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, device, computer-readable storage medium, electronic device and computer program product, which relates to the technical field of equipment support planning, wherein the method includes: determining the current equipment support task to be executed through a dynamic list scheduling algorithm; allocating the 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 solution corresponding to the current equipment support task; updating the dynamic list in the dynamic list scheduling algorithm according to the task status after executing the current equipment support task, and updating 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 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 in particular to an equipment support planning method, device, computer-readable storage medium, electronic device, and computer program product. Background Art

[0002] Equipment support plan refers to the basic concept of completing support tasks and implementing corresponding measures based on the characteristics of equipment use. Among them, the composition and deployment of equipment support forces are the core content of the equipment support plan. 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 equipment support departments based on support needs, and equipment support resources refer to equipment and personnel required to support combat operations. Under information conditions, various factors will affect the planning of equipment support tasks.

[0003] At present, the task planning method of related technologies occupies a large amount of computer memory and takes a long time to solve. It is not suitable for task planning problems with large problem scale, complex constraints and strong real-time requirements.

[0004] Therefore, relevant technologies cannot improve 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 related technologies that planning efficiency and accuracy cannot be improved.

[0006] In order to achieve the above-mentioned object, the first aspect of the present disclosure provides an equipment support planning method, comprising: 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, and the support point is used to store equipment resources and deploy the equipment resources. 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 plan corresponding to the current equipment support task until all equipment support tasks in the dynamic list are completed.

[0007] Optionally, further, determining the equipment support task to be currently executed 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.

[0008] Optionally, further, 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 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.

[0009] Optionally, further, 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.

[0010] Optionally, further, the iterative optimization of the initialized equipment support plan according to currently available equipment resources through global exploration and local exploitation strategies includes: 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.

[0011] Optionally, further, 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 includes: 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.

[0012] A second aspect of the present disclosure provides an equipment support planning device, including: A determination unit, used to determine the equipment support task to be executed currently through a dynamic list scheduling algorithm; An allocation unit is used 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 equipment resources and deploy the equipment resources; An updating unit is used 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 plan corresponding to the current equipment support task until all equipment support tasks in the dynamic list are completed.

[0013] A third aspect of the present disclosure provides a computer-readable storage medium, wherein 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 provided by any one of the first aspects.

[0014] The fourth aspect of the present disclosure provides an electronic device, comprising: 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 provided in any one of the first aspects.

[0015] A fifth aspect of the present disclosure provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the equipment support planning method provided in any one of the first aspects.

[0016] In the equipment support planning method provided in the embodiment of the present disclosure, a 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 tasks completed and the completion time, thereby improving the efficiency of equipment support planning; based on the demand information of the current equipment support tasks to be executed determined by the dynamic list scheduling algorithm, the currently available equipment resources are allocated through the Osprey optimization algorithm to find the optimal equipment support plan corresponding to the current equipment support task, thereby improving the accuracy of equipment support planning. Therefore, the optimal equipment support plan is determined by combining the dynamic list scheduling algorithm with the Osprey optimization algorithm to achieve the purpose of dynamic planning, thereby achieving the technical effect of improving the efficiency and accuracy of equipment support planning, thereby solving the technical problem that the related technology cannot improve the efficiency and accuracy of equipment support planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a scenario of an equipment support planning method provided in an embodiment of the present disclosure; Figure 2 A flowchart of an equipment support planning method provided in an embodiment of the present disclosure; Figure 3 A flowchart of an equipment support planning method provided in yet another embodiment of the present disclosure; Figure 4 A block diagram of an equipment support planning device provided in an embodiment of the present disclosure; Figure 5 A block diagram of an electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present disclosure, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] In the present disclosure, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present disclosure and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0022] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this disclosure can be understood according to specific circumstances.

[0023] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "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 a direct connection, or an indirect connection through an intermediate medium, or it can be 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 this disclosure can be understood according to specific circumstances.

[0024] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] At present, the task planning method of related technologies takes up a lot of computer memory and takes a long time to solve, which is not suitable for task planning problems with large problem scale, complex constraints and strong real-time requirements. Therefore, related technologies cannot improve planning efficiency and accuracy.

