Equipment Support Planning Method, Device, Computer Readable Storage Medium, Electronic Device and Computer Program Product

Abstract

The present disclosure provides an equipment support planning method, apparatus, computer-readable storage medium, electronic device, and computer program product, relating to the technical field of equipment support planning. The method includes: determining the currently to-be-executed equipment support tasks through a dynamic list scheduling algorithm; according to the requirement information of the current equipment support tasks, allocating the currently available equipment resources through an Osprey Optimization Algorithm (OOA) to determine the optimal equipment support plan corresponding to the current equipment support tasks; updating the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the current equipment support tasks, and updating the currently available equipment resources according to the optimal equipment support plan corresponding to the current equipment support tasks until all the equipment support tasks in the dynamic list are completed. The present disclosure achieves the technical effect of improving the efficiency and accuracy of equipment support planning.

Description

Technical Field

[0001] The present disclosure relates to the technical field of equipment support planning, and particularly relates to an equipment support planning method, device, computer-readable storage medium, electronic device, and computer program product. Background Art

[0002] An equipment support plan refers to the basic idea of completing support tasks and implementing corresponding measures based on the usage characteristics of equipment. Among them, the formation and deployment of equipment support forces are the core content of the equipment support plan, and the key issue is the planning of equipment support tasks, that is, the matching between support tasks and support resources. Equipment support tasks are determined by the equipment support department according to support requirements, and equipment support resources refer to equipment, personnel, etc. required for supporting combat operations. Under information-based conditions, various factors will affect the planning of equipment support tasks.

[0003] Currently, in the task planning methods of related technologies, a large amount of computer memory is occupied, and the solution time is relatively long, which is not applicable to task planning problems with large problem scales, complex constraints, and strong real-time requirements.

[0004] Therefore, the related technologies cannot improve the planning efficiency and accuracy. Summary of the Invention

[0005] The main purpose of the present disclosure is to provide an equipment support planning method, device, computer-readable storage medium, electronic device, and computer program product to solve the problem in the related technologies that the planning efficiency and accuracy cannot be improved.

[0006] To achieve the above purpose, the first aspect of the present disclosure provides an equipment support planning method, including:

[0007] Determine the currently to-be-executed equipment support task through a dynamic list scheduling algorithm;

[0008] According to the requirement information of the currently described equipment support task, allocate the currently available equipment resources through the Osprey Optimization Algorithm (OOA) to determine the optimal equipment support plan corresponding to the currently described equipment support task. The requirement information of the equipment support task includes task type, required equipment type, required equipment quantity, task duration, and support point information corresponding to the task. The equipment support plan includes equipment type, equipment quantity, equipment quality, equipment cost, and support point information. The equipment support plan is used to provide data basis for the equipment support planning of the currently described equipment support task, and the support point is used to store equipment resources and allocate the equipment resources;

[0009] Update the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the current equipment support task, and update the currently available equipment resources according to the optimal equipment support plan corresponding to the current equipment support task until all the equipment support tasks in the dynamic list are completed.

[0010] Optionally, further, determining the currently to-be-executed equipment support task through the dynamic list scheduling algorithm includes:

[0011] Taking the total task completion time as the first objective function, sorting each of the equipment support tasks in the task set to obtain the priorities of each of the equipment support tasks;

[0012] Inserting all the equipment support tasks into the dynamic list in descending order of priority; the dynamic list includes the priorities of the equipment support tasks;

[0013] Dynamically adjust the execution order of the equipment support tasks according to the current number of idle processors and the priorities of each of the equipment support tasks to determine the currently to-be-executed equipment support task.

[0014] Optionally, further, allocating the currently available equipment resources through the Osprey Optimization Algorithm (OOA) according to the requirement information of the current equipment support task to determine the optimal equipment support plan corresponding to the current equipment support task includes:

[0015] Initialize the equipment support plan through the OOA according to the requirement information of the current equipment support task;

[0016] Iteratively optimize the initialized equipment support plan through the global exploration and local exploitation strategies according to the currently available equipment resources;

[0017] Take the equipment support plan with the best fitness as the optimal equipment support plan for the current equipment support task;

[0018] Among them, for any one of the equipment support tasks, there is a constraint condition: the total resource capacity of the required resource quantity corresponding to each required equipment type is less than or equal to the total resource capacity of the resource quantity of the corresponding equipment type in the equipment support plan.

[0019] Optionally, further, initializing the equipment support plan through the OOA according to the requirement information of the current equipment support task includes:

[0020] Determine the dimension of the problem in the input vector according to the requirement information of the current equipment support task;

[0021] According to the dimensions of the problem, the number of OOA members, and the total number of iterations, an initial overall matrix is randomly generated through the OOA, and the initial overall matrix is used to represent the initialized set of OOA members;

[0022] Among them, for any one of the OOA members, the OOA member is used as a candidate equipment support plan.

[0023] Optionally, further, the initialized equipment support plan is iteratively optimized according to the currently available equipment resources through global exploration and local exploitation strategies, including:

[0024] For any OOA member in any iteration, the fish position set of the OOA member is updated through a position formula;

[0025] According to the fish position set of the OOA member, the fish selected by the OOA member is randomly determined;

[0026] Based on the movement simulation of the OOA member towards the selected fish, the new position of the OOA member is calculated until the optimal positions of all the OOA members are determined based on the currently available equipment resources, and the optimal positions are used as the optimal equipment support plans corresponding to the current equipment support tasks.

