Designing method and device for scenario simulation experiment

Abstract

The invention discloses a scenario simulation experiment design method and device, and the method comprises the steps: processing experiment demand information, and obtaining a simulation scenario; processing the simulation scenario by using an experiment design model to obtain a simulation experiment sample and experiment sample space description information; processing the simulation experiment sample and the experiment sample space description information by using an integrated operation model to obtain an experiment simulation result; performing situation monitoring on the simulation scenario by using a situation monitoring model to obtain simulation process data; and comprehensively evaluating the simulation process data and the experiment simulation result to obtain a comprehensive evaluation result. According to the method, the simulation scenario is subjected to experimental design, and the scenario simulation experiment efficiency is improved. The simulation process data and the experiment simulation result are comprehensively evaluated to obtain a comprehensive evaluation result, and rapid and efficient evaluation of the scenario simulation result is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of experimental simulation, and in particular to a method and device for designing a hypothetical simulation experiment. Background Art

[0002] When developing a simulation system, scenarios are often neglected, or even considered to be just the initialization of simulation system data. The scenario generation system is too highly coupled with the simulation system, resulting in poor reusability and scalability of simulation scenarios and scenario generation systems. Existing simulation scenario generation methods have certain problems of manual intervention and low efficiency. Simulation effectiveness evaluation has two main research significances: one is to evaluate the capabilities of the current simulation system and provide a basis for the improvement and optimization of the current system; the other is to guide the design and development planning of future simulation systems and provide guidance for the development and design of new simulation scenarios. It is of great significance to simulate scenarios through experiments. Therefore, it is necessary to study an experimental design method to simplify the scenario simulation experiment process and improve the efficiency of scenario simulation experiments. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for designing a scenario simulation experiment, and to perform simulation scenario analysis through an experimental method to simplify the scenario simulation experiment process and improve the efficiency of the scenario simulation experiment.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a method for designing a scenario simulation experiment, the method comprising:

[0005] S1, process the experimental requirement information to obtain simulation scenarios;

[0006] S2, using the experimental design model to process the simulation scenario to obtain simulation experiment samples and experimental sample space description information;

[0007] S3, using the integrated operation model, processing the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results;

[0008] S4, using a situation monitoring model to monitor the situation of the simulation scenario to obtain simulation process data;

[0009] S5, comprehensively evaluating the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result.

[0010] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the processing of the experimental requirement information to obtain the simulation scenario includes:

[0011] S11, parsing the experimental requirement information to obtain simulation information; the simulation information includes simulation scenario information, simulation device information, simulation deployment information and evaluation index information;

[0012] S12, using a preset simulation model, processing the simulation information to obtain a simulation scenario.

[0013] As an optional implementation, in the first aspect of the embodiment of the present invention, the use of the experimental design model to process the simulation scenario to obtain the simulation experiment sample and the experimental sample space description information includes:

[0014] S21, performing scenario analysis on the simulation scenario to obtain simulation model parameters;

[0015] S22, extracting the simulation model parameters to obtain key model parameters;

[0016] S23, optimizing the key model parameters to obtain optimized model parameters;

[0017] S24, processing the optimization model parameters to obtain a simulation scenario sample space description file and a simulation scenario sample file;

[0018] S25, setting the number of operations and the execution order of the simulation scenario sample space description file and the simulation scenario sample file to obtain simulation experiment samples and experiment sample space description information.

[0019] As an optional implementation manner, in the first aspect of the embodiment of the present invention, performing scenario analysis on the simulation scenario to obtain simulation model parameters includes:

[0020] S211, performing scenario analysis on the simulation scenario to obtain scenario file model information;

[0021] S212, performing integrity check on the assumed file model information to obtain a integrity check result;

[0022] S213, when the result of the integrity check is no, perform model component design on the assumed file model information; when the result of the integrity check is yes, perform model parameter extraction on the assumed file model information to obtain simulation model parameters.

[0023] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the use of the integrated operation model to process the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results includes:

[0024] S31, loading the simulation experiment sample and the experiment sample space description information;

[0025] S32, setting the number of runs and initial parameters of the simulation experiment;

[0026] S33, processing the simulation experiment samples and the experimental sample space description information according to the number of runs and the initial parameters to obtain an experimental simulation result.

[0027] As an optional implementation, in the first aspect of the embodiment of the present invention, the using of the situation monitoring model to perform situation monitoring on the simulation scenario to obtain simulation process data includes:

[0028] S41, parsing the simulation scenario to obtain a scenario file and model configuration data of the scenario file;

[0029] S42, performing simulation and deduction settings on the assumption file and the model configuration data to obtain simulation and deduction configuration information;

[0030] S43, performing simulation operation according to the simulation deduction configuration information to obtain simulation process data.

[0031] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the comprehensive evaluation of the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result includes:

[0032] S51, selecting to perform a performance evaluation calculation and analysis task or a display and analysis task of an existing performance evaluation result according to user needs;

[0033] S52, when selecting the task of displaying and analyzing the existing performance evaluation results, loading the existing simulation process data and experimental simulation results, and performing statistical analysis, correlation analysis and sensitivity analysis on the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result;

[0034] S53, when selecting to perform the performance evaluation calculation and analysis task, loading the experimental scheme result design information;

[0035] S54, processing the experimental scheme result design information to obtain a comprehensive evaluation result.

[0036] A second aspect of an embodiment of the present invention discloses a device for designing a scenario simulation experiment, the device comprising:

[0037] The simulation scenario module is used to process the experimental requirement information and obtain the simulation scenario;

[0038] An experimental design module is used to process the simulation scenario using an experimental design model to obtain simulation experiment samples and experimental sample space description information;

[0039] An integrated operation module, used to process the simulation experiment samples and the experimental sample space description information using the integrated operation model to obtain the experimental simulation results;

[0040] A situation monitoring module, using a situation monitoring model to monitor the situation of the simulation scenario and obtain simulation process data;

[0041] The comprehensive evaluation module is used to comprehensively evaluate the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result.

