Maintenance support system scheme generation and evaluation method

Abstract

The invention provides a maintenance support system scheme generation and evaluation method for solving the problems of an existing maintenance support system scheme generation method. According to the maintenance support system scheme established based on the OV model and the OPM language in the DoDAF, the maintenance support system scheme is presented in a graphic mode and has the advantages of being clear in logic, fixed in process, clear in continuity and capable of clearly describing tasks, and support is provided for writing the high-quality maintenance support scheme in a document form. Moreover, according to the generated maintenance support system scheme, a visual evaluation model is established in an Anylogic simulation platform, and the operation process of the maintenance support system scheme in a real scene can be simulated and the operation result can be recorded through the visual evaluation model. Based on the operation results, the reasonability of different maintenance support system schemes can be objectively evaluated, and a reference basis is provided for determining an optimal scheme.

Description

Technical Field

[0001] The invention relates to a maintenance guarantee system scheme generation and evaluation method. Background Art

[0002] With the development of information technology, more and more relevant factors need to be considered when solving major problems, and the uncertainty of the external environment of mobile equipment is becoming stronger and stronger. In view of the current characteristics of informatization, it is particularly important to establish a complete combat equipment maintenance support system. How to quickly form support capabilities with limited resources and improve support work efficiency is a new challenge facing the equipment maintenance support system.

[0003] At present, there are mainly the following problems in the method of quickly generating equipment maintenance support plans for different mission scenarios:

[0004] 1. Equipment maintenance and support plans at home and abroad are mainly in the form of documents, which often lead to unclear task descriptions and require a lot of manpower to communicate, and fail to promote communication and collaboration between engineers, stakeholders and customers.

[0005] 2. Maintenance and support plans are often dominated by the subjective consciousness of designers, resulting in unfocused logic and unstable processes.

[0006] 3. The maintenance guarantee plan is inefficient and costly to construct.

[0007] 4. The coherence of the maintenance support plan is unclear and the editing ability is weak.

[0008] 5. There is a lack of evaluation of the maintenance support plan, and it is impossible to determine whether the arrangements of personnel, equipment, work processes, etc. involved are reasonable, and the verifiability of the maintenance support system plan is poor. Summary of the invention

[0009] Aiming at the problems existing in the current maintenance support system solution generation method, the present invention proposes a maintenance support system solution generation and evaluation method.

[0010] The technical solution adopted by the present invention is:

[0011] A maintenance support system solution generation and evaluation method, which is special in that it includes the following steps:

[0012] Step 1: Generate maintenance support system plan;

[0013] Step 1.1: Construct a mission task and concept model based on the transport mission requirements and maintenance support requirements of armored vehicles in different working environments; the mission task and concept model is used to graphically describe the organizational units involved in the transport mission requirements and maintenance support requirements, the relationship between the organizational units, and the activity flow, including the OV-1 model and OV-5b model in DoDAF, and the cross-sectional work content scenario model that describes the work content of important nodes of the mission in the form of a time sequence diagram;

[0014] Step 1.2: Use OPM to establish a state determination model and a maintenance decision model, wherein the state determination model is used to graphically describe the method flow of the state determination link in the OV-5b model, and the maintenance decision model is used to graphically describe the method flow of the maintenance decision link in the OV-5b model;

[0015] Step 2: Build a visual evaluation model;

[0016] Step 2.1: Design the input and output data architecture of the visual evaluation model;

[0017] Step 2.2: Based on the maintenance support system solution generated in step 1, a visual evaluation model is constructed in the AnyLogic simulation platform for simulating the maintenance support system solution and outputting its comprehensive support data; the comprehensive support data includes mission integrity index, equipment availability index, support timeliness index, spare parts utilization index and personnel utilization index;

[0018] Step 3: Use the visual evaluation model constructed in step 2 to simulate different maintenance support system schemes. Each maintenance support system scheme is simulated multiple times to obtain the corresponding comprehensive support data.

[0019] Step 4: Based on the comprehensive support data obtained in step 3, evaluate each maintenance support system solution and take the solution with the highest score obtained by weighted sum as the best solution.

[0020] Furthermore, the input data architecture designed in step 2.1 includes the spare parts category and number of each node, the accompanying spare parts category and number, the support personnel category and number of each node, the MTBF value of each category of spare parts failure in the armored vehicle, the maintenance time and maintenance personnel requirements;

[0021] The output data architecture designed in step 2.1 includes the comprehensive support index data of the maintenance support system plan and the definition and calculation method of each index. The comprehensive support index data includes mission integrity index, equipment availability index, support timeliness index, spare parts utilization index and personnel utilization index.