[0027] Optionally, further, using the total task completion time as the first objective function, sorting each of the equipment support tasks in the task set to obtain the priorities of each of the equipment support tasks, including:

[0028] Determine the task set and the attributes of each of the equipment support tasks in the task set;

[0029] According to the attributes of each of the equipment support tasks, by calculating the total task completion time, the priorities of each of the equipment support tasks are optimized and iteratively obtained.

[0030] The second aspect of the present disclosure provides an equipment support planning device, including:

[0031] A determination unit, configured to determine the currently to-be-executed equipment support task through a dynamic list scheduling algorithm;

[0032] Allocation unit, configured to allocate the currently available equipment resources according to the requirement information of the current equipment support task through the Osprey Optimization Algorithm (OOA) to determine the optimal equipment support plan corresponding to the current equipment support task. The requirement information of the equipment support task includes task type, required equipment type, required equipment quantity, task duration, and support point information corresponding to the task. The equipment support plan includes equipment type, equipment quantity, equipment quality, equipment cost, and support point information. The equipment support plan is used to provide a data basis for the equipment support planning of the current equipment support task. The support point is used to store equipment resources and allocate the equipment resources;

[0033] Updating unit, configured to update the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the current equipment support task, and update the currently available equipment resources according to the optimal equipment support plan corresponding to the current equipment support task until all the equipment support tasks in the dynamic list are completed.

[0034] A third aspect of the present disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute the equipment support planning method provided in any one of the first aspects.

[0035] A fourth aspect of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to cause the at least one processor to execute the equipment support planning method provided in any one of the first aspects.

[0036] A fifth aspect of the present disclosure provides a computer program product including a computer program which, when executed by a processor, implements the equipment support planning method provided in any one of the first aspects.

[0037] In the equipment support planning method provided by the embodiments of the present disclosure, the dynamic list scheduling algorithm is adopted to dynamically and real-time adjust the order of equipment support tasks in the dynamic list, which can maximize the number of completed tasks and the completion time, thereby improving the efficiency of equipment support planning; based on the demand information of the currently to-be-executed equipment support tasks determined by the dynamic list scheduling algorithm, through the osprey optimization algorithm, the currently available equipment resources are allocated to find the optimal equipment support plan corresponding to the current equipment support tasks, thereby improving the accuracy of equipment support planning. Therefore, by combining the dynamic list scheduling algorithm with the osprey optimization algorithm, the optimal equipment support plan is determined to achieve the purpose of dynamic planning, thus realizing the technical effect of improving the efficiency and accuracy of equipment support planning, and further solving the technical problem that the related technology cannot improve the efficiency and accuracy of equipment support planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the related art, the following will briefly introduce the drawings required to be used in the description of the specific embodiments or the related art. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 Schematic diagram of the scenario of the equipment support planning method provided by the embodiments of the present disclosure;

[0040] Figure 2 Schematic flow chart of the equipment support planning method provided by the embodiments of the present disclosure;

[0041] Figure 3 Schematic flow chart of the equipment support planning method provided by another embodiment of the present disclosure;

[0042] Figure 4 Block diagram of the equipment support planning device provided by the embodiments of the present disclosure;

[0043] Figure 5 Block diagram of the electronic device provided by the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to enable those skilled in the art to better understand the solutions of the present disclosure, the following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some embodiments of the present disclosure, rather than all embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0045] It should be noted that in the description, claims and the above-mentioned drawings of the present disclosure, terms such as "first", "second", etc. are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so as to implement the embodiments of the present disclosure described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0046] In the present disclosure, the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", etc. is based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe the present disclosure and its embodiments, and are not used to limit that the indicated devices, elements or components must have a specific orientation, or be constructed and operated in a specific orientation.

[0047] Moreover, in addition to being used to represent the orientation or positional relationship, some of the above terms may also be used to represent other meanings. For example, the term "upper" may also be used to represent a certain attachment relationship or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in the present disclosure can be understood according to specific circumstances.

[0048] In addition, the terms "install", "set", "provided with", "connect", "connected", "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or there is an internal connection between two devices, elements or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0049] It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0050] Currently, in the task planning methods of related technologies, a large amount of computer memory is occupied and the solution time is relatively long, which is not applicable to task planning problems with large problem scales, complex constraints, and strong real-time requirements. Therefore, related technologies cannot improve the planning efficiency and accuracy.

[0051] To solve the above problems, the technical concept of the present disclosure is to adopt a dynamic list scheduling algorithm to continuously and dynamically adjust the task order to execute tasks with high priorities, thereby improving the equipment support planning. In combination with the osprey optimization algorithm, tasks are executed to obtain the optimal equipment support plan, improving the accuracy of the equipment support planning.

[0052] In practical applications, the execution entity of the present disclosure may be an equipment support planning device, which may be deployed in an electronic device such as a terminal device, a server, etc. A user can perform equipment support planning through the electronic device deployed with the equipment support planning device. The equipment here may be vehicles, weapons, communication equipment, etc. required for support tasks.

[0053] Exemplarily, as shown in Figure 1 shown, Figure 1 is a schematic diagram of the scenario of the equipment support planning method provided by an embodiment of the present disclosure. This scenario includes a data collection device 101 and a server 102 deployed with an equipment support planning device. Among them, the data collection device 101 is used to collect equipment support task information, demand information of equipment support tasks, etc. For example, for any equipment support task, the task type, required equipment type, required equipment quantity, task duration, and support point information corresponding to the task. The support point is used to store equipment resources and allocate equipment resources, and the equipment resources include equipment, personnel, etc. The server 102 is used to determine the currently required equipment support task (i.e., the currently pending equipment support task) through the dynamic list scheduling algorithm (Dynamic Level Scheduling, DLS), and based on the demand information of the current equipment support task, allocate the currently available equipment resources through the osprey optimization algorithm to find the optimal equipment support plan for the current equipment support task, achieving the purpose of equipment support planning. The equipment support plan here can be used as equipment support planning information or equipment support planning data.