[0042] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the processing of the experimental requirement information to obtain the simulation scenario includes:

[0043] S11, parsing the experimental requirement information to obtain simulation information; the simulation information includes simulation scenario information, simulation device information, simulation deployment information and evaluation index information;

[0044] S12, using a preset simulation model, processing the simulation information to obtain a simulation scenario.

[0045] As an optional implementation, in the second aspect of the embodiment of the present invention, the use of the experimental design model to process the simulation scenario to obtain the simulation experiment sample and the experimental sample space description information includes:

[0046] S21, performing scenario analysis on the simulation scenario to obtain simulation model parameters;

[0047] S22, extracting the simulation model parameters to obtain key model parameters;

[0048] S23, optimizing the key model parameters to obtain optimized model parameters;

[0049] S24, processing the optimization model parameters to obtain a simulation scenario sample space description file and a simulation scenario sample file;

[0050] S25, setting the number of operations and the execution order of the simulation scenario sample space description file and the simulation scenario sample file to obtain simulation experiment samples and experiment sample space description information.

[0051] As an optional implementation manner, in the second aspect of the embodiment of the present invention, performing scenario analysis on the simulation scenario to obtain simulation model parameters includes:

[0052] S211, performing scenario analysis on the simulation scenario to obtain scenario file model information;

[0053] S212, performing integrity check on the assumed file model information to obtain a integrity check result;

[0054] S213, when the result of the integrity check is no, perform model component design on the assumed file model information; when the result of the integrity check is yes, perform model parameter extraction on the assumed file model information to obtain simulation model parameters.

[0055] As an optional implementation, in the second aspect of the embodiment of the present invention, the use of the integrated operation model to process the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results includes:

[0056] S31, loading the simulation experiment sample and the experiment sample space description information;

[0057] S32, setting the number of runs and initial parameters of the simulation experiment;

[0058] S33, processing the simulation experiment samples and the experimental sample space description information according to the number of runs and the initial parameters to obtain an experimental simulation result.

[0059] As an optional implementation, in the second aspect of the embodiment of the present invention, the using of the situation monitoring model to perform situation monitoring on the simulation scenario to obtain simulation process data includes:

[0060] S41, parsing the simulation scenario to obtain a scenario file and model configuration data of the scenario file;

[0061] S42, performing simulation and deduction settings on the assumption file and the model configuration data to obtain simulation and deduction configuration information;

[0062] S43, performing simulation operation according to the simulation deduction configuration information to obtain simulation process data.

[0063] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the comprehensive evaluation of the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result includes:

[0064] S51, according to user needs, selecting to perform performance evaluation calculation and analysis tasks or to perform display and analysis tasks on existing performance evaluation results;

[0065] S52, when selecting the task of displaying and analyzing the existing performance evaluation results, loading the existing simulation process data and experimental simulation results, and performing statistical analysis, correlation analysis and sensitivity analysis on the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result;

[0066] S53, when selecting to perform the performance evaluation calculation and analysis task, loading the experimental scheme result design information;

[0067] S54, processing the experimental scheme result design information to obtain a comprehensive evaluation result.

[0068] The third aspect of the present invention discloses another device for designing a simulation experiment, the device comprising:

[0069] A memory storing executable program code;

[0070] a processor coupled to the memory;

[0071] The processor calls the executable program code stored in the memory to execute part or all of the steps in the hypothetical simulation experiment design method disclosed in the first aspect of the embodiment of the present invention.

[0072] The fourth aspect of the present invention discloses a computer-storable medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the hypothetical simulation experiment design method disclosed in the first aspect of an embodiment of the present invention.

[0073] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0074] The invention discloses a method for designing a scenario simulation experiment, which improves the efficiency of the scenario simulation experiment by designing an experiment for the simulation scenario, obtains a comprehensive evaluation result by comprehensively evaluating the simulation process data and the experimental simulation result, and realizes a fast and efficient evaluation of the scenario simulation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0076] Figure 1 It is a flow chart of a method for designing a simulation experiment disclosed in an embodiment of the present invention;

[0077] Figure 2 is a functional schematic diagram of an experimental design model disclosed in an embodiment of the present invention;

[0078] Figure 3 It is a schematic diagram of the workflow of the experimental design model disclosed in the embodiment of the present invention;

[0079] Figure 4It is a schematic diagram of the experimental design results disclosed in the embodiment of the present invention;

[0080] Figure 5 It is a functional schematic diagram of the integrated operation model disclosed in the embodiment of the present invention;

[0081] Figure 6 It is a schematic diagram of the workflow of the integrated operation model disclosed in the embodiment of the present invention;

[0082] Figure 7 is a functional schematic diagram of a situation monitoring model disclosed in an embodiment of the present invention;

[0083] Figure 8 It is a schematic diagram of the workflow of the situation monitoring model disclosed in the embodiment of the present invention;

[0084] Fig. 9 It is a flowchart of another method for designing a simulation experiment disclosed in an embodiment of the present invention;

[0085] Fig.10 is a schematic diagram of a statistical analysis model disclosed in an embodiment of the present invention;

[0086] Fig.11 It is a schematic diagram of the workflow of the statistical analysis model disclosed in the embodiment of the present invention;

[0087] Fig.12 It is a structural schematic diagram of a hypothetical simulation experiment design device disclosed in an embodiment of the present invention;

[0088] Fig.13 It is a structural schematic diagram of another hypothetical simulation experiment design device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0091] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0092] The present invention discloses a method and device for designing a simulation experiment of a hypothesis, the method comprising: processing experimental demand information to obtain a simulation hypothesis; using an experimental design model to process the simulation hypothesis to obtain simulation experiment samples and experimental sample space description information; using an integrated operation model to process the simulation experiment samples and the experimental sample space description information to obtain experimental simulation results; using a situation monitoring model to situation monitor the simulation hypothesis to obtain simulation process data; comprehensively evaluating the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result. The method of the present invention performs experimental design on simulation hypotheses to improve the efficiency of hypothesis simulation experiments. By comprehensively evaluating the simulation process data and the experimental simulation results, a comprehensive evaluation result is obtained, thereby realizing a rapid and efficient evaluation of the simulation results of the hypothesis. The following are detailed descriptions.