[0022] Furthermore, the mission integrity index is used to evaluate the ability of armored vehicles to maintain reliability and availability within a specific time frame, and its value is equal to the ratio of the number of mission successes to the number of experiments; the equipment availability index refers to the proportion of armored vehicles that are in a usable state within a period of time, and its value is equal to the ratio of normal working time to the total mission time; the support timeliness index is the reciprocal of the maintenance downtime; the spare parts utilization index is used to measure the spare parts deployment, and its value is equal to the ratio of the total number of spare parts minus the remaining number of spare parts to the total number of spare parts; the personnel utilization index is used to measure the deployment of support personnel, and its value is equal to the ratio of the support personnel used during the maintenance period to the total support personnel.

[0023] Furthermore, step 2.2 is specifically as follows:

[0024] Step 2.2.1: Input the input and output data structures designed in step 2.1 into the simulation interface and Main interface of the AnyLogic simulation platform respectively to create a scenario experiment setting interface;

[0025] Step 2.2.2: Select the main organizational units from the OV-1 model in the maintenance support system solution generated in step 1, and establish the intelligent agents of these main organizational units in the AnyLogic simulation platform, including the armored vehicle intelligent agent ArmoredVehicle, the command center intelligent agent Main, the node support intelligent agent Site and the companion support intelligent agent sePerson;

[0026] Step 2.2.3: Set up a visualization interface for the command center agent Main in the AnyLogic simulation platform, where the visualization interface can display the starting position, transportation route, node location, armored vehicle, and a switch button to switch to other agents;

[0027] Step 2.2.4: Based on the MTBF in the input data architecture designed in step 2.1, establish a random fault component state transition model and a fault quantity function in the AnyLogic simulation platform; the random fault component state transition model is used to simulate the fault conditions of the armored vehicle during the mission; the fault quantity function is used to simulate the number of various components that randomly generate faults;

[0028] Step 2.2.5: Establish a state transition model of the armored vehicle intelligent body ArmoredVehicle in the AnyLogic simulation platform according to the OV-5b model, and establish a warehouse inspection event process model, a fixed-point state inspection process simulation model when arriving at a route node for rest, and a maintenance time event simulation model according to the profile work content assumption model;

[0029] Step 2.2.6: Enter the node guarantee agent Site and the accompanying guarantee agent sePerson, add parameter array sPart to them respectively, and construct the self-developed function repairTime; the parameter array sPart represents the material allocation situation; the self-developed function repairTime is used to calculate the maintenance time of different maintenance guarantee methods, and after the self-developed function repairTime is called, the parameter array sPart in the node guarantee agent Site and the accompanying guarantee agent sePerson will be updated;

[0030] Step 2.2.7: constructing a security index function according to the output data architecture, wherein the security index function is used to calculate each index in the security comprehensive index data;

[0031] The above steps 2.2.3-2.2.6 can be interchanged in order.

[0032] Furthermore, the specific method of inputting the input data structure into the simulation interface of the Anylogic simulation platform in the step 2.2.1 is as follows: first, insert a picture as a background in the simulation interface, drag in multiple edit boxes and do the typesetting, and input the text part in the input data structure into the simulation interface through the text module of the Anylogic simulation platform to obtain the scenario experiment setting interface; then, drag in multiple variables to the simulation interface and name them staffing, sparePart, pNeed, repairT and MTBF respectively, set their data types and initial values, and then establish a link between the initial value and the corresponding edit box in the scenario experiment setting interface constructed in the simulation interface, so as to realize the function of changing the data in the edit box and the synchronous replacement of the variable data when running the visual evaluation model;

[0033] The specific method of inputting the input data architecture into the Main interface of the Anylogic simulation platform in the step 2.2.1 is as follows: drag the Anylogic built-in text file into the Main interface of the Anylogic simulation platform and name it file, and at the same time create a new output.txt file in the model folder location, select the file in the resource option of the file text file property interface and find the newly created output.txt file to realize the association between the output of the visual evaluation model and the external file; finally, drag the Anylogic built-in function into the Main interface and name it printOutput, and compile the function body for the printOutput function to realize the output of the comprehensive indicator guarantee.

[0034] Furthermore, the step 3 is specifically as follows: in the AnyLogic simulation platform, click to start running, and use the visual evaluation model constructed in step 2 to obtain a set of comprehensive guarantee data. The value of each indicator can be viewed in the output.txt file in the folder where the model is located.