[0054] The server 102 is also used to update the task status of the current equipment support task in the dynamic list, thereby subsequently dynamically adjusting the task order, and updating the currently available equipment resources for subsequent calculation of other equipment support tasks until all equipment support tasks are completed.

[0055] Through the above application scenario, the present disclosure can improve the efficiency and accuracy of equipment support planning by combining the osprey optimization algorithm with the DLS dynamic list.

[0056] It should be noted that in the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of information such as tasks, task data, or task requirements comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0057] The technical solutions of the present disclosure will be described in detail below with specific embodiments. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0058] An equipment support planning method is provided in an embodiment of the present disclosure. As Figure 2 shown, the method includes the following steps S201 to step S203:

[0059] Step S201: Determine the currently to-be-executed equipment support tasks through a dynamic list scheduling algorithm.

[0060] Step S202: According to the requirement information of the currently described equipment support tasks, allocate the currently available equipment resources through an osprey optimization algorithm to determine the optimal equipment support plan corresponding to the currently described equipment support tasks. The requirement information of the equipment support tasks includes task type, required equipment type, required equipment quantity, task duration, and support point information corresponding to the task. The equipment support plan includes equipment type, equipment quantity, equipment quality, equipment cost, and support point information. The equipment support plan is used to provide a data basis for the equipment support planning of the currently described equipment support tasks, and the support point is used to store equipment resources and allocate the equipment resources.

[0061] Step S203: Update the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the currently described equipment support tasks, and update the currently available equipment resources according to the optimal equipment support plan corresponding to the currently described equipment support tasks until all the equipment support tasks in the dynamic list are completed.

[0062] In an embodiment of the present disclosure, based on the dynamic list scheduling algorithm, the order of the equipment support tasks in the dynamic list is dynamically and real-time adjusted, which can maximize the number and completion time of tasks, thereby improving the efficiency of equipment support planning; based on the requirement information of the currently to-be-executed equipment support tasks determined by the dynamic list scheduling algorithm, the currently available equipment resources are allocated through the osprey optimization algorithm to find the optimal equipment configuration support plan corresponding to the currently described equipment support tasks, thereby improving the accuracy of equipment support planning. Therefore, by combining the dynamic list scheduling algorithm with the osprey optimization algorithm, the optimal equipment support plan is determined to achieve the purpose of dynamic planning, thereby improving the efficiency and accuracy of equipment support planning.

[0063] Among them, the equipment types include vehicles, weapons, communication equipment, etc. The equipment quality in the equipment support plan is used to ensure that the quality of the equipment meets the standards and can meet the usage requirements. The equipment costs include the procurement costs, maintenance costs, transportation costs, etc. of the equipment. The support point information includes: the location of the support point, the capacity of the support point, the coordination between support points, the staffing of the support point, and the emergency plan, etc.

[0064] Among them, the location of the support point: determine the geographical location of the support point to ensure that it can efficiently cover all usage points; the capacity of the support point: the storage capacity and allocation ability of each support point. The coordination between support points: the cooperation mechanism between different support points to ensure that resources can be quickly allocated when needed. The staffing of the support point: the number of personnel, skill requirements, and training plan of each support point. The emergency plan: the emergency support plan for different emergency situations, including the setting up of temporary support points and resource allocation.

[0065] The Dynamic List Scheduling algorithm DLS is an online task scheduling algorithm based on the greedy strategy. It is a variant of the dynamic list scheduling algorithm and is used to schedule tasks in a multi-processor system. The DLS algorithm inserts tasks into a task list according to their priorities and dynamically adjusts the order of tasks based on the current number of idle processors and the priorities of the tasks.

[0066] Specifically, when a new task arrives, the DLS algorithm checks the current number of idle processors. If there are idle processors, the task is assigned to one of the processors for execution. Otherwise, the algorithm selects a task with the lowest priority from the task list and readjusts its position to release a processor as soon as possible. In this way, the DLS algorithm can maximize the number of completed tasks and the completion time.

[0067] The Osprey Optimization Algorithm (OOA): Simulates the foraging behavior of ospreys. Specifically, the Osprey Optimization Algorithm consists of two stages: the first stage is for the osprey to identify the location of the fish and catch the fish (global exploration), and the second stage is to take the fish to a suitable location (local exploitation).

[0068] Among them, in the equipment support planning, multiple factors such as equipment requirements (such as equipment types, etc.), equipment quantity, equipment quality, and equipment costs are considered. The relationships between these factors are complex and need to be comprehensively considered and optimized. Specifically, the Osprey Optimization Algorithm is used to search for the best equipment configuration plan (here referring to the optimal equipment support plan), while the DLS dynamic list is used to store and update the cost-benefit information of various plans to achieve the purpose of dynamic programming. Combining the Osprey Optimization Algorithm with the DLS dynamic list and applying it to the equipment support planning can improve the efficiency and accuracy of the equipment support planning.

[0069] In the embodiments of the present disclosure, an osprey optimization algorithm combined with a dynamic list scheduling method is adopted to construct a model. The dynamic list method is used to screen the implementation guarantee tasks, and an adaptive learning factor is introduced to balance the global search and local search capabilities, which can effectively manage and optimize the equipment guarantee planning process, thereby improving the guarantee ability and reducing losses.

