheme, the dependency relationship of all devices is checked one by one, the device operation record is compared with the critical node in the dependency chain, the preconditions and subsequent conditions of each device operation are analyzed, and a device dependency verification table is generated;

[0043] Based on the device dependency verification table, the adjusted device operation process is verified one by one, the time sequence of the device operation is analyzed one by one, the actual device operation is matched with the dependency chain, and an operation process verification result is generated;

[0044] Based on the operation process verification result, the dependency chain mechanism of each device is called step by step, the matching degree of the device operation and its dependency condition is confirmed one by one, the dependency condition meets the sequence control requirement, and a device operation verification report is generated.

[0045] An intelligent substation sequence control system, which is used to execute the intelligent substation sequence control method described above, comprises:

[0046] The dependency analysis module: based on the device operation instruction, the operation record of each device is analyzed one by one, the state change of each device is extracted, the correspondence between the device state change and the time is analyzed, and the dependency chain between devices is judged by comparing the time difference, and a device dependency chain structure is generated;

[0047] The parallel execution module: based on the device dependency chain structure, the operation instruction of each device is analyzed one by one, the instruction set without time dependency relationship is screened out, the parallel task is executed in groups according to the idle period of the device, the time period of task execution is adjusted, and a parallel task execution plan is generated;

[0048] The load monitoring module: based on the parallel task execution plan, the resource occupation of each device is monitored in real time, the current task load of the device is collected step by step, the resource consumption of the device is analyzed through data flow compression, and the devices are sorted according to the resource occupation and the task load level in sequence, and a device load distribution suggestion is generated;

[0049] The conflict processing module: based on the device load distribution suggestion, real-time monitoring of the parallel operation execution of the device, detecting whether there is a task conflict in each device operation process one by one, analyzing the time conflict and resource competition of task execution, re-planning the task execution order and adjusting the time period, and generating a device conflict processing scheme;

[0050] The task scheduling module: based on the device conflict processing scheme, analyzing the execution time period and conflict record of each device, gradually distributing the execution time of multi-threaded tasks, optimizing the task distribution order of the device, adjusting the task execution period according to the task priority, rearranging the execution order of each task, and generating a multi-threaded task execution table;

[0051] The verification module: based on the multi-threaded task execution table, analyzing the dependency chain of all devices, confirming the matching of device operation and dependency chain one by one, verifying the dependency condition of device operation, and generating a device operation verification record.

[0052] Compared with the prior art, the advantages and positive effects of the present application are:

[0053] In the present application, the resource occupation of the device is monitored through the data flow compression algorithm, and the load balancing is carried out, the device load level is evaluated in real time, and the dynamic distribution and adjustment of the load are realized, which effectively relieves the pressure of high-load devices, ensures that the resource use of each device in the system is more balanced, introduces an operation conflict judgment mechanism, identifies and solves the potential conflict of the device in parallel execution in real time, ensures the smooth execution of parallel tasks, calculates the execution time of the key device, delays the non-key task, ensures the priority execution of the device on the critical path, improves the scheduling flexibility of the system, effectively reduces the delay and conflict that may occur in the device execution process, and guarantees the sustainability and reliability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The workflow diagram of the present application is shown in the figure;

[0055] Figure 2 The system flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0057] Example 1

[0058] Please refer to Figure 1 The present application provides a technical scheme: an intelligent substation sequence control method, comprising the following steps:

[0059] Step 1: Based on the equipment operation instructions of the intelligent substation, the logical sequence of equipment operations is analyzed, the equipment status change information is extracted, and the operation analysis of the equipment status and time correlation is performed. The dependency relationship between the equipment statuses is determined by the time difference, and an equipment operation dependency model is generated.

[0060] Step 2: Based on the device operation dependency model, group device operation instructions, filter out independent instruction sets, calculate the execution time and idle time periods of each device, schedule parallel execution according to the time window, and generate a parallel operation plan.

