Armored equipment maintenance scheduling method and device

Abstract

The invention provides a maintenance scheduling method and device for armored equipment, and relates to the technical field of maintenance scheduling, and the method comprises the steps: determining all current maintenance tasks after the armored equipment is lost; endowing each current maintenance task with a corresponding weight by using a hypergraph generative adversarial network; based on each current maintenance task and the corresponding weight, determining maintenance time corresponding to each current maintenance task, and determining weighting completion time of all the current maintenance tasks; and adopting a flood optimization algorithm to optimize by taking the minimization of the weighting completion time of all the current maintenance tasks as an optimization target, and determining an optimal maintenance sequence of all the optimized current maintenance tasks. According to the method, the optimal maintenance sequence is determined, so that the weighting completion time required by maintenance is shortest, the efficiency of maintenance scheduling of the armored equipment is improved, post-maintenance scheduling of the armored equipment is efficiently realized, and the problem that efficient maintenance scheduling of the armored equipment cannot be realized in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance scheduling, and in particular to a maintenance scheduling method and device for armored equipment. Background Art

[0002] In the context of economic globalization, armored equipment plays an important hub role in the frequent flow of materials. With the increase in armored throughput and the increase in mechanized operations, armored equipment is often in an overloaded operating state. In addition, mechanized operations in open-air scenes are easily affected by bad weather and cause armored equipment failures. Therefore, reasonable armored equipment maintenance scheduling has become the key to maintaining efficient operation of armored equipment. However, related technologies cannot achieve efficient maintenance scheduling of armored equipment.

[0003] With regard to the problem that related technologies cannot achieve efficient maintenance and scheduling of armored equipment, no effective technical solution has been proposed so far. Summary of the invention

[0004] The main purpose of the present invention is to provide a maintenance scheduling method and device for armored equipment to solve the problem that in the related art, it is impossible to achieve efficient maintenance scheduling for armored equipment.

[0005] In order to achieve the above-mentioned object, the first aspect of the present disclosure provides a maintenance scheduling method for armored equipment, comprising: Identify all current repair tasks after armored equipment loss; Use hypergraph generative adversarial network to assign corresponding weights to each current maintenance task; Based on each current maintenance task and the corresponding weight, determine the maintenance time corresponding to each current maintenance task, and determine the weighted completion time of all current maintenance tasks; The flood optimization algorithm is used to minimize the weighted completion time of all current maintenance tasks as the optimization goal, and the optimal maintenance sequence of all current maintenance tasks after optimization is determined.

[0006] Optionally, a hypergraph generative adversarial network is used to assign corresponding weights to each current maintenance task, including: Initialize the hypergraph generative adversarial network, use the steepest descent method with momentum to preliminarily train the hypergraph generative adversarial network in a parallel training mode, and obtain the hypergraph generative adversarial network after the preliminary training, wherein the hypergraph generative adversarial network uses the S-type function as the transfer function; Input the maintenance workload ratio and maintenance cycle of each historical maintenance task into the hypergraph generative adversarial network after preliminary training, output the result, and determine the error based on the results of two consecutive outputs. Based on the error, correct the hidden layer weights and thresholds of the hypergraph generative adversarial network after preliminary training, and repeat the training; When the training is repeated until the sum of squares of the errors is less than or equal to a preset error tolerance, or when the number of repeated training reaches a preset maximum number of iterations, the training is stopped to obtain a hypergraph generative adversarial network after repeated training; By analyzing and comparing the maintenance workload ratio and maintenance cycle of each historical maintenance task, the accuracy of the hypergraph generative adversarial network after repeated training is determined, and the hypergraph generative adversarial network after repeated training is tested to obtain the weight corresponding to each current maintenance task of the armored equipment; According to the normal maintenance state or emergency maintenance state of the armored equipment, the current maintenance task of the armored equipment and the change of the corresponding weight of the current maintenance task are determined.

[0007] Furthermore, the maintenance workload ratio and maintenance cycle of each historical maintenance task are input into the hypergraph generative adversarial network after preliminary training, including: The maintenance workload ratio and maintenance cycle of each historical maintenance task are normalized to obtain normalized historical maintenance data, and the normalized historical maintenance data is input into the hypergraph generative adversarial network after preliminary training. After obtaining the hypergraph generative adversarial network after repeated training, the method further includes: The normalized historical maintenance data is denormalized to obtain the maintenance workload ratio and maintenance cycle of each historical maintenance task.

[0008] Optionally, the hypergraph generative adversarial network is an interactive hyperedge neuron module or MRL-AHF.

