Equipment guarantee efficiency comprehensive evaluation method and related device

Abstract

The invention discloses an equipment guarantee efficiency comprehensive evaluation method and a related device, and relates to the technical field of equipment guarantee efficiency comprehensive evaluation, and the method comprises the steps: employing a Monte Carlo simulation method to carry out the simulation of a work process and a maintenance process of an equipment completion task for multiple times, and determining a reliability evaluation result, an availability evaluation result, a supportability evaluation result and a maintainability evaluation result according to results of multiple simulation, and determining the equipment support efficiency based on the reliability evaluation result, the availability evaluation result, the supportability evaluation result and the maintainability evaluation result. According to the method, evaluation results in multiple aspects can be efficiently determined, the equipment support efficiency is further determined, the comprehensive evaluation efficiency of the equipment support efficiency can be improved, and compared with a manual analysis mode, the accuracy and reliability can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of comprehensive evaluation of equipment support effectiveness, and in particular to a comprehensive evaluation method for equipment support effectiveness based on Monte Carlo simulation method and hierarchical analysis method and related devices. Background Art

[0002] System effectiveness is the degree or capability of a system to complete specified tasks under specified conditions and achieve the expected goals. From the definition, it can be seen that the concept of system effectiveness includes five elements: system, specified conditions, specified tasks, expected goals, and the degree or capability of formation. Based on this, equipment support effectiveness refers to the ability of the support system to ensure that the equipment meets the requirements of equipment integrity and mission continuity under the expected or specified tasks or use conditions. Indicators such as equipment integrity rate, mission success, and operational availability are the expected goals of equipment support effectiveness and are the top-level measurements of the level of general quality characteristics of equipment.

[0003] Since there are many factors that affect the effectiveness of equipment support, traditional evaluation methods mostly rely on manual analysis and are inefficient. Summary of the invention

[0004] The purpose of this application is to provide a method and related device for comprehensive evaluation of equipment support effectiveness, which can improve the efficiency of comprehensive evaluation of equipment support effectiveness.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for comprehensive evaluation of equipment support effectiveness, the method comprising:

[0007] Use the Monte Carlo simulation method to simulate the work process and maintenance process of the equipment to complete the task multiple times, and determine the reliability assessment results, availability assessment results, supportability assessment results and maintainability assessment results based on the results of multiple simulations;

[0008] Based on the reliability assessment results, availability assessment results, supportability assessment results and maintainability assessment results, a comprehensive assessment of the equipment support effectiveness is conducted to determine the equipment support effectiveness.

[0009] In a second aspect, the present application provides a device for comprehensive evaluation of equipment support effectiveness, the device comprising:

[0010] A simulation module is used to simulate the working process and maintenance process of the equipment to complete the task multiple times using the Monte Carlo simulation method, and determine the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results based on the results of the multiple simulations;

[0011] The comprehensive evaluation module is used to conduct a comprehensive evaluation of the equipment support effectiveness based on the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results to determine the equipment support effectiveness.

[0012] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for comprehensive evaluation of equipment support effectiveness.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for comprehensive evaluation of equipment support effectiveness.

[0014] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned equipment support effectiveness comprehensive evaluation method.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects:

[0016] The present application provides a method and related devices for comprehensively evaluating equipment support effectiveness, which utilizes the Monte Carlo simulation method to simulate the working process and maintenance process of the equipment to complete the task multiple times, and determines the reliability evaluation results, availability evaluation results, supportability evaluation results, and maintainability evaluation results based on the results of the multiple simulations. The evaluation results of multiple aspects can be efficiently determined, and the equipment support effectiveness can be further determined based on the evaluation results of multiple aspects. The efficiency of the comprehensive evaluation of equipment support effectiveness can be improved, and compared with the manual analysis method, the accuracy and reliability can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 This is an application environment diagram of a comprehensive evaluation method for equipment support effectiveness provided in Example 1 of the present application.

[0019] Figure 2 A flowchart of a method for comprehensive evaluation of equipment support effectiveness provided in Example 1 of the present application.

[0020] Figure 3 A schematic diagram of the overall process of a method for comprehensive evaluation of equipment support effectiveness provided in Example 1 of the present application.

[0021] Figure 4 A schematic diagram of the principle of the comprehensive evaluation method of equipment support effectiveness based on the Monte Carlo simulation method and the hierarchical analysis method provided in Example 1 of the present application.

[0022] Figure 5 A schematic diagram of the idea of ​​establishing an index system for comprehensive evaluation of equipment support effectiveness provided in Example 1 of the present application.

[0023] Figure 6 A schematic diagram of the security organization structure provided in Example 1 of the present application.

[0024] Figure 7 Schematic diagram of the hierarchical security activities provided in Example 1 of the present application.

[0025] Figure 8 A schematic diagram of the modeling architecture of the performance assurance simulation model provided in Example 1 of the present application.

[0026] Fig. 9 A schematic diagram of a sub-model of the performance assurance simulation model provided in Example 1 of the present application.

[0027] Fig.10 This is an example diagram of the activity network diagram provided in Example 1 of the present application.

[0028] Fig.11 This is an example diagram of multiple selections and multiple activities provided in Example 1 of the present application.

[0029] Fig.12 This is an example diagram of a multiple-choice activity provided in Example 1 of the present application.

[0030] Fig.13 This is an example diagram of the call reliability model provided in Example 1 of the present application.

[0031] Fig.14 This is an example diagram of transferring to a sub-activity network provided in Example 1 of the present application.

[0032] Fig.15 A schematic diagram of the hierarchical system of the analytic hierarchy process provided in Example 1 of the present application.

[0033] Fig.16 Schematic diagram of the calculation principle of the hierarchical analysis method provided in Example 1 of the present application.

[0034] Fig.17 A schematic diagram of the hierarchical model provided in Example 1 of the present application.

[0035] Fig.18 A schematic diagram of the functional modules of a device for comprehensive evaluation of equipment support effectiveness provided in Example 2 of the present application.