[0061] Step 3: Based on the parallel operation plan, a data stream compression algorithm is used to monitor the resource usage of each device, analyze the device load level, select high-load devices, reallocate tasks to low-load devices, adjust the device's task priority and resource allocation, and generate a device load distribution table;

[0062] Step 4: Based on the device load distribution table, monitor the device's operation execution in real time, determine operation conflicts, detect the device's parallel execution status, identify possible conflicts, select different operation paths to adjust the operation sequence, and generate a device conflict resolution plan;

[0063] Step 5: Based on the device conflict handling solution, perform multi-threaded task processing for parallel operations, allocate device execution time periods, optimize the task allocation order, ensure that multi-threaded tasks are executed according to the time window, and generate multi-threaded processing results;

[0064] Step 6: Based on the multi-threaded processing results, identify the critical path in the sequential control process, calculate the execution time of each device operation on the critical path, postpone the execution of non-critical tasks, optimize the execution order and time of key devices, and generate a critical path optimization plan;

[0065] Step 7: Based on the critical path optimization plan, check the dependencies of all devices, verify the adjusted device operation process, ensure that the dependency conditions of each device meet the sequence control requirements, call the dependency chain mechanism to perform operation verification, and generate the device operation verification report.

[0066] See also Figure 2 ,The specific process of generating the device operation dependency model is:

[0067] Based on the equipment operation instructions of the intelligent substation, the operation records of each device are extracted, the status changes of the devices are analyzed one by one, the operation duration is matched with the status switching time, and the status changes of each device are sorted out in chronological order to generate the device status identification and time data set;

[0068] Based on the device status identification and time data set, by comparing the device status changes with the timestamps, the device status changes are associated with their corresponding operation times one by one, the time difference of each device operation is calculated, the operation sequence of each device is obtained, and the device operation sequence table is generated;

[0069] Based on the device operation sequence table, further analyze the associated operations between devices, identify the dependency chains between devices one by one, analyze the sequence of operation states, and construct the dependency chain to generate a device operation dependency model;

[0070] Based on the device operation records, a time series parsing algorithm is used. The parameters include the start timestamp and the end timestamp. The start timestamp is the time when the device operation starts, and the end timestamp is the time when the device operation is completed. The device operation records are parsed one by one, and the state switching time in the operation record is compared by timestamp to extract the device state change information. The start time and end time of each operation are matched to obtain the device state switching time and the corresponding state identifier. The state changes of each device are sorted out to generate a device state identifier and time data set;

[0071] Based on the device status identification and time data set, a time series comparison algorithm is adopted. The parameters include the status change timestamp and status identification of each device. The status change timestamp is used to identify the state change moment of each device, and the status identification is used to identify the device status. The state change of each device is compared with its timestamp one by one, and the state change of each device is associated with its operation time. The operation time difference between devices is calculated. The operation time difference is calculated by the time series comparison function. The calculation function receives two timestamps as input and outputs their time difference. The operation sequence of each device is obtained and the device operation sequence table is generated.

[0072] Based on the device operation sequence table, a dependency identification algorithm is adopted. The parameters include the device operation sequence, device status identifier, and time difference. The operation sequence is used to identify the execution sequence of the devices, the device status identifier is used to determine the type of operation, and the time difference is used to identify the dependency relationship between devices. The operation sequence of the devices is parsed one by one, and the sequence of device operations is analyzed in turn. The time difference is used to judge the dependency relationship between devices and construct the dependency chain structure of the devices. The dependency chain structure is generated by a traversal algorithm. The traversal algorithm receives the operation sequence table and dependency conditions as input, matches the time sequence of operations with the device status, and generates a device operation dependency model.

[0073] See also Figure 2 ,The specific process of generating a parallel operation plan is:

[0074] Based on the device operation dependency model, all device operation instructions are analyzed one by one, instructions without time correlation are screened out, device operations are grouped, and the device instructions in each group are arranged one by one to generate independent operation instruction sets;

[0075] Based on the independent operation instruction set, the execution time window of each device is analyzed and its idle time period is calculated. The idle period is calculated by comparing the operation time and status change of the device, and the idle time of each device is recorded one by one to generate a device idle time table;

[0076] Based on the equipment idle time table, the idle time periods of each equipment are arranged, and the execution windows of equipment operations are allocated one by one. Parallel operation tasks are arranged to ensure the simultaneous operation of equipment and generate parallel operation plans.