[0009] Furthermore, when the hypergraph generative adversarial network is an interactive hyperedge neuron module, the interactive hyperedge neuron module is used as a generator to capture the complex relationship between data, and the discriminator is MLP; Determine the characteristics of the nodes in the l+1th layer according to the following formulas and the characteristics of the hyperedge at layer l+1 : , in, is the activation function, is the hypergraph incidence matrix, yes The transpose of is the feature of the hyperedge at layer l, is the feature of the node in layer l, is the weight matrix of the hyperedge at layer l, is the weight matrix of the nodes in layer l, is a hyperparameter; The nodes in the last layer are used as the weights corresponding to the current maintenance task.

[0010] Furthermore, when the hypergraph generative adversarial network is MRL-AHF, the latent representation is fed into encoders EA and EB respectively to obtain the representation and , and the two generated hypergraphs use the incidence matrix and Indicates that hypergraph fusion is achieved through adversarial training strategy to obtain vertex features : , in, and They are and The vertex degree matrix of , , and They are and The hyperedge degree matrix of yes The transpose of is the weight matrix; The features of the vertices are used as the weights corresponding to the current maintenance task.

[0011] Optionally, according to the normal maintenance state or the emergency maintenance state of the armored equipment, determining the current maintenance task of the armored equipment and the change of the weight corresponding to the current maintenance task includes: When the armored equipment is in normal maintenance status, the first objective function is established according to the following formula: , The first constraint is: , Among them, the first objective function is the weighted completion time of all current maintenance tasks under normal maintenance status, n is the total number of current maintenance tasks, is the weight of the i-th maintenance task in the maintenance sequence under normal maintenance status, is the cumulative completion time of the first i maintenance tasks in the maintenance sequence under normal maintenance conditions, is the maintenance time of the ith maintenance task in the maintenance sequence under normal maintenance conditions; Indicates whether the i-th maintenance task is to repair the j-th maintenance equipment, Indicates that the i-th maintenance task is to repair the j-th maintenance equipment, Indicates that the i-th maintenance task is not to repair the j-th maintenance equipment; is the time to repair the jth maintenance equipment, is the weight of maintaining the jth maintenance equipment, is the maintenance time of the mth maintenance task in the maintenance sequence; The first constraint conditions include: only one maintenance device can be repaired at a time, and a maintenance task can only have one position in the maintenance sequence.

[0012] Furthermore, when the armored equipment is in an emergency maintenance state, the second objective function is established according to the following formula: , The second constraint is: , Among them, the second objective function is the weighted completion time of all current maintenance tasks in the emergency maintenance state, is the weight of the i-th maintenance task in the maintenance sequence under emergency maintenance status, is the cumulative completion time of the first i maintenance tasks in the maintenance sequence under emergency maintenance status, is the maintenance time of the i-th maintenance task in the maintenance sequence under emergency maintenance status, is the maintenance time of the mth maintenance task in the maintenance sequence under emergency maintenance status; Indicates whether the maintenance method of the i-th maintenance task is k, Indicates that the maintenance method of the i-th maintenance task is k, Indicates that the maintenance method of the i-th maintenance task is not k, k=0 indicates normal maintenance, k=1 indicates major maintenance, and k=2 indicates minor maintenance; and The time for major repairs and the time for minor repairs are respectively and are the volatility of maintenance task weights caused by overhaul and minor repair, respectively; a and b are the minimum and maximum loss degrees of maintenance equipment for overhaul, respectively; e and f are the minimum and maximum loss degrees of maintenance equipment for minor repair, respectively. is the loss degree of the i-th maintenance task in the maintenance sequence under emergency maintenance status; The second constraint conditions include: only one maintenance equipment can be repaired at a time, a maintenance task can only have one position in the maintenance sequence, the maintenance task in the maintenance sequence is one of normal maintenance, overhaul and minor repair, the degree of wear and tear of the maintenance equipment for overhaul of any maintenance task in the maintenance sequence under emergency maintenance conditions is in the range of [a,b], and the degree of wear and tear of the maintenance equipment for minor repair of any maintenance task in the maintenance sequence under emergency maintenance conditions is in the range of [e,f].

[0013] Optionally, a flood optimization algorithm is used to optimize the minimization of the weighted completion time of all current maintenance tasks as the optimization goal, and the optimal maintenance sequence of all current maintenance tasks after optimization is determined, including: The number, time and weight of all current maintenance tasks are input into the flood optimization algorithm, and the optimal maintenance sequence of all current maintenance tasks and the maintenance time and weight corresponding to each current maintenance task in the optimal maintenance sequence are output.