[0036] Fig.19 A schematic diagram of the structure of a computer device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] Example 1

[0039] The equipment support effectiveness comprehensive evaluation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send a pending comprehensive evaluation request (used to request a comprehensive evaluation of the equipment support effectiveness) to the server. After the server receives the pending comprehensive evaluation request, for the pending comprehensive evaluation request, the server uses the Monte Carlo simulation method to simulate the working process and maintenance process of the equipment to complete the task multiple times, and determines the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results based on the results of multiple simulations; based on the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results, the equipment support effectiveness is comprehensively evaluated to determine the equipment support effectiveness. The server can feedback the obtained equipment support effectiveness for the comprehensive evaluation request to the terminal.

[0040] In addition, in some embodiments, the equipment support effectiveness comprehensive evaluation method can also be implemented independently by a server or a terminal. For example, the terminal can directly process the comprehensive evaluation request to be processed, or the server can obtain the comprehensive evaluation request to be processed from the data storage system and process the comprehensive evaluation request to be processed.

[0041] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0042] In an exemplary embodiment, Figure 2 As shown, a method for comprehensive evaluation of equipment support effectiveness is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The following steps are used to illustrate the server in the example.

[0043] Step S1, using the Monte Carlo simulation method to simulate the working process and maintenance process of the equipment to complete the task multiple times, and determine the reliability assessment results, availability assessment results, security assessment results and maintainability assessment results based on the results of the multiple simulations.

[0044] Step S2, comprehensively evaluating the equipment support effectiveness based on the reliability evaluation results, the availability evaluation results, the supportability evaluation results and the maintainability evaluation results, and determining the equipment support effectiveness.

[0045] By implementing the above steps S1 to S2, this embodiment uses the Monte Carlo simulation method to simulate the working process and maintenance process of the equipment to complete the task multiple times, and determines the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results based on the results of multiple simulations, which can efficiently determine the evaluation results of multiple aspects, and further determine the equipment support effectiveness based on the evaluation results of multiple aspects, solving the problem of low efficiency in traditional evaluation methods. At the same time, compared with the manual analysis method, it can improve accuracy and reliability.

[0046] This embodiment provides a comprehensive evaluation method for equipment support effectiveness based on Monte Carlo simulation method and analytic hierarchy process, which is widely used in the field of equipment support to improve the efficiency, accuracy and reliability of equipment support effectiveness evaluation. Among them, the Monte Carlo simulation method is a method based on random sampling, which is used to estimate the behavior, performance or results of a complex system through a large number of random samples, and is particularly suitable for dealing with problems related to uncertainty and risk. The analytic hierarchy process shows its unique superiority when dealing with a complex system composed of many interrelated and mutually constrained factors that often lacks quantitative data. At present, the analytic hierarchy process has been widely used in the determination of weight coefficients and the optimization of schemes. There are many factors that affect the effectiveness of equipment support. In traditional evaluation methods, single indicators are usually used to evaluate the effectiveness of equipment support. However, single indicators can only evaluate the effectiveness of equipment support from a certain dimension, and cannot evaluate the effectiveness of equipment support from the overall perspective. It is difficult to choose between different equipment support schemes and cannot provide suggestions in the corresponding decision-making links. Moreover, most traditional evaluation methods rely on manual experience and qualitative analysis, which are inefficient and lack systematicity, scientificity and objectivity. Therefore, it is particularly important to use an effective comprehensive evaluation method to quantitatively evaluate the equipment support effectiveness. In this embodiment, when comprehensively evaluating the equipment support effectiveness, the Monte Carlo simulation method and the analytic hierarchy process are combined to improve the efficiency, accuracy and reliability of the evaluation, ensuring that the evaluation of the equipment support effectiveness is systematic, scientific, objective, effective, accurate and reliable.

[0047] This embodiment can be applied to various equipment, such as aircraft and vehicles executing formations.

[0048] like Figure 3 and Figure 4 As shown, the comprehensive evaluation method of this embodiment is further introduced through the following five steps:

[0049] (1) Establish an indicator system for comprehensive evaluation of equipment support effectiveness.

[0050] The establishment of an index system for comprehensive evaluation of equipment support effectiveness is an important part of comprehensive evaluation of equipment support effectiveness. In order to avoid the problem of arbitrariness and subjectivity in selecting evaluation indicators, it is necessary to conduct field research, clarify the purpose of comprehensive evaluation of equipment support effectiveness, deeply analyze the main factors affecting equipment support effectiveness, and fully consult relevant literature and repeatedly seek expert opinions. The basic ideas for establishing an index system for comprehensive evaluation of equipment support effectiveness are as follows: Figure 5 As shown in the figure, finally 51 evaluation indicators in four categories, namely reliability, availability, security and maintainability, were formed, as shown in Tables 1 to 4 below.

[0051] Table 1 Reliability index

[0052]

[0053] In Table 1, regeneration tasks refer to tasks that occur suddenly during the execution of preset tasks, and the rate of all tasks that can be executed refers to the proportion of tasks that can be executed by the equipment to the total tasks.

[0054] Table 2 Availability indicators

[0055]

[0056] In Table 2, the intact rate refers to the proportion of intact equipment to all equipment.

[0057] Table 3 Security indicators

[0058]

[0059]

[0060] In Table 3, the satisfaction rate refers to the probability of being able to meet the needs, and the utilization rate refers to the proportion of the used quantity to the total quantity.

[0061] Table 4 Maintainability index

[0062]

[0063] (2) Construct a security effectiveness simulation model.

[0064] The basic principles of modeling the performance assurance simulation model include:

[0065] 1) Simulation modeling should at least include task unit modeling, equipment modeling, task modeling, support resource modeling, support organization modeling and support process modeling.

[0066] 2) Mission unit modeling should describe the type and quantity of deployed equipment.

[0067] 3) Equipment modeling needs to objectively and truly reflect the actual configuration of the equipment and the general quality characteristics of its components. The granularity of modeling depends on the stage of the equipment when the assessment is carried out and the data that can be obtained at that stage.

[0068] 4) Mission modeling should cover various mission scenarios in the top-level requirements of equipment.

[0069] 5) Support resource modeling includes the types and quantities of support resources, and should cover support resources such as spare parts, support equipment, and human resources as much as possible.

[0070] 6) Support process modeling needs to cover activities such as equipment use support, preventive maintenance and corrective maintenance.

