Multi-device cooperative task execution management method and system for power distribution scene

Abstract

The invention provides a power distribution scene-oriented multi-device cooperative task execution management method and system, and relates to the technical field of distributed control, and the method comprises the steps: loading a power distribution scene demand target to a task rule base, and carrying out the decomposition to obtain a plurality of atomic subtasks; performing task parallel dependency analysis, and constructing a task sequence execution chain; performing device multiplexing analysis to obtain P multiplexing time chains of the P multiplexing devices and W task execution time windows of the W non-multiplexing devices; performing task execution control fitting, and outputting a plurality of task execution control parameters; a task cooperative control chain is obtained; and driving the P multiplexing devices and the W non-multiplexing devices to execute device space-time cooperative control management. According to the method, the technical problem that the task execution efficiency in a power distribution scene is low due to the fact that a multi-device task scheduling method in the prior art usually neglects the time-space coordination problem between devices and resource scrambling and time conflict occur during device scheduling is solved.

Description

Technical Field

[0001] The present invention relates to the field of distributed control technology, and in particular to a multi-device collaborative task execution management method and system for power distribution scenarios. Background Art

[0002] Tasks in power distribution scenarios typically involve the collaboration of multiple devices, such as device monitoring, status updates, and data collection. These tasks are sometimes executed in parallel and sometimes sequentially. Device scheduling must consider not only the capabilities and location of the devices, but also the dependencies between tasks and device availability. Therefore, task scheduling and device collaborative execution require a highly optimized management approach.

[0003] In the existing technology, the spatiotemporal coordination issues between devices are usually ignored when executing tasks. Problems such as conflicts between devices, path overlaps, and execution timing conflicts are not effectively resolved, resulting in resource competition and time conflicts during device scheduling. This causes delays in task execution and waste of device resources, and cannot meet the real-time and efficient task execution requirements. Especially when multiple tasks are executed in parallel, conflicts between devices are inevitable, resulting in low task execution efficiency in power distribution scenarios. Summary of the Invention

[0004] The present application provides a multi-device collaborative task execution management method and system for power distribution scenarios, aiming to solve the technical problem that the multi-device task scheduling methods in the existing technology usually ignore the spatiotemporal coordination problems between devices, resource competition and time conflicts occur during device scheduling, resulting in reduced device scheduling efficiency, and further leading to low task execution efficiency in power distribution scenarios.

[0005] The first aspect disclosed in the present application provides a multi-device collaborative task execution management method for power distribution scenarios, the method comprising: loading the real-time uploaded power distribution scenario demand target into a task rule library, decomposing it into multiple atomic subtasks; performing task parallel dependency analysis on the multiple atomic subtasks, and constructing a task sequential execution chain, wherein the task sequential execution chain includes multiple serial parallel subtask groups; performing device reuse analysis based on the task sequential execution chain, and obtaining P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices; performing task execution control fitting on the multiple atomic subtasks according to the power distribution scenario architectural information, and outputting multiple task execution control parameters; associating the multiple task execution control parameters according to the task sequential execution chain, and obtaining a task collaborative control chain; using the P multiplexing time chains and W task execution time windows as device parallel scheduling constraints, and driving the P multiplexing devices and W non-multiplexing devices to perform device spatiotemporal collaborative control management based on the task collaborative control chain.

[0006] The second aspect disclosed in the present application provides a multi-device collaborative task execution management system for power distribution scenarios, which is used for the above-mentioned multi-device collaborative task execution management method for power distribution scenarios. The system includes: a subtask acquisition module for loading the real-time uploaded power distribution scenario demand target into the task rule library to decompose it into multiple atomic subtasks; a task parallel dependency analysis module for performing task parallel dependency analysis on the multiple atomic subtasks and constructing a task sequential execution chain, wherein the task sequential execution chain includes multiple serial parallel subtask groups; and a device reuse analysis module for performing device reuse analysis based on the task sequential execution chain. P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices are obtained; a task execution control fitting module is used to perform task execution control fitting on the multiple atomic subtasks according to the distribution scenario architectural information, and output multiple task execution control parameters; a collaborative control chain acquisition module is used to associate the multiple task execution control parameters according to the task sequence execution chain to obtain a task collaborative control chain; a control management module is used to use the P multiplexing time chains and W task execution time windows as device parallel scheduling constraints, and drive the P multiplexing devices and W non-multiplexing devices to perform device spatiotemporal collaborative control management based on the task collaborative control chain.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: By loading the real-time uploaded distribution scenario demand targets into the task rule library and decomposing the tasks into multiple atomic subtasks, it is ensured that the tasks can be refined and executed step by step, thereby enhancing the flexibility and response speed of the system; performing task parallel dependency analysis on multiple atomic subtasks and building a task sequential execution chain, this process improves the efficiency of parallel execution and avoids execution delays between tasks; through equipment reuse analysis, generating P reuse time chains for P reused devices and P task execution time windows for W non-reused devices, so that reused devices and non-reused devices can work effectively without interfering with each other during task execution, optimizing the balance between equipment use and task execution; and performing task execution according to the distribution scenario building information. The system performs row control fitting and generates task execution control parameters to ensure that the equipment executes the task along the optimal path. This process reduces equipment conflicts caused by spatial layout and physical obstacles, ensuring efficient completion of tasks. By associating task execution control parameters, a task collaborative control chain is generated to clarify the order, path and coordination method of equipment execution. This provides a systematic control solution for the collaboration between devices and improves the coordination of devices in task execution. Based on the task collaborative control chain and equipment scheduling constraints, devices can achieve spatiotemporal coordinated control management when executing tasks, avoiding execution bottlenecks caused by time conflicts or path overlaps, ensuring efficient collaboration between devices, and reducing conflicts and delays that may occur during task execution.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of a multi-device collaborative task execution management method for power distribution scenarios provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of a multi-device collaborative task execution management system for power distribution scenarios provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: subtask acquisition module 10 , task parallel dependency analysis module 20 , device reuse analysis module 30 , task execution control fitting module 40 , collaborative control chain acquisition module 50 , control management module 60 . DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a multi-device collaborative task execution management method and system for power distribution scenarios, which solves the technical problem that the multi-device task scheduling methods in the existing technology usually ignore the time and space coordination problems between devices, and resource competition and time conflicts occur during device scheduling, resulting in reduced device scheduling efficiency, and further leading to low task execution efficiency in power distribution scenarios.

[0013] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0014] Example 1, as Figure 1 As shown, an embodiment of the present application provides a multi-device collaborative task execution management method for power distribution scenarios, the method comprising: By loading the real-time uploaded distribution scenario demand targets into the task rule library, they are decomposed into multiple atomic subtasks.

[0015] The distribution scenario demand target refers to the tasks or operation targets that need to be completed in the system. For example, a task requires collecting data, starting equipment, or monitoring the status of equipment at a specific location. The real-time uploaded distribution scenario demand targets are analyzed in detail, the key elements of the tasks are extracted, and matched with the rules in the task rule library to determine how each task should be decomposed into smaller atomic subtasks. For example, the task target involves the operation of device A, and this operation needs to be split into multiple subtasks, such as device startup, data collection, operation feedback, etc. Based on the decomposition method defined in the task rule library, the distribution scenario demand target is broken down into multiple atomic subtasks. The atomic subtask is the most basic and non-decomposable unit, which includes specific execution actions, time, equipment requirements, etc.