[0093] Embodiment 1

[0094] See also Figure 1 , Figure 1 1 is a flow chart of a method for designing a simulation experiment disclosed in an embodiment of the present invention. Figure 1 The described method for designing a scenario simulation experiment is applied to the field of experimental simulation, and is used to design an experiment for a simulation scenario, thereby improving the efficiency of the scenario simulation experiment. Figure 1 As shown, the scenario simulation experiment design method may include the following operations:

[0095] S1, process the experimental requirement information to obtain simulation scenarios;

[0096] S2, using the experimental design model to process the simulation scenario to obtain simulation experiment samples and experimental sample space description information;

[0097] S3, using the integrated operation model, processing the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results;

[0098] S4, using a situation monitoring model to monitor the situation of the simulation scenario to obtain simulation process data;

[0099] S5, comprehensively evaluating the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result.

[0100] Optionally, the processing of the experimental requirement information to obtain a simulation scenario includes:

[0101] S11, parsing the experimental requirement information to obtain simulation information; the simulation information includes simulation scenario information, simulation device information, simulation deployment information and evaluation index information;

[0102] S12, using a preset simulation model, processing the simulation information to obtain a simulation scenario.

[0103] First, a simulation knowledge base is constructed; the simulation knowledge base includes simulation scenario information, simulation scenario information, simulation device information, simulation deployment information, evaluation index information, simulation distribution information, initial situation information and simulation execution information;

[0104] Using the simulation knowledge base, training the large language model to obtain an optimized large language model;

[0105] Using the optimized large language model, searching in the simulation knowledge base according to the simulation information to obtain the assumed text information;

[0106] Parsing the scenario text information to obtain core data information required for the scenario; the core data information is data information after redundancy is removed;

[0107] The optimized large language model is used to process the core data information required for the scenario to realize the simulation scenario. The preset simulation model is the optimized large language model.

[0108] The simulation scenario is generated by inputting text or table description information into a computer and storing it in a formalized standard format (which can be understood as a format similar to json or xml). The large language model used in this embodiment is the text2vec model, which can represent the entire text as a vector sequence, thereby retaining the semantic information of the text.

[0109] Optionally, the method for processing the experimental requirement information to obtain the simulation scenario is:

[0110] a) Based on the experimental requirement information, select and simulate the required database, including environment model, equipment model, service model, cognitive model, message model, etc.;

[0111] b) Set the participants of the simulation, such as our side, friendly side, etc.;

[0112] c) Deployment of physical units of the participants;

[0113] d) Conduct airspace design and route planning based on environmental requirements;

[0114] e) Setting and assigning tasks to deployed physical units;

[0115] f) Conduct a rehearsal of the formulated scenarios, modify and improve the scenarios based on the rehearsal process and results, and form simulation scenarios.

[0116] Among them, the route planning method is:

[0117] 1) According to the physical deployment plan and the simulated airspace, a three-dimensional model is established, the cost function and constraint conditions of the UAV path planning are determined, and the particle swarm algorithm is initialized;

[0118] 2) Use Logistic chaotic mapping to generate the initial individuals of the particle swarm and calculate the fitness value of the individuals;

[0119] 3) Using nonlinear energy factors to update individuals, so that individuals move towards lower fitness values;

[0120] 4) Use chaotic mapping to search the local solution space to make it jump out of the local optimal value;

[0121] 5) Repeat 3) and 4), and when the algorithm termination condition is met, output the optimal fitness value; the optimal fitness value is the simulation path information.

[0122] The task setting and assignment methods are:

[0123] Step 1: Load the relevant information of the drone and the mission target, and calculate the distance matrix and other data needed in the calculation process;

[0124] Step 2: Initialize the population and calculate the fitness value of each individual;

[0125] Step 3: Divide the population into 10 groups of 8, select the best individual in each group to perform a quasi-3-opt local search, and replace all individuals in the corresponding group with the searched new individuals, that is, at this time, the 8 individuals in each group are exactly the same;

[0126] Step 4: For each group, perform 7 operations on the 7 individuals in it. The last individual does not perform any operation, and a new group is obtained. All new groups constitute a new population.

[0127] The 7 operation modes are: a dual-vector encoding method combining allocation vector and breakpoint vector, based on three operators: crossover mutation, single-point sliding and region flipping, 7 operation modes are designed, including crossover mutation, sliding and region flipping of allocation vector, random generation of breakpoint vector, and a combination of three operations of allocation vector (crossover mutation, sliding and region flipping, random generation of breakpoint vector) and random generation of breakpoint vector.

[0128] Step 5: Update the optimal individual related information and check whether the iteration limit is reached. If so, output the task setting and assignment results. Otherwise, jump to Step 3.