[0026] In order to solve the above problems, the technical concept of the present invention is to adopt a dynamic list scheduling algorithm to continuously and dynamically adjust the task order to execute high-priority tasks, thereby improving the equipment support planning, and combining the Osprey optimization algorithm to execute tasks to use the optimal equipment support plan, thereby improving the accuracy of equipment support planning.

[0027] In practical applications, the execution subject of the present disclosure may be an equipment support planning device, which may be deployed in electronic devices, such as terminal devices, servers, etc. Users may perform equipment support planning through electronic devices deployed with the equipment support planning device, where the equipment may be vehicles, weapons, communication equipment, etc. required for the support mission.

[0028] For example, see Figure 1 As shown, Figure 1 A schematic diagram of a scenario of an equipment support planning method provided in an embodiment of the present disclosure. The scenario includes a data collection device 101 and a server 102 on which an equipment support planning device is deployed. The data collection device 101 is used to collect equipment support task information, equipment support task demand information, etc., such as the task type, required equipment type, required equipment quantity, task duration, and task corresponding support point information for any equipment support 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 equipment support task that needs to be executed (i.e., the equipment support task to be executed currently) through a dynamic list scheduling algorithm (Dynamic Level Scheduling, DLS), and based on the demand information of the current equipment support task, the currently available equipment resources are allocated through the Osprey optimization algorithm to find the optimal equipment support plan for the current equipment support task, so as to achieve the purpose of equipment support planning. The equipment support plan here can be used as equipment support planning information or equipment support planning data.

[0029] The server 102 is also used to update the task status of the current equipment support task in the dynamic list, so as to dynamically adjust the task order, and update the currently available equipment resources, so as to calculate other equipment support tasks, until all equipment support tasks are completed.

[0030] Through the above application scenarios, the present disclosure can improve the efficiency and accuracy of equipment support planning by combining the Osprey optimization algorithm with the DLS dynamic list.

[0031] It should be noted that in the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of information such as tasks, task data or task requirements involved are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0032] The technical solution of the present disclosure is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0033] The present disclosure provides an equipment support planning method, such as Figure 2 As shown, the method includes the following steps S201 to S203: Step S201: Determine the equipment support task to be executed currently through a dynamic list scheduling algorithm.

[0034] Step S202: According to the demand information of the current equipment support task, the currently available equipment resources are allocated through the Osprey optimization algorithm 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 a data basis for the equipment support planning of the current equipment support task, and the support point is used to store equipment resources and deploy the equipment resources.

[0035] Step S203: 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 equipment support tasks in the dynamic list are completed.

[0036] In the disclosed embodiment, based on the dynamic list scheduling algorithm, the order of the equipment support tasks in the dynamic list is adjusted dynamically and in real time, which can maximize the number of tasks completed and the completion time, thereby improving the efficiency of the equipment support planning; based on the demand information of the current equipment support tasks to be executed 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 current equipment support task, thereby improving the accuracy of the equipment support planning. Therefore, the optimal equipment support plan is determined by combining the dynamic list scheduling algorithm with the Osprey optimization algorithm to achieve the purpose of dynamic planning, thereby improving the efficiency and accuracy of the equipment support planning.

[0037] 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 use requirements. Equipment costs include equipment procurement costs, maintenance costs, transportation costs, etc. Support point information includes: support point location, support point capacity, coordination between support points, support point personnel configuration, and emergency plans.

[0038] Among them, the location of the security point: determine the geographical location of the security point to ensure that it can efficiently cover all usage points; the capacity of the security point: the storage capacity and deployment capacity of each security point. Coordination between security points: the collaboration mechanism between different security points to ensure that resources can be quickly deployed when needed. Staffing of security points: the number of personnel, skill requirements and training plans of each security point. Emergency plan: emergency support plan for different emergency situations, including the establishment of temporary security points and resource deployment.

[0039] The dynamic list scheduling algorithm DLS is an online task scheduling algorithm based on a 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 priority and dynamically adjusts the order of tasks based on the current number of idle processors and the priority of the tasks.

[0040] 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 repositions it to free up a processor as soon as possible. In this way, the DLS algorithm can maximize the number of tasks completed and the completion time.

[0041] Osprey optimization algorithm (OOA): simulates the hunting behavior of osprey. Specifically, the osprey optimization algorithm consists of two stages: the first stage is for the osprey to identify the location of fish and catch fish (global exploration), and the second stage is to bring the fish to the appropriate location (local exploitation).