[0077] Based on the device operation dependency model, a timestamp comparison algorithm is used. Parameters include the operation time of each device and the dependency relationship between devices. Device operation time is identified by timestamps, and dependency relationships are identified by device status changes. The operation instructions of all devices are analyzed one by one, and timestamp comparison is used to filter out instructions without time correlation. The independence of each instruction is determined by the dependency relationship. The filtered non-dependent instructions are arranged according to device priority, and the device instruction order is arranged one by one to generate an independent operation instruction set.

[0078] Based on an independent operation instruction set, a time window calculation algorithm is used. Parameters include the device's operation time and state changes. The operation time is determined by a timestamp, and the state change is used to mark the start and end of the device operation. The execution time window of each device is analyzed, and the time window function is used to calculate the operation time of each device. By comparing the device's state change with its idle time period, the idle time of each device is calculated one by one, generating a device idle time table.

[0079] Based on the device idle time table, a task scheduling algorithm is adopted. The parameters include the device's idle time period and operation window. The idle time period is used to determine the device's available time, and the operation window is used to identify the operation time when the device can perform operations. The idle time periods of each device are arranged, and the task allocation of the devices is analyzed one by one. Task allocation is performed by comparing the idle time period with the operation window. The execution order of the devices is arranged in sequence, and tasks that can be performed in parallel are arranged to generate a parallel operation plan.

[0080] See also Figure 2 ,The specific process of generating the device load distribution table is:

[0081] Based on the parallel operation plan, the data stream compression algorithm is used to monitor the resource usage of each device one by one, collect the load data of the current task, analyze the resource consumption and task processing status of the device, sort the devices by resource consumption, and generate a device resource usage list;

[0082] Based on the device resource usage list, select high-load devices, analyze task types and execution durations, allocate some tasks from high-load devices to low-load devices, readjust the task execution order according to task priority, and generate a task reallocation table;

[0083] Based on the task reallocation table, adjust the resource allocation ratio of all devices, balance the resource utilization between devices, ensure the load balance of each device when executing tasks, and generate a device load distribution table;

[0084] Based on the parallel operation plan, a traffic monitoring and compression algorithm is adopted. The parameters include the real-time resource occupancy rate of the device, the progress of task processing, and the operating load of the device. The resource occupancy rate is collected in real time by the device monitoring module, the progress of task processing is tracked by the task queue, and the device load is identified by the usage rate of the processor and memory. The resource occupancy of each device is monitored one by one. The load information of the current task is collected by compressing the real-time data stream. The resource consumption of each device is analyzed using the load analysis function. The task processing status is sorted according to the device resource occupancy rate to generate a device resource occupancy list.

[0085] Based on the device resource occupancy list, a task decomposition and allocation algorithm is adopted. The parameters include the task queue of the high-load device, the idle queue of the low-load device, the priority of the task, and the task execution time. The task queue and the idle queue are used to identify the allocation position of the task. The task priority identifies the order of task processing. The task execution time is obtained through the real-time monitoring module. The high-load device is analyzed and the tasks that need to be decomposed are selected. The decomposition function is used to separate some tasks from the task queue of the high-load device. The allocation function is used to reallocate the tasks to the idle queue of the low-load device. The new task queue is adjusted according to the task priority, and a task reallocation table is generated.

[0086] Based on the task reallocation table, a resource allocation algorithm is adopted. The parameters include the load of each device, resource occupancy rate, and task execution time period. The load is obtained through the real-time monitoring module. The resource occupancy rate is used to calculate the resource allocation ratio of each device. The task execution time period identifies the resource usage of each device at different times. The resource allocation ratio of all devices is adjusted, and the resource allocation function is used to balance the resource occupancy of each device. The execution window of the task is adjusted one by one to generate a device load distribution table.