[0129] Optionally, the use of the experimental design model to process the simulation scenario to obtain simulation experiment samples and experimental sample space description information includes:

[0130] S21, performing scenario analysis on the simulation scenario to obtain simulation model parameters;

[0131] S22, extracting the simulation model parameters to obtain key model parameters;

[0132] Extract sensitive device parameters, the number and proportion of entities or devices that users are concerned about;

[0133] S23, optimizing the key model parameters to obtain optimized model parameters; including setting the threshold range of key model parameter attribute values, legality check, etc.; providing commonly used experimental design methods, and being able to flexibly expand according to user needs to complete parameter modification;

[0134] S24, processing the optimization model parameters to obtain a simulation scenario sample space description file and a simulation scenario sample file;

[0135] Specifically, the optimization model parameters are processed, an experimental design method (such as orthogonal experimental design and uniform experimental design, etc.) is adopted, and one or more test equipment samples or test scenario samples for simulation test, simulation scenario sample space description files and simulation scenario sample files are set;

[0136] S25, setting the number of operations and the execution order of the simulation scenario sample space description file and the simulation scenario sample file to obtain simulation experiment samples and experiment sample space description information, including setting the number of operations and the execution order of each simulation sample, and storing the simulation space in a format such as XML.

[0137] Optionally, performing scenario analysis on the simulation scenario to obtain simulation model parameters includes:

[0138] S211, performing scenario analysis on the simulation scenario to obtain scenario file model information; the scenario file model information is the environment information, entity information, etc. included in the simulation scenario;

[0139] S212, performing integrity check on the assumed file model information to obtain a integrity check result;

[0140] S213, when the integrity check result is negative, perform model component design on the assumption file model information; when the integrity check result is positive, perform model parameter extraction on the assumption file model information to obtain simulation model parameters, including the number of entities or devices, the proportional relationship of entities or devices, etc.;

[0141] Among them, model component design is to supplement the missing information in the assumption file model information to meet the completeness check.

[0142] Optionally, the utilizing the integrated operation model to process the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results includes:

[0143] S31, loading the simulation experiment sample and the experiment sample space description information;

[0144] S32, setting the number of runs and initial parameters of the simulation experiment;

[0145] S33, according to the number of runs and the initial parameters, the simulation experiment samples and the experimental sample space description information are processed to obtain the experimental simulation results. This includes the initial setting of the number of parallel simulation cores, and online or offline modification of the CPU and memory occupancy of the "Resource Window" according to the requirements and the running process. The simulation start and stop control is performed through the "Control Tool" in the menu bar, including starting simulation, pausing simulation and resetting simulation. Visual display can be supported during the simulation process: view the running status and key parameters of each sample according to the information displayed in the "Run Window"; select the target sample by clicking the sample monitoring view, view the main configuration information and simulation progress of the sample in the "Sample Information" window, or view the simulation progress of each sample through the assumed experimental sample progress bar in the "Task List"; after selecting the target sample, click the "2D" or "3D" button in the "Sample Information" window to view the two-dimensional / three-dimensional situation display of the experimental sample in the pop-up window.

[0146] Optionally, the using of a situation monitoring model to perform situation monitoring on the simulation scenario to obtain simulation process data includes:

[0147] S41, parsing the simulation scenario to obtain a scenario file and model configuration data of the scenario file;

[0148] The simulation scenario is parsed to obtain the scenario file and its supporting model configuration data. The scenario plot is initialized on the interface by parsing these two parts, and the simulation initialization information is parsed and stored in the memory. The input is the scenario data file (.sdata), process data definition file (.pdef), and result data definition file (.rdef). The output is the simulation process data (.pdata) and simulation result data (.rdata);

[0149] S42, performing simulation and deduction settings on the assumption file and the model configuration data to obtain simulation and deduction configuration information, including deduction step length, simulation acceleration ratio, random algorithm, data recording path, etc.;

[0150] S43, performing simulation operation according to the simulation deduction configuration information to obtain simulation process data.

[0151] Optionally, the comprehensive evaluation of the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result includes:

[0152] S51, according to user needs, selecting to perform performance evaluation calculation and analysis tasks or to perform display and analysis tasks on existing performance evaluation results;

[0153] S52, when selecting the task of displaying and analyzing the existing performance evaluation results, loading the existing simulation process data and experimental simulation results, and performing statistical analysis, correlation analysis and sensitivity analysis on the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result;

[0154] S53, when selecting to perform the performance evaluation calculation and analysis task, loading the experimental scheme result design information;

[0155] The experimental scheme result design information is the experimental simulation results and simulation process data obtained according to steps S1 to S4 for the current experimental requirement information;

[0156] S54, processing the experimental scheme result design information to obtain a comprehensive evaluation result.

[0157] Step S54 is specifically as follows:

[0158] According to the evaluation index information obtained in S11, the experimental scheme result design information is processed to obtain a comprehensive evaluation result.

[0159] In this embodiment, the evaluation index information includes the total time consumed after the simulation scenario is completed, the overall operation time consumed by a single entity object, the average network connectivity and the network invulnerability;

[0160] Commonly used definitions are as follows:

[0161] Entity: An identifiable simulation object that exists in a certain state and has independent behavior capabilities or can independently complete a task. It can be either a behavior subject or a behavior object. Simulation entities are the main components of simulation scenarios.

[0162] Simulation component: the components that make up the simulation entity, which may include physical components, motion components, action components, decision components, etc. Corresponding to the software program, the simulation component is a relatively independent and reusable software module that encapsulates the component design and implementation and can provide an interface to the outside.

[0163] Simulation event: At a certain moment, the result of the change of the state of a simulation entity or the instantaneous behavior that causes the change of the state of a simulation entity is called an event.

[0164] Simulation running thread: A concept in the field of computer science, which refers to the basic unit of scheduling and execution of computer processors. In the simulation calculation process, threads are usually used to run the event functions of simulation entities.

[0165] The total time T after the simulation scenario is completed is defined as:

[0166] a. The number of all entities running in a single thread

[0167] k=∑oi (where i=1, 2, 3, ...; oi is the entity running in a single thread; k is the total number of entities running in a single thread).

[0168] b. Each sampling period runs a single thread at a double speed

[0169] R=△t / t (where R is the running speed multiple of a single thread, △t is the difference between the simulation timestamps of the last and first simulation events executed by the thread within a sampling period, and t is the actual physical time taken by the thread to execute all simulation events within a sampling period).