[0014] A second aspect of the present disclosure provides a maintenance scheduling device for armored equipment, comprising: A task determination unit, which is used to determine all current repair tasks after the loss of armored equipment; A weight assignment unit, used to assign a corresponding weight to each current maintenance task using a hypergraph generative adversarial network; A time determination unit, used to determine the maintenance time corresponding to each current maintenance task based on each current maintenance task and the corresponding weight, and determine the weighted completion time of all current maintenance tasks; The optimization unit is used for adopting a flood optimization algorithm, optimizing by taking the minimization of the weighted completion time of all current maintenance tasks as the optimization target, and determining the optimal maintenance sequence of all current maintenance tasks after optimization.

[0015] A third aspect of the present disclosure provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the maintenance scheduling method for armored equipment provided by any one of the first aspects.

[0016] 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 maintenance scheduling method for armored equipment provided in any one of the first aspects.

[0017] In the armored equipment maintenance scheduling method provided by the present disclosure, all current maintenance tasks after the armored equipment is damaged are determined; a corresponding weight is assigned to each current maintenance task using a hypergraph generative adversarial network; the hypergraph generative adversarial network has strong nonlinear fitting ability, simple learning rules, and is easy to implement by computer, so as to simulate the comprehensive consideration of the value of maintenance equipment by humans and assign a reasonable weight to each current maintenance task; Based on each current maintenance task and the corresponding weight, the maintenance time corresponding to each current maintenance task is determined, and the weighted completion time of all current maintenance tasks is determined; the flood optimization algorithm is adopted, and the minimization of the weighted completion time of all current maintenance tasks is used as the optimization goal for optimization, and the optimal maintenance sequence of all current maintenance tasks after optimization is determined. The flood optimization algorithm is adopted to determine the minimized weighted completion time of all current maintenance tasks, determine the optimal maintenance sequence of all current maintenance tasks, find the optimal scheduling plan to minimize the weighted completion time required for maintenance, improve the efficiency of armored equipment maintenance scheduling, and efficiently realize the post-maintenance scheduling of armored equipment, solving the problem that the related technology cannot realize efficient maintenance scheduling of armored equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 A schematic diagram of a maintenance scheduling method for armored equipment provided in an embodiment of the present disclosure; Figure 2 A block diagram of a maintenance scheduling device for armored equipment provided by an embodiment of the present disclosure; Figure 3 A block diagram of an electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] 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.

[0023] In the context of economic globalization, armored equipment plays an important hub role in the frequent flow of materials. With the increase in armored throughput and the increase in mechanized operations, armored equipment is often in an overloaded operating state. In addition, mechanized operations in open-air scenes are easily affected by bad weather and cause armored equipment failures. Therefore, reasonable armored equipment maintenance scheduling has become the key to maintaining efficient operation of armored equipment. However, related technologies cannot achieve efficient maintenance scheduling of armored equipment.

[0024] In order to solve the above problems, the present disclosure provides a maintenance scheduling method for armored equipment, such as Figure 1 As shown, the method includes the following steps S11 to S14: Step S11: determining all current maintenance tasks after the armored equipment is damaged; Step S12: using a hypergraph generative adversarial network to assign a corresponding weight to each current maintenance task; using a hypergraph generative adversarial network to quantify the weight of the current maintenance task, using the hypergraph generative adversarial network's strong nonlinear fitting ability, simple learning rules, and ease of computer implementation, to simulate manual comprehensive consideration of the value of maintenance equipment, and assign a reasonable weight to each current maintenance task or each maintenance equipment; Step S13: Based on each current maintenance task and the corresponding weight, determine the maintenance time corresponding to each current maintenance task, and determine the weighted completion time of all current maintenance tasks; Step S14: Adopting the flood optimization algorithm, the minimization of the weighted completion time of all current maintenance tasks is optimized as the optimization goal, and the optimal maintenance sequence of all current maintenance tasks after optimization is determined. The optimal maintenance sequence is the optimal maintenance scheduling plan. The position of each maintenance task in the optimal maintenance sequence can be determined by the sequence of each maintenance task in the optimal maintenance sequence. When the maintenance conditions are met, the weighted completion time considering the weight of the maintenance task can be reduced by reasonably arranging the maintenance sequence of each current maintenance task.

[0025] The present invention utilizes a hypergraph generative adversarial network to assign a weight to each current maintenance task, considers the minimized weighted completion time of all current maintenance tasks, and uses a flood optimization algorithm (FLA) for optimization to find the optimal maintenance scheduling plan so that the weighted completion time required for maintenance is the shortest, thereby improving the efficiency of armored equipment maintenance scheduling, and efficiently realizing post-maintenance scheduling of armored equipment, thereby solving the problem in related technologies that efficient maintenance scheduling of armored equipment cannot be realized.