[0087] Data stream compression algorithm, according to the formula:

[0088]

[0089] Where: R is the average resource occupancy rate of the device, T i The time taken by the i-th device to execute the task, W i is the task weight coefficient of the i-th device, C i is the total resource capacity of the i-th device, E i is the energy consumption compensation coefficient of the i-th device, and n is the number of devices;

[0090] Execution process: First, for each device i, determine its task execution time T i and task weight coefficient W i , task weight coefficient W i The weight is assigned based on the importance or priority of the task. Complex or important tasks may be given higher weight values. The total resource capacity C of the computing device i and energy consumption compensation coefficient E i The sum of the energy consumption compensation coefficient E i It can be obtained based on the historical energy consumption data of the device or real-time measurement to reflect the energy consumption of the device and the task execution time T of the device. i and task weight coefficient W i Multiplying the two represents the task execution time of the device under weight correction, and then dividing it by the sum of the total resource capacity of the device and the energy consumption compensation C i +E i , this ratio represents the resource utilization efficiency of the device when executing the task. Finally, the resource utilization efficiency of all devices is accumulated and divided by the total number of devices n to calculate the average device resource utilization rate R.

[0091] See also Figure 2 ,The specific process of generating a device conflict resolution solution is as follows:

[0092] Based on the device load distribution table, the system monitors the operation execution status of each device in real time, detects the parallel operation status of the devices in turn, determines whether there is conflict or resource competition, and generates operation status conflict records;

[0093] Based on the conflict records of operation status, analyze the equipment and tasks that may cause operation conflicts, identify the task group where the conflict point is located, select the low-conflict path to adjust the operation sequence, rearrange the task priorities, and generate an operation sequence adjustment plan;

[0094] Based on the operation sequence adjustment plan, the execution path and time window of the equipment are reallocated, and real-time adjustments are made to enable parallel execution of tasks, generating a solution for handling equipment conflicts.

[0095] Based on the device load distribution table, a parallel task monitoring algorithm is adopted. The parameters include the device's execution status, operation time window, and resource occupancy rate. The device's execution status is obtained through the real-time monitoring module. The operation time window is used to identify the parallel execution time period of the task. The resource occupancy rate is used to detect the task's occupation of device resources. The operation execution status of each device is monitored in real time. By cross-comparing the device execution status with the resource occupancy rate, the parallel operation status of the device is detected in turn to determine whether there is an operation conflict or resource competition. The conflict information is recorded through the state conflict judgment function, and an operation state conflict record is generated.

[0096] Based on the conflict records of operation status, a conflict path identification algorithm is adopted. The parameters include the task queue of the conflicting device, the task execution time, and the conflict point. The task queue is used to identify the device operation that caused the conflict. The task execution time is marked with a timestamp. The conflict point identifies the specific task group that caused the conflict. The conflicting devices and their tasks are analyzed. The conflict path identification function is used to identify the task group where the conflict point is located. The conflict point analysis function is used to determine the cause and path of the conflict. The low-conflict path is selected and the operation order is adjusted. The task priorities are rearranged and an operation order adjustment plan is generated.

[0097] Based on the operation sequence adjustment scheme, the device execution path adjustment algorithm is adopted. The parameters include device execution path, task execution time window, and operation priority. The device execution path is used to identify the operation path of the device, the task execution time window is used to adjust the time period of the task, and the operation priority is used to reallocate the execution order of the tasks. The execution paths and time windows of all devices are reallocated, and the parallel execution of tasks is dynamically adjusted through the real-time path adjustment function. The execution order and time period of the tasks are updated one by one to generate a device conflict handling plan.

[0098] See also Figure 2 , the specific process of generating multi-threaded processing results is:

[0099] Based on the device conflict handling solution, the system allocates parallel operation tasks to each device one by one, analyzes the task execution period and the device conflict records, determines the execution time period based on the device load, adjusts the task execution order, and generates a device execution schedule;

[0100] Based on the equipment execution schedule, the task allocation sequence is optimized. Tasks are analyzed one by one according to task priority and equipment idle time, the execution sequence of equipment tasks is adjusted, the execution sequence of tasks within the time period is rearranged, and the task optimization sequence table is generated.

[0101] Based on the task optimization sequence table, parallel operation tasks are executed, and time windows are allocated for each device task to ensure that each task is executed in sequence within the time period. The operation tasks of all devices are executed through parallel tasks to generate multi-threaded processing results;

[0102] Based on the device conflict handling solution, a parallel task scheduling algorithm is adopted. The parameters include device load, conflict records, and task execution time period. The load status is obtained through the device's real-time monitoring module, and the conflict records are analyzed through the conflict handling solution. The task execution time period is identified by the task queue. The parallel operation tasks of each device are assigned one by one. The device load data is used to determine the optimal time period for task execution. The time sequence of task execution is adjusted according to the device load status to generate a device execution schedule.