[0070] Optionally, determining the currently to-be-executed equipment guarantee task through the dynamic list scheduling algorithm includes:

[0071] Taking the total task completion time as the first objective function, sorting each of the equipment guarantee tasks in the task set to obtain the priorities of each of the equipment guarantee tasks;

[0072] Inserting all the equipment guarantee tasks into the dynamic list in descending order of priority; the dynamic list includes the priorities of the equipment guarantee tasks;

[0073] According to the current number of idle processors and the priorities of each of the equipment guarantee tasks, dynamically adjust the execution order of the equipment guarantee tasks to determine the currently to-be-executed equipment guarantee task.

[0074] Among them, the dynamic list also includes: the basic information of the task, the dependency relationship of the task, the scheduling status of the task, and the dynamic adjustment information of the task. Here, the task refers to a task that requires a processor to perform calculations, such as an equipment guarantee task, which will not be elaborated below.

[0075] Basic information of the task: task ID, computation amount, communication overhead. Priority information of the task (here refers to the priority of the equipment guarantee task): static priority, dynamic priority. Dependency relationship of the task: predecessor task, successor task. Scheduling status of the task: start time, completion time, processor allocation. Dynamic adjustment information of the task: dynamic level, scheduling status flag.

[0076] Among them, task ID: the unique identifier of the task, used to distinguish different tasks. Computation Cost of the task: represents the time required for the task to be executed on the processor. CommunicationCost of the task: if there is a dependency relationship between tasks, data transmission between tasks will generate communication overhead.

[0077] Static Level (SL): The static priority of a task is usually based on the Critical Path Length (CPL) or the Earliest Finish Time (EFT) of the task; the higher the static priority, the greater the impact of the task on the completion time of the overall task set. Dynamic Level (DL): The dynamic priority combines the static priority of the task and the current resource state of the system (such as the number of idle processors); the dynamic priority is adjusted in real time according to the execution progress of the task and the changes in system resources.

[0078] Predecessors: The list of all predecessor tasks of a task, indicating that the task i cannot start execution until these tasks are completed. Successors: The list of all successor tasks of a task, indicating that these tasks cannot start execution until the task i is completed. i is completed. i

[0079] Start Time of a task: The actual start execution time of the task on the processor. Finish Time of a task: The actual completion time of the task on the processor. Processor Allocation of a task: Which processor the task is allocated to for execution.

[0080] Dynamic Level (DL) of a task: The dynamic level of a task is used to dynamically adjust the execution order of the task. The calculation formula for the dynamic level is:

[0081] (1)

[0082] where is the dynamic level of task i ; is the static level of task i ; is the set of successor tasks of task i ; is the communication overhead between task i and task j . When there is no communication overhead between tasks, this is 0.

[0083] ​Tasks are sorted according to dynamic priorities from high to low, and tasks with higher priorities are scheduled first. The DLS list (i.e., the dynamic list) is used to store all tasks to be scheduled, and dynamically adjusts the execution order of tasks according to the dynamic priorities of tasks and the status of system resources. According to the task priorities in the DLS list and the idle status of the processor, tasks are assigned to appropriate processors for execution. During the execution of tasks, the DLS list is updated in real time according to the completion of tasks and changes in system resources.

[0084] Optionally, taking the total task completion time as the first objective function, sorting each of the equipment support tasks in the task set to obtain the priorities of each of the equipment support tasks includes:

[0085] Determine the task set and the attributes of each of the equipment support tasks in the task set;

[0086] According to the attributes of each of the equipment support tasks, by calculating the total task completion time, optimize and iterate to obtain the priorities of each of the equipment support tasks.

[0087] Among them, the attributes of each task include computing time (execution time of the task), dependency relationship (predecessor tasks and successor tasks of the task), resource requirements (i.e., resources required by the task), and completion time (expected completion time of the task).

[0088] In the embodiments of the present disclosure, a task dependency graph is established according to the task set and task attributes: the directed acyclic graph (DAG) is used to represent the dependency relationship between tasks: nodes represent tasks, and edges represent the dependency relationship between tasks (for example, task T i must be completed before task T i and the execution order of tasks is determined by topological sorting. According to the above formula (1) and when it is 0, the priorities of the equipment support tasks are calculated.

[0089] Exemplarily, in order to achieve effective planning of resource support, this planning model can be divided into two parts: task description and resource description. On the basis of analyzing the core elements of equipment support tasks, this model mainly includes elements such as the types (i.e., resource types) and quantities of resources required for each equipment support task, the (coordinate) positions of support points, and task durations.

[0090] The support tasks and support resources are described as:

[0091] (2)

[0092] Among them, the vector X represents an N-item task guarantee set (for example, N candidate task guarantee solutions) and m-item equipment guarantee requirements (for example, m requirements or m problems or m variables; for example: the number of equipment, resource capabilities (such as equipment quality), task duration, etc.). The input is the specific requirements of the equipment guarantee task, including: task type, types and quantities of required resources, task duration, etc., the current available equipment guarantee resources, and the parameter settings of the osprey optimization algorithm and dynamic list scheduling (such as: the dimension of the problem, the number of ospreys, the total number of iterations, etc.). The output is the optimal equipment guarantee plan and its corresponding fitness value, the task execution plan (such as task status) and resource usage (such as the situation of the remaining available resources).