[0071] This embodiment first collects data, specifically collecting various types of data, including: equipment data, mission data, support resource data, support organization data, and support process data.

[0072] Equipment data includes: equipment types and quantities in the mission unit, equipment usage data, equipment models, equipment configurations and their RMS (Reliability, Maintainability, Supportability) attribute data, such as MTBF (Mean Time Between Failure), fault isolation rate, fault detection rate, etc.

[0073] Mission data includes: mission type, mission duration, mission phase, mission cycle, required equipment quantity, mission equipment list, success criteria, etc. The mission equipment list includes the model and quantity of the support equipment required for the mission.

[0074] The resource security data include: spare parts quantity, matching quantity, maintenance objects, usage time, main purpose, deployment method, etc.

[0075] Support organization data includes: support sites and site maintenance levels, etc. Support organizations generally adopt a two-level or three-level maintenance support model. The three-level maintenance support model is as follows: Figure 6 As shown in Figure 1, the claiming and supply of support resources are carried out between different and adjacent support sites.

[0076] The guarantee process data is as follows:

[0077] The assurance process is divided into three levels according to the assurance tasks, assurance activities and assurance operations. The assurance process data collection needs to be refined to each assurance operation level, such as Figure 7 shown.

[0078] 1) Support tasks: configuration conversion support tasks, operation support tasks, maintenance support tasks, and training support tasks.

[0079] 2) Support activities: The first-level support process has an initial definition in different models, but users can reset it according to the actual process. For example, support tasks include support activities such as pre-dispatch preparation, post-dispatch inspection and re-dispatch preparation, and maintenance support tasks include support activities such as preventive maintenance support and corrective maintenance support.

[0080] 3) Assurance Operations: The next level of assurance activities, the modeling of assurance operations is open and can be customized by users according to their needs.

[0081] This embodiment further establishes a support effectiveness simulation model, specifically using various types of collected and processed data to construct the support effectiveness simulation model. The support effectiveness simulation model includes: equipment model, task model, support resource model, support organization model, support process model and evaluation model of single evaluation index. The support effectiveness simulation model is as follows: Figure 8 and Fig. 9 shown.

[0082] It should be noted that modeling can be carried out after obtaining the data. The modeling steps include: first, establishing an equipment model; on this basis, the task model, support resource model, and support organization model can be established in parallel; then the support process model is established; then the support process model is associated with the task model, and at the same time, the support resource model is associated with the support process model. The final form of the model is a project file (*.PP), and the content of the project file is a data structure of a support effectiveness simulation model.

[0083] (3)Simulation.

[0084] based on Figure 8 and Fig. 9 The support effectiveness simulation model shown in the figure has the following specific steps of simulation: using the processed equipment use data and maintenance support data as the input of the simulation software based on the Monte Carlo simulation method, the equipment use data mainly includes equipment use time, equipment load time, fault records and other data, and the maintenance support data mainly includes maintenance records, spare parts management, maintenance plan and other data, establishing the mapping of equipment, support system and other elements in the real scene in the computer program, determining the equipment failure distribution input by the Monte Carlo simulation method, simulating the equipment workflow and maintenance process under the preset task, extracting the simulation result data, and determining the values ​​of multiple evaluation indicators based on the result data of multiple simulations. The equipment failure distribution is obtained according to statistical laws, and the Monte Carlo simulation method is a discrete simulation method. For example, it is known that the failure data follows the normal distribution probability, and the Monte Carlo simulation method is used to perform normal discretization sampling to obtain a failure data (failure component and failure time), and simulation is performed based on the failure data. After multiple simulations, the value of each evaluation indicator is obtained. For example, the simulation outputs the result of whether the task is successful. At this time, the task success rate is equal to the ratio of the number of successful tasks in multiple simulations to the total number of simulations. Other evaluation indicators are also determined in this way and will not be described here one by one.

[0085] Based on the Monte Carlo simulation method, the support effectiveness simulation model is run to obtain the value of the single indicator of equipment support effectiveness, which includes: based on the Monte Carlo simulation method, according to the collected and processed usage data and maintenance support data, the fault trigger mechanism and maintenance activity process are constructed, the simulation running time and number are defined, and the triggering and maintenance activities of equipment faults are simulated by discrete event driving through the support effectiveness simulation model. After multiple simulation runs, the value of the single indicator of equipment support effectiveness within a certain time interval is statistically obtained.

[0086] During the simulation process, the equipment allocation simulation algorithm is used to simulate equipment allocation: the status of equipment is divided into three types: available, cocked, and in use. In use includes equipment that is performing tasks or in maintenance. Equipment in both available and in-use states can participate in task allocation. Equipment allocation is a dynamic process. When the simulation starts, the simulation program (also known as the simulation system, simulator, simulation software, etc.) starts to perform allocation according to the task profile information. The number of equipment required for the task includes the minimum number, the maximum number, and the spare number. Before the task is dispatched, the simulation program will allocate the maximum number of equipment and spare equipment for the task as much as possible. If the equipment allocated for the first time is less than the sum of the maximum number and the spare number, the task will enter the waiting queue until equipment is released from other tasks to continue allocation. If the minimum number of equipment is not allocated before the cancellation time parameter of the task, the task will be cancelled, and the equipment allocated to the task will be returned to the available equipment pool. If a fault is found in the equipment during the pre-dispatch inspection work, and it has not been completed before the task is cancelled or dispatched, the equipment will be released after the troubleshooting is completed. Spare equipment is used to replace other equipment on the same mission when faults are found before deployment. If there are no problems with other equipment on the same mission, the spare equipment will be released after the mission deployment time and will participate in pre-deployment preparations.

[0087] During the simulation process, the fault occurrence simulation algorithm is used to simulate the fault occurrence process: when encountering a dispatch mission or other mission, the simulation program will call the fault generator to randomly simulate the occurrence of faults in equipment or equipment systems or components in the system. Each type of faulty component has a fault timer, which tells the simulation program when the fault will occur. After each task is executed, the fault timer will subtract the execution time of the task. When the fault timer reaches zero, the fault generator will take two measures:

[0088] 1) Generate a fault and execute the predefined troubleshooting activities in the simulation. For different components, the time unit of the fault timer is different. For example, the navigation system of the equipment will be used all the time during the dispatch process, so the unit of the fault timer is the dispatch hour. For other components, the time unit also includes the number of uses, calendar time, etc.