[0103] Based on the equipment execution schedule, a priority sorting algorithm is used. The parameters include task priority, equipment idle period, and task execution time. Task priority indicates the urgency of task processing. Equipment idle period is obtained through the time window function. Task execution time is used to arrange the task sequence and optimize the task allocation order. Tasks and equipment idle periods are analyzed one by one. The execution order of tasks is adjusted according to task priority. The execution order of tasks within the time period is rearranged through the sorting function to generate a task optimization sequence table.

[0104] Based on the task optimization sequence table, a parallel task execution algorithm is adopted. The parameters include task time window, device execution path, and task execution order. The task time window is identified by a time period. The device execution path is used to define the operation steps of the task. The task execution order identifies the processing order of the tasks. The parallel operation tasks are executed. The multi-threaded execution function is used to allocate the task time window of each device, and the execution order of the tasks is arranged one by one so that each task is executed according to the task optimization sequence table. The operations of all devices are processed through parallel tasks to generate multi-threaded processing results.

[0105] See also Figure 2 ,The specific process of generating the critical path optimization plan is:

[0106] Based on the multi-threaded processing results, the key equipment operations in the sequential control process are analyzed one by one, and the task execution time of each key equipment is calculated. By recording the equipment operation time, the operation time of all key tasks is identified, and a key equipment operation schedule is generated;

[0107] Based on the critical equipment operation schedule, non-critical tasks are identified one by one, their execution periods are analyzed to see if they conflict with critical tasks, the execution of non-critical tasks is delayed to reduce interference with critical tasks, the execution time of tasks is adjusted, and a non-critical task adjustment table is generated;

[0108] Based on the non-critical task adjustment table, the execution order of key equipment is optimized one by one, the operation time period of key tasks is adjusted, unnecessary delays are reduced, time windows are reallocated, key tasks are prioritized, and a critical path optimization plan is generated;

[0109] Based on the results of multi-threaded processing, an equipment operation duration calculation algorithm is adopted. The parameters include the task execution time, operation duration, and task queue of key equipment. The task execution time is identified by a timestamp, and the operation duration is recorded in the equipment operation log. The task queue is used to identify key tasks. The key equipment operations in the sequential control process are analyzed one by one, and the task execution time of each key equipment is calculated by the timestamp. The operation duration calculation function is used to record the duration of the equipment operation, and the operation duration of all key tasks is identified to generate a key equipment operation schedule.

[0110] Based on the critical equipment operation schedule, a task conflict detection algorithm is adopted. The parameters include the execution period of non-critical tasks, the critical task time period, and the task queue. The non-critical task execution period is identified by the task time window function, the critical task time period is defined by the critical equipment operation schedule, and the task queue identifies the task priority. Each non-critical task is identified one by one, and the conflict between its execution period and the time conflict of the critical task is analyzed through the conflict detection function. The delay task function is used to adjust the execution period of the non-critical task to reduce the interference with the critical task, reallocate the task execution time, and generate a non-critical task adjustment table.

[0111] Based on the non-critical task adjustment table, a critical path optimization algorithm is adopted. The parameters include critical task operation time period, equipment execution sequence, and time window. The critical task operation time period is defined by the operation schedule. The equipment execution sequence is used to arrange the operation sequence of critical equipment. The time window identifies the execution period of equipment tasks. The execution sequence of critical equipment is optimized one by one. The operation time period of critical tasks is adjusted through the sequence optimization function to reduce unnecessary delays. The time window function is used to reallocate the execution time of critical tasks to generate a critical path optimization plan.

[0112] See also Figure 2 , the specific process of generating the equipment operation verification report is as follows:

[0113] Based on the critical path optimization solution, the dependencies of all devices are checked one by one. The device operation records are compared with the key nodes in the dependency chain. The pre- and post-conditions of each device operation are analyzed to generate a device dependency verification table.