[0035] Furthermore, step 4 is specifically as follows:

[0036] Step 4.1: Calculate the evaluation index of each maintenance guarantee system scheme;

[0037] Step 4.2: Determine the weight of each evaluation indicator;

[0038] Step 4.3: Normalize each evaluation index to obtain a normalized evaluation index;

[0039] Step 4.4: For each maintenance support system solution, multiply the normalized evaluation by its corresponding weight, and then add the results to obtain a weighted sum score;

[0040] Step 4.5: Select the solution with the highest weighted score as the best solution.

[0041] The beneficial effects of the present invention are:

[0042] 1. The present invention is based on the OV model and OPM language in DoDAF to establish a maintenance support system solution. The maintenance support system solution is presented in a graphical manner and has the advantages of clear logic, fixed process, clear coherence, and the ability to clearly describe tasks, providing support for writing high-quality maintenance support solutions in the form of documents.

[0043] 2. The maintenance support system solution of the present invention is a graphical model constructed through a fixed process and a standardized modeling language. Compared with the traditional text-based solution construction method, the method of the present invention is more efficient in generating solutions, and the generated solutions can be integrated into system modeling and simulation tools such as Enterprise Architect and M-Arch. It is very convenient when the solution needs to be modified, and all stakeholders in the solution can participate in the model formulation of the solution.

[0044] 3. The standardized modeling language is used in the existing system modeling and simulation tools to make the solution generation very fast and free from the limitations of documents.

[0045] 4. Based on the generated maintenance support system solution, the present invention establishes a visual evaluation model in the Anylogic simulation platform. Through the visual evaluation model, the operation process of the maintenance support system solution in the real scene can be simulated and the operation results can be recorded. Based on these operation results, the rationality of different maintenance support system solutions can be objectively evaluated, providing a reference basis for determining the best solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the method of the present invention.

[0047] Figure 2 It is an OV-1 maintenance support mission scenario description model constructed in an embodiment of the present invention.

[0048] Figure 3 It is the OV-5b activity phase description model constructed in the embodiment of the present invention.

[0049] Figure 4 It is a cross-sectional content assumption model of the warehouse state constructed in the embodiment of the present invention.

[0050] Figure 5 It is a profile content assumption model of the entry security node constructed in the embodiment of the present invention.

[0051] Figure 6 Schematic diagram of an armored vehicle state determination model constructed in an embodiment of the present invention.

[0052] Figure 7 Schematic diagram of the armored vehicle maintenance decision model constructed in an embodiment of the present invention.

[0053] Figure 8-Figure 11 This is an example of an interface obtained after the designed input parameter architecture is input into the simulation interface of the AnyLogic simulation platform in an embodiment of the present invention.

[0054] Fig.12 It is a state transition diagram of the armored vehicle intelligent agent ArmoredVehicle established in an embodiment of the present invention.

[0055] Fig.13 It is a state transition diagram of a random fault component established in an embodiment of the present invention.

[0056] Fig.14 is a fault quantity function established in the embodiment of the present invention.

[0057] Fig.15 This is an example of setting a visualization interface for the command center agent Main in an embodiment of the present invention.

[0058] Fig.16 It is a discrete event simulation maintenance and status inspection diagram established in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0060] Example:

[0061] This embodiment is described by taking the generation and evaluation of a maintenance support system plan for a certain type of armored vehicle as an example.

[0062] A certain type of armored vehicle needs to adapt to a variety of working environments, including deserts, mountains, cities and other complex terrains. Different working environments have different wear, failure modes and maintenance requirements for vehicles. For this type of armored vehicle, the present invention can be used to generate a maintenance support system solution for it and conduct an evaluation to ensure that this type of armored vehicle can continue to efficiently perform transportation tasks in different working environments.

[0063] The specific steps are as follows:

[0064] Step 1: Generate maintenance support system plan;

[0065] Step 1.1: Build mission and concept models;

[0066] According to the transport mission requirements and maintenance support requirements of armored vehicles in different working environments, a mission task and concept model is constructed. The mission task and concept model is used to graphically describe the organizational units involved in the transport mission requirements and maintenance support requirements, the relationship between each organizational unit, and the activity flow.

[0067] Specifically, the mission and conceptual models include the OV-1 model (or view) and OV-5b model (or view) in DoDAF (DoDAF is the top-level, comprehensive framework and conceptual model for architecture development), as well as the profile work content scenario model that describes the work content of important nodes of the mission in the form of a timing diagram.

[0068] In this embodiment:

[0069] The OV-1 view created is as follows Figure 2 As shown, it describes the organizational units (ground station, relay satellite, command center, maintenance support group and armored vehicle) involved in the XX maintenance and support mission of armored vehicles and the relationship between them.