[0026] In an optional implementation of the present disclosure, step S12 includes: Initialize the hypergraph generative adversarial network, use the steepest descent method including momentum to preliminarily train the hypergraph generative adversarial network in a parallel training mode, and obtain the hypergraph generative adversarial network after the preliminary training, wherein the hypergraph generative adversarial network uses the S-type function as the transfer function, and the hypergraph generative adversarial network includes 1 or 2 hidden layers; Input the maintenance workload ratio and maintenance cycle of each historical maintenance task into the hypergraph generative adversarial network after preliminary training, output the result, and determine the error based on the results of two consecutive outputs. Correct the hidden layer weights and thresholds of the hypergraph generative adversarial network after preliminary training based on the error, and repeat the training; wherein, the maintenance workload ratio refers to the ratio of the time for repairing a certain maintenance equipment to the total time for repairing the same type of equipment. The maintenance workload ratio can ensure the rationality of the weight of a type of equipment maintenance; for example, due to the variety of armored equipment, the same type of armored equipment also has many subdivisions. Armored cantilever gantry cranes are divided into double cantilevers and single cantilevers. The workload of repairing the two types of equipment is originally different, which will affect the determination of the equipment maintenance weight. The embodiment of the present disclosure uses the maintenance workload ratio instead of the maintenance workload as the basis for determining the weight, so as to ensure the rationality of the subsequent determination of the maintenance task weight; When the training is repeated until the sum of squares of the errors is less than or equal to the preset error tolerance, or when the number of repeated training reaches the preset maximum number of iterations, the training is stopped to obtain the hypergraph generative adversarial network after repeated training; when the training is repeated until the sum of squares of the errors is less than or equal to the preset error tolerance, the hypergraph generative adversarial network converges, and the preset maximum number of iterations can be 1000 times; By analyzing and comparing the maintenance workload ratio and maintenance cycle of each historical maintenance task, the accuracy of the hypergraph generative adversarial network after repeated training is determined, and the hypergraph generative adversarial network after repeated training is tested to obtain the weight corresponding to each current maintenance task of the armored equipment; through the maintenance workload ratio and maintenance cycle of each historical maintenance task, a hypergraph generative adversarial network that can more accurately assign weights to maintenance tasks is trained; According to the normal maintenance state or emergency maintenance state of the armored equipment, the current maintenance task of the armored equipment and the change of the corresponding weight of the current maintenance task are determined.

[0027] The basic idea of ​​the learning algorithm of the hypergraph generative adversarial network is to train the hypergraph generative adversarial network to complete a task by providing it with training examples (such as various historical maintenance tasks). In this process, the weight of each unit is adjusted to reduce the error between the expected output and the actual output. The input of the hypergraph generative adversarial network is the maintenance cycle and the proportion of maintenance workload, and the output is the weight of the maintenance task.

[0028] In a preferred embodiment of the present disclosure, the maintenance workload ratio and maintenance cycle of each historical maintenance task are input into the hypergraph generative adversarial network after preliminary training, including: The maintenance workload ratio and maintenance cycle of each historical maintenance task are normalized to obtain normalized historical maintenance data, and the normalized historical maintenance data is input into the hypergraph generative adversarial network after preliminary training. After obtaining the hypergraph generative adversarial network after repeated training, the method further includes: The normalized historical maintenance data is denormalized to obtain the maintenance workload ratio and maintenance cycle of each historical maintenance task.

[0029] By normalizing the maintenance workload ratio and maintenance cycle before input and then performing denormalization after training, the convergence speed and algorithm accuracy of the hypergraph generative adversarial network can be improved.

[0030] In an optional embodiment of the present disclosure, the hypergraph generative adversarial network is an interactive hyper edge neuron module or MRL-AHF. HGGAN proposes to use an interactive hyper edge neuron module (IHEN) as a generator to capture the complex relationship between data; MRL-AHF utilizes the complementarity between multiple modalities and the interaction within multiple modalities to improve the representation learning ability and multimodal fusion performance.

[0031] In a preferred embodiment of the present disclosure, when the hypergraph generative adversarial network is an interactive hyperedge neuron module, the interactive hyperedge neuron module is used as a generator to capture the complex relationship between data, and the discriminator is an MLP; Determine the characteristics of the nodes in the l+1th layer according to the following formulas and the characteristics of the hyperedge at layer l+1 : , in, is the activation function, is the hypergraph incidence matrix, yes The transpose of is the feature of the hyperedge at layer l, is the feature of the node in layer l, is the weight matrix of the hyperedge at layer l, is the weight matrix of the nodes in layer l, is a hyperparameter; The nodes in the last layer are used as the weights corresponding to the current maintenance task.