[0114] Based on the device dependency verification table, the adjusted device operation process is verified in sequence, the time sequence of device operations is analyzed one by one, the actual device operation is matched with the dependency chain, and the operation process verification result is generated;

[0115] Based on the operation process verification results, the dependency chain mechanism of each device is called step by step to confirm the matching degree between the device operation and its dependency conditions one by one. If the dependency conditions meet the sequential control requirements, a device operation verification report is generated;

[0116] Based on the critical path optimization solution, a dependency chain comparison algorithm is adopted. The parameters include equipment operation records, dependency chain key nodes, equipment preconditions and postconditions. Equipment operation records are used to record the operation time and status of each equipment. Dependency chain key nodes are generated through the dependency chain. Preconditions and postconditions are used to identify the dependency relationships of equipment. The dependency relationships of all equipment are checked one by one, and the equipment operation records are compared with the dependency chain key nodes. The comparison function is used to analyze whether the preconditions and postconditions of each equipment operation meet the dependency requirements, and an equipment dependency verification table is generated.

[0117] Based on the device dependency verification table, a time sequence matching algorithm is used. Parameters include the device operation time sequence, dependency chain nodes, and device operation process. The device operation time sequence is recorded using timestamps. The dependency chain nodes are used to define the dependency relationship of each operation. The operation process is used to represent the operation sequence of the device. The adjusted device operation processes are verified in sequence. The actual operation of each device is compared with the dependency chain through the time sequence matching function. The order of the device operation time is analyzed one by one to see if it meets the dependency relationship, and the operation process verification result is generated.

[0118] Based on the operation process verification results, the dependency chain mechanism verification algorithm is adopted. The parameters include device dependency conditions, device operation records, and dependency chain nodes. Device dependency conditions are used to define the prerequisites and follow-up conditions of device operations. Operation records are used to record the execution status of the equipment. Dependency chain nodes are used to identify the specific nodes that the equipment depends on. The dependency chain mechanism of each device is called step by step. The dependency chain call function is used to confirm the matching degree of each device operation and its dependency conditions, verify whether the dependency conditions meet the sequential control requirements, and generate a device operation verification report.

[0119] An intelligent substation sequential control system, the system includes:

[0120] Dependency parsing module: Based on device operation instructions, it analyzes the operation records of each device one by one, extracts the status changes of each device, analyzes the corresponding relationship between device status changes and time, and determines the dependency chain between devices by comparing time differences, and generates the device dependency chain structure;

[0121] Parallel execution module: Based on the device dependency chain structure, it analyzes the operating instructions of each device one by one, filters out instruction sets that have no time dependencies, executes parallel tasks in groups according to the idle periods of the devices, adjusts the time periods for task execution, and generates a parallel task execution plan;

[0122] Load monitoring module: Based on the parallel task execution plan, it monitors the resource usage of each device in real time, gradually collects the current task load of the device, analyzes the resource consumption of the device through data stream compression, sorts the devices according to their resource usage and task load level, and generates device load distribution recommendations;

[0123] Conflict handling module: Based on device load distribution recommendations, it monitors the parallel operation execution of devices in real time, detects whether there are task conflicts during each device operation, analyzes the time conflicts and resource competition of task execution, re-plans the task execution order and adjusts the time period, and generates a device conflict handling plan;

[0124] Task scheduling module: Based on the device conflict handling solution, it analyzes the execution time period and conflict records of each device, gradually allocates the execution time of multi-threaded tasks, optimizes the task allocation order of the devices, adjusts the task execution period according to the task priority, rearranges the execution order of each task, and generates a multi-threaded task execution table;

[0125] Verification module: Based on the multi-threaded task execution table, analyze the dependency chains of all devices, confirm the matching of device operations with the dependency chains one by one, verify the dependency conditions of device operations, and generate device operation verification records.