[0042] Among them, in equipment support planning, multiple factors such as equipment requirements (for example, equipment type, etc.), equipment quantity, equipment quality and equipment cost are considered. The relationship between these factors is complex and needs to be comprehensively considered and optimized. Specifically, the Osprey optimization algorithm is used to search for the best equipment configuration plan (here refers 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 planning. The Osprey optimization algorithm is combined with the DLS dynamic list and applied to equipment support planning to improve the efficiency and accuracy of equipment support planning.

[0043] In the disclosed embodiment, the Osprey optimization algorithm is combined with the dynamic list scheduling method to construct the model, and the dynamic list method is used to screen the implementation of the support tasks. At the same time, an adaptive learning factor is introduced to balance the global search and local search capabilities, which can achieve effective management and optimization of the equipment support planning process, thereby improving the support capability and reducing losses.

[0044] Optionally, determining the equipment support task to be currently executed by 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.

[0045] The dynamic list also includes: basic information of the task, task dependencies, task scheduling status, and task dynamic adjustment information. The task here refers to a task that requires a processor to perform calculations, such as an equipment support task, which will not be described in detail below.

[0046] Basic information of the task: task ID, computational effort, communication overhead. Priority information of the task (here refers to the equipment support task priority): static priority, dynamic priority. Dependencies 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.

[0047] Among them, Task ID: 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. Communication Cost of the task: if there is a dependency between tasks, data transmission between tasks will generate communication overhead.

[0048] Static Priority (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 Priority (DL): The dynamic priority combines the static priority of the task and the current system resource status (such as the number of idle processors); the dynamic priority will be adjusted in real time according to the execution progress of the task and the changes in system resources.

[0049] Predecessors: Tasks i A list of all predecessor tasks, indicating that the task i These tasks must be completed before execution can begin. i A list of all the successor tasks that must be completed before the task i Execution can only begin after completion.

[0050] The start time of a task (Start Time): The actual time when the task starts executing on the processor. The finish time of a task (Finish Time): The actual time when the task is completed on the processor. The processor allocation of a task (ProcessorAllocation): The processor on which the task is allocated for execution.

[0051] Dynamic Level (DL) of a task: The dynamic level of a task is used to dynamically adjust the execution order of tasks. The calculation formula for the dynamic level is: (1) in, It's a task i Dynamic level of It's a task i The static level of It's a task i The set of successor tasks; It's a task i and tasksj When there is no communication overhead between tasks, the is 0.

[0052] Tasks are sorted from high to low according to dynamic priority, and high-priority tasks are scheduled first. The DLS list (i.e., dynamic list) is used to store all tasks to be scheduled, and dynamically adjust the execution order of tasks according to the dynamic priority of the tasks and the status of system resources. According to the task priority in the DLS list and the idle status of the processor, the tasks are assigned to the appropriate processor for execution. During the task execution process, the DLS list is updated in real time according to the completion of the task and the changes in system resources.

[0053] Optionally, taking the total task completion time as the first objective function, sorting the equipment support tasks in the task set, and obtaining the priority of each equipment support task includes: 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.

[0054] Among them, the attributes of each task include computing time (task execution time), dependencies (predecessor and successor tasks of the task), resource requirements (i.e. resources required for the task), and completion time (estimated completion time of the task).

[0055] In the embodiment of the present disclosure, a task dependency graph is established based on the task set and task attributes: a 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 in task T i Before the task is completed, the execution order of the task is determined by topological sorting. According to the above formula (1) and When it is 0, the priority of the equipment support task is calculated.

[0056] For example, in order to achieve effective planning of resource support, the planning model can be divided into two parts: task description and resource description. Based on the analysis of the core elements of equipment support tasks, the model mainly includes the types and quantities of resources required for each equipment support task, the (coordinate) location of the support point, and the duration of the task.

[0057] The guarantee tasks and guarantee resources are described as follows: (2) Among them, the vector X represents N mission support sets (for example, N candidate mission support solutions) and m equipment support requirements (for example, m requirements or m problems or m variables; for example: equipment quantity, resource capacity (for example, equipment quality), mission duration, etc.). The input is the specific requirements of the equipment support task, including: task type, required resource type and quantity, mission duration, etc., the current available equipment support resources, and the parameter settings of the Osprey optimization algorithm and dynamic list scheduling (for example: the dimension of the problem, the number of Ospreys, the total number of iterations, etc.). The output is the optimal equipment support solution and its corresponding fitness value, the task execution plan (for example, the task status) and the resource usage (for example, the current remaining available resources).