[0032] In a preferred embodiment of the present disclosure, when the hypergraph generative adversarial network is MRL-AHF, the potential representation is sent to the encoders EA and EB respectively to obtain the representation and , and the two generated hypergraphs use the incidence matrix and Indicates that hypergraph fusion is achieved through adversarial training strategy to obtain vertex features : , in, and They are and The vertex degree matrix of , , and They are and The hyperedge degree matrix of yes The transpose of is the weight matrix; The features of the vertices are used as the weights corresponding to the current maintenance task.

[0033] In an optional implementation of the present disclosure, determining a current maintenance task of the armored equipment and a change in a weight corresponding to the current maintenance task according to a normal maintenance state or an emergency maintenance state of the armored equipment includes: When the armored equipment is in normal maintenance status, the first objective function is established according to the following formula: , The first constraint is: , Among them, the first objective function is the weighted completion time of all current maintenance tasks under normal maintenance status, n is the total number of current maintenance tasks, is the weight of the i-th maintenance task in the maintenance sequence under normal maintenance status, is the cumulative completion time of the first i maintenance tasks in the maintenance sequence under normal maintenance conditions, is the maintenance time of the ith maintenance task in the maintenance sequence under normal maintenance conditions; Indicates whether the i-th maintenance task is to repair the j-th maintenance equipment, Indicates that the i-th maintenance task is to repair the j-th maintenance equipment, Indicates that the i-th maintenance task is not to repair the j-th maintenance equipment; is the time to repair the jth maintenance equipment, is the weight of maintaining the jth maintenance equipment, is the maintenance time of the mth maintenance task in the maintenance sequence; The first constraint condition includes: only one maintenance equipment can be repaired at a time, and a maintenance task can only have one position in the maintenance sequence. For example, when a ship enters the port to unload cargo, if the armored quay crane and gantry crane fail, a maintenance team can only complete the repair of the armored quay crane equipment first to ensure the unloading of the ship, and then repair the gantry crane to ensure the cargo enters the yard.

[0034] In addition, since armored equipment may be damaged or worn out suddenly, the present disclosure considers both normal maintenance status and maintenance scheduling under emergency maintenance status; emergency maintenance status means that in the established maintenance schedule, some equipment to be repaired has a maintenance status change, and the original maintenance method cannot complete the maintenance; for example, the armored crane originally scheduled for maintenance only has a loose conveyor belt, which only needs to be re-fixed, but the conveyor belt is stuck in the conveyor bearing during use by the operator, causing the bearing to be misaligned when forced to start. At this time, the simple re-fixing maintenance is changed to bearing resetting, which not only increases the maintenance workload, but also affects the overall life of the crane if not handled in time, increasing safety hazards; In an emergency maintenance state, the original maintenance task weight changes, and the maintenance time and maintenance method will also change. The disclosed embodiment visualizes this change as a change in the degree of wear and tear of maintenance equipment; the degree of equipment wear and tear refers to the ratio of the equipment's service life to its expected service life. A lower degree of wear and tear of maintenance equipment indicates that the frequency of use is lower than normal, and there is an unreasonable expense problem. For example, the automatic guided vehicle to be repaired only has a dent in the outer shell and can work, but because the maintenance record has been placed, the work efficiency of the guided vehicle is reduced; the degree of wear and tear of maintenance equipment increases, and the equipment has a greater hidden danger of failure, which is the focus of maintenance. The change in the degree of wear and tear of maintenance equipment will change the basic steps required for maintenance accordingly, and the corresponding maintenance tasks, maintenance methods and maintenance time will change. This change will be directly reflected in the total time required to complete the maintenance. These changes must be taken into account to ensure the rationality and economy of the maintenance sequence. Therefore, in an emergency maintenance state, the embodiment of the present disclosure defines the maintenance of equipment with a higher degree of equipment loss as a major repair, and defines the maintenance of equipment with a lower degree of equipment loss as a minor repair; the embodiment of the present disclosure reasonably arranges the order of maintenance tasks in maintenance scheduling to avoid some important maintenance tasks from being arranged in the wrong maintenance position, thereby reducing maintenance delays, time losses and economic losses.