[0170] c. The total time taken to execute simulation events on each thread in each sampling cycle

[0171] The total time taken by a single thread to execute a single entity: T object =∑T j (where j = 1, 2, 3 ... n; T object The total time it takes to execute a single entity simulation event for this thread; T j The time taken for the jth simulation event to be executed for this entity).

[0172] Total execution time of a single thread: T thread =∑T object (k)(where k = 1, 2, 3 ...; T thread The total time it takes to execute simulation events for this thread; T object The total execution time of the kth entity in this thread).

[0173] d. Total time consumed after the simulation scenario is completed

[0174] Overall running time: T = Max{T thread (n)} (where n = 1, 2, 3...; T is the overall running time of the engine; T thread The total time taken to execute simulation events for the nth thread).

[0175] The overall running time Tso of a single entity object is defined as:

[0176] The total execution time of a single entity object and a single thread: T object =∑T j (where j = 1, 2, 3, ..., T object : The total time taken to execute a single entity simulation event for this thread; T j The time taken for the jth simulation event to be executed for this entity).

[0177] The total execution time of all events of a single entity object: T ao =∑T object (n)(where n=1, 2, 3…, T object(n): the execution time of the entity in the nth thread; T ao The total time it takes to execute simulation events in all threads for this entity).

[0178] The overall running time of a single entity object is: Tso = Max{T object (n)} (where n = 1, 2, 3..., Tso: the running time of the entity object after the simulation is completed; T object (n) is the time taken for the simulation event of the entity object to be executed in the nth thread).

[0179] The average network connectivity Z is defined as:

[0180] The network constructed by the simulation experiment results is processed using the network average connectivity calculation model to obtain the network average connectivity Z; the network average connectivity calculation model is:

[0181]

[0182] in, i=1,2,L,n, where n is the number of nodes in the undirected connected graph G of the network constructed by the simulation experiment results, G=(V,E), V={v1,v2,L,v n} is a set of nodes in the undirected connected graph G, v1,v2,L,v n represents n nodes, For edge sets, Represents node v i With node v j The lth, 0≤l≤m ij Direct links, m ij For node v i With node v j The number of direct links between ij For node v i With node v j The degree of connectivity between

[0183] Using node v i With node v j The connectivity calculation model between nodes v i With node v j The connectivity between ij ; The node v i With node v j The calculation model of the connectivity between is:

[0184]

[0185] in k>1 is node v iWith node v j The indirect connectivity between them, k is the number of hops, j=1,2,L,n.

[0186] Network survivability is defined as:

[0187] Using the method of randomly attacking network nodes, the network constructed by the simulation experiment results is attacked N times;

[0188] The average connectivity of the network constructed by the simulation experiment result after N attacks is calculated to obtain the network invulnerability ZL.

[0189] The comprehensive evaluation result S is:

[0190]

[0191] Among them, γ1+γ2+γ3+γ4=1, γ1, γ2, γ3, and γ4 are weight coefficients set by experiments, and the comprehensive evaluation result S is normalized to between 0 and 1. The larger the value of S, the better the comprehensive evaluation result.

[0192] Figure 2 is a functional schematic diagram of an experimental design model disclosed in an embodiment of the present invention; Figure 3 It is a schematic diagram of the workflow of the experimental design model disclosed in the embodiment of the present invention; Figure 4 It is a schematic diagram of the experimental design results disclosed in the embodiment of the present invention; Figure 5 It is a functional schematic diagram of the integrated operation model disclosed in the embodiment of the present invention; Figure 6 It is a schematic diagram of the workflow of the integrated operation model disclosed in the embodiment of the present invention; Figure 7 is a functional schematic diagram of a situation monitoring model disclosed in an embodiment of the present invention; Figure 8 It is a schematic diagram of the workflow of the situation monitoring model disclosed in the embodiment of the present invention.

[0193] It can be seen that the present invention discloses a method for designing a scenario simulation experiment, which improves the efficiency of the scenario simulation experiment by designing an experiment for the simulation scenario. By comprehensively evaluating the simulation process data and the experimental simulation results, a comprehensive evaluation result is obtained, thereby realizing a rapid and efficient evaluation of the scenario simulation results.

[0194] Embodiment 2

[0195] See also Fig. 9 , Fig. 9 FIG. 1 is a flow chart of another method for designing a simulation experiment disclosed in an embodiment of the present invention. Fig. 9 The described method for designing a scenario simulation experiment is applied to the field of experimental simulation, and is used to design an experiment for a simulation scenario, thereby improving the efficiency of the scenario simulation experiment. Fig. 9 As shown, the scenario simulation experiment design method may include the following operations:

[0196] (1) According to the experimental requirements, use the scenario editing tool to edit the simulation scenario, mainly including deployment, mission, airspace, route settings, etc., and output the simulation scenario file in a given format;

[0197] (2) Using the experimental design model to load the simulation experiment scenario as the basic scenario, on this basis, the scenario parameters or the equipment parameters involved in the scenario can be modified according to certain rules to batch generate simulation experiment samples and corresponding experimental sample space description files (or data);

[0198] (3) Use the integrated operation model to load simulation scenario files or simulation experiment samples, control the simulation engine and model to perform single, multiple, and parallel large-scale simulation operations and output simulation results. In the case of a single operation, it can also support online situation monitoring and manual intervention at the same time;

[0199] (4) Using 3D situation display tools, based on 3D virtual globe and 3D models, the commonly used assumption elements, simulation entities, simulation process effects, information relationships between entities and natural environment elements in the simulation process can be displayed in two ways: playback and online, and support close-range situation effect display;

[0200] (5) Using the statistical analysis model, an indicator statistical script is constructed to perform preliminary statistical processing on the original simulation result data, and an efficiency indicator system is constructed. The efficiency indicators are calculated and displayed based on the original simulation results and preliminary statistical results. In addition, the correlation analysis between the experimental design parameters and the efficiency evaluation results can be carried out in combination with the experimental sample space description file.