[0016] Performing task parallel dependency analysis on the multiple atomic subtasks to construct a task sequential execution chain, wherein the task sequential execution chain includes multiple serial parallel subtask groups.

[0017] Analyze the parallel dependencies of multiple atomic subtasks to determine which atomic subtasks can be executed in parallel and which must be executed serially. For example, device A and device B can execute a subtask group simultaneously, while device C must be executed after device A and device B complete their tasks. Find the dependencies by traversing the task connection association matrix and mark the order of tasks.

[0018] The task dependencies are converted into a task topology graph, where each node represents a subtask and each edge represents a dependency. Based on the task topology graph, we analyze which subtasks can be executed in parallel and which need to be executed serially. By analyzing these task dependencies, we construct a sequential task execution chain. Parallel subtask groups are those that can be executed in the same time period and are independent of each other. For example, "Parallel group 1 {device A, device B} → serial task 1 {device C}" means that devices A and B can execute tasks simultaneously, but device C must wait until devices A and B complete their execution before it can begin.

[0019] Device reuse analysis is performed based on the task sequence execution chain to obtain P reuse time chains of P multiplexed devices and W task execution time windows of W non-multiplexed devices.

[0020] Based on the execution requirements of each atomic subtask, the corresponding device attributes are matched. Device attributes include device ID, task execution benchmark duration, etc., and combined with the task dependencies in the task sequence execution chain, the device is analyzed to determine whether it can be reused. For example, if certain atomic subtasks in the task sequence require the same device to operate at different time periods, these atomic subtasks will share the device. Through device reuse analysis, P reuse time chains are generated for P reused devices, i.e., the usage time period of each device. This is used to ensure that device usage is properly arranged without conflict. P is the number of reused devices, which is a positive integer. For those devices that cannot be reused, i.e., W non-reuse devices, they are assigned W corresponding task execution time windows. This means that these non-reuse devices can only operate within a specific time period in the task execution chain. W is the number of non-reuse devices, which is a positive integer.

[0021] Task execution control fitting is performed on the multiple atomic subtasks according to the power distribution scenario building information, and multiple task execution control parameters are output.

[0022] Analyze the building information of the power distribution scenario, including the building structure, such as the geographic and physical layout of the power distribution scenario, and the coordinate distribution of existing physical obstacles. Project the operational objectives of the power distribution scenario into the spatial structure to determine the data collection locations during task execution. For example, some tasks require data collection or action execution at specific locations. These collection locations are determined by the power distribution scenario building information. Using physical obstacles and building structure as constraints, determine the task execution control parameters for each task. For example, if a device needs to avoid certain physical obstacles during execution, it may be necessary to adjust the device's path or action sequence. Based on the device's location, task requirements, and physical obstacles, multiple task execution control parameters are fitted and output.

[0023] The plurality of task execution control parameters are associated with each other according to the task sequential execution chain to obtain a task collaborative control chain.

[0024] The task execution control parameters of each parallel task group are associated with the relevant tasks in the task sequential execution chain. The task sequential execution chain has determined which atomic subtasks are parallel and which are serial. Therefore, based on this information, the execution order, resource allocation and device configuration of the devices in each task can be adjusted. Specifically, multiple task execution control parameters are merged according to the structure of the task sequential execution chain. Based on the parallel task groups and serial task groups in the task sequential execution chain, it is clear which devices need to be executed simultaneously and which devices need to be executed in sequence. In this way, the task execution control parameters of each task group are aggregated to ensure that the devices in the task group can coordinately execute their respective tasks. Finally, a task collaborative control chain is obtained, which means that the execution order, execution conditions and collaborative methods of all devices are precisely defined to ensure that the execution of the entire task is efficient and coordinated.

[0025] The P multiplexing time chains and W task execution time windows are used as device parallel scheduling constraints, and the P multiplexing devices and W non-multiplexing devices are driven to perform device spatiotemporal collaborative control management based on the task collaborative control chain.

[0026] For P multiplexed time chains, the corresponding P multiplexed devices will be called multiple times in the task, so their usage time needs to be planned and coordinated to avoid conflicts; for W task execution time windows, the task execution time windows of the corresponding W non-multiplexed devices are fixed, so it is necessary to ensure that these non-multiplexed devices execute tasks within a specific time period.

[0027] Device parallel scheduling is performed based on the task collaborative control chain. Specifically, the parallel execution of devices is arranged according to the constraints of P multiplexing time chains and W task execution time windows. For example, the task collaborative control chain specifies that devices A and B execute a task at the same time, while device C must execute after A and B complete their execution.

[0028] Through the task coordination control chain, P multiplexed devices and W non-multiplexed devices are driven to perform tasks according to the specified time and space conditions. The scheduling of multiplexed devices needs to take into account their switching and coordination between different tasks. Non-multiplexed devices are strictly executed according to the plan based on the task time window. For example, device A and device B need to perform tasks in the same spatial area. Their scheduling needs to ensure that there will be no conflicts within the same time, and physical obstacles must also be avoided.

[0029] Furthermore, performing task parallel dependency analysis on the multiple atomic subtasks and constructing a task sequential execution chain, the method includes: The plurality of atomic subtasks are used to traverse a task connection association matrix to obtain a plurality of groups of dependent subtasks; the plurality of atomic subtasks are connected according to the out-degrees of the plurality of groups of dependent subtasks to output a task topology graph; the task topology graph is decomposed to obtain a plurality of parallel subtask groups without dependency relationships; the plurality of parallel subtask groups are serially connected according to the inter-group dependency relationships of the plurality of parallel subtask groups to obtain the task sequential execution chain.

[0030] The task connection association matrix is ​​a two-dimensional array, in which each element represents the connection relationship between one atomic subtask and another atomic subtask, such as dependency, sequence, etc. For example, if atomic subtask 1 must be executed after atomic subtask 2, then the corresponding position in the task connection association matrix is ​​marked as "dependency". By traversing the task connection association matrix, multiple groups of dependent subtasks are identified. These dependent subtasks are composed of several atomic subtasks. The several atomic subtasks in each group of dependent subtasks have dependencies on each other and must be executed in a specific order. For example, if atomic subtask 1 depends on atomic subtask 2, and atomic subtask 2 depends on atomic subtask 3, then subtask 1, subtask 2 and subtask 3 will be divided into a group, and the tasks in this group will be executed one by one according to the dependency relationship.

[0031] Out-degree refers to the number of other atomic subtasks that an atomic subtask points to in the dependency chain. By counting the out-degree of each atomic subtask, the dependencies of the tasks can be clearly identified. The task topology graph is a directed acyclic graph that shows the dependencies of all atomic subtasks, ensuring that each atomic subtask is executed in the correct order. In the task topology graph, each atomic subtask is a node, and the dependencies between atomic subtasks are represented by directed edges. The direction of the arrow indicates the direction of the dependency. For example, if Task 1 depends on Task 2 and Task 3, then in the task topology graph, Task 2 and Task 3 will point to Task 1, indicating that Task 1 must wait until Task 2 and Task 3 are completed before it can be executed.