[0093] Calculate the priority coefficient:

[0094] In the process of solving the task resource guarantee problem, there are two difficulties. The first difficulty is that when a single task has the optimal schedulable resources, it does not necessarily result in a globally optimal solution. The second difficulty is that in large-scale resource guarantee problems, due to the need for multiple iterations, the waste of computing power takes a long time. To solve these problems, the total task completion time can be used as the objective function, and the priority coefficient can be used to sort problems such as group contention to eliminate conflicts. The specific formula is as follows (based on formula (1), applied to the calculation of the priority of equipment guarantee tasks, and formula (3) is derived):

[0095] (3)

[0096] Among them, : The priority value of task i . : The computation time of task i . : The set of successor tasks of task i . : The maximum priority value among all successor tasks of task i . : The sum of the priority values of all successor tasks of task i .

[0097] According to the guarantee task set relationship graph and the task priority definition formula (such as formula (3)), the priority coefficient of each task (here refers to the task priority or task priority value) can be calculated.

[0098] Among them, assuming that the task can be guaranteed, but to ensure the smooth completion of the task, it is necessary to meet the resource demand constraints, requiring that the total capacity of each type of resource in the resource capacity vector is not less than the corresponding resource capacity quantity required by the task, that is (constraint condition):

[0099]

[0100] Optionally, according to the requirement information of the current equipment support task, the available equipment resources are allocated through the osprey optimization algorithm to determine the optimal equipment support plan corresponding to the current equipment support task, including:

[0101] Initializing an equipment support plan according to the requirement information of the current equipment support task through the osprey optimization algorithm;

[0102] Iteratively optimizing the initialized equipment support plan according to the currently available equipment resources through the global exploration and local exploitation strategies;

[0103] Taking the equipment support plan with the best fitness as the optimal equipment support plan for the current equipment support task;

[0104] Among them, for any of the equipment support tasks, there is a constraint condition: the total resource capacity of the required resource quantity corresponding to each required equipment type is less than or equal to the total resource capacity of the resource quantity of the corresponding equipment type in the equipment support plan. For example, the sum of the resource quantities of each equipment type in the equipment support plan is not less than the sum of the required resource quantities of the corresponding equipment types, and / or, the sum of the resource qualities (i.e., equipment qualities) of each equipment type in the equipment support plan is not less than the sum of the required resource qualities of the corresponding equipment types. The resources here can include equipment.

[0105] The osprey optimization algorithm adopted here is the quadratic osprey optimization algorithm.

[0106] In the embodiments of the present disclosure, the optimization scheme of the osprey optimization algorithm includes the following steps:

[0107] Step a1: Population initialization

[0108] Randomly initialize the osprey population using the following formula:

[0109] (4)

[0110] (5)

[0111] Among them, N is the number of ospreys (here referring to the overall size of the OOA or the number of OOA members. In practical applications, for example, N is the number of candidate equipment support plans), m is the dimension of the problem, and the fitness value is calculated according to the optimization problem after initializing the position:

[0112] (6)

[0113] Among them, formula (6) is the fitness function, which serves as the objective function, such as the second objective function.

[0114] Step a2: Global exploration (the first stage: position recognition and fishing)

[0115] Ospreys are powerful hunters. Due to their strong eyesight, they can detect the positions of fish underwater. After determining the position of the fish, they attack it and hunt the fish by diving underwater. The first stage of population update in OOA is modeled based on the simulation of this natural behavior of ospreys. Modeling the attack of ospreys on fish will cause significant changes in the positions of ospreys in the search space, which increases the exploration ability of OOA in identifying the optimal region and escaping from local optima. In the design of OOA, for each osprey, the positions of other ospreys in the search space with better objective function values are regarded as fish underwater. The position of each osprey is specified by the following formula.

[0116] (7)

[0117] Where, is the position set of the i th osprey (for example, the position set of the i th OOA member), is the position of the best osprey (i.e., the best solution or optimal solution, such as: the optimal equipment support plan).

[0118] The osprey randomly detects the position of one of the fish and attacks it. Based on the simulation of the osprey's movement towards the fish, the new position of the corresponding osprey is calculated using the following formula. If the value of the objective function of this new position is better, it replaces the previous position of the osprey.

[0119] (8)

[0120] (9)

[0121] (10)

[0122] Where, is the new position of the i th osprey in the first stage at its j th dimension, is its corresponding fitness value. is a random number between [0, 1], is a random number in the set {1, 2}.

[0123] Step a3: Local exploitation (the second stage: taking the fish to a suitable position)

[0124] After catching a fish, the osprey takes it to a suitable (safe for him) location and eats it there. The second stage of updating the population in OOA is based on the simulation modeling of this natural behavior of the osprey. Modeling the behavior of taking the fish to a suitable location causes a slight change in the position of the osprey in the search space, resulting in an increase in the exploitation ability of OOA in local search and convergence to a better solution near the discovered solution. In the design of OOA, to simulate this natural behavior of the osprey, first, for each member of the population, a new random position is calculated as the "suitable location for eating fish" using the following formula. Then, if the value of the objective function is improved at this new position, the previous position of the corresponding osprey is replaced.

[0125] (11)

[0126] (12)

[0127] (13)

[0128] where is the new position of the i-th osprey in the d-th dimension at the second stage, j and is its corresponding fitness value. r is a random number between [0, 1], and t and T are the current iteration number and the maximum iteration number respectively.

[0129] Optionally, initializing the equipment support plan through the OOA according to the demand information of the current equipment support task includes:

[0130] Determining the dimension of the problem in the input vector according to the demand information of the current equipment support task;

[0131] Randomly generating an initial population matrix through the OOA according to the dimension of the problem, the number of OOA members, and the total number of iterations, where the initial population matrix is used to represent the initialized set of OOA members;

[0132] where, for any one of the OOA members, the OOA member serves as a candidate equipment support plan.

