Intelligent substation sequence control method and system

Abstract

The invention relates to the technical field of automatic control systems, in particular to an intelligent substation sequence control method and system, and the method comprises the following steps: based on an equipment operation instruction of an intelligent substation, analyzing the logic sequence of equipment operation, extracting the state change information of equipment, and carrying out the operation analysis of equipment state and time correlation; and judging the dependency relationship through the time difference of the equipment state, and generating an equipment operation dependency model. According to the method, the resource occupation condition of the equipment is monitored through a data stream compression algorithm, load balancing is carried out, the load level of the equipment is evaluated in real time, dynamic distribution and adjustment of loads are achieved, the pressure of high-load equipment is effectively relieved, potential conflicts of the equipment during parallel execution are recognized and solved in real time by introducing an operation conflict judgment mechanism, and the safety of the equipment is improved. Smooth execution of parallel tasks is guaranteed, delay and conflict possibly occurring in the equipment execution process are effectively reduced, and continuity and reliability of a power system are guaranteed.

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

[0003] The intelligent substation sequential control method aims to achieve linkage control, protection action and rapid fault recovery of substation equipment through technical means such as automated dispatching, remote control and intelligent response, thereby improving the safety, automation and management efficiency of power grid operation, reducing the workload of operators, improving the response speed of substations, and ensuring continuous and stable power supply.

[0004] Traditional substation sequential control methods rely on pre-set fixed rules or static resource allocation modes in terms of resource allocation and task scheduling, and lack real-time load adjustment capabilities. As a result, in actual operations, some equipment is in a high-load state for a long time and cannot 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 during parallel execution of equipment, 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 in 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, analyze the logical sequence of equipment operation, extract the equipment status change information, conduct operation analysis related to equipment status and time, determine the dependency relationship through the time difference of equipment status, and generate the equipment operation dependency model;

[0008] Step 2: Based on the device operation dependency model, group the device operation instructions, filter the 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, make operation conflict judgments, 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 handling plan;

[0011] Step 5: Based on the device conflict handling solution, perform multi-threaded task processing of 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 window, 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 key 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 sequential 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:

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

[0016] Based on the device status identification and time data set, by comparing the device status change with the timestamp, the device status change is associated with its corresponding operation time 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;

[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, the 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, its idle time period is calculated, 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:

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

[0024] Based on the device resource occupancy list, select a high-load device, analyze the task type and execution duration, allocate part of the tasks of the high-load device to the low-load device, readjust the task execution order according to the task priority, 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 ith device, E i is the energy consumption compensation coefficient of the ith 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:

[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 turn, 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 equipment and tasks that may cause the operation conflict, 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 path and time window of the device are reallocated, and real-time adjustment is performed 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 scheme, 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 period, 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 tasks of each device are allocated to ensure that each task is executed in sequence within a 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, the 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, so as to generate 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 time 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 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.

[0041] As a further solution of the present invention, the specific process of generating the equipment operation verification report is as follows:

[0042] Based on the critical path optimization scheme, the dependency relationships of all devices are checked one by one, the device operation records are compared with the key nodes in the dependency chain, the preconditions and postconditions 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 in sequence, the time sequence of device operations is analyzed one by one, the actual operation of the device is matched with the dependency chain, and the operation process verification result is generated;

[0044] Based on the operation process verification results, the dependency chain mechanism of each device is called step by step, and the matching degree between the device operation and its dependency conditions is confirmed one by one. If the dependency conditions meet the sequential control requirements, a device operation verification report is generated.

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

[0046] Dependency parsing module: Based on the device operation instructions, analyze the operation records of each device one by one, extract the state changes of each device, analyze the corresponding relationship between the device state changes and time, and judge the dependency chain between devices by comparing the time difference, and generate the device dependency chain structure;

[0047] Parallel execution module: Based on the device dependency chain structure, analyze the operation instructions of each device one by one, filter out instruction sets without time dependencies, execute parallel tasks in groups according to the idle time of the device, adjust the time period of task execution, and generate a parallel task execution plan;

[0048] Load monitoring module: Based on the parallel task execution plan, monitor the resource usage of each device in real time, gradually collect the current task load of the device, analyze the resource consumption of the device through data stream compression, sort the devices according to their resource usage and task load level, and generate device load distribution suggestions;

[0049] Conflict handling module: Based on the equipment load distribution suggestion, monitor the parallel operation execution of the equipment in real time, detect whether there is a task conflict in the operation process of each equipment one by one, analyze the time conflict and resource competition of task execution, re-plan the task execution order and adjust the time period, and generate a device conflict handling plan;

[0050] 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 device, 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;

[0051] 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.