[0032] In the task topology graph, any task without dependencies can be considered parallel. By analyzing the task topology graph, we can identify those subtask sets without dependencies. For example, if there is no dependency between task A, task B, and task C, they can be executed in parallel within the same time period. Multiple parallel subtask groups can be decomposed from the task topology graph. The tasks in each parallel subtask group are independent of each other and can be performed simultaneously.

[0033] There are inter-group dependencies between multiple parallel subtask groups. For example, the execution of a parallel subtask group depends on the completion of another parallel subtask group. By analyzing the dependencies between parallel subtask groups, the order of task execution is determined. By arranging multiple parallel subtask groups in series according to the dependencies between them, a task sequence execution chain is finally obtained. The task sequence execution chain ensures that each task is executed in the correct order and optimizes the parallelism and dependencies of the tasks.

[0034] Furthermore, the task coordination control chain is obtained by associating the plurality of task execution control parameters according to the task sequential execution chain, and the method includes: Aggregate the multiple task execution control parameters based on the multiple parallel subtask groups to obtain multiple multi-device collaborative control parameters; map and splice the multiple multi-device collaborative control parameters based on the connection relationship of the multiple parallel subtask groups in the task sequence execution chain to obtain the task collaborative control chain; use the P multiplexed time chains and W task execution time windows as device parallel scheduling constraints, and drive the P multiplexed devices and W non-multiplexed devices to perform device spatiotemporal collaborative control management in the multiple parallel subtask groups based on the task collaborative control chain.

[0035] Each parallel subtask group has corresponding task execution control parameters that describe the operational requirements of the devices within that parallel subtask group. Within any parallel subtask group, multiple devices must collaborate to execute tasks. The aggregated multi-device collaborative control parameters ensure the collaborative work of multiple devices, ensuring they can effectively cooperate within the same time period. Aggregation takes into account resource sharing, scheduling conflicts, and the efficiency of collaborative execution between devices. By merging the multiple task execution control parameters of multiple parallel subtask groups, multiple multi-device collaborative control parameters are generated. These multi-device collaborative control parameters define how devices coordinate their work during parallel execution, ensuring that tasks can be completed simultaneously and efficiently.

[0036] In a task sequence execution chain, the execution order of multiple parallel subtask groups is dependent on each other. For example, one parallel subtask group must be executed after another parallel subtask group. By analyzing the connection relationship in the task sequence execution chain, the multiple multi-device collaborative control parameters of multiple parallel subtask groups are mapped and spliced. The mapping process refers to integrating the multiple multi-device collaborative control parameters of multiple parallel subtask groups into a unified execution framework, and splicing refers to connecting multiple multi-device collaborative control parameters in sequence to form a complete task collaborative control chain. The task collaborative control chain defines the scheduling, execution time, coordination method, and other contents of all devices in the entire task process to ensure that multiple devices can work together to successfully complete the task.

[0037] Through the task coordination control chain, the execution order of P multiplexed devices and W non-multiplexed devices is scheduled according to the constraints of P multiplexed time chains and W task execution time windows. The multiple multi-device coordination control parameters in the task coordination control chain guide the device scheduling, execution time, coordination method, etc. During the execution of multiple parallel subtask groups, the coordination of devices in time and space is ensured. For example, the scheduling of multiplexed devices must ensure that they can switch smoothly between multiple parallel subtask groups, while non-multiplexed devices must execute tasks according to fixed time windows. Temporal and spatial coordination control management ensures that conflicts do not occur between devices and that each device can complete its task according to the specified time and space conditions.

[0038] Furthermore, device reuse analysis is performed based on the task sequential execution chain to obtain P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices. The method includes: Multiple task execution attributes of the multiple atomic subtasks are obtained by matching in the task attribute library, wherein the task execution attributes include device ID and task execution benchmark time consumption; based on the multiple task execution attributes, a serial task device multiplexing analysis is performed on the task sequential execution chain to obtain P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices.

[0039] The task attribute library contains various information required for task execution, especially the task execution attributes of each atomic subtask. Among them, the task execution attributes include the device ID and the task execution benchmark time. The device ID indicates the device required to execute the atomic subtask. Each atomic subtask depends on certain specific devices; the task execution benchmark time indicates the basic time required to execute each atomic subtask, that is, under ideal conditions, without considering any external factors, the time required to complete the atomic subtask. This attribute is used to evaluate task execution duration and device scheduling.

[0040] In a task sequential execution chain, any task may have different device requirements. For reusable devices, they may be used in turn among multiple tasks to improve device utilization. Serial task device reuse analysis is to determine which devices can be shared among multiple tasks and which devices must be used separately.

[0041] P multiplexed devices are devices that are used multiple times during the task execution process. For each multiplexed device, analyze and determine its multiplexing time period between different tasks in the task sequential execution chain. Based on the device ID in the task execution attributes and the task execution benchmark duration, calculate the multiplexing time chain of each multiplexed device. P multiplexing time chains refer to the usage time periods of P multiplexed devices between different tasks. It is necessary to ensure that there is no time conflict between the multiplexed devices between different tasks.

[0042] W non-multiplexed devices are devices that cannot be reused in the task sequential execution chain. Each non-multiplexed device can only execute one task. Therefore, each non-multiplexed device has a fixed task execution time window, that is, it must complete the task within a specific time period. Based on the device ID in the task execution attributes and the task execution benchmark duration, the task execution time window of each non-multiplexed device is calculated. Each task execution time window is fixed and cannot conflict with other tasks.

[0043] Furthermore, the method further comprises: Extract multiple task execution benchmark times and multiple device IDs from the multiple task execution attributes; convert the multiple task execution benchmark times into multiple task execution time windows based on the execution sequence characteristics of the multiple atomic subtasks in the task sequence execution chain; separate Q multiplexed devices and an initial non-multiplexed device set based on the recurrence frequency of the multiple device IDs; extract Q groups of task execution time windows associated with the Q multiplexed devices from the multiple task execution time windows, perform time overlap conflict resolution on the Q groups of task execution time windows, and generate the P multiplexed time chains of the P multiplexed devices; incorporate the QP multiplexed devices eliminated in the conflict resolution process into the initial non-multiplexed device set to obtain the W non-multiplexed devices; use the P multiplexed time chains as time occupancy constraints, and call the W task execution time windows corresponding to the W non-multiplexed devices from multiple task execution time windows.

[0044] For each atomic subtask, the task execution benchmark duration and device ID are extracted from the corresponding task execution attributes.

[0045] The task sequence execution chain defines the execution order of multiple atomic subtasks. By analyzing the execution order of these tasks, the start time of each atomic subtask is determined. The task execution time window represents the time range occupied by the atomic subtask during the execution process. By combining the task execution benchmark time of each task with the start time of the task, the task execution time window of each atomic subtask is determined.

[0046] Traverse the device IDs in the task execution attributes and count the recurrence frequency of each device ID. The recurrence frequency indicates how many times the device is reused in multiple tasks. Devices with a high recurrence frequency are more reusable. Based on the recurrence frequency, device IDs are divided into two categories: Q reused devices have a high recurrence frequency and are shared across multiple tasks, where Q is the number of reused devices, a positive integer. The initial set of non-reuseable devices has a low recurrence frequency and cannot be shared across multiple tasks.