[0133] Optionally, iteratively optimizing the initialized equipment support plan through global exploration and local exploitation strategies according to the currently available equipment resources includes:

[0134] For any OOA member in any iteration, updating the fish position set of the OOA member through the position formula;

[0135] Randomly determining the fish selected by the OOA member according to the fish position set of the OOA member

[0136] Based on the motion simulation of the OOA members towards the selected fish, calculate the new positions of the OOA members until the optimal positions of all the OOA members are determined based on the currently available equipment resources, and the optimal positions are used as the optimal equipment support plan corresponding to the current equipment support task.

[0137] Specifically, taking the equipment support plan as an example of the OOA members' scenario, the process of the quadratic osprey optimization algorithm includes:

[0138] Start OOA.

[0139] Input: problem information (variables, objective function, and constraints; the variables here include m equipment support requirements, such as: equipment quantity, equipment quality, mission duration, etc.).

[0140] Set the overall size (N) and total number of iterations (T) of OOA; where the overall size of OOA is the number of OOA members.

[0141] Randomly generate the initial overall matrix using formulas (4) and (5).

[0142] Calculate the objective function according to formula (6).

[0143] For t = 1, …, T

[0144] For i = 1, …, N

[0145] First stage: position recognition and fishing

[0146] Update the fish position set of the i-th OOA member using formula (7);

[0147] Randomly determine the fish selected by the i-th osprey

[0148] Calculate the new position of the i-th OOA member based on the first stage of OOA using formula (8).

[0149] Check the boundary conditions of the new position of the OOA member using formula (9).

[0150]

[0151] Update the i-th OOA member using formula (10).

[0152]

[0153] Second stage: take the fish to the appropriate position

[0154] Calculate the new position of the \(i\)-th OOA member in the second stage of OOA using formula (11).

[0155]

[0156] Check the boundary conditions of the new position of the OOA member using formula (12).

[0157]

[0158] Update the \(i\)-th OOA member using formula (13).

[0159] End

[0160] Save the currently found optimal candidate solution.

[0161] End OOA.

[0162] Therefore,

[0163] Exemplarily, refer to Figure 3 as shown Figure 3 is a flowchart of the equipment support planning method provided by another embodiment of the present disclosure. For the problem of precise collaborative support, a dynamic list scheduling combined with a quadratic osprey optimization algorithm can be used for joint solution. First, select the equipment support tasks that need to be currently executed through the dynamic list, and then select the optimal solution for executing the current equipment support tasks from all the current resources through the quadratic osprey optimization algorithm, that is, the optimal equipment support plan for the current equipment task support.

[0164] In the embodiment of the present disclosure, when starting to execute the operation, first extract the support process and formulate a support timing diagram (for example, a relationship diagram of the support task set); then calculate the task priority coefficient, select the support tasks, optimize through the osprey optimization algorithm, and judge whether there is a feasible structure (for example: the optimal solution); if there is no feasible structure, release the occupied state of all current resources and continue to optimize through the osprey optimization algorithm; if there is a feasible structure, generate a support plan, update the task status and available resource information, judge whether there are unfinished tasks, if there are unfinished tasks, continue to select support tasks and repeat the above operations, if there are no unfinished tasks, end the operation.

[0165] Specifically, the process of the osprey optimization algorithm combined with the dynamic list algorithm is as follows:

[0166] Step b1, task selection. Select the equipment support tasks that need to be currently executed according to the task priority in the dynamic list.

[0167] Step b2. Resource allocation. Use the osprey optimization algorithm to allocate the currently available equipment resources to find the optimal equipment configuration plan.

[0168] (1) Initialize the osprey population (i.e., equipment configuration plan or equipment support plan) according to the task requirements.

[0169] (2) Use the global exploration and local exploitation strategies to iteratively optimize the osprey population.

[0170] (3) Select the osprey with the best fitness (i.e., the optimal equipment configuration plan or the optimal equipment support plan) as the resource allocation plan or resource support plan for the current task.

[0171] Step b3. Task execution and update. Execute the current task and update the task status and equipment resource information according to the actual situation.

[0172] Step b4. According to the updated task list and resource information, repeat the above task selection and resource allocation steps until all tasks are completed.

[0173] The osprey optimization algorithm has high search performance and high convergence speed. Applying the osprey optimization algorithm combined with dynamic list scheduling to the campaign cluster military equipment support planning can improve the efficiency and quality of precise equipment support.

[0174] From the above description, it can be seen that the present disclosure achieves the following technical effects: First, according to the current equipment requirements and resource supply situation, it can automatically find the optimal equipment scheduling plan, thereby improving the scheduling efficiency and equipment utilization rate. Second, when an emergency occurs, it is necessary to quickly schedule and allocate campaign cluster military equipment to meet different task requirements. Through the dynamic list scheduling method, the task and resource information can be updated in real time, and the fast search ability of the osprey optimization algorithm can help quickly find the optimal scheduling plan, thus achieving timely response. Finally, through the prediction ability of the osprey optimization algorithm, according to historical data and trends, it can predict the possible future equipment requirements, thereby helping the emergency management department to do a good job in equipment reserve and scheduling preparation work and improving the campaign cluster military equipment support ability.