[0058] Calculate the priority coefficient: There are two difficulties in solving the task resource guarantee problem. The first difficulty is that when the schedulable resources of a single task are optimal, it is not necessarily possible to obtain the global optimal solution. The second difficulty is that in large-scale resource guarantee problems, since multiple iterations are required, computing power is wasted and takes a long time. In order 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 the problems such as grouping 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, formula (3) is inferred): (3) in, :Task i The priority value of the task. :Task i Computation Time. :Task i The set of successor tasks. :Task i The maximum priority value among all successor tasks. :Task i The sum of the priority values ​​of all successor tasks.

[0059] According to the guarantee task set relationship diagram and the task priority definition formula (such as formula (3)), each task priority coefficient (here refers to the task priority or task priority value) can be calculated.

[0060] Among them, it is assumed that the task can be guaranteed, but in order to ensure that the task can be completed smoothly, the resource demand constraint needs to be met, requiring that the total capacity of each type of resource in the resource capacity vector is not less than the corresponding resource capacity required by the task, that is (constraint condition):

[0061] Optionally, the currently available equipment resources are allocated by the Osprey optimization algorithm according to the demand information of the current equipment support task to determine the optimal equipment support solution corresponding to the current equipment support task, including: Initialize the equipment support plan based on the demand information of the current equipment support task through the Osprey optimization algorithm; 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. For example, the sum of the resource quantity of each equipment type in the equipment support plan is not less than the sum of the resource quantity required for each corresponding equipment type, and / or, the sum of the resource quality (i.e., equipment quality) of each equipment type in the equipment support plan is not less than the sum of the resource quality required for each corresponding equipment type. The resources here may include equipment.

[0062] The Osprey optimization algorithm here uses the quadratic Osprey optimization algorithm.

[0063] In the disclosed embodiment, the optimization scheme of the Osprey optimization algorithm includes the following steps: Step a1: Population initialization The osprey population is randomly initialized using the following formula: (4) (5) Among them, N is the number of ospreys (here refers to the overall size of OOA or the number of OOA members. In practical applications, for example, N is the number of candidate equipment support solutions), m is the dimension of the problem, and the fitness value is calculated according to the optimization problem after the initialization position: (6) Wherein, formula (6) is the fitness function, which is used as an objective function, such as the second objective function.

[0064] Step a2: Global exploration (Phase 1: Location identification and fishing) Ospreys are powerful hunters that are able to detect the location of fish underwater due to their powerful eyesight. After determining the location of the fish, they attack it and hunt the fish by diving underwater. The first stage of population updating in OOA is modeled based on the simulation of this natural behavior of ospreys. Modeling the osprey attacking fish causes significant changes in the positions of the ospreys in the search space, which increases the exploration ability of OOA in identifying optimal areas and escaping from local optima. In OOA design, for each osprey, the positions of other ospreys with better objective function values ​​in the search space are considered as underwater fish. The position of each osprey is specified using the following formula.

[0065] (7) in, For the i The set of positions of the ospreys (e.g. i OOA member location set), is the best Osprey position (i.e., the best solution or optimal solution, such as the best equipment support solution).

[0066] 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 corresponding osprey's new position is calculated using the following formula. This new position, if its value of the objective function is better, replaces the osprey's previous position.

[0067] (8) (9) (10) in, For the i Ospreys in the first stage, its j The new location of the is its corresponding fitness value. is a random number between [0,1], is a random number from the set {1,2}.

[0068] Step a3: Local exploitation (Phase 2: Bringing the fish to the right location) After catching the fish, the osprey brings it to a suitable (safe for him) location and eats it there. The second stage of updating the population in OOA is modeled based on the simulation of this natural behavior of the osprey. The modeling of bringing the fish to a suitable location leads to a small change in the position of the osprey in the search space, which leads to an increase in the exploitation ability of the OOA in the local search and convergence to a better solution near the found solution. In the design of OOA, in order to simulate this natural behavior of the osprey, first, for each member of the population, a new random position is calculated as a "suitable position 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.