[0070] The OV-5b view is created as follows Figure 3 As shown in the figure, it describes a series of activity processes carried out by the main organizational units (armored vehicles, command centers, node maintenance and support, and accompanying maintenance and support) to achieve the transportation mission objectives after the armored vehicle receives the transportation mission instruction. This series of activity processes includes task dispatching, task receiving, task execution, status determination, and maintenance decision-making.

[0071] The established cross-section content assumption model is as follows Figure 4 , Figure 5As shown in the figure, it describes the task profile of the armored vehicle to carry out resource inventory and inspection in the warehouse, as well as the task profile of the inspection work entering the guarantee node. More detailed task profiles can also be set at other nodes such as the start and during transportation according to task requirements, which will not be repeated here.

[0072] Step 1.2: Construct maintenance support activity model;

[0073] The maintenance support activity model is used to further refine the OV-5b model in step 1.1, including a state determination model and a maintenance decision model. The state determination model is used to graphically describe the method flow of the state determination link in the OV-5b model, and the maintenance decision model is used to graphically describe the method flow of the maintenance decision link in the OV-5b model.

[0074] Both the state determination model and the maintenance decision model can be established using OPM. In this embodiment, the state determination model established using OPM is as follows: Figure 6 As shown in Figure 2, the maintenance decision model is as follows: Figure 7 shown.

[0075] At this point, a maintenance guarantee system solution with clear logic, fixed process, clear coherence and good editability described in a graphical form is obtained, and the maintenance guarantee system solution generation method is completed.

[0076] Step 2: Build a visual evaluation model;

[0077] Step 2.1: Design the input and output data architecture of the visual evaluation model;

[0078] The input data architecture designed in this embodiment is as shown in Tables 1 and 2, and the output data architecture is as shown in Table 3, wherein the specific values ​​in Table 1 are arbitrarily assigned initial values ​​and can be updated during simulation evaluation; the MTBF values, maintenance time values, and maintenance personnel requirement values ​​in Table 2 are all assigned based on the historical average data of similar transportation tasks.

[0079] Table 1

[0080]

[0081] Table 2

[0082]

[0083] Table 3

[0084]

[0085] Step 2.2: Build a visual evaluation model in the AnyLogic simulation platform;

[0086] Step 2.2.1: Input the input and output data structures designed in step 2.1 into the simulation interface and Main interface of the AnyLogic simulation platform respectively, and create the output file of the scenario experiment setting interface and the visual evaluation model;

[0087] With the help of AnyLogic, construct the input data structure in step 2.1 into the simulation interface of the AnyLogic simulation platform. First, insert a picture as the background in the simulation interface, drag in multiple edit boxes and do the typesetting. Use the "text module" of the AnyLogic simulation platform to input the text part of the input data structure into the simulation interface, and get the following: Figure 8-11 Then, drag multiple variables to the simulation interface and name them as shown in Table 4 below, set their data types and initial values, and then link the initial values ​​to the corresponding edit boxes in the "Scenario Experiment Setting Interface" constructed in the simulation interface. This allows the data in the edit box to be changed and the variable data to be replaced synchronously when running the visual evaluation model, preparing for the replacement of the simulated maintenance support plan in the subsequent simulation.

[0088] It should be noted that when the text part occupies multiple interfaces, it is necessary to write code in the simulation interface to realize the switching of each interface (which can be achieved by conventional technical means).

[0089] Table 4

[0090]

[0091] According to the output data architecture designed in step 2.1, drag the AnyLogic built-in text file into the Main interface of the AnyLogic simulation platform and name the text file as file. At the same time, create a new output.txt file in the model folder. Select the file in the resource option of the file text file property interface and find the newly created output.txt file to realize the association between the output of the visual evaluation model and the external file. Finally, drag the AnyLogic built-in function into the Main interface and name it printOutput. Write the following function body for the printOutput function to realize the subsequent output of the comprehensive guarantee index:

[0092] / / Output performance value

[0093] file.println("\"Task integrity\":"+RcrOutput+",");

[0094] file.println("\"Equipment availability\":"+A0Output+",");

[0095] file.println("\"Ensure timeliness\":"+JOutput+",");

[0096] file.println("\"Spare parts utilization\":"+PbOutput+",");

[0097] file.println("\"Staff Utilization\":"+PsumOutput+",");

[0098] Later in the state transition model Fig.14 By calling the printOutput() function in the performance calculation, the comprehensive index data of the maintenance support system plan can be automatically input into the output.txt file after running a simulation.

[0099] Step 2.2.2: Create the intelligent agents of the main organizational units in the AnyLogic simulation platform;

[0100] Select the main organizational units from the OV-1 model in the maintenance support system solution generated in step 1, namely armored vehicles, command centers, node maintenance support, and accompanying maintenance support. Create four new intelligent agents in the AnyLogic simulation platform and name them ArmoredVehicle, Main, Main, Site, and sePerson.