[0175] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0176] The present disclosure embodiment also provides an equipment support planning device for implementing the above equipment support planning method embodiment, as Figure 4 shown, the equipment support planning device 40 includes:

[0177] A determination unit 401, configured to determine the currently to-be-executed equipment support task through a dynamic list scheduling algorithm;

[0178] An allocation unit 402, configured to allocate the currently available equipment resources according to the requirement information of the current equipment support task through the Osprey Optimization Algorithm (OOA) to determine the optimal equipment support plan corresponding to the current equipment support task. The requirement information of the equipment support task includes task type, required equipment type, required equipment quantity, task duration, and support point information corresponding to the task. The equipment support plan includes equipment type, equipment quantity, equipment quality, equipment cost, and support point information, and is used to provide a data basis for the equipment support planning of the current equipment support task. The support point is used to store equipment resources and allocate the equipment resources.

[0179] An update unit 403, configured to update the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the current equipment support task, and update the currently available equipment resources according to the optimal equipment support plan corresponding to the current equipment support task until all the equipment support tasks in the dynamic list are completed.

[0180] Optionally, when the determination unit 401 determines the currently to-be-executed equipment support task through the dynamic list scheduling algorithm, it specifically includes:

[0181] Taking the total task completion time as the first objective function, sorting each of the equipment support tasks in the task set to obtain the priorities of each of the equipment support tasks;

[0182] Inserting all the equipment support tasks into the dynamic list in descending order of priority; the dynamic list includes the priorities of the equipment support tasks;

[0183] Dynamically adjusting the execution order of the equipment support tasks according to the current number of idle processors and the priorities of each of the equipment support tasks to determine the currently to-be-executed equipment support task.

[0184] Optionally, when the allocation unit 402 allocates the currently available equipment resources according to the requirement information of the current equipment support task through the Osprey Optimization Algorithm (OOA) to determine the optimal equipment support plan corresponding to the current equipment support task, it specifically includes:

[0185] Initializing the equipment support plan according to the requirement information of the current equipment support task through the OOA;

[0186] Iteratively optimizing the initialized equipment support plan according to the currently available equipment resources through the global exploration and local exploitation strategies;

[0187] Take the equipment support plan with the best fitness as the optimal equipment support plan for the current equipment support task;

[0188] Among them, for any one of the equipment support tasks, there is a constraint condition: the total resource capacity of the required resource quantity corresponding to each required equipment type is less than or equal to the total resource capacity of the resource quantity of the corresponding equipment type in the equipment support plan.

[0189] Optionally, when the allocation unit 402 initializes the equipment support plan through the OOA according to the demand information of the current equipment support task, it specifically includes:

[0190] Determine the dimension of the problem in the input vector according to the demand information of the current equipment support task;

[0191] Randomly generate an initial overall matrix through the OOA according to the dimension of the problem, the number of OOA members, and the total number of iterations. The initial overall matrix is used to represent the initialized set of OOA members;

[0192] Among them, for any one of the OOA members, the OOA member is used as a candidate equipment support plan.

[0193] Optionally, when the allocation unit 402 iteratively optimizes the initialized equipment support plan through the global exploration and local exploitation strategies according to the currently available equipment resources, it specifically includes:

[0194] For any OOA member in any iteration, update the fish position set of the OOA member through the position formula;

[0195] Randomly determine the fish selected by the OOA member according to the fish position set of the OOA member;

[0196] Based on the movement simulation of the OOA member towards the selected fish, calculate the new position of the OOA member until the optimal positions of all the OOA members are determined based on the currently available equipment resources. The optimal position is used as the optimal equipment support plan corresponding to the current equipment support task.

[0197] Optionally, when the allocation unit 402 ranks each of the equipment support tasks in the task set by taking the total task completion time as the first objective function to obtain the priorities of each of the equipment support tasks, it specifically includes:

[0198] Determine the task set and the attributes of each of the equipment support tasks in the task set;

[0199] According to the attributes of each of the equipment support tasks, the priorities of each of the equipment support tasks are optimized and iterated by calculating the total task completion time.

[0200] The specific manners of the execution operations of each unit in the above device embodiments have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0201] An embodiment of the present disclosure further provides an electronic device, as Figure 5 shown. The electronic device includes one or more processors 51 and a memory 52. Figure 5 Here, one processor 51 is taken as an example.

[0202] The controller may further include: an input device 53 and an output device 54.

[0203] The processor 51, the memory 52, the input device 53, and the output device 54 may be connected through a bus or other means. Figure 5 Here, connection through a bus is taken as an example.

[0204] The processor 51 may be a central processing unit (Central Processing Unit, abbreviated as CPU), and the processor 51 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, abbreviated as DSP), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), field programmable gate arrays (Field-Programmable Gate Array, abbreviated as FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips. The general-purpose processor may be a microprocessor or any conventional processor.

[0205] The memory 52, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the control method in the embodiments of the present disclosure. The processor 51 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 52, that is, implements the equipment support planning method in the above method embodiments.

[0206] The memory 52 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the processing device operating on the server, etc. In addition, the memory 52 may include a high-speed random access memory and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 52 may optionally include a memory remotely provided with respect to the processor 51, and these remote memories may be connected to the network connection device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0207] The input device 53 may receive input digital or character information and generate key signal inputs related to user settings and function controls of the processing device of the server. The output device 54 may include display devices such as a display screen.

[0208] One or more modules are stored in the memory 52 and, when executed by one or more processors 51, perform the method as shown above.

[0209] The embodiments of the present disclosure also provide a computer-readable storage medium storing computer instructions for causing a computer to execute the equipment support planning method as described above.

[0210] The embodiments of the present disclosure also provide a computer program product including a computer program that, when executed by a processor, implements the equipment support planning method as described above.