[0201] The simulation experiment design method in this embodiment mainly includes two operation modes: simulation deduction and simulation experiment:

[0202] (1) Simulation experiment mode: This mode is mainly used to conduct large-scale experiments with multiple scenarios and samples. In the simulation experiment mode, each model is responsible for solving various processes and result data on the computing server and recording them in the local database to improve efficiency. After the simulation is completed, the operation control model will uniformly collect the result data and store them in the network database for evaluation and playback.

[0203] (2) Playback mode: This mode is mainly used to play back specific simulation experiments in order to observe and display the process and results. In playback mode, each model directly reads various intermediate processes and result data of the previous simulation from the local database and plays and displays them accordingly.

[0204] The purpose of experimental design is to analyze indicators, research programs or evaluate effectiveness. Different experimental purposes have different elements of focus and adjustment. The experimental design model is based on the simulation assumptions and simulation system model system. It can extract assumption parameters or equipment parameters as experimental variables according to the experimental purpose and requirements, set the variable value method and number, and generate simulation experiment samples based on orthogonal design, uniform design and other methods to provide support for subsequent integrated operation and statistical analysis. The experimental design model can be used to design simulation experiments according to the experimental purpose, and can generate a series of simulation experiment samples with important comparative research significance, thereby improving the effectiveness and pertinence of simulation experiments.

[0205] The functional components of the experimental design model are as follows: Figure 2 As shown, it mainly consists of five parts: data management, parameter design, sample generation, sample setting, and record export.

[0206] Data management: extract the element information and the hierarchical relationship and affiliation in the original scenario file output by the scenario editing software, and display them in a structured manner. The input is the experimental design definition file (.edef) and the scenario data file (.sdata), and the output is the experimental design data (.edata);

[0207] Parameter design: Select the parameters of the assumed elements according to the test requirements, and re-value the attribute values ​​of the selected parameters through design methods such as manual intervention;

[0208] Sample generation: Modify any parameter to generate the corresponding test sample. Sample generation is to realize the function of exporting the generated samples.

[0209] Sample setting: Set the number of executions and the execution order of the generated samples to provide support for subsequent simulation control;

[0210] Record export: any modification content involved in the parameter design process is recorded and can be exported in the form of Word or Excel.

[0211] The operation process is as follows Figure 3 shown.

[0212] a) Start or switch to the experimental design model and enter b);

[0213] b) Scenario analysis: First, load the scenario file and perform a completeness check on the scenario file model information. If the model data information is incomplete, perform model component analysis, and perform model component design or model component equipment. If the model data information is complete, perform model parameter extraction, and perform a structured display of the affiliation and hierarchical relationship between the extracted model data, and then proceed to c);

[0214] c) Parameter selection: From the factors that affect the experimental results, the sensitive parameters of the equipment, the number and proportion of entities or equipment that the user is concerned about can be extracted and entered into d);

[0215] d) Parameter setting: supports parameter setting and combination optimization, including parameter attribute value threshold range setting, legality check, etc.; provides commonly used experimental design methods, and can be flexibly expanded according to user needs, completes parameter modification, and enters e);

[0216] e) Experimental sample generation: Assume that the experimental sample generation is based on the design results, adopt the experimental design method (such as orthogonal experimental design and uniform experimental design, etc.), set one or more test equipment samples or test scenario samples for simulation test, generate simulation scenario sample space description file and simulation scenario sample file, and enter f);

[0217] f) Experimental sample setting: set the number of operations and execution order of each simulation sample, store the simulation space in XML and other formats, and enter g);

[0218] g) End.

[0219] The integrated operation model uses standardized simulation model description specifications, and can quickly build a system simulation system with diverse functions and scalable size by building blocks. Through the operation auxiliary tools, simulation experiment samples can be loaded in batches, and the system can be controlled to perform multi-sample and large-scale system confrontation simulation operations. Finally, the simulation results are output according to the sample number and operation order, providing input for the next statistical analysis. The functions of the integrated operation model are mainly composed of four parts: data management, simulation operation control, simulation auxiliary display, and simulation parameter setting.

[0220] Data management: The integrated operation model needs to load three types of file information: assumption data (.sdata), model configuration data (.mdata) and experimental design information (.edata). Basic data management operations should be provided for these three types of data, and the compatibility between the three should be checked;

[0221] Simulation operation control: design and select operation tasks, including experimental sample loading, sample selection and setting of independent operation times for each sample; and overall control of the simulation process, including starting simulation, pausing simulation and resetting simulation functions, which can be operated on independent samples or batch operations on sample sets;

[0222] Simulation auxiliary display: provides visual display during the simulation process, including operation control equipment resource display, operation status monitoring, sample information display of selected target samples, 2D situation display, 3D situation display, and simulation progress display of a single sample or sample set;

[0223] Simulation parameter setting: Initialize key simulation parameter settings and modify configuration parameters online during simulation, including network settings, parallel simulation settings, simulation step size settings, etc.

[0224] The operation process of the integrated operation model is described as follows:

[0225] a) Start or switch to the integrated operation mode and enter b);

[0226] b) Load the experimental sample: Select the experimental sample and the number of times to be run in the "Task List" and enter c);

[0227] c) Setting the number of simulation cores: Then perform the initial setting of the number of parallel simulation cores, and modify it online or offline according to the requirements and the CPU and memory usage of the "Resource Window" during operation, and enter d);

[0228] d) Simulation start and stop control: Use the "Control Tools" menu bar to control the simulation start and stop, including starting simulation, pausing simulation and resetting simulation, and enter e);

[0229] e) Simulation display: Visual display is supported during the simulation process: view the running status and key parameters of each sample according to the information displayed in the "Run Window"; select the target sample by clicking the sample monitoring view, and view the main configuration information and simulation progress of the sample in the "Sample Information" window, or view the simulation progress of each sample through the assumed experimental sample progress bar in the "Task List"; after selecting the target sample, click the "2D" or "3D" button in the "Sample Information" window to view the two-dimensional / three-dimensional situation display of the experimental sample in the pop-up window;

[0230] f) End.