[0047] Based on multiple task execution time windows, Q groups of task execution time windows corresponding to Q multiplexed devices are extracted. Any group of task execution time windows represents the multiple time ranges within which the corresponding multiplexed device executes multiple tasks. When multiple task execution time windows of any multiplexed device overlap, this can lead to conflicting use of the multiplexed device. To resolve this conflict, these overlaps must be resolved to ensure that the multiplexed device is not used by multiple tasks within the same time period.

[0048] For example, suppose that multiplexed device A is applied to task 1 and task 2, and the execution time windows of task 1 and task 2 overlap, that is, multiplexed device A needs to execute these two tasks within the same time period, then device A cannot complete these two tasks at the same time, resulting in a time conflict. In this case, the multiplexed device A that cannot execute these tasks will be removed from the multiplexed device set, that is, the original multiplexed device A will be used as a non-multiplexed device alone to one of task 1 or task 2, and the remaining tasks will be executed by other devices.

[0049] After conflict resolution, P multiplexing time chains of P multiplexing devices are generated, where P is a positive integer less than Q. These multiplexing time chains represent the time allocation of each multiplexing device during task execution, ensuring that the usage time period of each device does not conflict.

[0050] After the conflict resolution process, those devices that cannot be reused have been eliminated from the multiple reused devices, namely QP reused devices. These QP reused devices are reclassified as non-reuseable devices. These devices cannot be shared multiple times during task execution. Therefore, their use is fixed to a certain task and no longer reused between multiple tasks. These devices are added to the initial non-reuseable device set, and finally W non-reuseable devices are obtained.

[0051] During the scheduling process, the P multiplexing time chains of P multiplexing devices are used as time occupancy constraints. This means that during the usage period of each multiplexing device, other tasks cannot use the device. These multiplexing time chains provide time windows for device scheduling, ensuring that tasks are not delayed due to device conflicts. Based on the time windows of each task in the task sequential execution chain, the W task execution time windows of W non-multiplexed devices are called according to the P multiplexing time chains. The time windows of non-multiplexed devices have been fixed in the previous step, so they must execute tasks within the specified time. At this time, the multiplexing time chains of the multiplexing devices and the task execution time windows of the non-multiplexed devices are used together as scheduling constraints to ensure that devices do not conflict when executing tasks.

[0052] Furthermore, task execution control fitting is performed on the multiple atomic subtasks according to the power distribution scene building information, and multiple task execution control parameters are output. The method includes: Analyze the power distribution scenario building information to obtain the power distribution scenario spatial structure and physical obstacle coordinate distribution; project the operation target of the power distribution scenario demand target to the power distribution scenario spatial structure to locate the task data collection site; use the physical obstacle coordinate distribution as a spatial constraint condition, perform device control parameter fitting according to the task data collection site and the W device starting points of the W non-multiplexed devices, and obtain W task execution control parameters; update the starting points of the P multiplexed devices according to the P multiplexed time chains to obtain P starting position sequences; use the physical obstacle coordinate distribution as a spatial constraint condition, perform multi-level progressive control parameter fitting according to the task data collection site and the P starting position sequences to obtain P groups of task execution control parameters, wherein the W task execution control parameters and the P groups of task execution control parameters constitute the multiple task execution control parameters.

[0053] The spatial structure of a power distribution scenario refers to spatial elements such as building layout, walls, room size, passageways, doors, and windows. These will affect equipment layout and task execution. For example, some equipment needs to operate within a specific area or must avoid certain areas. Physical obstacles include walls, columns, equipment spacing, doors, etc. The coordinate distribution of physical obstacles directly affects equipment movement and task execution, providing necessary constraints for the spatial planning of subsequent tasks.

[0054] The demand objectives of a power distribution scenario refer to the tasks or operational goals that need to be completed in the system. For example, a task may require collecting data at a specific location, starting a device, or monitoring the device's status. These operational goals are projected into the power distribution scenario's spatial structure. For example, if a task requires collecting device data in a specific area, a suitable collection location must be selected within that area. The power distribution scenario's spatial structure provides the physical environment for task execution, while the demand objectives provide the operational requirements for the task. Based on the operational goals and the power distribution scenario's spatial structure, the task data collection locations are determined. These are the specific locations where equipment needs to collect information when performing tasks. Equipment must be positioned and scheduled based on these locations when performing tasks.

[0055] Physical obstacle coordinate distribution ensures that devices avoid collisions during mission execution. For example, during mission execution, the device's path must avoid obstacles to ensure normal operation. Each mission requires the device to execute from a different starting point. For non-multiplexed devices, the starting point is fixed, typically the mission's required starting position. The mission data collection location influences the device's path selection, speed control, operating mode, and other factors. Device control parameters must adapt to the mission's execution requirements based on the mission data collection location.

[0056] Based on the coordinate distribution of physical obstacles, the task data collection locations, and the device's starting point, device control parameters are fitted. These parameters include the device's start time, path planning, execution speed, and movement direction. For example, if the task requires the device to start at location A, collect data through certain specific areas, and avoid obstacles, the device control parameters will be fitted based on these requirements. This fitting ensures that the device can both follow the planned path and avoid physical obstacles during the task, ultimately completing the task on time. Through this fitting process, W task execution control parameters for W non-multiplexed devices are ultimately derived. Each task execution control parameter ensures that the device can complete the task within the planned space while meeting all physical constraints and task requirements.

[0057] Based on the usage time of the devices in the P multiplexing time chains, the starting position of the devices is calculated. The starting position of the devices is adjusted as the multiplexing time chains change, especially when the devices switch between multiple tasks. For example, if the device needs to execute task B immediately after completing task A, the starting position of task B should be updated at the end position of task A to ensure that the devices can seamlessly connect. Through the above analysis, a sequence of P starting positions of P multiplexing devices is generated. This sequence provides accurate time and space information for the starting position of each multiplexing device, which is convenient for subsequent task scheduling and control.

[0058] Similarly, the coordinate distribution of physical obstacles is used as a spatial constraint condition. According to the task data collection site, multi-level progressive control parameter fitting is performed in combination with P starting position sequences. That is, a multi-level progressive control method is adopted to gradually fit the device control parameters. The progressive fitting process is carried out step by step. Starting from the initial position of the device, the path, speed, execution time and other parameters of the device are calculated until the task is completed. Through progressive control parameter fitting, P groups of task execution control parameters are finally obtained. These task execution control parameters include information such as the device's path, execution time, speed, etc., to ensure that the device can successfully complete the task.

[0059] Combining the W task execution control parameters of W non-multiplexed devices with the P group of task execution control parameters of P group of multiplexed devices ultimately yields multiple task execution control parameters. These parameters provide complete information for task scheduling and device control, ensuring that devices can be scheduled and tasks executed as expected.

[0060] Furthermore, the method includes: using the multiple atomic subtasks to traverse the task connection association matrix to obtain multiple groups of dependent subtasks. Combining and enumerating the multiple atomic-level subtasks to obtain multiple groups of atomic-level subtasks; extracting multiple first connection direction recurrence frequencies of the multiple groups of atomic-level subtasks from historical task logs; extracting multiple first total execution frequencies of the multiple first subtasks in the multiple groups of atomic-level subtasks from the historical task logs; using the multiple first connection direction recurrence frequency mapping divided by the multiple first total execution frequencies to obtain multiple first connection direction association strength values ​​of the multiple groups of atomic-level subtasks; calculating multiple second connection direction association strength values ​​of the multiple groups of atomic-level subtasks by analogy; after screening the multiple first connection direction association strength values ​​and the multiple second connection direction association strength values ​​based on a preset association strength threshold, constructing the task connection association matrix based on the screening results.