[0101] Step 2.2.3: Set up a visualization interface for the command center agent Main;

[0102] In the AnyLogic simulation platform, a visualization interface is set up for the command center agent Main. The visualization interface is required to display the starting position, transportation route, node location, armored vehicle, and switch buttons to other agents. Furthermore, fault prompt symbols can also be displayed, such as Fig.15As shown, drag multiple parameters from the AnyLogic panel at the same time, create new parameters in the Main interface, and name them staffing, sparePart, pNeed, repairT, and MTBF respectively. Then return to the simulation interface of step 2.2.1, click on the blank space and you can see the property interface of the simulation experiment. Select the highest agent Main, and there will be new parameters such as staffing in the drop-down parameter list. Write staffing in the blank box after the parameter staffing to synchronize the variables in the simulation with the parameters on the Main interface. Similarly, write the corresponding variable names for sparePart, pNeed, repairT, and MTBF.

[0103] Step 2.2.4: Based on the MTBF in the input data architecture designed in step 2.1, establish the random fault component state transition model and fault quantity function in the AnyLogic simulation platform, as follows: Fig.13 and Fig.14 The random fault component state transition model is used to simulate the failure of armored vehicles during mission execution. Fig.13 In the simulation, the fault transition of four types of components are simulated respectively. The following takes the transition of type A components as an example. The specific transition principle is shown in Table 5:

[0104] Table 5

[0105]

[0106] Fig.13 In the above example, SJ indicates the fault quantity function that needs to be called (a self-developed function), and its function body content is as follows: Fig.14 As shown, calling SJ(0) can simulate the number of randomly generated faulty components A and assign it to the variable BsPart[0], while giving the fault information main.faultSignal = false; the variable BsPart records the number of damaged components, and its data type is double[]; the initial value is {0,0,0,0}; min_max represents the component damage interval, which is int[][] type and can be set to {{1,2},{1,2},{1,2},{1,2}}, indicating that the single damage number interval is 1-2; the variable pNeed is the required personnel category and total number, which is determined by Fig.10 The given value is assigned, and the variable staffing represents the number of personnel required, where staffing[0] represents the number of electronic maintenance personnel required, staffing[1] represents the number of software personnel required, and staffing[2] represents the number of mechanical personnel required, and their values ​​are calculated by the function SJ.

[0107] Step 2.2.5: Based on the OV-5b model in step 1.1, establish the state transition model of the armored vehicle intelligent agent ArmoredVehicle, and based on the profile work content assumption model in step 1.1, establish the warehouse inspection event process model entertask1 (such as Fig.16 As shown in the figure), the fixed-point status inspection process simulation model entertask2 when arriving at the route node for rest and recuperation (as shown in the figure) Fig.16 As shown), and the maintenance time event simulation model entertask3 (as shown Fig.16 shown).

[0108] Based on the OV-5b model in the maintenance support system solution generated in step 1, the state transition model of the armored vehicle intelligent agent ArmoredVehicle is established in the AnyLogic simulation platform, such as Fig.12 The state transition principle and the built-in functions contained in each state are shown in Table 6 below.

[0109] The state transition model of the armored vehicle agent ArmoredVehicle is constructed according to the following method to construct the state parameters and variables:

[0110] Step a1: Construct parameters and name them numOfExpRun and numOfExp, where numOfExpRun is used to record the number of times a single simulation scheme visualization true evaluation model is run (one simulation run is one scheme input, and clicking Run after input will repeat the experiment multiple times continuously), and numOfExp indicates the number of repeated experiments, and the initial value is 3;

[0111] Step a2: Construct parameters and name them Tu, Tua, Tub, A0, sumT, where Tu is used to record the normal operation time of the armored vehicle under a single simulation, Tua represents the entry time of normal operation, Tub represents the exit time of normal operation, A0 represents the availability of single experimental equipment, and sumT represents the total time of a single simulation;

[0112] Step a3: Construct parameters and name them Pst, Pa, W, sumP, where Pa represents the total number of workers during maintenance, Pst represents the total number of workers available in the surrounding environment during maintenance, W represents the number of maintenance times for a single task, and sumP represents the personnel utilization rate for a single task;

[0113] Step a4: Create a variable and name it BsPart to record the number of damaged parts;

[0114] Step a5: Construct variables and name them B1, B2, and Pb, where B2 is the total number of remaining spare parts, B1 is the total number of spare parts for each support node and its companions, and Pb is the utilization rate of spare parts for a single experiment;