[0069] (11) (12) (13) in, is the number of the i-th osprey in the second stage. j The new location of the is its corresponding fitness value. r is a random number between [0,1], t and T are the current iteration number and the maximum iteration number respectively.

[0070] Optionally, 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.

[0071] Optionally, the iterative optimization of the initialized equipment support plan according to currently available equipment resources through global exploration and local exploitation strategies includes: 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.

[0072] Specifically, taking the equipment support plan as an example of an OOA member, the process of the secondary Osprey optimization algorithm includes: Start OOA.

[0073] 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.).

[0074] Set the OOA total size (N) and total number of iterations (T); the OOA total size is the number of OOA members.

[0075] The initial population matrix is ​​randomly generated using formula (4) and formula (5).

[0076] The objective function is calculated according to formula (6).

[0077] For t=1,…,T For i=1,…,N Phase 1: Location Identification and Fishing Use formula (7) to update the fish position set of the i-th OOA member; Randomly determine the fish chosen by the i-th osprey The new position of the i-th OOA member is calculated based on the first stage of OOA using formula (8).

[0078] Use equation (9) to check the boundary conditions of the new positions of the OOA members.

[0079]

[0080] Update the i-th OOA member using formula (10).

[0081]

[0082] Stage 2: Bringing the fish to the right location The new position of the i-th OOA member is calculated based on the second stage of OOA using formula (11).

[0083]

[0084] Use equation (12) to check the boundary conditions of the new positions of the OOA members.

[0085]

[0086] Update the i-th OOA member using formula (13).

[0087] Finish Save the best candidate solution found so far.

[0088] End OOA.

[0089] therefore, For example, see Figure 3 As shown, Figure 3 A flowchart of an equipment support planning method provided by another embodiment of the present disclosure. For the problem of precise collaborative support, dynamic list scheduling can be used in conjunction with the secondary osprey optimization algorithm to solve the problem. First, the equipment support task that needs to be executed currently is selected through the dynamic list, and then the secondary osprey optimization algorithm is used to select the optimal solution for executing the current equipment support task from all current resources, that is, the optimal equipment support solution for the current equipment task guarantee.

[0090] In the disclosed embodiment, the operation is started by first extracting the guarantee process and formulating a guarantee timing diagram (for example, a guarantee task set relationship diagram); then the task priority coefficient is calculated, the guarantee task is selected, and the osprey optimization algorithm is used to find the optimal solution to determine whether there is a feasible structure (for example, the optimal solution); if there is no feasible structure, all current resource occupancy states are released, and the osprey optimization algorithm is continued to find the optimal solution; if there is a feasible structure, a guarantee plan is generated, and the task status and available resource information are updated to determine whether there are any unfinished tasks. If there are any unfinished tasks, the guarantee tasks are continued to be selected, and the above operations are repeated. If there are no unfinished tasks, the operation is terminated.

[0091] Specifically, the Osprey optimization algorithm combined with the dynamic list algorithm process is: Step b1: Task selection: Select the equipment support task that needs to be performed currently according to the task priority in the dynamic list.

[0092] Step b2. Resource allocation: Use the Osprey optimization algorithm to allocate currently available equipment resources to find the optimal equipment configuration solution.

[0093] (1) Initialize the Osprey population (i.e., equipment configuration plan or equipment support plan) based on mission requirements.

[0094] (2) Iteratively optimize the osprey population using global exploration and local exploitation strategies.

[0095] (3) Select the Osprey with the best fitness (i.e., the optimal equipment configuration plan or the optimal equipment guarantee plan) as the resource allocation plan or resource guarantee plan for the current task.

[0096] Step b3. Task execution and update: Execute the current task and update the task status and equipment resource information according to the actual situation.

[0097] Step b4. Repeat the above task selection and resource allocation steps according to the updated task list and resource information until all tasks are completed.

[0098] The Osprey optimization algorithm has high search performance and high convergence speed. Applying the Osprey optimization algorithm in combination with dynamic list scheduling to the military equipment support planning of campaign clusters can improve the efficiency and quality of precise equipment support.