[0089] 2) According to the equipment failure distribution (such as exponential distribution), a new random failure occurrence time is calculated using the Monte Carlo simulation method, and the failure timer is reset.

[0090] During the simulation process, the operation and support activities execute simulation algorithms: During the simulation, the activity network diagram is used to simulate the equipment formation operation and support process. The simulation program tracks the position of each equipment in the activity network diagram, and also selects the activities to be performed by each equipment based on the reliability data of the equipment's system or components and other factors.

[0091] Here, the activity types of the activity network diagram are introduced:

[0092] In the activity network diagram, each activity has an activity name, each interruption point must also be marked by numbers or letters, and the activity line and interruption point list must be fully defined. In addition, each specific support activity should also define an identifier so that the computer program can determine the specific activity type of the equipment, system, and component. This embodiment specifically defines different activity types and their identifiers, as follows: D-execution activity, S-execution of activities at an agreed time, B-activities that are executed only after the preceding activities are completed, A-multiple selections of multiple activities, G-multiple selections of multiple (at least one) activities, E-multiple selections of one activity, F-calling reliability model, TD-calling testability model, M-calling maintainability model, C-transferring to sub-activity network, H-activities that are executed when there is no failure. Each identifier defines a type of activity, and some identifiers require additional information. The meanings and required information of these identifiers are described as follows.

[0093] 1) D-Execution activities

[0094] The identifier D indicates that the activity is executed as soon as it is entered, and its selection does not require any additional conditions. Fig.10 As shown, the identifier D is usually used to execute tasks sequentially.

[0095] 2) S-Perform activities at the agreed time

[0096] The identifier S represents an activity with a start time caused by a sortie or other activity affected by the sortie activity data. When encountering this type of activity, the simulation program will select the corresponding sortie activity data to determine the earliest time that the activity can start. If the previous activity has been completed and other conditions are met (such as sufficient available equipment), the activity can be started.

[0097] 3) B-Activities that are performed only after the predecessor activities are completed

[0098] The identifier B indicates that all the preceding parallel activities should be completed before this activity is executed. Before the activity with identifier B starts, the simulation program will determine whether the preceding activities connected to it are completed. If there is no such identifier, any activity in the multiple parallel activities will be completed, and the subsequent activities will be executed, resulting in multiple wrong executions.

[0099] 4) A-Multiple selections and multiple activities

[0100] The identifier A indicates that multiple activities are selected from multiple parallel activities. During operation, if an activity with identifier A is found, each activity parallel to it will be checked in sequence, and whether the activity is selected will be determined based on the value of its probability factor. For example, if the probability factor is 0.1, the activity it represents will be selected once every 10 times on average. Compare the probability factor and generate a random number between 0 and 1 to select the activity. If the random number is less than or equal to the probability factor, the activity may be selected, otherwise, it will not be selected. In this way, the activity actually selected is random.

[0101] like Fig.11 As shown in the figure, each parallel activity has a different probability factor. According to the calculation results, it is possible that none of the activities in the parallel activities will be selected, one will be selected, multiple will be selected, or all will be selected. However, after a long period of operation, the frequency of its selection must be close to its probability factor. The identifier A is often used to determine which of the systems or components fail. This identifier expresses the uncertainty of the real world, that is, it is impossible to know in advance which system or component fails. As expected, the above probability factors should be statistically calculated and determined from a large amount of failure data.

[0102] 5) G-Multiple-select (select at least one) activities

[0103] Type G activities are the same as Type A activities, except that more than one activity must be selected. If no activity has the probability to be selected after the random operation, the simulation program re-executes the random operation until more than one activity can be selected.

[0104] 6) E-Multiple Choice Activities

[0105] The identifier E indicates that among multiple parallel activities, only one activity is selected based on the activity probability factor. The selection process is similar to type A. Each activity has its probability factor, and the generated random number will be compared with the probability factor. The activity with the earliest probability factor less than or equal to the random number will be selected. The sum of all probability factors of parallel activities must be 1 to ensure that one of the multiple activities is selected.

[0106] The description of the identifier E is as follows Fig.12 Note that all probability factors sum to 1. This indicator is often used to decide whether a component is repaired at the crew level or at the depot level. The indicator E simulates the process of giving a component the chance of being repaired at both the crew level and the depot level.

[0107] 7) F-call reliability model

[0108] When the equipment is in the dispatched state, parts may fail, and the failure must be repaired before the equipment can perform the mission. This failure and other failures are triggered by the reliability model, and the identifier F indicates the call to the reliability model. For a given activity network diagram, the failure rate determines whether its activity will be selected.

[0109] like Fig.13 As shown in the figure, it shows the execution process using the identifier F. For example, a piece of equipment that performs a specific activity, its hydraulic pump (component H1) fails and must be replaced after the dispatch. To execute the work activities that include removing, replacing and repairing the pump to be completed by the hydraulic system technician, there must be an F-type activity first: Dispatch to the hydraulic system. This activity tells the model that when the pump fails, the dispatch to the hydraulic system and the subsequent repair activities will be performed; if the pump does not fail, those activities will not be selected.

[0110] 8)TD-call test model

[0111] Call the test model to perform fault diagnosis and isolation.

[0112] 9) M-call maintainability model

[0113] Call the maintainability model to troubleshoot product-specific failure modes.

[0114] 10) C- Transfer to sub-activity network

[0115] When an activity is represented by the identifier C, it will reference the pre-built sub-activity network. The activity with identifier C is actually virtual. It has no time and resource requirements, and only represents the entrance to the sub-activity network. The activities of the sub-activity network are executed in sequence. When all the activities of the sub-activity network are completed, the simulation program will return to the main network and immediately execute other activities following the C activity. The description of identifier C is as follows Fig.14 .