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

[0053] In the present invention, the resource occupancy of the equipment is monitored and load balancing is performed through a data stream compression algorithm, the equipment load level is evaluated in real time, and dynamic distribution and adjustment of the load are realized, thereby effectively alleviating the pressure of high-load equipment and ensuring a more balanced use of resources of each device in the system. By introducing an operation conflict judgment mechanism, potential conflicts of equipment during parallel execution are identified and resolved in real time, thereby ensuring the smooth execution of parallel tasks. By calculating the execution time of key equipment and postponing non-critical tasks, priority execution of equipment on the critical path is ensured, thereby improving the scheduling flexibility of the system, effectively reducing possible delays and conflicts during equipment execution, and ensuring the continuity and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0055] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] Embodiment 1

[0058] See also Figure 1 The present invention provides a technical solution: an intelligent substation sequential control method, comprising the following steps:

[0059] Step 1: Based on the equipment operation instructions of the intelligent substation, analyze the logical sequence of equipment operation, extract the equipment status change information, conduct operation analysis related to equipment status and time, determine the dependency relationship through the time difference of equipment status, and generate the equipment operation dependency model;

[0060] Step 2: Based on the device operation dependency model, group the device operation instructions, filter the 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;

[0061] Step 3: Based on the parallel operation plan, the 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;

[0062] Step 4: Based on the equipment load distribution table, monitor the operation execution of the equipment in real time, make operation conflict judgments, detect the parallel execution status of the equipment, identify possible conflicts, select different operation paths to adjust the operation sequence, and generate equipment conflict handling solutions;

[0063] Step 5: Based on the device conflict handling solution, perform multi-threaded task processing of 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 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 equipment, and generate a critical path optimization plan;

[0065] 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 sequential control requirements, call the dependency chain mechanism for operation verification, and generate an equipment 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 state changes of the equipment are analyzed one by one, the operation duration is matched with the state switching time, and the state changes of each device are sorted out in chronological order to generate the equipment state identification and time data set;

[0068] Based on the equipment status identification and time data set, by comparing the equipment status change and timestamp, the equipment status change and its corresponding operation time are associated one by one, the time difference of each equipment operation is calculated, the operation sequence of each equipment is obtained, and the equipment 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 operation records of the equipment, 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 equipment starts the operation, and the end timestamp is the time when the equipment operation is completed. The operation records of the equipment are parsed one by one. The state switching time in the operation record is compared with the timestamp to extract the state change information of the equipment. The start time and end time of each operation are matched to obtain the switching time of the equipment state and the corresponding state identification. The state changes of each equipment are sorted out to generate the equipment state identification and time data set;

[0071] Based on the equipment 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 equipment. The status change timestamp is used to identify the status change moment of each equipment. The status identification is used to identify the equipment status. The equipment status change and its timestamp are compared one by one. The status change of each equipment is associated with its operation time. The operation time difference between the equipment 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 equipment is obtained and the equipment 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 between devices. The device operation sequence is parsed one by one, and the sequence of device operations is analyzed in turn. The dependency relationship between devices is judged using the time difference, and the dependency chain structure of the device is constructed. 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, the operation instructions of all devices are analyzed one by one, the 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;

[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 state 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, the execution windows of equipment operations are allocated one by one, and the operation tasks that can be operated in parallel are arranged to ensure the simultaneous operation of the equipment and generate a parallel operation plan;

[0077] Based on the device operation dependency model, a timestamp comparison algorithm is used. The parameters include the operation time of each device and the dependency relationship between devices. The device operation time is marked by the timestamp, and the dependency relationship is marked by the device state change. The operation instructions of all devices are analyzed one by one, and the instructions without time association are screened out by timestamp comparison. The independence of each instruction is judged by the dependency relationship. The screened non-dependent instructions are arranged according to the device priority, and the device instruction order is arranged one by one to generate an independent operation instruction set;

[0078] Based on the independent operation instruction set, a time window calculation algorithm is adopted. The parameters include the operation time and state change of the device. The operation time is determined by the 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 operation time of each device is calculated using the time window function. The operation time of the device is compared with the idle time period through the device state change, and the idle time of each device is calculated one by one to generate the device idle time table.