[0099] From the above description, it can be seen that the present disclosure achieves the following technical effects: First, according to the current equipment demand and resource supply situation, it can automatically find the optimal equipment scheduling plan, thereby improving scheduling efficiency and equipment utilization. Secondly, when an emergency occurs, it is necessary to quickly dispatch and allocate the military equipment of the campaign cluster to meet the needs of different tasks. Through the dynamic list scheduling method, the task and resource information can be updated in real time, and the fast search capability of the Osprey optimization algorithm can help quickly find the optimal scheduling plan, so as to achieve timely response. Finally, through the prediction ability of the Osprey optimization algorithm, it is possible to predict the equipment needs that may appear in the future based on historical data and trends, thereby helping the emergency management department to prepare for equipment reserves and scheduling, and improve the military equipment support capabilities of the campaign cluster.

[0100] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0101] The present disclosure also provides an equipment support planning device for implementing the above equipment support planning method embodiment. Figure 4 As shown, the equipment support planning device 40 includes: The determination unit 401 is used to determine the equipment support task to be executed currently through a dynamic list scheduling algorithm; The allocation unit 402 is used 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 equipment resources and deploy the equipment resources; The updating unit 403 is used 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 plan corresponding to the current equipment support task until all equipment support tasks in the dynamic list are completed.

[0102] Optionally, the determining unit 401, when determining the equipment support task to be currently executed 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.

[0103] Optionally, the allocation unit 402, when allocating currently available equipment resources through the Osprey optimization algorithm OOA according to the demand information of the current equipment support task to determine the optimal equipment support solution corresponding to the current equipment support task, specifically includes: 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.

[0104] Optionally, the allocation unit 402, when executing the initialization of the equipment support plan through the OOA according to the demand information of the current equipment support task, specifically 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.

[0105] Optionally, the allocating unit 402, when iteratively optimizing the initialized equipment support plan according to currently available equipment resources through global exploration and local exploitation strategies, specifically includes: 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.

[0106] Optionally, the allocating unit 402, when executing the ordering of the equipment support tasks in the task set by taking the total task completion time as the first objective function to obtain the priority of each equipment support task, specifically includes: 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.

[0107] The specific manner in which each unit in the above device embodiment performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0108] The present disclosure also provides an electronic device, such as Figure 5 As shown, the electronic device includes one or more processors 51 and a memory 52. Figure 5 A processor 51 is taken as an example.

[0109] The controller may further include: an input device 53 and an output device 54 .

[0110] The processor 51, the memory 52, the input device 53 and the output device 54 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0111] The processor 51 may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. The general-purpose processor may be a microprocessor or any conventional processor.

[0112] The memory 52 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the control method in the embodiment of the present disclosure. The processor 51 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 52, that is, the equipment support planning method of the above method embodiment is implemented.

[0113] The memory 52 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of a processing device operated by the server, etc. In addition, the memory 52 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 52 may optionally include a memory remotely arranged relative to the processor 51, and these remote memories may be connected to a network connection device via 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.

[0114] The input device 53 can receive input digital or character information and generate key signal input related to user settings and function control of the processing device of the server. The output device 54 can include display devices such as a display screen.

[0115] One or more modules are stored in the memory 52, and when executed by one or more processors 51, the method shown above is performed.

[0116] The embodiment of the present disclosure also provides a computer-readable storage medium, wherein 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 as described above.

[0117] The embodiments of the present disclosure also provide a computer program product, including a computer program, which implements the equipment support planning method as described above when executed by a processor.

[0118] Those skilled in the art can understand that the implementation of all or part of the processes in the above method embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FM), a hard disk (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0119] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all 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, and the support point is used to store equipment resources and deploy the equipment resources. 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 plan corresponding to the current equipment support task until all equipment support tasks in the dynamic list are completed.

2. The method according to claim 1, characterized in that The dynamic list scheduling algorithm is used to determine the equipment support tasks to be performed, including: 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.

3. The method according to claim 1 or 2, 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.

4. The method according to claim 3, 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.

5. The method according to claim 4, 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, the fish position set of the OOA member is updated by 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.

6. The method according to claim 2, 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.

7. An equipment support planning device, characterized in that: The device also includes: A determination unit, used to determine the equipment support task to be currently executed through a dynamic list scheduling algorithm; An allocation unit is used 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 equipment resources and deploy the equipment resources; An updating unit is used 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 plan corresponding to the current equipment support task until all equipment support tasks in the dynamic list are completed.

8. 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 6.

9. 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 6.

10. 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 6.

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