[0115] Step a6: Construct variables and name them Tba and T, where Tba[0], Tba[1], and Tba[2] represent the warehouse activity time, node activity time, and maintenance state time of a single simulation, respectively, and T is used to record the maintenance time of a single experiment;

[0116] Step a7: Construct variables and name them as state information, lg, and sucF, where state information is used to record the state of the armored vehicle, lg represents the mission integrity of a single experiment, and sucF represents the number of successful assurances under this solution;

[0117] Step a8: construct variables and name them WX and target. WX=1 indicates that companion protection is performed, WX=2 indicates that node protection is performed, and target indicates the target selected after a failure (it can be a node protection or companion protection).

[0118] Step a9: Construct the self-developed functions of run; bSelectSite; bjoint; goSite, refer to Table 6 for details according to the required functions, and use conventional technical means to write relevant codes according to the contents recorded in the function column in Table 6 to achieve it.

[0119] Table 6

[0120]

[0121]

[0122]

[0123] The functions stop()\send()\time()\setLocation()\take() called in the state transition model of the armored vehicle intelligent agent ArmoredVehicle are all built-in functions of the Anylogic simulation platform and can be called directly.

[0124] Step 2.2.6: Enter the node support agent Site and the companion support agent sePerson and add the parameter array sPart, sPart represents the material allocation situation. Build the self-developed function repairTime and Fig.16 The function repairTime is called by the accompanying maintenance time and node maintenance time in. The main functions of the function repairTime are two: Function 1 is to calculate the relevant maintenance time according to the different WX (that is, the different selected guarantee methods), requiring that the time is related to the configured personnel, and the more personnel, the less time it takes; Function 2 is that after calling this function, the sPart parameters in the node guarantee agent Site and the accompanying guarantee agent sePerson will be updated after consumption, and the specific coding is target.sPart[i]-=BsPart[i];.

[0125] Step 2.2.7: Construct five support index functions as shown in Table 7 to calculate the comprehensive support data of a single experiment. The specific steps are to drag five functions into the ArmoredVehicle interface of the intelligent body and name them as single experiment mission integrity, single experiment equipment availability, single experiment support timeliness, single experiment spare parts utilization, and single experiment personnel utilization. Write the codes shown in Table 7 in the function bodies of the five functions. The steps of code writing are shown in Table 7.

[0126] Table 7

[0127]

[0128]

[0129] Steps 2.2.3-2.2.6 are interchangeable, i.e., can be performed in any order.

[0130] At this point, the construction of the visual evaluation model is completed.

[0131] Step 3: Use the visual evaluation model constructed in step 2 to simulate different maintenance support system solutions and obtain corresponding comprehensive support data;

[0132] In the AnyLogic simulation platform, click Start to run, and use the visual evaluation model built in step 2 to obtain a set of comprehensive guarantee data. The relevant content can be viewed in the output.txt file in the folder where the model is located, as follows:

[0133] Mission integrity: 0.9;

[0134] Equipment availability: 0.9;

[0135] Guarantee timeliness: 0.1;

[0136] Spare parts utilization rate: 0.6;

[0137] Staff utilization rate: 0.5;

[0138] Change the input data in the scenario experiment setting interface constructed in the simulation interface of step 2.2.1 in the AnyLogic simulation platform to form maintenance support system solutions with different configurations, click Run again, and obtain a new set of comprehensive support data;

[0139] In this way, n groups of input data are input in the scenario experiment setting interface of the simulation interface to form n different maintenance support system solutions, and n groups of corresponding comprehensive support data are obtained; in this embodiment, n=2.

[0140] Step 4: Based on the comprehensive support data obtained in step 3, evaluate each maintenance support system solution. This step is performed outside the simulation platform.

[0141] Step 4.1: Calculate the evaluation index of each maintenance support system scheme according to the following scheme;

[0142] The visual evaluation model uses the various indicator functions designed in step 2.2.6 to calculate the various indicator values ​​in the comprehensive data of a single experiment. Fig.12 The data processing method in the medium performance calculation state processes the comprehensive support data of repeated experiments on a certain scheme to obtain a final set of comprehensive support data for the scheme; adjusting the input scheme to run the simulation program can obtain another set of comprehensive support data.

[0143] In this embodiment, there are two maintenance guarantee system solutions, and the evaluation indicators are shown in Table 8 below.

[0144] Table 8

[0145]

[0146] Step 4.2: Determine the weight of each evaluation indicator;

[0147] A weight value is determined for each evaluation indicator to indicate its relative importance in the final evaluation. The weight value can be determined by expert judgment, subjective evaluation, or based on historical data. Assume that the weight values ​​determined for mission integrity, equipment availability, support timeliness, spare parts utilization, and personnel utilization in this embodiment are 0.2, 0.2, 0.2, 0.2, 0.2, respectively.