[0079] Based on the equipment idle time table, a task scheduling algorithm is adopted. The parameters include the equipment's idle time period and operation window. The idle time period is used to determine the equipment's available time, and the operation window is used to identify the operation time when the equipment can perform operations. The idle time periods of each equipment are arranged, and the task allocation of the equipment is analyzed one by one. Task allocation is performed by comparing the idle time periods with the operation windows. The execution order of the equipment 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 equipment 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 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, assign some tasks of high-load devices to low-load devices, readjust the task execution order according to task priorities, 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 that each device is load balanced when executing tasks, and generate a device load distribution table;

[0084] Based on the parallel operation plan, the flow monitoring and compression algorithm is adopted. The parameters include the real-time resource occupancy rate of the equipment, the progress of task processing, and the operation load of the equipment. The resource occupancy rate is collected in real time through the equipment monitoring module, the progress of task processing is tracked through the task queue, and the equipment load is identified by the usage rate of the processor and memory. The resource occupancy of each device is monitored one by one, and the load information of the current task is collected by compressing the real-time data stream. The resource consumption of each device is analyzed by the load analysis function, and 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, and 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 the 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 ith 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 It is the weight 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 convert the task execution time T of the device into i and task weight coefficient W i Multiplying 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 handling solution is:

[0092] Based on the equipment load distribution table, the operation execution status of each equipment is monitored in real time, the parallel operation status of the equipment is detected in turn, whether there is a conflict or resource competition, and the operation status conflict record is generated;

[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 low-conflict paths to adjust the operation sequence, rearrange 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 tasks to be executed in parallel, generating a solution for handling equipment conflicts;

[0095] Based on the equipment load distribution table, a parallel task monitoring algorithm is adopted. The parameters include the execution status of the equipment, the operation time window, and the resource occupancy rate. The execution status of the equipment 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 occupation of the equipment resources by the task. The operation execution status of each equipment is monitored in real time. The equipment execution status and resource occupancy rate are cross-compared, and the parallel operation status of the equipment 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 to generate an operation state conflict record;

[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 causes the conflict. The task execution time is marked by a timestamp. The conflict point identifies the specific task group that causes 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 cause and path are determined by the conflict point analysis function. The low-conflict path is selected and the operation order is adjusted. The task priority is rearranged to generate an operation order adjustment plan.

[0097] Based on the operation sequence adjustment plan, 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 equipment conflict handling solution, the parallel operation tasks of each equipment are assigned one by one, the conflict records of the task execution period and the equipment are analyzed, the execution time period is determined according to the load of the equipment, the execution order of the tasks is adjusted, and the equipment execution schedule is generated;

[0100] Based on the equipment execution schedule, optimize the task allocation order, analyze the tasks one by one according to the task priority and the equipment's idle time period, adjust the execution order of equipment tasks, rearrange the execution order of tasks within the time period, and generate a task optimization sequence table;

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

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

[0103] Based on the equipment execution schedule, a priority sorting algorithm is adopted. 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 time window function. Task execution time is used to arrange the order of tasks and optimize the order of task allocation. 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 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 the 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, and the task time window of each device is allocated by the multi-threaded execution function. The execution order of the tasks is arranged one by one, so that each task is executed according to the task optimization sequence table, and 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 solution is:

[0106] Based on the multi-threaded processing results, analyze the key equipment operations in the sequential control process one by one, calculate the task execution time of each key equipment, identify the operation time of all key tasks by recording the equipment operation time, and generate a key equipment operation schedule;

[0107] Based on the operation schedule of key equipment, identify non-critical tasks one by one, analyze the time conflicts between their execution periods and key tasks, delay the execution of non-critical tasks, reduce interference with key tasks, adjust the execution time of tasks, and generate a non-critical task adjustment table;

[0108] Based on the non-critical task adjustment table, optimize the execution order of key equipment one by one, adjust the operation time period of key tasks, reduce unnecessary delays, reallocate time windows, ensure that key tasks are executed first, and generate a critical path optimization plan;

[0109] Based on the multi-threaded processing results, the 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 marked 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, and the critical task time period is defined by the critical equipment operation schedule. 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 critical task is analyzed through the conflict detection function. The execution period of non-critical tasks is adjusted using the delayed task function to reduce interference with critical tasks, reallocate task execution time, and generate a non-critical task adjustment table.

[0111] Based on the non-critical task adjustment table, the 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, check the dependencies of all devices one by one, compare the device operation records with the key nodes in the dependency chain, analyze the pre- and post-conditions of each device operation, and generate a device dependency verification table;

[0114] Based on the equipment dependency verification table, verify the adjusted equipment operation process in sequence, analyze the time sequence of equipment operations one by one, match the actual equipment operation with the dependency chain, and generate the operation process verification result;

[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, the device operation verification report is generated;

[0116] Based on the critical path optimization scheme, a dependency chain comparison algorithm is adopted. The parameters include equipment operation records, dependency chain key nodes, equipment preconditions and subsequent conditions. Equipment operation records are used to record the operation time and status of each equipment. Dependency chain key nodes are generated through dependency chains. Preconditions and subsequent conditions are used to identify the dependency relationship 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 subsequent conditions of each equipment operation meet the dependency requirements, and the equipment dependency verification table is generated;

[0117] Based on the device dependency verification table, a time sequence matching algorithm is adopted. The parameters include the device operation time sequence, dependency chain nodes, and device operation process. The device operation time sequence is recorded by 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 process is verified in turn. The actual operation of each device is compared with the dependency chain through the time sequence matching function. The sequence of 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 prerequisite and subsequent 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 matching degree of each device operation and its dependency conditions is confirmed through the dependency chain calling function. It is verified whether the dependency conditions meet the sequential control requirements and generate an equipment operation verification report.