[0231] The functional components of the situation monitoring model are as follows: Figure 7 As shown, it mainly consists of four parts: data management, scenario simulation operation, scenario simulation assistance and situation display setting.

[0232] Data management: The data managed by the data comes from the scenario editing. The main data is divided into the scenario file and its supporting model configuration data. The scenario plot is initialized on the interface by parsing these two parts, and the simulation initialization information is parsed and stored in the memory. The input is the scenario data file (.sdata), process data definition file (.pdef), and result data definition file (.rdef). The output is the simulation process data (.pdata) and simulation result data (.rdata);

[0233] Simulation operation: First, the simulation entity and model engine are initialized according to the simulation settings and assumption information, and the simulation operation is controlled by the simulation control tool to finally generate the simulation process and result data.

[0234] Scenario simulation assistance: The main function of the scenario simulation software is to provide users with a visual interface for the simulation process, and therefore provides users with a series of auxiliary functions: an overview interface for hypothetical scenarios, a query interface for simulation unit attribute information, a display and printing interface for simulation key events, an online statistical interface for detailed information, a two- and three-dimensional display switching function, etc.

[0235] Situation display settings: The entity plotting settings interface can control the visibility of plots such as graphics, airspace, and routes. The map operation settings can set the map type, two- and three-dimensional switching, and map zoom ratio, etc.

[0236] The main workflow of the situation monitoring model is as follows.

[0237] a) Open the scenario file;

[0238] b) Perform simulation settings, including simulation step size, simulation acceleration ratio, random algorithm, data recording path, etc.;

[0239] c) Start simulation and control simulation operation;

[0240] d) After the simulation is completed, the process data and result data are automatically saved according to the set path.

[0241] The running process of the statistical analysis model is described as follows:

[0242] a) Start or switch to the statistical analysis model and enter b);

[0243] b) Task type determination: First, according to user needs, choose to perform performance evaluation calculation and analysis tasks or display and analyze existing performance evaluation results. If you choose to display and analyze results, you need to load the existing performance evaluation calculation result data, and perform statistical analysis, correlation analysis, sensitivity analysis and other analysis and display of the calculation results, and output display reports, charts, etc. If you choose to perform performance evaluation calculation and analysis tasks, the system executes the subsequent regular performance evaluation task process and enters c);

[0244] c) Create / open performance evaluation scheme: Open an existing performance evaluation scheme user configuration from a local file or database, or create a blank performance evaluation scheme user configuration. According to the user configuration content, display the existing indicator system scheme content and the existing custom operator library content, and enter d);

[0245] d) Loading experimental scheme result design information: The experimental scheme result design information includes information such as the definition fields of the model simulation output results, and provides the lowest-level unit information for building the indicator system. For a newly created blank performance evaluation scheme, it is necessary to load the experimental scheme result design information that the current system can provide; for an opened performance evaluation scheme, it is assumed that it already includes the experimental scheme result design information it needs. The relevant information will eventually be displayed in a list on the custom operator interface, and then enter e);

[0246] e) Construction of indicator system / custom operator design: According to the existing indicator system scheme or custom operator library provided, select the target for content display and modification; or create a blank indicator system scheme or a blank custom operator scheme. In the indicator system construction interface, you can drag and drop the available operators in the operator library to define the indicator operator weights, calculation relationships, dimensions, ranges and other information to build the indicator system results. In the custom operator interface, you can drag and drop the experimental scheme result design information, the available operators in the operator library, the operation rules, etc. to build a custom operator, and you can save the operator object into the library to form iterative utilization and enter f);

[0247] f) Loading of simulation experiment data: Open the assumed simulation experiment design configuration scheme and the corresponding simulation result data in the specified directory. The experimental design configuration scheme and simulation result data should be saved in a unified manner and correspond to each other. The experimental design configuration scheme, simulation result data, and experimental scheme result design information are the three main basic data supporting the operation of the statistical analysis model. The simulation result data must be able to match the output result field in the experimental result design. Therefore, during the loading process, the compatibility of the simulation result data and the experimental result design information must be checked. If the check fails, a prompt should appear and return to the simulation experiment data path selection node, or the experimental result design information loading node for reselection. If the check passes, execute the next step and enter g);

[0248] g) Extraction and storage of simulation result data: Extract simulation result data according to the output result fields defined in the experimental result design information, and store all extracted data in the database to speed up the subsequent indicator evaluation calculation speed, and enter h);

[0249] h) Performance evaluation calculation: directly obtain data from the database, and calculate the corresponding indicator value according to the constructed indicator system and the definition of the custom operator. The corresponding results are displayed on the indicator system display interface. After the calculation is completed, jump directly to the result analysis display interface and enter i);

[0250] i) Result analysis display and export: You can directly load existing performance evaluation calculation data from the specified directory, or receive the performance evaluation calculation result data of the current calculation. During the display process, read the experimental design configuration plan, evaluate the calculation results and display them, and perform statistical, correlation and other analyses. The performance evaluation calculation data is packaged with the required experimental design configuration plan and experimental plan result design information, and exported to the specified path; the display chart can be directly exported to the specified path; the corresponding calculation data can also be formed into a report in the specified format and exported, enter j);

[0251] j) End.

[0252] It can be seen that the present invention discloses a method for designing a scenario simulation experiment, which improves the efficiency of the scenario simulation experiment by designing an experiment for the simulation scenario. The machine learning model is used to learn the mechanism in the simulation evaluation process, so as to achieve a fast and efficient evaluation of the scenario simulation results and improve the design efficiency.