[0061] Through the task decomposition process, each task is decomposed into multiple atomic subtasks, and multiple atomic subtasks are combined and enumerated. That is, any two atomic subtasks are combined in pairs to obtain multiple groups of atomic subtasks for subsequent dependency analysis.

[0062] The historical task log records information about past tasks, including the start and end times of atomic subtasks, the devices used, and the dependencies between atomic subtasks. The recurrence frequency of the connection direction indicates how often two atomic subtasks are executed in a specific order in the historical task log. For example, atomic subtask A1 of task A typically executes before atomic subtask B1 of task B. The recurrence frequency of this sequence can be expressed as the recurrence frequency of the first connection direction from atomic subtask A1 to atomic subtask B1.

[0063] For multiple groups of atomic subtasks, determine multiple first subtasks, that is, the atomic subtask that is ranked before the previous one in each group of atomic subtasks. For example, in the previous example, atomic subtask A1 is taken as the first subtask. In the historical task log, find out the execution status of the first subtask in each task execution. The first total execution frequency represents the frequency of execution of the corresponding first subtask during the past task execution process.

[0064] The first connection direction recurrence frequency mapping is divided by the corresponding first total execution frequency to obtain the first connection direction association strength value of each group of atomic subtasks, which reflects the strength of the dependency relationship between two atomic subtasks.

[0065] The first connection direction association strength value is the dependency strength from the previous atomic subtask to the next atomic subtask in any group of atomic subtasks. The second connection direction association strength value is the opposite, indicating the dependency strength from the next atomic subtask to the previous atomic subtask. The second connection direction association strength value is calculated using a calculation process similar to that of the first connection direction association strength value.

[0066] To screen out valid connection relationships, a pre-set association strength threshold is used. Only when the association strength of a connection direction exceeds this threshold is it considered a valid dependency relationship that needs to be considered in task scheduling. Based on the association strength threshold, multiple first connection direction association strength values ​​and multiple second connection direction association strength values ​​are screened, retaining those connection relationships whose association strength exceeds the association strength threshold. In this way, weak dependencies can be filtered out, focusing only on the critical dependencies in task execution. Based on the screened connection directions, a task connection association matrix is ​​constructed. Each matrix element in this matrix represents whether a dependency relationship exists between two atomic-level subtasks and the strength of this dependency relationship.

[0067] Furthermore, the method comprises: using the plurality of atomic subtasks to traverse the task connection association matrix to obtain a plurality of groups of dependent subtasks; A task association threshold is predefined, wherein the task association threshold is greater than A times the association strength threshold; in the process of traversing the task connection association matrix using the multiple atomic subtasks, the task association threshold is used to screen the associated dependent subtasks, and a set of direct dependent pairs is output; the set of direct dependent pairs is aggregated according to the multiple atomic subtasks, and the multiple groups of dependent subtasks are output.

[0068] A predefined task association threshold is greater than A times the association strength threshold. A is a preset amplification factor used to limit the relationship between the task association threshold and the association strength threshold. For example, if the A times the association strength threshold is 0.6, then the task association threshold should be greater than 0.6 and can be set to 0.7 or other suitable values.

[0069] In the task linkage matrix, each row and column represents an atomic subtask, and each element in the matrix indicates the strength of the association between two atomic subtasks. By traversing the task linkage matrix, we examine the association between each pair of atomic subtasks. For each element in the matrix, we compare its association strength with a predefined task linkage threshold. If the association strength of a pair of atomic subtasks exceeds the set task linkage threshold, it is considered that there is a valid dependency relationship between the two atomic subtasks, and they are directly dependent pairs. This screening process outputs a set of directly dependent pairs: those task pairs with strong dependencies.

[0070] From the set of direct dependency pairs, we can find that some atomic subtask pairs have higher-level dependencies. For example, if A depends on B, and B depends on C, then there is an indirect dependency between A and C. By aggregating these direct dependency pairs, we can form larger dependency groups. Each dependency group consists of multiple atomic subtasks, and the execution order and dependencies between them are determined by the direct dependency pairs. After aggregation, multiple groups of dependent subtasks are output, each of which reflects the overall dependency relationship between multiple atomic subtasks.

[0071] Furthermore, in the process of driving the P multiplexed devices and W non-multiplexed devices to perform device spatiotemporal collaborative control management based on the task collaborative control chain, the P multiplexed devices are subjected to continuous and smooth transition processing of the switching process across parallel subtask groups.

[0072] Multiplexing devices usually switch between multiple tasks. For example, device A needs to execute task group 1 first, and then switch to task group 2 for execution. Since task group 1 and task group 2 may be executed in parallel, this means that device A needs to make a smooth transition when switching task groups to avoid delays or interruptions in the device during the task switching process. For example, during the switching process, the scheduling strategy of the multiplexing device is dynamically adjusted. If there is time overlap or path conflict in the task group switching, the execution order or startup time of the multiplexing device is adjusted in real time to avoid conflicts in task execution. In order to avoid the multiplexing device from changing the execution state immediately during the task switching process, a progressive switching strategy can be adopted. For example, when task group 1 is nearing completion, the multiplexing device can prepare the resources or configuration of task group 2 in advance to reduce the switching delay. Driven by the task collaborative control chain, the multiplexing device can switch according to information such as time constraints, path planning, and execution order between task groups, and optimize execution efficiency to ensure smooth transition of the device between parallel task groups.

[0073] Furthermore, the method further comprises: W first task energy consumptions are calculated based on the W task execution control parameters; W historical energy consumption record sets and W theoretical energy consumption test sets are retrieved based on W non-multiplexed IDs; device energy consumption fluctuations are solved based on the W historical energy consumption record sets and W theoretical energy consumption test sets to obtain a first benchmark fluctuation scale; the W first task energy consumptions are compensated using the first benchmark fluctuation scale to obtain W first steady-state energy consumptions; and the W non-multiplexed devices are retrieved and called using the W non-multiplexed IDs and the W first steady-state energy consumptions.

[0074] Based on the W task execution control parameters, the energy consumed by each non-multiplexed device when executing a task is calculated using a predefined energy consumption model or empirical formula. Common calculation methods include inferring the task energy consumption based on the product of device power and execution time. Ultimately, the W first task energy consumptions are obtained to provide input for subsequent steps.

[0075] The non-multiplexed ID is a unique device ID for each non-multiplexed device. These IDs can be used to extract the corresponding device's energy consumption data from historical energy consumption data and theoretical energy consumption test data sets. The W historical energy consumption record sets contain energy consumption data for devices during past missions. This data is derived from previous mission executions, device performance monitoring, and operation logs. The W theoretical energy consumption test sets contain estimated energy consumption data based on device specifications and mission requirements, typically obtained through theoretical calculations or simulations in a standard experimental environment.

[0076] By comparing W historical energy consumption record sets with W theoretical energy consumption test sets, the fluctuations in the energy consumption of the device during task execution are analyzed. For example, if the energy consumption of a device fluctuates greatly between different tasks, it indicates that the device's load or task execution environment is unstable. Using mathematical methods such as standard deviation and coefficient of variation, the amplitude of the device's energy consumption fluctuation is calculated. This amplitude represents the range of energy consumption changes when the device performs tasks and serves as the first benchmark fluctuation scale.