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

[0120] Dependency parsing module: Based on the device operation instructions, analyze the operation records of each device one by one, extract the state changes of each device, analyze the corresponding relationship between the device state changes and time, and judge the dependency chain between devices by comparing the time difference, and generate the device dependency chain structure;

[0121] Parallel execution module: Based on the device dependency chain structure, analyze the operation instructions of each device one by one, filter out the instruction sets without time dependency, execute parallel tasks in groups according to the idle time of the device, adjust the time period of task execution, and generate 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 suggestions;

[0123] Conflict handling module: Based on the equipment load distribution suggestions, it monitors the parallel operation execution of the equipment in real time, detects whether there is a task conflict in the operation process of each equipment one by one, analyzes the time conflict and resource competition of task execution, re-plans the task execution order and adjusts the time period, and generates equipment conflict handling solutions;

[0124] 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 device, 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;

[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 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. 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, analyze the logical sequence of equipment operation, extract the equipment status change information, conduct operation analysis related to equipment status and time, determine the dependency relationship through the time difference of equipment status, and generate the equipment operation dependency model; Step 2: Based on the device operation dependency model, group the device operation instructions, filter the 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, make operation conflict judgments, 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 handling plan; Step 5: Based on the device conflict handling solution, perform multi-threaded task processing of 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 window, 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 sequential 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 is 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 state changes of the equipment are analyzed one by one, the operation duration is matched with the state switching time, and the state changes of each device are sorted out in chronological order to generate the equipment state identification and time data set; Based on the device status identification and time data set, by comparing the device status change with the timestamp, the device status change is associated with its corresponding operation time 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; 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 is 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, the 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, its idle time period is calculated, 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 is 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 occupancy of each device one by one, collect the load data of the current task, analyze the resource consumption and task processing of the device, sort the devices by resource consumption, and generate a device resource occupancy list; Based on the device resource occupancy list, select a high-load device, analyze the task type and execution duration, allocate part of the tasks of the high-load device to the low-load device, readjust the task execution order according to the task priority, 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 is 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 ith device, E i is the energy consumption compensation coefficient of the ith device, and n is the number of devices.

6. The intelligent substation sequential control method according to claim 1 is 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 turn, 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 equipment and tasks that may cause the operation conflict, 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 path and time window of the device are reallocated, and real-time adjustment is performed 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 is characterized in that: The specific process of generating the multi-threaded processing result is: Based on the device conflict handling scheme, 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 period, 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 tasks of each device are allocated to ensure that each task is executed in sequence within a 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 is characterized in that: The specific process of generating the critical path optimization solution is as follows: Based on the multi-threaded processing results, the 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, so as to generate 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 time 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 scheme, the dependency relationships of all devices are checked one by one, the device operation records are compared with the key nodes in the dependency chain, the preconditions and postconditions of each device operation are analyzed, and a device dependency verification table is generated; 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 operation of the device is matched with the dependency chain, and the operation process verification result is generated; Based on the operation process verification results, the dependency chain mechanism of each device is called step by step, and the matching degree between the device operation and its dependency conditions is confirmed 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 the intelligent substation sequential control method according to any one of claims 1 to 9, the system comprises: Dependency parsing module: Based on the device operation instructions, analyze the operation records of each device one by one, extract the state changes of each device, analyze the corresponding relationship between the device state changes and time, and judge the dependency chain between devices by comparing the time difference, and generate the device dependency chain structure; Parallel execution module: Based on the device dependency chain structure, analyze the operation instructions of each device one by one, filter out instruction sets without time dependencies, execute parallel tasks in groups according to the idle time of the device, adjust the time period of task execution, and generate a parallel task execution plan; Load monitoring module: Based on the parallel task execution plan, monitor the resource usage of each device in real time, gradually collect the current task load of the device, analyze the resource consumption of the device through data stream compression, sort the devices according to their resource usage and task load level, and generate device load distribution suggestions; Conflict handling module: Based on the equipment load distribution suggestion, monitor the parallel operation execution of the equipment in real time, detect whether there is a task conflict in the operation process of each equipment one by one, analyze the time conflict and resource competition of task execution, re-plan the task execution order and adjust the time period, and generate 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 device, 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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