[0211] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, it may include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory (FM), a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0212] While embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for equipment support planning, characterized in that: include: Determine the equipment support tasks currently to be performed through the dynamic list scheduling algorithm; According to the demand information of the current equipment support task, the currently available equipment resources are allocated through the Osprey optimization algorithm OOA to determine the optimal equipment support plan corresponding to the current equipment support task. The demand information of the equipment support task includes the task type, the required equipment type, the required equipment quantity, the task duration, and the support point information corresponding to the task. The equipment support plan includes the equipment type, the equipment quantity, the equipment quality, the equipment cost and the support point information. The equipment support plan is used to provide data basis for the equipment support planning of the current equipment support task. The support point is used to store equipment resources and deploy the equipment resources. The support point information includes: the location of the support point, the capacity of the support point, the cooperation mechanism between the support points, the personnel configuration of the support point and the emergency plan. The equipment resources include the vehicles, weapons and communication equipment required for the equipment support task. The dynamic list in the dynamic list scheduling algorithm is updated according to the task status after executing the current equipment support task, and the currently available equipment resources are updated according to the optimal equipment support solution corresponding to the current equipment support task until all equipment support tasks in the dynamic list are completed; The method of determining the equipment support task to be executed currently by means of a dynamic list scheduling algorithm includes: Taking the total task completion time as the first objective function, sorting the equipment support tasks in the task set to obtain the priority of each equipment support task; Inserting all the equipment support tasks into a dynamic list according to the priority from high to low; the dynamic list includes the equipment support task priority; According to the current number of idle processors and the priority of each of the equipment support tasks, the execution order of the equipment support tasks is dynamically adjusted to determine the equipment support task currently to be executed.

2. The method according to claim 1, characterized in that The currently available equipment resources are allocated according to the demand information of the current equipment support task through the Osprey optimization algorithm OOA to determine the optimal equipment support solution corresponding to the current equipment support task, including: Initialize the equipment support plan through the OOA according to the demand information of the current equipment support task; According to the currently available equipment resources, the initialized equipment support plan is iteratively optimized through global exploration and local exploitation strategies; The equipment support plan with the best adaptability is taken as the optimal equipment support plan for the current equipment support task; Among them, for any of the equipment support tasks, there is a constraint condition: the total resource capacity of the required resource quantity corresponding to each required equipment type is less than or equal to the total resource capacity of the resource quantity of the corresponding equipment type in the equipment support plan.

3. The method according to claim 2, characterized in that Initializing the equipment support plan through the OOA according to the demand information of the current equipment support task includes: Determine the dimension of the problem in the input vector according to the requirement information of the current equipment support task; According to the dimension of the problem, the number of OOA members, and the total number of iterations, an initial overall matrix is ​​randomly generated through the OOA, and the initial overall matrix is ​​used to represent the initialized OOA member set; Wherein, for any of the OOA members, the OOA member serves as a candidate equipment support solution.

4. The method according to claim 3, characterized in that The initialized equipment support scheme is iteratively optimized based on currently available equipment resources through global exploration and local mining strategies, including: For any OOA member in any iteration, update the fish position set of the OOA member by using the position formula; Randomly determine the fish selected by the OOA member based on the fish position set of the OOA member; Based on the movement simulation of the OOA members toward the selected fish, the new positions of the OOA members are calculated until the optimal positions of all the OOA members are determined based on the currently available equipment resources, and the optimal positions serve as the optimal equipment support solution corresponding to the current equipment support task.

5. The method according to any one of claims 1 to 4, characterized in that: The total task completion time is used as the first objective function to sort the equipment support tasks in the task set to obtain the priority of each equipment support task, including: Determining the task set and the attributes of each of the equipment support tasks in the task set; According to the attributes of each of the equipment support tasks, the priority of each of the equipment support tasks is obtained by calculating the total task completion time, optimizing and iterating.

6. An equipment support planning device, characterized in that: The device comprises: A determination unit, used to determine the equipment support task to be executed currently through a dynamic list scheduling algorithm; an allocation unit, configured to allocate currently available equipment resources through the Osprey optimization algorithm (OOA) according to the demand information of the current equipment support task, so as to determine the optimal equipment support plan corresponding to the current equipment support task, wherein the demand information of the equipment support task includes the task type, the required equipment type, the required equipment quantity, the task duration, and the support point information corresponding to the task, and the equipment support plan includes the equipment type, the equipment quantity, the equipment quality, the equipment cost, and the support point information, and the equipment support plan is used to provide data basis for the equipment support planning of the current equipment support task, and the support point is used to store and deploy the equipment resources, and the support point information includes: the support point location, the support point capacity, the cooperation mechanism between the support points, the personnel configuration of the support point, and the emergency plan, and the equipment resources include the vehicles, weapons, and communication equipment required for the equipment support task; An updating unit, configured to update the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the current equipment support task, and to update the currently available equipment resources according to the optimal equipment support solution corresponding to the current equipment support task, until all equipment support tasks in the dynamic list are completed; The determining unit, when determining the equipment support task to be executed currently through the dynamic list scheduling algorithm, specifically includes: Taking the total task completion time as the first objective function, sorting the equipment support tasks in the task set to obtain the priority of each equipment support task; Inserting all the equipment support tasks into a dynamic list according to the priority from high to low; the dynamic list includes the equipment support task priority; According to the current number of idle processors and the priority of each of the equipment support tasks, the execution order of the equipment support tasks is dynamically adjusted to determine the equipment support task currently to be executed.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the equipment support planning method described in any one of claims 1 to 5.

8. An electronic device, characterized in that: The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the equipment support planning method described in any one of claims 1 to 5.

9. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the equipment support planning method according to any one of claims 1 to 5.

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