[0035] In an optional implementation of the present disclosure, when the armored equipment is in an emergency maintenance state, the second objective function is established according to the following formula: , The second constraint is: , Among them, the second objective function is the weighted completion time of all current maintenance tasks in the emergency maintenance state, is the weight of the i-th maintenance task in the maintenance sequence under emergency maintenance status, is the cumulative completion time of the first i maintenance tasks in the maintenance sequence under emergency maintenance status, is the maintenance time of the i-th maintenance task in the maintenance sequence under emergency maintenance status, is the maintenance time of the mth maintenance task in the maintenance sequence under emergency maintenance status; Indicates whether the maintenance method of the i-th maintenance task is k, Indicates that the maintenance method of the i-th maintenance task is k, Indicates that the maintenance method of the i-th maintenance task is not k, k=0 indicates normal maintenance, k=1 indicates major maintenance, and k=2 indicates minor maintenance; and The time for major repairs and the time for minor repairs are respectively and are the volatility of maintenance task weights caused by overhaul and minor repair, respectively; a and b are the minimum and maximum loss degrees of maintenance equipment for overhaul, respectively; e and f are the minimum and maximum loss degrees of maintenance equipment for minor repair, respectively. is the loss degree of the i-th maintenance task in the maintenance sequence under emergency maintenance status; The second constraint conditions include: only one maintenance equipment can be repaired at a time, a maintenance task can only have one position in the maintenance sequence, the maintenance tasks in the maintenance sequence are either normal maintenance, overhaul or minor repair, the range of the degree of wear and tear of the maintenance equipment for overhaul of any maintenance task in the maintenance sequence under emergency maintenance is [a, b], and the range of the degree of wear and tear of the maintenance equipment for minor repair of any maintenance task in the maintenance sequence under emergency maintenance is [e, f]. When the degree of wear and tear is within [a, b], overhaul is performed, and when the degree of wear and tear is within [e, f], minor repair is performed; the maintenance time of each maintenance task is determined according to the actual situation, and the thresholds for overhaul and minor repair are not fixed values, but are related to the specific maintenance equipment.

[0036] In an optional implementation of the present disclosure, step S14 includes: The number, time and weight of all current maintenance tasks are input into the flood optimization algorithm, and the optimal maintenance sequence of all current maintenance tasks and the maintenance time and weight corresponding to each current maintenance task in the optimal maintenance sequence are output.

[0037] The disclosed embodiment solves the post-maintenance scheduling problem through a flood optimization algorithm. The flood optimization algorithm introduces a new meta-heuristic optimization algorithm, which draws inspiration from the complex movement and flow patterns of water masses in flood events in a watershed. The flood optimization algorithm mathematically models key phenomena, such as the movement of water toward slopes, flow velocity over time, soil permeability effects, and periodic increases and decreases in water levels caused by precipitation and losses. Using these models, the algorithm guides the potential solution population to move and evolve in the direction of enhanced optimality. In the disclosed embodiment, the input of the flood optimization algorithm is the number, time and corresponding weight of all current maintenance tasks, and the output is the optimal maintenance sequence of all current maintenance tasks, as well as the maintenance time and weight corresponding to each current maintenance task in the optimal maintenance sequence.

[0038] The present invention aims at the maintenance scheduling problem of armored equipment after damage, that is, the scheduling problem of post-maintenance. By analyzing the post-maintenance scheduling arrangement of armored equipment, a hypergraph generative adversarial network algorithm is used to quantify the weights of armored equipment to be repaired, and a flood optimization algorithm is used to minimize the weighted completion time of the maintenance task, so as to obtain the optimized maintenance scheduling sequence and the corresponding maintenance time arrangement; through the maintenance example of armored equipment, the application of the optimized scheduling model in port machinery and equipment is demonstrated, the maintenance sequence of port machinery and equipment is clarified, and the maintenance time is saved while ensuring the completion of the maintenance task, which provides a reference for the maintenance plan of armored equipment.

[0039] From the above description, it can be seen that the present disclosure achieves the following technical effects: The present invention uses a hypergraph generative adversarial network to assign a weight to each current maintenance task, considers the minimized weighted completion time of all current maintenance tasks, and uses a flood optimization algorithm for optimization to find the optimal maintenance scheduling solution so that the weighted completion time required for maintenance is the shortest, thereby improving the efficiency of armored equipment maintenance scheduling, and efficiently realizing the post-maintenance scheduling of armored equipment, thus solving the problem in related technologies that it is impossible to realize efficient maintenance scheduling of armored equipment. The position of each maintenance task in the optimal maintenance sequence can be determined by the sequence of each maintenance task in the optimal maintenance sequence. When the maintenance conditions are met, the weighted completion time considering the weight of the maintenance task can be reduced by reasonably arranging the maintenance sequence of each current maintenance task.

[0040] 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.