[0116] Subnets are constructed in the same way as mainnet activities. All activity type identifiers can be used, including the identifier C itself. Thus, a mainnet can be transferred into subnets, which can be transferred into sub-subnets in sequence, and so on. This feature allows activity network diagrams to be constructed hierarchically and allows hierarchically constructed subnets to be reused multiple times in a mainnet. In some specific uses, a subnet can even call itself, for example, to repeatedly repair a possibly faulty component, where only one repair can correct the fault.

[0117] 11)H-Activities to be performed when no failure occurs

[0118] H type activities are just related to F type. If a related clock is zero, the H type activity is blocked and cannot be executed. Otherwise, the H type activity will be executed. H type can usually be used to establish "IF" conditional branch structures and repeated loop structures.

[0119] Here, the execution process of the activity network diagram is introduced:

[0120] The execution entry of the activity network diagram is defined by the dispatch activities to be performed. For each equipment that performs the dispatch activities, the simulation program has a dedicated data record. When the starting activity in the activity network diagram is selected, the next activity will be registered in the equipment record. When a new activity is selected, its next activity is registered in the equipment record. According to this method, the position of the equipment in the activity network diagram can be known at any time. Each activity is assigned a unique number, and the activity number in the equipment record determines which activity in the entire activity network diagram will occur. When the activity network diagram indicates that a part on the equipment needs to be replaced, a new record will be constructed to represent the replaced part. If a part is separated from the equipment, a record of the part will be generated.

[0121] When an activity is executed, resource requirements are determined. Resource availability determines whether an activity can be started or delayed. Whenever an activity is completed, the simulator must re-determine what the next activity is. This process continues until the equipment, system, or component is ready to be used or ready to be used. For multiple activities that meet the selection criteria at the same time, the start order is determined based on the activity priority and the activity execution time. The activity with the highest priority will be executed first, and the activity with the shortest execution time will be executed first among the same priority.

[0122] It should be noted that the activity network diagram is created by the user according to the order of task activities, and different tasks correspond to different activity network diagrams.

[0123] At this time, in this embodiment, the Monte Carlo simulation method is used to perform multiple simulations on the working process and maintenance process of the equipment to complete the task, specifically including:

[0124] 1) Obtain equipment data, mission data, support resource data, support organization data and support process data.

[0125] 2) Construct an equipment model based on equipment data, construct a task model based on task data, construct a support resource model based on support resource data, construct a support organization model based on support organization data, and construct a support process model based on support process data; combine the equipment model, task model, support resource model, support organization model and support process model into a support effectiveness simulation model.

[0126] 3) Determine equipment failure distribution, equipment workflow and equipment maintenance process based on equipment usage data and maintenance support data.

[0127] 4) For each simulation, the faulty components and fault time are determined based on the equipment fault distribution. The faulty components and fault time are used as inputs. According to the equipment work flow and equipment maintenance process, the support effectiveness simulation model is used to simulate the work process and maintenance process of the equipment to complete the task.

[0128] According to the equipment work process and equipment maintenance process, the support effectiveness simulation model is used to simulate the work process and maintenance process of the equipment to complete the task, including:

[0129] 1) Design an activity network diagram based on the equipment workflow and equipment maintenance process.

[0130] 2) According to the activity network diagram, use the support effectiveness simulation model to simulate the work process and maintenance process of the equipment to complete the task.

[0131] Among them, the activity network diagram is composed of multiple activities connected, and the activity types of multiple activities include: execution activities, execution activities at agreed time, activities that are executed only after the predecessor activities are completed, multiple-select multiple activities, multiple-select multiple activities with at least one selected, multiple-select one activity, calling reliability model, calling testability model, calling maintainability model, transferring to sub-activity network and activities executed when there is no failure.

[0132] The reliability evaluation results, availability evaluation results, security evaluation results and maintainability evaluation results are determined based on the results of multiple simulations. The reliability evaluation results include the values ​​of reliability indicators, the availability evaluation results include the values ​​of availability indicators, the security evaluation results include the values ​​of security indicators, and the maintainability evaluation results include the values ​​of maintainability indicators.

[0133] Reliability indicators include mission success rate, regeneration mission success rate, mission reliability, mission dispatch time ratio, mission area occupancy rate, dispatch completion rate, number of dispatch completions and the rate of all tasks that can be executed.

[0134] Availability indicators include operational availability, readiness rate, combat sortie intensity, cumulative number of sorties, daily sortie hours for a single unit, cumulative daily sortie hours and number of sorties for a single unit.

[0135] The support indicators include total maintenance support costs, personnel satisfaction rate, personnel utilization rate, support equipment satisfaction rate, spare parts satisfaction rate, spare parts utilization rate, data satisfaction rate, support equipment utilization rate, data utilization rate, average personnel delay time, cumulative personnel delay time, cumulative personnel application times, cumulative personnel delay times, average support resource delay time, cumulative resource delay time, cumulative resource application times, cumulative resource delay times, cumulative maintenance preparation delay time, cumulative preventive maintenance delay time, cumulative corrective maintenance delay time, average support waiting time, average maintenance waiting time, average pre-dispatch preparation time and average post-dispatch inspection time.

[0136] Maintainability indicators include direct maintenance hours per dispatch hour, cumulative maintenance preparation hours, cumulative maintenance preparation time, direct maintenance hours per dispatch hour, average maintenance preparation time, cumulative preventive maintenance hours, cumulative preventive maintenance time, preventive maintenance hours per dispatch hour, cumulative corrective maintenance hours, cumulative corrective maintenance time, corrective maintenance hours per dispatch hour and average troubleshooting time.

[0137] (4) Establish a hierarchical model to obtain the weights of the index system for comprehensive evaluation of equipment support effectiveness.

[0138] On the basis of the index system for comprehensive evaluation of equipment support effectiveness and the support effectiveness simulation model, starting from the analysis of the index system, the relevant influencing factors and indicators are sorted out and classified, and reasonable weights are given to each important influencing factor and indicator. The weight of the index system needs to be considered from many aspects. For example, how to set the weight, the basis for setting the weight, and whether it is accurate will be the key issues to pay attention to. From a mathematical point of view, the weight setting of the index system belongs to a multi-objective decision analysis method, which quantifies empirical judgment and is a decision-making method that organically combines qualitative analysis and quantitative analysis. This embodiment specifically introduces the hierarchical analysis method. The basic idea of ​​the hierarchical analysis method to solve the problem is: first, according to the nature of the problem and the goal to be achieved, the various influencing indicators of the problem are decomposed, and a hierarchical structure model is formed according to the relationship between the indicators, and then the analysis is carried out layer by layer to obtain the weight of each indicator for the highest level (overall goal). The problem to be solved by the hierarchical analysis method is about the relative importance of the lowest level to the highest level, such as Fig.15 As shown, including:

[0139] Goal layer: Propose overall goals, such as a support process model consisting of the use process and maintenance process.