[0077] The purpose of compensation is to determine the necessary energy consumption required for task execution. Based on the first benchmark fluctuation scale, the W first task energy consumptions are adjusted. For example, if the energy consumption of a device fluctuates greatly, it is compensated by adding or subtracting a compensation factor based on the fluctuation scale to make the energy consumption more consistent with the energy consumption requirements during actual operation. After compensation, W first steady-state energy consumptions are obtained. These values ​​reflect the actual energy consumption of the device during task execution.

[0078] Based on W non-multiplexed IDs and the first steady-state energy consumption of each device, we search the device pool for devices that can provide the corresponding energy consumption, ensuring that the steady-state energy consumption of each device is within the specified range. By searching and calling, we obtain W non-multiplexed devices to ensure that the task can be executed smoothly.

[0079] Furthermore, the method further comprises: Performing smooth transition fitting on the P groups of task execution control parameters to obtain P task execution control chains; calculating P second task energy consumptions based on the P task execution control chains; solving device energy consumption fluctuations based on historical energy consumption data to obtain P second benchmark fluctuation scales; and performing device retrieval and matching based on the compensation results of the P second task energy consumptions using the P second benchmark fluctuation scales to obtain the P multiplexed devices.

[0080] Because multiplexed devices need to switch between tasks when executing multiple tasks, the transition process may be uneven. Smooth transition fitting methods, such as linear interpolation and smooth curve fitting, are used to reduce sudden changes in the device during task switching, making the device's execution behavior more stable. This fitting method smoothly transitions P groups of task execution control parameters during task switching, ensuring that the device seamlessly connects to the next task. After smooth transition fitting, P task execution control chains are generated. These control chains describe the smooth transition and execution control method for each multiplexed device when executing multiple tasks.

[0081] P task execution control chains describe various control parameters of the device when executing a task, including the task execution duration, load, resource consumption, etc. These parameters are the basis for calculating energy consumption. Based on the P task execution control chains, the energy consumption of the multiplexed device during task execution is calculated. The calculation formula is power multiplied by execution time multiplied by load factor, where power is the power consumption of the device, execution time is the execution duration of the task, and load factor reflects the load of the device when executing the task. After calculation, the P second task energy consumptions are obtained, that is, the energy consumption values ​​of the P multiplexed devices during task execution.

[0082] Based on historical task execution records, we obtain historical energy consumption data for devices during past task execution. This data includes information about energy consumption fluctuations across different tasks. By comparing the energy consumption fluctuation ranges of devices during historical tasks, we analyze the variability of device energy consumption. For example, we calculate the standard deviation or coefficient of variation of each device's energy consumption to measure its energy consumption fluctuation. This calculation yields a second baseline fluctuation scale for each reused device, representing a reference value for energy consumption fluctuations during task execution.

[0083] The energy consumption of the P second tasks is compensated using P second benchmark fluctuation scales. Specifically, the second task energy consumption is added or subtracted by a fluctuation compensation factor. The fluctuation compensation factor is calculated based on the second benchmark fluctuation scale and aims to eliminate energy consumption fluctuations caused by changes in device load or task switching.

[0084] Based on the compensated energy consumption data, device search and matching is performed. Device search and matching compares the device's steady-state energy consumption with the task's energy requirements to ensure that the selected device can perform the task within the expected energy consumption range. Ultimately, P multiplexed devices are obtained, ensuring system energy efficiency and successful task completion.

[0085] In summary, the multi-device collaborative task execution management method for power distribution scenarios provided by the embodiments of the present application has the following technical effects: By loading the real-time uploaded distribution scenario demand targets into the task rule library and decomposing the tasks into multiple atomic subtasks, it is ensured that the tasks can be refined and executed step by step, thereby enhancing the flexibility and response speed of the system; performing task parallel dependency analysis on multiple atomic subtasks and building a task sequential execution chain, this process improves the efficiency of parallel execution and avoids execution delays between tasks; through equipment reuse analysis, generating P reuse time chains for P reused devices and P task execution time windows for W non-reused devices, so that reused devices and non-reused devices can work effectively without interfering with each other during task execution, optimizing the balance between equipment use and task execution; and performing task execution according to the distribution scenario building information. The system performs row control fitting and generates task execution control parameters to ensure that the equipment executes the task along the optimal path. This process reduces equipment conflicts caused by spatial layout and physical obstacles, ensuring efficient completion of tasks. By associating task execution control parameters, a task collaborative control chain is generated to clarify the order, path and coordination method of equipment execution. This provides a systematic control solution for the collaboration between devices and improves the coordination of devices in task execution. Based on the task collaborative control chain and equipment scheduling constraints, devices can achieve spatiotemporal coordinated control management when executing tasks, avoiding execution bottlenecks caused by time conflicts or path overlaps, ensuring efficient collaboration between devices, and reducing conflicts and delays that may occur during task execution.

[0086] Embodiment 2 is based on the same inventive concept as the multi-device collaborative task execution management method for power distribution scenarios in the above embodiment. Figure 2 As shown, an embodiment of the present application provides a multi-device collaborative task execution management system for power distribution scenarios, the system comprising: A subtask acquisition module 10 is configured to load the real-time uploaded distribution scenario demand targets into a task rule library to decompose the target into multiple atomic subtasks. A task parallel dependency analysis module 20 is configured to perform task parallel dependency analysis on the multiple atomic subtasks and construct a task sequential execution chain, wherein the task sequential execution chain includes multiple serial parallel subtask groups. A device reuse analysis module 30 is configured to perform device reuse analysis based on the task sequential execution chain to obtain P multiplexing time chains for P multiplexing devices and W task execution time windows for W non-multiplexing devices. A task execution control fitting module 40 is configured to perform task execution control fitting on the multiple atomic subtasks based on the distribution scenario architectural information and output multiple task execution control parameters. A collaborative control chain acquisition module 50 is configured to associate the multiple task execution control parameters according to the task sequential execution chain to obtain a task collaborative control chain. A control management module 60 is configured to use the P multiplexing time chains and W task execution time windows as device parallel scheduling constraints and, based on the task collaborative control chain, drive the P multiplexing devices and W non-multiplexing devices to perform device spatiotemporal collaborative control management.

[0087] Furthermore, the task parallel dependency analysis module 20 is configured to perform the following steps: The plurality of atomic subtasks are used to traverse a task connection association matrix to obtain a plurality of groups of dependent subtasks; the plurality of atomic subtasks are connected according to the out-degrees of the plurality of groups of dependent subtasks to output a task topology graph; the task topology graph is decomposed to obtain a plurality of parallel subtask groups without dependency relationships; the plurality of parallel subtask groups are serially connected according to the inter-group dependency relationships of the plurality of parallel subtask groups to obtain the task sequential execution chain.

[0088] Furthermore, the collaborative control chain acquisition module 50 is configured to perform the following steps: Aggregate the multiple task execution control parameters based on the multiple parallel subtask groups to obtain multiple multi-device collaborative control parameters; map and splice the multiple multi-device collaborative control parameters based on the connection relationship of the multiple parallel subtask groups in the task sequence execution chain to obtain the task collaborative control chain; use the P multiplexed time chains and W task execution time windows as device parallel scheduling constraints, and drive the P multiplexed devices and W non-multiplexed devices to perform device spatiotemporal collaborative control management in the multiple parallel subtask groups based on the task collaborative control chain.