[0041] The disclosed embodiment also provides a maintenance scheduling device for armored equipment for implementing the above method embodiment, such as Figure 2 As shown, the maintenance scheduling device 20 includes: A task determination unit 21, used to determine all current maintenance tasks after the armored equipment is damaged; A weight assigning unit 22, used for assigning a corresponding weight to each current maintenance task by using a hypergraph generative adversarial network; A time determination unit 23, for determining the maintenance time corresponding to each current maintenance task based on each current maintenance task and the corresponding weight, and determining the weighted completion time of all current maintenance tasks; The optimization unit 24 is used to adopt a flood optimization algorithm to optimize the minimization of the weighted completion time of all current maintenance tasks as an optimization target, and determine the optimal maintenance sequence of all current maintenance tasks after optimization.

[0042] 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.

[0043] The present disclosure also provides an electronic device, such as Figure 3 As shown, the electronic device includes one or more processors 31 and a memory 32. Figure 3 A processor 31 is taken as an example.

[0044] The controller may further include: an input device 33 and an output device 34 .

[0045] The processor 31, the memory 32, the input device 33 and the output device 34 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0046] The processor 31 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.

[0047] The memory 32 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the control method in the embodiment of the present disclosure. The processor 31 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 32, that is, the maintenance scheduling method of the armored equipment in the above method embodiment is realized.

[0048] The memory 32 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 32 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 32 may optionally include a memory remotely arranged relative to the processor 31, 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.

[0049] The input device 33 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 34 can include display devices such as a display screen.

[0050] One or more modules are stored in the memory 32, and when executed by one or more processors 31, the execution is as follows: Figure 1 The method shown.

[0051] 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.

[0052] 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 maintenance scheduling method for armored equipment, characterized in that: include: Identify all current repair tasks after armored equipment loss; Use hypergraph generative adversarial network to assign corresponding weights to each current maintenance task; Based on each current maintenance task and the corresponding weight, determine the maintenance time corresponding to each current maintenance task, and determine the weighted completion time of all current maintenance tasks; The flood optimization algorithm is used to minimize the weighted completion time of all current maintenance tasks as the optimization goal, and the optimal maintenance sequence of all current maintenance tasks after optimization is determined.

2. The method according to claim 1, characterized in that The method of using a hypergraph to generate an adversarial network to assign a corresponding weight to each current maintenance task includes: Initializing a hypergraph generative adversarial network, using a steepest descent method including momentum to preliminarily train the hypergraph generative adversarial network in a parallel training manner, and obtaining a hypergraph generative adversarial network after the preliminary training, wherein the hypergraph generative adversarial network uses an S-type function as a transfer function; Input the maintenance workload ratio and maintenance cycle of each historical maintenance task into the hypergraph generative adversarial network after the preliminary training, output the result, and determine the error according to the results of two consecutive outputs, correct the hidden layer weights and thresholds of the hypergraph generative adversarial network after the preliminary training based on the error, and repeat the training; When the training is repeated until the sum of squares of the errors is less than or equal to a preset error tolerance, or when the number of repeated training reaches a preset maximum number of iterations, the training is stopped to obtain a hypergraph generative adversarial network after repeated training; By analyzing and comparing the maintenance workload ratio and maintenance cycle of each historical maintenance task, the accuracy of the hypergraph generative adversarial network after repeated training is determined, and the hypergraph generative adversarial network after repeated training is tested to obtain the weight corresponding to each current maintenance task of the armored equipment; According to the normal maintenance state or the emergency maintenance state of the armored equipment, the current maintenance task of the armored equipment and the change of the weight corresponding to the current maintenance task are determined.

3. The method according to claim 2, characterized in that in, The step of inputting the maintenance workload ratio and maintenance cycle of each historical maintenance task into the hypergraph generative adversarial network after the preliminary training is completed includes: Normalizing the maintenance workload ratio and maintenance cycle of each historical maintenance task to obtain normalized historical maintenance data, and inputting the normalized historical maintenance data into the hypergraph generative adversarial network after the preliminary training; After obtaining the hypergraph generative adversarial network after repeated training, the method further includes: The normalized historical maintenance data is denormalized to obtain the maintenance workload ratio and maintenance cycle of each historical maintenance task.

4. The method according to claim 2, characterized in that: The hypergraph generative adversarial network is an interactive hyperedge neuron module or MRL-AHF.

5. The method according to claim 4, characterized in that When the hypergraph generative adversarial network is an interactive hyperedge neuron module, the interactive hyperedge neuron module is used as a generator to capture the complex relationship between data, and the discriminator is MLP; Determine the characteristics of the nodes in the l+1th layer according to the following formulas and the characteristics of the hyperedge at layer l+1 : ， in, is the activation function, is the hypergraph incidence matrix, yes The transpose of is the feature of the hyperedge at layer l, is the feature of the node in layer l, is the weight matrix of the hyperedge at layer l, is the weight matrix of the nodes in layer l, is a hyperparameter; The nodes in the last layer are used as the weights corresponding to the current maintenance task.