[0140] Criteria layer: the main influencing factors of the target layer, such as the support resource model composed of support resources and maintenance strategies.

[0141] Indicator layer: Available options, such as task models.

[0142] like Fig.16 As shown, the weights are calculated as follows:

[0143] 1) Construct a judgment matrix and find the maximum eigenvalue and eigenvector

[0144] The key to constructing a judgment matrix is ​​to use what scale to represent the comparison results between elements. Commonly used scaling methods include nominal scaling, ordinal scaling, interval scaling, and ratio scaling. This embodiment constructs a judgment matrix based on a 1-9 ratio scale, compares elements pairwise, and uses the sum method to obtain the eigenvector.

[0145] Table 5 Ratio scale table

[0146]

[0147]

[0148] Suppose a judgment matrix obtained after investigation is:

[0149]

[0150] Among them, a ij The value range is 1 to 9, 1 / 2 to 1 / 9.

[0151] There are many methods for finding the eigenvector of a judgment matrix, such as the power method (eigenroot method or eigenvector method), the sum method, the root method, etc. This embodiment uses the sum method to find the eigenvector of the matrix:

[0152] For the judgment matrix A=(a ij ) n×n , normalized by row, that is have to:

[0153]

[0154] For the matrix Using the formula Sum by row, we get:

[0155]

[0156] right Normalize by row, that is Get the eigenvector:

[0157] ω=(ω1ω2…ω n );

[0158] The elements of this feature vector are the weights of the corresponding factors on the target, as shown in Table 6.

[0159] Table 6. Corresponding weights of criterion layers

[0160]

[0161] 2) Consistency test of judgment matrix

[0162] To determine whether the weights obtained in the previous step are mathematically reasonable, it is necessary to perform a consistency check on the judgment matrix. If the inconsistency of the judgment matrix is ​​within the allowable range, the judgment matrix and the weights obtained are reasonable. Otherwise, the judgment matrix needs to be improved and the weights recalculated. Let λ max is the largest characteristic root of the n-order judgment matrix A, λ max The larger it is than n, the more serious the inconsistency of A is. Therefore, the value of λ-n can be used to measure the degree of inconsistency of A.

[0163] Before finding the weights, the judgment matrix A must be checked for consistency using certain indicators. The following are the steps for the consistency check:

[0164] Calculate the consistency index:

[0165]

[0166] Calculate the random consistency index:

[0167]

[0168] Among them, CI is the consistency index; λ max is the maximum eigenvalue of the judgment matrix A; n is the order of the judgment matrix A; RI is the average random consistency index, which can adopt the values ​​listed in Table 7 below.

[0169] Table 7 Average random consistency index

[0170] n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0171] Consistency test criteria for judgment matrix:

[0172] When the random consistency index CR≤0.1 or CI≤0.1RI, it is generally considered that the consistency of the judgment matrix is ​​acceptable. Otherwise, the element values ​​in the judgment matrix A must be readjusted and recalculated until the consistency requirements are met.

[0173] According to the above method, the weight of each criterion in the criterion layer relative to the total goal in the goal layer and the weight of each indicator in the indicator layer relative to each criterion in the criterion layer can be determined.

[0174] 3) Calculate the composite weight of each layer element to the overall target

[0175] After the consistency check, the weight values ​​of indicators at all levels can be used to calculate the composite weight of each layer of elements to the overall goal. The method is to use the square root method to obtain the composite weight, and finally rank the elements or make further calculations based on the obtained composite weight value.

[0176] After calculating the weight of each layer to the layer above it, we also need to calculate the weight of all factors of the indicator system to the highest layer (total goal), which is called the total ranking of the levels. This process is carried out from the highest level to the lowest level, as follows:

[0177] Criteria level A level m factors A1, A2, …, A m The ranking of the weights of the total target Z is:

[0178] a1,a2,…,a m ;

[0179] The n factors in the indicator layer B layer have an impact on the factor A in the upper criterion layer A layer. j The hierarchical order of is:

[0180] b 1j ,b 2j ,…,b nj (j=1,2,…,m);

[0181] The total hierarchical order of the indicator layer B is:

[0182]

[0183] That is, the weight of the i-th factor in the target layer B to the total target is:

[0184]

[0185] Further consistency check of the total hierarchical order is performed:

[0186] Assume that the index layer B is B1, B2, ..., B n For factor A in the upper layer (layer A) j(j＝1,2,…,m) The hierarchical single-rank consistency index is CI j , the random consistency index is RI j , then the consistency ratio of the total hierarchical order is:

[0187]

[0188] When CR is less than 0.1, it is considered that the hierarchical total ranking has passed the consistency test and has satisfactory consistency. Otherwise, it is necessary to readjust the element values ​​of the judgment matrix with high consistency ratios.

[0189] At this point, the impact of the indicator layer on the target layer is calculated based on the total hierarchical ranking of the indicator layer, and the final decision is made to obtain the weight of each evaluation indicator.

[0190] At this time, in this embodiment, the equipment support effectiveness is comprehensively evaluated based on the reliability evaluation results, availability evaluation results, supportability evaluation results, and maintainability evaluation results to determine the equipment support effectiveness, specifically including:

[0191] 1) Use the analytic hierarchy process to determine the weight of each evaluation index, which includes reliability index, availability index, security index and maintainability index.

[0192] 2) Determine the equipment support effectiveness based on the value and weight of each evaluation indicator.