[0089] Furthermore, the device reuse analysis module 30 is configured to perform the following steps: Multiple task execution attributes of the multiple atomic subtasks are obtained by matching in the task attribute library, wherein the task execution attributes include device ID and task execution benchmark time consumption; based on the multiple task execution attributes, a serial task device multiplexing analysis is performed on the task sequential execution chain to obtain P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices.

[0090] Furthermore, the device reuse analysis module 30 is configured to perform the following steps: Extract multiple task execution benchmark times and multiple device IDs from the multiple task execution attributes; convert the multiple task execution benchmark times into multiple task execution time windows based on the execution sequence characteristics of the multiple atomic subtasks in the task sequence execution chain; separate Q multiplexed devices and an initial non-multiplexed device set based on the recurrence frequency of the multiple device IDs; extract Q groups of task execution time windows associated with the Q multiplexed devices from the multiple task execution time windows, perform time overlap conflict resolution on the Q groups of task execution time windows, and generate the P multiplexed time chains of the P multiplexed devices; incorporate the QP multiplexed devices eliminated in the conflict resolution process into the initial non-multiplexed device set to obtain the W non-multiplexed devices; use the P multiplexed time chains as time occupancy constraints, and call the W task execution time windows corresponding to the W non-multiplexed devices from multiple task execution time windows.

[0091] Furthermore, the task execution control fitting module 40 is configured to perform the following operation steps: Analyze the power distribution scenario building information to obtain the power distribution scenario spatial structure and physical obstacle coordinate distribution; project the operation target of the power distribution scenario demand target to the power distribution scenario spatial structure to locate the task data collection site; use the physical obstacle coordinate distribution as a spatial constraint condition, perform device control parameter fitting according to the task data collection site and the W device starting points of the W non-multiplexed devices, and obtain W task execution control parameters; update the starting points of the P multiplexed devices according to the P multiplexed time chains to obtain P starting position sequences; use the physical obstacle coordinate distribution as a spatial constraint condition, perform multi-level progressive control parameter fitting according to the task data collection site and the P starting position sequences to obtain P groups of task execution control parameters, wherein the W task execution control parameters and the P groups of task execution control parameters constitute the multiple task execution control parameters.

[0092] Furthermore, the task parallel dependency analysis module 20 is configured to perform the following steps: Combining and enumerating the multiple atomic-level subtasks to obtain multiple groups of atomic-level subtasks; extracting multiple first connection direction recurrence frequencies of the multiple groups of atomic-level subtasks from historical task logs; extracting multiple first total execution frequencies of the multiple first subtasks in the multiple groups of atomic-level subtasks from the historical task logs; using the multiple first connection direction recurrence frequency mapping divided by the multiple first total execution frequencies to obtain multiple first connection direction association strength values ​​of the multiple groups of atomic-level subtasks; calculating multiple second connection direction association strength values ​​of the multiple groups of atomic-level subtasks by analogy; after screening the multiple first connection direction association strength values ​​and the multiple second connection direction association strength values ​​based on a preset association strength threshold, constructing the task connection association matrix based on the screening results.

[0093] Furthermore, the task parallel dependency analysis module 20 is configured to perform the following steps: A task association threshold is predefined, wherein the task association threshold is greater than A times the association strength threshold; in the process of traversing the task connection association matrix using the multiple atomic subtasks, the task association threshold is used to screen the associated dependent subtasks, and a set of direct dependent pairs is output; the set of direct dependent pairs is aggregated according to the multiple atomic subtasks, and the multiple groups of dependent subtasks are output.

[0094] Furthermore, in the process of driving the P multiplexed devices and W non-multiplexed devices to perform device spatiotemporal collaborative control management based on the task collaborative control chain, the P multiplexed devices are subjected to continuous and smooth transition processing of the switching process across parallel subtask groups.

[0095] Furthermore, the device reuse analysis module 30 is configured to perform the following steps: W first task energy consumptions are calculated based on the W task execution control parameters; W historical energy consumption record sets and W theoretical energy consumption test sets are retrieved based on W non-multiplexed IDs; device energy consumption fluctuations are solved based on the W historical energy consumption record sets and W theoretical energy consumption test sets to obtain a first benchmark fluctuation scale; the W first task energy consumptions are compensated using the first benchmark fluctuation scale to obtain W first steady-state energy consumptions; and the W non-multiplexed devices are retrieved and called using the W non-multiplexed IDs and the W first steady-state energy consumptions.

[0096] Furthermore, the device reuse analysis module 30 is configured to perform the following steps: Performing smooth transition fitting on the P groups of task execution control parameters to obtain P task execution control chains; calculating P second task energy consumptions based on the P task execution control chains; solving device energy consumption fluctuations based on historical energy consumption data to obtain P second benchmark fluctuation scales; and performing device retrieval and matching based on the compensation results of the P second task energy consumptions using the P second benchmark fluctuation scales to obtain the P multiplexed devices.

[0097] Through the above detailed description of the multi-device collaborative task execution management method for power distribution scenarios in this specification, technical personnel in this field can clearly understand the multi-device collaborative task execution management system for power distribution scenarios in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant matters, please refer to the method part description.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-device collaborative task execution management method for power distribution scenarios, characterized by: The method comprises: By loading the real-time uploaded power distribution scenario demand targets into the task rule library, multiple atomic subtasks are decomposed; Performing task parallel dependency analysis on the multiple atomic subtasks to construct a task sequential execution chain, wherein the task sequential execution chain includes multiple serial parallel subtask groups; Perform device reuse analysis based on the task sequence execution chain to obtain P reuse time chains of P reused devices and W task execution time windows of W non-reused devices; Performing task execution control fitting on the multiple atomic-level subtasks according to the power distribution scenario building information, and outputting multiple task execution control parameters; Associating the plurality of task execution control parameters according to the task sequential execution chain to obtain a task collaborative control chain; The P multiplexing time chains and W task execution time windows are used as device parallel scheduling constraints, and the P multiplexing devices and W non-multiplexing devices are driven to perform device spatiotemporal collaborative control management based on the task collaborative control chain.

2. The multi-device collaborative task execution management method for power distribution scenarios according to claim 1, characterized in that: Performing task parallel dependency analysis on the multiple atomic subtasks to construct a task sequential execution chain, the method comprising: Using the multiple atomic subtasks to traverse the task connection association matrix to obtain multiple groups of dependent subtasks; Connecting the multiple atomic subtasks according to the out-degrees of the multiple groups of dependent subtasks, and outputting a task topology graph; Decomposing the task topology graph to obtain multiple parallel subtask groups without dependencies; The plurality of parallel subtask groups are serially arranged according to the inter-group dependencies of the plurality of parallel subtask groups to obtain the task sequential execution chain.

3. The multi-device collaborative task execution management method for power distribution scenarios according to claim 1, characterized in that: Associating the plurality of task execution control parameters according to the task sequential execution chain to obtain a task collaborative control chain, the method comprising: aggregating the plurality of task execution control parameters according to the plurality of parallel subtask groups to obtain a plurality of multi-device collaborative control parameters; Mapping and splicing the multiple multi-device collaborative control parameters according to the connection relationship of the multiple parallel subtask groups in the task sequential execution chain to obtain the task collaborative control chain; The P multiplexing time chains and W task execution time windows are used as device parallel scheduling constraints, and based on the task collaborative control chain, the P multiplexing devices and W non-multiplexing devices are driven to perform device spatiotemporal collaborative control management in the multiple parallel subtask groups.