[0126] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent substation sequential control method, characterized in that: The following steps are involved: Step 1: Based on the equipment operation instructions of the intelligent substation, the logical sequence of equipment operations is analyzed, the equipment status change information is extracted, and the operation analysis of the equipment status and time correlation is performed. The dependency relationship between the equipment statuses is determined by the time difference, and an equipment operation dependency model is generated. Step 2: Based on the device operation dependency model, group device operation instructions, filter instruction sets without dependencies, calculate the execution time and idle time period of each device, arrange parallel execution according to the time window, and generate a parallel operation plan; Step 3: Based on the parallel operation plan, a data stream compression algorithm is used to monitor the resource usage of each device, analyze the device load level, select high-load devices, reallocate tasks to low-load devices, adjust the task priority and resource allocation of the devices, and generate a device load distribution table; Step 4: Based on the device load distribution table, monitor the operation execution of the device in real time, perform operation conflict judgment, detect the parallel execution status of the device, identify possible conflicts, select different operation paths to adjust the operation sequence, and generate a device conflict resolution plan; Step 5: Based on the device conflict handling solution, perform multi-threaded task processing for parallel operations, allocate time periods for device execution, optimize the order of task allocation, ensure that multi-threaded tasks are executed according to the time windows, and generate multi-threaded processing results; Step 6: Based on the multi-threaded processing results, identify the critical path in the sequential control process, calculate the execution time of each device operation on the critical path, postpone the execution of non-critical tasks, optimize the execution order and time of key devices, and generate a critical path optimization plan; Step 7: Based on the critical path optimization solution, check the dependencies of all devices, verify the adjusted device operation process, ensure that the dependency conditions of each device meet the sequence control requirements, call the dependency chain mechanism for operation verification, and generate a device operation verification report.

2. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the device operation dependency model is as follows: Based on the equipment operation instructions of the intelligent substation, the operation records of each device are extracted, the status changes of the devices are analyzed one by one, the operation duration is matched with the status switching time, and the status changes of each device are sorted out in chronological order to generate the device status identification and time data set; Based on the device status identification and time data set, by comparing the device status changes with the timestamps, the device status changes are associated with the corresponding operation times one by one, the time difference of each device operation is calculated, the operation sequence of each device is obtained, and a device operation sequence table is generated; Based on the device operation sequence table, the associated operations between devices are further analyzed, the dependency chains between devices are identified one by one, the sequence of operation states is analyzed, and the dependency chains are constructed to generate a device operation dependency model.

3. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the parallel operation plan is as follows: Based on the device operation dependency model, the operation instructions of all devices are analyzed one by one, instructions without time association are screened out, the device operations are grouped, and the device instructions in each group are arranged one by one to generate an independent operation instruction set; Based on the independent operation instruction set, the execution time window of each device is analyzed to calculate its idle time period, the idle period is calculated by comparing the operation time and state change of the device, and the idle time of each device is recorded one by one to generate a device idle time table; Based on the device idle time table, the idle time periods of each device are arranged, the execution windows of device operations are allocated one by one, and parallel operation tasks are arranged to ensure the simultaneous operation of the devices and generate a parallel operation plan.

4. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the device load distribution table is as follows: Based on the parallel operation plan, a data stream compression algorithm is used to monitor the resource usage of each device one by one, collect the load data of the current task, analyze the resource consumption and task processing status of the device, sort the devices by resource consumption, and generate a device resource usage list; Based on the device resource occupancy list, select high-load devices, analyze task types and execution durations, allocate some tasks of the high-load devices to low-load devices, readjust the task execution order according to task priorities, and generate a task reallocation table; Based on the task reallocation table, the resource allocation ratio of all devices is adjusted to balance the resource utilization between devices, ensure that the load of each device is balanced when executing tasks, and generate a device load distribution table.

5. The intelligent substation sequential control method according to claim 1, characterized in that: The data stream compression algorithm is based on the formula: Where: R is the average resource occupancy rate of the device, T i The time taken by the i-th device to execute the task, W i is the task weight coefficient of the i-th device, C i is the total resource capacity of the i-th device, E i is the energy consumption compensation coefficient of the i-th device, and n is the number of devices.

6. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the device conflict handling solution is as follows: Based on the device load distribution table, the operation execution status of each device is monitored in real time, the parallel operation status of the devices is detected in sequence, whether there is a conflict or resource competition, and an operation status conflict record is generated; Based on the operation status conflict records, analyze the devices and tasks that may cause operation conflicts, identify the task group where the conflict point is located, select a low-conflict path to adjust the operation sequence, rearrange the task priorities, and generate an operation sequence adjustment plan; Based on the operation sequence adjustment plan, the execution paths and time windows of the devices are reallocated, and real-time adjustments are made to enable tasks to be executed in parallel, thereby generating a device conflict handling plan.

7. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the multi-threaded processing result is: Based on the device conflict handling solution, the parallel operation tasks of each device are assigned one by one, the execution period of the task and the conflict record of the device are analyzed, the execution time period is determined according to the load of the device, the execution order of the task is adjusted, and the device execution schedule is generated; Based on the equipment execution schedule, the task allocation sequence is optimized, tasks are analyzed one by one according to task priority and equipment idle time, the execution sequence of equipment tasks is adjusted, the execution sequence of tasks within the time period is rearranged, and an optimized task sequence table is generated; Based on the task optimization sequence table, parallel operation tasks are executed, and time windows for each device task are allocated to ensure that each task is executed in sequence within the time period. The operation tasks of all devices are executed through parallel tasks to generate multi-threaded processing results.

8. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the critical path optimization solution is as follows: Based on the multi-threaded processing results, key equipment operations in the sequential control process are analyzed one by one, the task execution time of each key equipment is calculated, and the operation time of all key tasks is identified by recording the equipment operation time, thereby generating a key equipment operation schedule; Based on the critical equipment operation schedule, non-critical tasks are identified one by one, their execution periods are analyzed for conflicts with critical tasks, the execution of non-critical tasks is delayed to reduce interference with critical tasks, the execution time of tasks is adjusted, and a non-critical task adjustment table is generated; Based on the non-critical task adjustment table, the execution order of critical equipment is optimized one by one, the operation time period of critical tasks is adjusted, unnecessary delays are reduced, time windows are reallocated, critical tasks are ensured to be executed first, and a critical path optimization plan is generated.

9. The intelligent substation sequential control method according to claim 1, characterized in that: The specific process of generating the equipment operation verification report is as follows: Based on the critical path optimization solution, the dependency relationships of all devices are checked one by one, and the device operation records are compared with the key nodes in the dependency chain. The pre- and post-conditions of each device operation are analyzed to generate a device dependency verification table. Based on the device dependency verification table, the adjusted device operation process is verified in sequence, the time sequence of the device operations is analyzed one by one, the actual device operation is matched with the dependency chain, and the operation process verification result is generated; Based on the verification results of the operation process, the dependency chain mechanism of each device is called step by step to confirm the matching degree of the device operation and its dependency conditions one by one. If the dependency conditions meet the sequential control requirements, a device operation verification report is generated.

10. An intelligent substation sequential control system, characterized in that: According to any one of claims 1 to 9, the intelligent substation sequential control method comprises: Dependency parsing module: Based on device operation instructions, it analyzes the operation records of each device one by one, extracts the status changes of each device, analyzes the corresponding relationship between device status changes and time, and determines the dependency chain between devices by comparing time differences, and generates the device dependency chain structure; Parallel execution module: Based on the device dependency chain structure, it analyzes the operation instructions of each device one by one, filters out instruction sets without time dependencies, executes parallel tasks in groups according to the idle periods of the devices, adjusts the time periods for task execution, and generates a parallel task execution plan; Load monitoring module: Based on the parallel task execution plan, it monitors the resource usage of each device in real time, gradually collects the current task load of the device, analyzes the resource consumption of the device through data stream compression, sorts the devices according to their resource usage and task load level, and generates device load distribution suggestions; Conflict handling module: Based on the device load distribution recommendations, it monitors the parallel operation execution of devices in real time, detects whether there are task conflicts during each device operation, analyzes the time conflicts and resource competition of task execution, re-plans the task execution order and adjusts the time period, and generates a device conflict handling plan; Task scheduling module: Based on the device conflict handling solution, analyze the execution time period and conflict records of each device, gradually allocate the execution time of multi-threaded tasks, optimize the task allocation order of the devices, adjust the task execution period according to the task priority, rearrange the execution order of each task, and generate a multi-threaded task execution table; Verification module: Based on the multi-threaded task execution table, analyze the dependency chains of all devices, confirm the matching of device operations with the dependency chains one by one, verify the dependency conditions of device operations, and generate device operation verification records.

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