[0193] The weight of each evaluation indicator is determined by using the hierarchical analysis method, including:

[0194] 1) Construct a hierarchical model; the hierarchical model includes the target layer, the criterion layer and the indicator layer; the target layer includes the comprehensive evaluation indicators of equipment support effectiveness; the criterion layer includes the availability rate, operational reliability, mission success rate, spare parts satisfaction rate and spare parts utilization rate; the indicator layer includes all evaluation indicators.

[0195] Hierarchical model such as Fig.17 shown.

[0196] 2) Use the hierarchical analysis method to determine the weight of each evaluation indicator in the indicator layer compared with the comprehensive evaluation index of equipment support effectiveness in the target layer, and obtain the weight of each evaluation indicator.

[0197] Using the hierarchical analysis method to determine the weight of each evaluation indicator can more objectively determine the weight of each evaluation indicator and improve objectivity.

[0198] (5) Calculate comprehensive evaluation indicators.

[0199] Multiply each individual index of equipment support effectiveness by the weight and sum them up to obtain the comprehensive evaluation index of equipment support effectiveness. The formula is as follows:

[0200]

[0201] Among them, C is the equipment support effectiveness; n is the total number of evaluation indicators; W i is the weight of the i-th evaluation indicator; X i is the value of the i-th evaluation indicator.

[0202] At this time, in this embodiment, the equipment support effectiveness is determined based on the value and weight of each evaluation indicator, specifically including:

[0203] 1) For each evaluation indicator, calculate the product of the value of the evaluation indicator and the weight to obtain the weighted value of the evaluation indicator.

[0204] 2) Calculate the sum of the weighted values ​​of all evaluation indicators to obtain the equipment support effectiveness.

[0205] The present embodiment provides a method for comprehensive evaluation of equipment support effectiveness based on Monte Carlo simulation method and analytic hierarchy process, including establishing a support effectiveness simulation model, Monte Carlo simulation, analytic hierarchy process, comprehensive evaluation and result analysis, specifically constructing a support effectiveness simulation model, using the collected and processed equipment use and maintenance support data as the input of the simulation software based on the Monte Carlo simulation method, establishing a mapping of elements such as equipment and support systems in real scenarios in a computer program, simulating the use and maintenance process of equipment under a task, constructing a fault triggering mechanism and a maintenance activity process based on the Monte Carlo simulation method, defining the simulation running time and number, using discrete events to drive the simulation of equipment fault triggering and maintenance activities, and after multiple simulation runs, statistically analyzing the equipment failures and maintenance activities. Get a set of single indicators of equipment support effectiveness within a certain time interval, establish a hierarchical model from the three levels of target layer, criterion layer and indicator layer, construct a judgment matrix according to the 1-9 scale, compare the elements two by two, use the sum method to find the eigenvector, and perform consistency test on the judgment matrix. If the random consistency index CR≤0.1 or CI≤0.1RI, the consistency of the judgment matrix is ​​acceptable. Use the hierarchical analysis method to distribute weights, use the square root method to find the synthetic weights, and finally rank the advantages and disadvantages or make further calculations based on the obtained synthetic weight values ​​to ensure the scientificity and rationality of the evaluation results. The comprehensive evaluation and result analysis is to multiply the single indicators of support effectiveness by the weights and sum them to obtain the comprehensive indicators of support effectiveness. The innovation of this embodiment mainly lies in the comprehensive evaluation of support effectiveness, which is not only applicable to the field of equipment support, but also can be extended to other fields of equipment support and performance evaluation. Through the combination of Monte Carlo simulation method and hierarchical analysis method, the effective processing of complex system evaluation is realized, which promotes the development of related evaluation technology. This embodiment can be applied in the evaluation of equipment support effectiveness. After data collection and the establishment of a simulation model, the hierarchical analysis method is used to comprehensively analyze various influencing factors, and the equipment support effectiveness evaluation results are quickly obtained. By sorting the weights of factors at each level, decision makers are assisted in formulating reasonable support strategies to improve the combat readiness and task execution capabilities of the formation. Compared with traditional methods, the method of this embodiment is more scientific and accurate, can effectively optimize resource allocation, and improve the evaluation efficiency of equipment support effectiveness. At the same time, through the interpretation of the simulation model, it is possible to better understand and deal with uncertainties in actual operations.

[0206] The present application also provides an application scenario, which applies the above-mentioned equipment support effectiveness comprehensive evaluation method. Specifically, the equipment support effectiveness comprehensive evaluation method provided in this embodiment can be applied in an evaluation scenario, and the evaluation scenario includes an evaluation link and a result display link. The evaluation link is used to comprehensively evaluate the equipment support effectiveness, and the result display link is used to display the equipment support effectiveness to the user. The equipment support effectiveness comprehensive evaluation method provided in this embodiment belongs to the evaluation link.

[0207] Example 2

[0208] Based on the same inventive concept, the embodiment of the present application also provides an equipment support effectiveness comprehensive evaluation device for implementing the equipment support effectiveness comprehensive evaluation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more equipment support effectiveness comprehensive evaluation device embodiments provided below can refer to the limitations of the equipment support effectiveness comprehensive evaluation method above, and will not be repeated here.

[0209] In an exemplary embodiment, Fig.18 As shown, a device for comprehensive evaluation of equipment support effectiveness is provided, and the device for comprehensive evaluation of equipment support effectiveness includes:

[0210] The simulation module M1 is used to simulate the working process and maintenance process of the equipment to complete the task multiple times using the Monte Carlo simulation method, and determine the reliability assessment results, availability assessment results, supportability assessment results and maintainability assessment results based on the results of the multiple simulations.

[0211] The comprehensive evaluation module M2 is used to comprehensively evaluate the equipment support effectiveness based on the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results to determine the equipment support effectiveness.

[0212] Example 3

[0213] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.19As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for comprehensive evaluation of equipment support effectiveness is implemented.

[0214] Those skilled in the art will understand that Fig.19 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0215] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method for comprehensive evaluation of equipment support effectiveness in Example 1 is implemented.

[0216] Example 4

[0217] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the equipment support effectiveness comprehensive evaluation method in Example 1.

[0218] Example 5

[0219] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the equipment support effectiveness comprehensive evaluation method in Example 1 when executed by a processor.