4. The multi-device collaborative task execution management method for power distribution scenarios according to claim 1, characterized in that: Performing device reuse analysis based on the task sequence execution chain to obtain P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices, the method comprising: Matching and obtaining a plurality of task execution attributes of the plurality of atomic subtasks in a task attribute library, wherein the task execution attributes include a device ID and a benchmark time consumption for task execution; According to the multiple task execution attributes, serial task device multiplexing analysis is performed on the task sequential execution chain to obtain P multiplexing time chains of P multiplexing devices and W task execution time windows of W non-multiplexing devices.

5. The multi-device collaborative task execution management method for power distribution scenarios according to claim 4, characterized in that: The method further comprises: extracting a plurality of task execution benchmark consuming times and a plurality of device IDs from the plurality of task execution attributes; Converting the plurality of task execution benchmark times into a plurality of task execution time windows according to execution sequence characteristics of the plurality of atomic subtasks in the task sequential execution chain; Separate Q multiplexed devices and an initial non-multiplexed device set according to the recurrence frequencies of the multiple device IDs; After extracting Q groups of task execution time windows associated with the Q multiplexing devices from the multiple task execution time windows, performing time overlap conflict resolution on the Q groups of task execution time windows to generate the P multiplexing time chains of the P multiplexing devices; Merging the QP multiplexed devices eliminated in the conflict resolution process into the initial non-multiplexed device set to obtain the W non-multiplexed devices; The P multiplexing time chains are used as time occupancy constraints, and the W task execution time windows corresponding to the W non-multiplexed devices are called from a plurality of task execution time windows.

6. The multi-device collaborative task execution management method for power distribution scenarios according to claim 1, characterized in that: Performing task execution control fitting on the multiple atomic-level subtasks based on the power distribution scene building information and outputting multiple task execution control parameters, the method includes: Analyze the building information of the power distribution scene to obtain the spatial structure of the power distribution scene and the coordinate distribution of physical obstacles; Projecting the operation target of the power distribution scenario demand target to the power distribution scenario spatial structure and locating the task data collection site; Using the coordinate distribution of the physical obstacle as a spatial constraint, performing device control parameter fitting based on the task data collection location and the W device starting points of the W non-multiplexed devices to obtain W task execution control parameters; updating the starting points of the P multiplexing devices according to the P multiplexing time chains to obtain P starting position sequences; Taking the coordinate distribution of the physical obstacle as a spatial constraint condition, multi-level progressive control parameter fitting is performed according to the task data collection site and P starting position sequences to obtain P groups of task execution control parameters, wherein the W task execution control parameters and the P groups of task execution control parameters constitute the multiple task execution control parameters.

7. The multi-device collaborative task execution management method for power distribution scenarios according to claim 2, characterized in that: The method includes: using the multiple atomic subtasks to traverse the task connection association matrix to obtain multiple groups of dependent subtasks. Combining and enumerating the multiple atomic subtasks to obtain multiple groups of atomic subtasks; Extracting recurrence frequencies of multiple first connection directions of the multiple groups of atomic subtasks from historical task logs; Extracting a plurality of first total execution frequencies of a plurality of first subtasks in the plurality of groups of atomic subtasks from the historical task log; Dividing the plurality of first connection direction recurrence frequency maps by the plurality of first total execution frequencies to obtain a plurality of first connection direction association strength values ​​of the plurality of groups of atomic subtasks; Calculating by analogy a plurality of second connection direction association strength values ​​of the plurality of groups of atomic-level subtasks; After filtering the plurality of first connection direction association strength values ​​and the plurality of second connection direction association strength values ​​based on a preset association strength threshold, the task connection association matrix is ​​constructed based on the filtering results.

8. The multi-device collaborative task execution management method for power distribution scenarios according to claim 7, characterized in that: The method comprises: using the plurality of atomic subtasks to traverse a task connection association matrix to obtain a plurality of groups of dependent subtasks. predefined task relevance threshold, wherein the task relevance threshold is greater than A times the relevance strength threshold; In the process of traversing the task connection association matrix using the multiple atomic subtasks, the task association threshold is used to screen the associated dependent subtasks, and a set of directly dependent pairs is output; The direct dependency pair sets are aggregated according to the multiple atomic subtasks, and the multiple groups of dependent subtasks are output.

9. The multi-device collaborative task execution management method for power distribution scenarios according to claim 3, characterized in that: In the process of driving the P multiplexed devices and W non-multiplexed devices to perform device spatiotemporal collaborative control management based on the task collaborative control chain, continuous smooth transition processing of the switching process across parallel subtask groups is performed on the P multiplexed devices.

10. The multi-device collaborative task execution management method for power distribution scenarios according to claim 6, characterized in that: The method further comprises: Calculating W first task energy consumptions according to the W task execution control parameters; Based on W non-multiplexed ID retrieval, W historical energy consumption record sets and W theoretical energy consumption test sets are obtained; Solving equipment energy consumption fluctuations based on the W historical energy consumption record sets and the W theoretical energy consumption test sets to obtain a first benchmark fluctuation scale; Compensating the W first task energy consumptions using the first reference fluctuation scale to obtain W first steady-state energy consumptions; The W non-multiplexed devices are retrieved and called using the W non-multiplexed IDs and the W first steady-state energy consumptions.

11. The multi-device collaborative task execution management method for power distribution scenarios according to claim 6, characterized in that: The method further comprises: Performing smooth transition fitting on the P groups of task execution control parameters to obtain P task execution control chains; Calculating energy consumption of P second tasks according to the P task execution control chains; Solve the equipment energy consumption fluctuation based on historical energy consumption data and obtain P second benchmark fluctuation scales; The compensation results of the energy consumption of the P second tasks using the P second reference fluctuation scales are used to perform device retrieval and matching to obtain the P multiplexing devices.

12. A multi-device collaborative task execution management system for power distribution scenarios, characterized by: A system for implementing the multi-device collaborative task execution management method for power distribution scenarios according to any one of claims 1 to 11, comprising: The subtask acquisition module is used to load the real-time uploaded distribution scenario demand targets into the task rule library and decompose them into multiple atomic subtasks; A task parallel dependency analysis module is used to perform task parallel dependency analysis on the multiple atomic subtasks and construct a task sequential execution chain, wherein the task sequential execution chain includes multiple serial parallel subtask groups; A device reuse analysis module is used to perform device reuse analysis based on the task sequence execution chain to obtain P reuse time chains of P multiplexed devices and W task execution time windows of W non-multiplexed devices; A task execution control fitting module is used to perform task execution control fitting on the multiple atomic subtasks according to the power distribution scene building information, and output multiple task execution control parameters; A collaborative control chain acquisition module, configured to associate the plurality of task execution control parameters according to the task sequential execution chain to obtain a task collaborative control chain; The control management module is used to use the P multiplexing time chains and W task execution time windows as device parallel scheduling constraints, and drive the P multiplexing devices and W non-multiplexing devices to perform device spatiotemporal collaborative control management based on the task collaborative control chain.

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