[0148] Step 4.3: Normalize each evaluation index to obtain a normalized evaluation index;

[0149] Each evaluation index is normalized so that the value range of each evaluation index is consistent to eliminate the dimension difference. This embodiment selects standardization for normalization. Standardization is to convert the original value x of each evaluation index into a normal distribution value with a mean of 0 and a standard deviation of 1. The following formula can be used for standardization:

[0150] x'=(x-mean(x)) / std(x)

[0151] Among them, x' is the value of the normalized evaluation index, x is the value of the original evaluation index, mean(x) and std(x) are the mean and standard deviation of the evaluation index respectively.

[0152] The normalized comprehensive index parameters obtained in this embodiment are shown in Table 9 below.

[0153] Table 9

[0154]

[0155] Step 4.4: Perform weighted summation to obtain the weighted scores of each maintenance support system solution;

[0156] For each maintenance support system solution, the normalized evaluation is multiplied by its corresponding weight, and then the results are added together to obtain a weighted sum score. The weighted sum score is:

[0157] Si=w1*x1i+w2*x2i+...+w5*x5i

[0158] Among them, wi is the weight of evaluation index i, and xi is the normalized value of the evaluation index of scheme i.

[0159] The weighted score S1 of the maintenance support system solution 1 obtained in this embodiment is 0.6;

[0160] The weighted score S2 of the maintenance support system solution 1 obtained in this embodiment is -0.6.

[0161] Step 4.5: Determine the best maintenance support system solution;

[0162] The weighted summation results of the maintenance guarantee system solutions obtained in step 4.4 are compared, and the solution with the highest weighted score is the best solution. In this embodiment, the weighted score of maintenance guarantee system solution 1 is the highest, indicating that its comprehensive index performance is the best, and it is selected as the best solution.

Claims

1. A maintenance support system solution generation and evaluation method, characterized in that: The following steps are involved: Step 1: Generate maintenance support system plan; Step 1.1: Construct a mission task and concept model based on the transport mission requirements and maintenance support requirements of armored vehicles in different working environments; the mission task and concept model is used to graphically describe the organizational units involved in the transport mission requirements and maintenance support requirements, the relationship between the organizational units, and the activity flow, including the OV-1 model and OV-5b model in DoDAF, and the cross-sectional work content scenario model that describes the work content of important nodes of the mission in the form of a time sequence diagram; Step 1.2: Use OPM to establish a state determination model and a maintenance decision model, wherein the state determination model is used to graphically describe the method flow of the state determination link in the OV-5b model, and the maintenance decision model is used to graphically describe the method flow of the maintenance decision link in the OV-5b model; Step 2: Build a visual evaluation model; Step 2.1: Design the input and output data architecture of the visual evaluation model; Step 2.2: Based on the maintenance support system solution generated in step 1, a visual evaluation model is constructed in the AnyLogic simulation platform for simulating the maintenance support system solution and outputting its comprehensive support data; the comprehensive support data includes mission integrity index, equipment availability index, support timeliness index, spare parts utilization index and personnel utilization index; Step 3: Use the visual evaluation model constructed in step 2 to simulate different maintenance support system schemes. Each maintenance support system scheme is simulated multiple times to obtain the corresponding comprehensive support data. Step 4: Based on the comprehensive support data obtained in step 3, evaluate each maintenance support system solution and take the solution with the highest score obtained by weighted sum as the best solution.

2. The maintenance support system solution generation and evaluation method according to claim 1, characterized in that: The input data structure designed in step 2.1 includes the spare parts category and number of each node, the accompanying spare parts category and number, the support personnel category and number of each node, the MTBF value of each category of spare parts failure in the armored vehicle, the maintenance time and maintenance personnel requirements; The output data architecture designed in step 2.1 includes the comprehensive support index data of the maintenance support system plan and the definition and calculation method of each index. The comprehensive support index data includes mission integrity index, equipment availability index, support timeliness index, spare parts utilization index and personnel utilization index.

3. The maintenance support system solution generation and evaluation method according to claim 2, characterized in that: The mission integrity index is used to evaluate the ability of armored vehicles to maintain reliability and availability within a specific time frame, and its value is equal to the ratio of the number of successful missions to the number of experiments; the equipment availability index refers to the proportion of armored vehicles that are in a usable state within a period of time, and its value is equal to the ratio of normal working time to the total mission time; the support timeliness index is the reciprocal of the maintenance downtime; the spare parts utilization index is used to measure the spare parts deployment, and its value is equal to the ratio of the total number of spare parts minus the remaining number of spare parts to the total number of spare parts; the personnel utilization index is used to measure the deployment of support personnel, and its value is equal to the ratio of the support personnel used during the maintenance period to the total support personnel.