6. The method according to claim 4, characterized in that When the hypergraph generative adversarial network is MRL-AHF, the potential representation is sent to encoders EA and EB respectively to obtain the representation and , and the two generated hypergraphs use the incidence matrix and Indicates that hypergraph fusion is achieved through adversarial training strategy to obtain vertex features : ， in, and They are and The vertex degree matrix of , , and They are and The hyperedge degree matrix of yes The transpose of is the weight matrix; The features of the vertices are used as the weights corresponding to the current maintenance task.

7. The method according to claim 2, characterized in that The determining, according to the normal maintenance state or the emergency maintenance state of the armored equipment, the current maintenance task of the armored equipment and the change of the weight corresponding to the current maintenance task includes: When the armored equipment is in a normal maintenance state, the first objective function is established according to the following formula: ， The first constraint is: ， Among them, the first objective function is the weighted completion time of all current maintenance tasks under normal maintenance status, is the total number of current maintenance tasks, It is the first in the maintenance sequence under normal maintenance status. The weight of the maintenance task, The maintenance sequence is completed in normal maintenance state. The cumulative completion time of each maintenance task is It is the first in the maintenance sequence under normal maintenance status. Maintenance time for each maintenance task; Indicates Is the maintenance task the first Maintenance equipment, Indicates that the i-th maintenance task is to repair the Maintenance equipment, Indicates This maintenance task is not the first Maintenance equipment; For maintenance The time to repair the equipment, For maintenance The weight of the maintenance equipment, is the maintenance time of the mth maintenance task in the maintenance sequence; The first constraint conditions include: only one maintenance device can be maintained at a time, and a maintenance task can only have one position in the maintenance sequence.

8. The method according to claim 7, characterized in that When the armored equipment is in an emergency maintenance state, the second objective function is established according to the following formula: ， The second constraint is: ， Among them, the second objective function is the weighted completion time of all current maintenance tasks in the emergency maintenance state, is the weight of the i-th maintenance task in the maintenance sequence under emergency maintenance status, is the cumulative completion time of the first i maintenance tasks in the maintenance sequence under emergency maintenance status, is the maintenance time of the i-th maintenance task in the maintenance sequence under emergency maintenance status, is the maintenance time of the mth maintenance task in the maintenance sequence under emergency maintenance status; Indicates whether the maintenance method of the i-th maintenance task is k, Indicates that the maintenance method of the i-th maintenance task is k, Indicates that the maintenance method of the i-th maintenance task is not k, k=0 indicates normal maintenance, k=1 indicates major maintenance, and k=2 indicates minor maintenance; and The time for major repairs and the time for minor repairs are respectively and are the volatility of maintenance task weights caused by overhaul and minor repair, respectively; a and b are the minimum and maximum loss degrees of maintenance equipment for overhaul, respectively; e and f are the minimum and maximum loss degrees of maintenance equipment for minor repair, respectively. is the loss degree of the i-th maintenance task in the maintenance sequence under emergency maintenance status; The second constraint conditions include: only one maintenance equipment can be repaired at a time, a maintenance task can only have one position in the maintenance sequence, the maintenance task in the maintenance sequence is one of normal maintenance, overhaul and minor repair, the degree of wear and tear of the maintenance equipment for overhaul of any maintenance task in the maintenance sequence under emergency maintenance conditions is in the range of [a,b], and the degree of wear and tear of the maintenance equipment for minor repair of any maintenance task in the maintenance sequence under emergency maintenance conditions is in the range of [e,f].

9. The method according to claim 1, characterized in that: The flood optimization algorithm is used to optimize the minimization of the weighted completion time of all current maintenance tasks as the optimization goal, and the optimal maintenance sequence of all current maintenance tasks after optimization is determined, including: The quantity, time and weight of all current maintenance tasks are input into the flood optimization algorithm, and the optimal maintenance sequence of all current maintenance tasks and the maintenance time and weight corresponding to each current maintenance task in the optimal maintenance sequence are output.

10. A maintenance and scheduling device for armored equipment, characterized in that: include: A task determination unit, which is used to determine all current repair tasks after the loss of armored equipment; A weight assignment unit, used to assign a corresponding weight to each current maintenance task using a hypergraph generative adversarial network; A time determination unit, used to determine the maintenance time corresponding to each current maintenance task based on each current maintenance task and the corresponding weight, and determine the weighted completion time of all current maintenance tasks; The optimization unit is used for adopting a flood optimization algorithm, optimizing by taking the minimization of the weighted completion time of all current maintenance tasks as the optimization target, and determining the optimal maintenance sequence of all current maintenance tasks after optimization.

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