[0220] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0221] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0222] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A comprehensive evaluation method for equipment support effectiveness, characterized in that: The equipment support effectiveness comprehensive evaluation method includes: Use the Monte Carlo simulation method to simulate the work process and maintenance process of the equipment to complete the task multiple times, and determine the reliability assessment results, availability assessment results, supportability assessment results and maintainability assessment results based on the results of multiple simulations; Based on the reliability assessment results, availability assessment results, supportability assessment results and maintainability assessment results, a comprehensive assessment of the equipment support effectiveness is conducted to determine the equipment support effectiveness.

2. The comprehensive evaluation method for equipment support effectiveness according to claim 1 is characterized in that: The Monte Carlo simulation method is used to simulate the working process and maintenance process of the equipment to complete the task multiple times, including: Acquire equipment data, mission data, support resource data, support organization data, and support process data; Build an equipment model based on equipment data, build a task model based on task data, build a support resource model based on support resource data, build a support organization model based on support organization data, and build a support process model based on support process data; combine the equipment model, task model, support resource model, support organization model and support process model into a support effectiveness simulation model; Determine equipment failure distribution, equipment workflow and equipment maintenance process based on equipment usage data and maintenance support data; For each simulation, the faulty components and failure time are determined based on the equipment failure distribution. The faulty components and failure time are used as inputs. According to the equipment work flow and equipment maintenance process, the support effectiveness simulation model is used to simulate the work process and maintenance process of the equipment to complete the task.

3. The comprehensive evaluation method for equipment support effectiveness according to claim 2 is characterized in that: According to the equipment work process and equipment maintenance process, the support effectiveness simulation model is used to simulate the work process and maintenance process of the equipment to complete the task, including: Design activity network diagram based on equipment workflow and equipment maintenance process; According to the activity network diagram, the support effectiveness simulation model is used to simulate the work process and maintenance process of the equipment to complete the task; Among them, the activity network diagram is composed of multiple activities connected, and the activity types of multiple activities include: execution activities, execution activities at agreed time, activities that are executed only after the predecessor activities are completed, multiple-select multiple activities, multiple-select multiple activities with at least one selected, multiple-select one activity, calling reliability model, calling testability model, calling maintainability model, transferring to sub-activity network and activities executed when there is no failure.

4. The comprehensive evaluation method for equipment support effectiveness according to claim 1 is characterized in that: The reliability evaluation result includes the value of the reliability index, the availability evaluation result includes the value of the availability index, the supportability evaluation result includes the value of the supportability index, and the maintainability evaluation result includes the value of the maintainability index; Reliability indicators include mission success rate, regeneration mission success rate, mission reliability, mission dispatch time ratio, mission area occupancy rate, dispatch completion rate, number of dispatch completions and the rate of all missions that can be executed; Availability indicators include operational availability, readiness rate, combat sortie intensity, cumulative number of sorties, daily sortie hours of a single unit, cumulative daily sortie hours and number of sorties of a single unit; The support indicators include total maintenance support costs, personnel satisfaction rate, personnel utilization rate, support equipment satisfaction rate, spare parts satisfaction rate, spare parts utilization rate, data satisfaction rate, support equipment utilization rate, data utilization rate, average personnel delay time, cumulative personnel delay time, cumulative number of personnel applications, cumulative number of personnel delays, average support resource delay time, cumulative resource delay time, cumulative number of resource applications, cumulative number of resource delays, cumulative maintenance preparation delay time, cumulative preventive maintenance delay time, cumulative corrective maintenance delay time, average support waiting time, average maintenance waiting time, average pre-dispatch preparation time and average post-dispatch inspection time; Maintainability indicators include direct maintenance hours per dispatch hour, cumulative maintenance preparation hours, cumulative maintenance preparation time, direct maintenance hours per dispatch hour, average maintenance preparation time, cumulative preventive maintenance hours, cumulative preventive maintenance time, preventive maintenance hours per dispatch hour, cumulative corrective maintenance hours, cumulative corrective maintenance time, corrective maintenance hours per dispatch hour and average troubleshooting time.

5. The comprehensive evaluation method for equipment support effectiveness according to claim 4 is characterized in that: Based on the reliability assessment results, availability assessment results, supportability assessment results and maintainability assessment results, the equipment support effectiveness is comprehensively assessed to determine the equipment support effectiveness, including: The weight of each evaluation index is determined by using the hierarchical analysis method; the evaluation indexes include reliability index, availability index, supportability index and maintainability index; Based on the value and weight of each evaluation indicator, the equipment support effectiveness is determined.

6. The comprehensive evaluation method for equipment support effectiveness according to claim 5 is characterized in that: The weight of each evaluation indicator is determined by using the hierarchical analysis method, including: Construct a hierarchical model; the hierarchical model includes the target layer, the criterion layer and the indicator layer; the target layer includes comprehensive evaluation indicators of equipment support effectiveness; the criterion layer includes intact rate, operational reliability, mission success rate, spare parts satisfaction rate and spare parts utilization rate; the indicator layer includes all evaluation indicators; The weight of each evaluation indicator in the indicator layer compared with the comprehensive evaluation indicator of equipment support effectiveness in the target layer is determined by using the hierarchical analysis method to obtain the weight of each evaluation indicator; Based on the value and weight of each evaluation indicator, the equipment support effectiveness is determined, including: For each evaluation indicator, calculate the product of the value of the evaluation indicator and the weight to obtain the weighted value of the evaluation indicator; Calculate the sum of the weighted values ​​of all evaluation indicators to obtain the equipment support effectiveness.

7. A comprehensive evaluation device for equipment support effectiveness, characterized in that: The equipment support effectiveness comprehensive evaluation device comprises: A simulation module is used to simulate the working process and maintenance process of the equipment to complete the task multiple times using the Monte Carlo simulation method, and determine the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results based on the results of the multiple simulations; The comprehensive evaluation module is used to conduct a comprehensive evaluation of the equipment support effectiveness based on the reliability evaluation results, availability evaluation results, supportability evaluation results and maintainability evaluation results to determine the equipment support effectiveness.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the equipment support effectiveness comprehensive evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for comprehensive evaluation of equipment support effectiveness described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for comprehensive evaluation of equipment support effectiveness described in any one of claims 1 to 6 is implemented.

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