4. The maintenance support system solution generation and evaluation method according to claim 3 is characterized by: Step 2.2 is as follows: Step 2.2.1: Input the input and output data structures designed in step 2.1 into the simulation interface and Main interface of the AnyLogic simulation platform respectively to create a scenario experiment setting interface; Step 2.2.2: Select the main organizational units from the OV-1 model in the maintenance support system solution generated in step 1, and establish the intelligent agents of these main organizational units in the AnyLogic simulation platform, including the armored vehicle intelligent agent ArmoredVehicle, the command center intelligent agent Main, the node support intelligent agent Site and the companion support intelligent agent sePerson; Step 2.2.3: Set up a visualization interface for the command center agent Main in the AnyLogic simulation platform, where the visualization interface can display the starting position, transportation route, node location, armored vehicle, and a switch button to switch to other agents; Step 2.2.4: Based on the MTBF in the input data architecture designed in step 2.1, establish a random fault component state transition model and a fault quantity function in the AnyLogic simulation platform; the random fault component state transition model is used to simulate the fault conditions of the armored vehicle during the mission; the fault quantity function is used to simulate the number of various components that randomly generate faults; Step 2.2.5: Establish a state transition model of the armored vehicle intelligent body ArmoredVehicle in the AnyLogic simulation platform according to the OV-5b model, and establish a warehouse inspection event process model, a fixed-point state inspection process simulation model when arriving at a route node for rest, and a maintenance time event simulation model according to the profile work content assumption model; Step 2.2.6: Enter the node guarantee agent Site and the accompanying guarantee agent sePerson, add parameter array sPart to them respectively, and construct the self-developed function repairTime; the parameter array sPart represents the material allocation situation; the self-developed function repairTime is used to calculate the maintenance time of different maintenance guarantee methods, and after the self-developed function repairTime is called, the parameter array sPart in the node guarantee agent Site and the accompanying guarantee agent sePerson will be updated; Step 2.2.7: constructing a security index function according to the output data architecture, wherein the security index function is used to calculate each index in the security comprehensive index data; The above steps 2.2.3-2.2.6 can be interchanged in order.

5. The maintenance support system solution generation and evaluation method according to claim 4 is characterized in that: The specific method of inputting the input data structure into the simulation interface of the Anylogic simulation platform in step 2.2.1 is as follows: first, insert a picture as the background in the simulation interface, drag in multiple edit boxes and do the typesetting, and input the text part in the input data structure into the simulation interface through the text module of the Anylogic simulation platform to obtain the scenario experiment setting interface; then, drag in multiple variables to the simulation interface and name them staffing, sparePart, pNeed, repairT and MTBF respectively, set their data types and initial values, and then establish a link between the initial value and the corresponding edit box in the scenario experiment setting interface constructed in the simulation interface, so as to realize the function of changing the data in the edit box and the synchronous replacement of the variable data when running the visual evaluation model; The specific method of inputting the input data architecture into the Main interface of the Anylogic simulation platform in the step 2.2.1 is as follows: drag the Anylogic built-in text file into the Main interface of the Anylogic simulation platform and name it file, and at the same time create a new output.txt file in the model folder location, select the file in the resource option of the file text file property interface and find the newly created output.txt file to realize the association between the output of the visual evaluation model and the external file; finally, drag the Anylogic built-in function into the Main interface and name it printOutput, and compile the function body for the printOutput function to realize the output of the comprehensive indicator guarantee.

6. The maintenance support system solution generation and evaluation method according to claim 5 is characterized by: The step 3 is specifically as follows: in the AnyLogic simulation platform, click to start running, and use the visual evaluation model constructed in step 2 to obtain a set of comprehensive guarantee data. The value of each indicator can be viewed in the output.txt file in the folder where the model is located.

7. The maintenance support system solution generation and evaluation method according to claim 6 is characterized by: Step 4 is as follows: Step 4.1: Calculate the evaluation index of each maintenance guarantee system scheme; Step 4.2: Determine the weight of each evaluation indicator; Step 4.3: Normalize each evaluation index to obtain a normalized evaluation index; Step 4.4: For each maintenance support system solution, multiply the normalized evaluation by its corresponding weight, and then add the results to obtain a weighted sum score; Step 4.5: Select the solution with the highest weighted score as the best solution.

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