[0253] Fig.10 is a schematic diagram of a statistical analysis model disclosed in an embodiment of the present invention; Fig.11 It is a schematic diagram of the workflow of the statistical analysis model disclosed in the embodiment of the present invention.

[0254] Embodiment 3

[0255] See also Fig.12 , Fig.12 Schematic diagram of a device for designing a simulation experiment disclosed in an embodiment of the present invention. Fig.12 The described device for designing a simulation experiment is applied to the field of experimental simulation, and is used to design an experiment for a simulation scenario, thereby improving the efficiency of the simulation experiment. Fig.12 As shown, the assumption simulation experiment design device may include the following operations:

[0256] S301, a simulation scenario module, is used to process the experimental requirement information to obtain a simulation scenario;

[0257] S302, an experiment design module, used to process the simulation scenario using the experiment design model to obtain simulation experiment samples and experimental sample space description information;

[0258] S303, an integrated operation module, used to process the simulation experiment samples and the experimental sample space description information using the integrated operation model to obtain the experimental simulation results;

[0259] S304, a situation monitoring module, using a situation monitoring model to perform situation monitoring on the simulation scenario to obtain simulation process data;

[0260] S305, a comprehensive evaluation module is used to comprehensively evaluate the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result.

[0261] It can be seen that the present invention discloses a method for designing a scenario simulation experiment, which improves the efficiency of the scenario simulation experiment by designing an experiment for the simulation scenario. The machine learning model is used to learn the mechanism in the simulation evaluation process, so as to achieve a fast and efficient evaluation of the scenario simulation results and improve the design efficiency.

[0262] Embodiment 4

[0263] See also Fig.13 , Fig.13 Schematic diagram of another device for designing a simulation experiment according to an embodiment of the present invention. Fig.13 The described device for designing a simulation experiment is applied to the field of experimental simulation, and is used to design an experiment for a simulation scenario, thereby improving the efficiency of the simulation experiment. Fig.13 As shown, the assumption simulation experiment design device may include the following operations:

[0264] A memory 401 storing executable program codes;

[0265] a processor 402 coupled to the memory 401;

[0266] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the hypothetical simulation experiment design method described in the first and second embodiments.

[0267] Embodiment 5

[0268] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps in the hypothetical simulation experiment design method described in Embodiment 1 and Embodiment 2.

[0269] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0270] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0271] Finally, it should be noted that the hypothetical simulation experiment design method and device disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions recorded in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for designing a scenario simulation experiment, characterized in that: The method comprises: S1, process the experimental requirement information to obtain simulation scenarios; S2, using the experimental design model to process the simulation scenario to obtain simulation experiment samples and experimental sample space description information; S3, using the integrated operation model, processing the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results; S4, using a situation monitoring model to perform situation monitoring on the simulation scenario to obtain simulation process data; S5, comprehensively evaluating the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result.

2. The method for designing a scenario simulation experiment according to claim 1, characterized in that: The processing of the experimental requirement information to obtain the simulation scenario includes: S11, parsing the experimental requirement information to obtain simulation information; the simulation information includes simulation scenario information, simulation device information, simulation deployment information and evaluation index information; S12, using a preset simulation model, processing the simulation information to obtain a simulation scenario.

3. The scenario simulation experiment design method according to claim 1, characterized in that: The experimental design model is used to process the simulation scenario to obtain simulation experiment samples and experimental sample space description information, including: S21, performing scenario analysis on the simulation scenario to obtain simulation model parameters; S22, extracting the simulation model parameters to obtain key model parameters; S23, optimizing the key model parameters to obtain optimized model parameters; S24, processing the optimization model parameters to obtain a simulation scenario sample space description file and a simulation scenario sample file; S25, setting the number of operations and the execution order of the simulation scenario sample space description file and the simulation scenario sample file to obtain simulation experiment samples and experiment sample space description information.

4. The method for designing a scenario simulation experiment according to claim 3, characterized in that: The performing scenario analysis on the simulation scenario to obtain simulation model parameters includes: S211, performing scenario analysis on the simulation scenario to obtain scenario file model information; S212, performing integrity check on the assumed file model information to obtain a integrity check result; S213, when the result of the integrity check is no, perform model component design on the assumed file model information; when the result of the integrity check is yes, perform model parameter extraction on the assumed file model information to obtain simulation model parameters.

5. The scenario simulation experiment design method according to claim 1, characterized in that: The integrated operation model is used to process the simulation experiment samples and the experimental sample space description information to obtain the experimental simulation results, including: S31, loading the simulation experiment sample and the experiment sample space description information; S32, setting the number of runs and initial parameters of the simulation experiment; S33, processing the simulation experiment samples and the experimental sample space description information according to the number of runs and the initial parameters to obtain an experimental simulation result.

6. The method for designing a scenario simulation experiment according to claim 1, characterized in that: The method of using the situation monitoring model to monitor the situation of the simulation scenario and obtain simulation process data includes: S41, parsing the simulation scenario to obtain a scenario file and model configuration data of the scenario file; S42, performing simulation and deduction settings on the assumption file and the model configuration data to obtain simulation and deduction configuration information; S43, performing simulation operation according to the simulation deduction configuration information to obtain simulation process data.

7. The scenario simulation experiment design method according to claim 1, characterized in that: The comprehensive evaluation of the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result includes: S51, according to user needs, selecting to perform performance evaluation calculation and analysis tasks or to perform display and analysis tasks on existing performance evaluation results; S52, when selecting the task of displaying and analyzing the existing performance evaluation results, loading the existing simulation process data and experimental simulation results, and performing statistical analysis, correlation analysis and sensitivity analysis on the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result; S53, when selecting to perform the performance evaluation calculation and analysis task, loading the experimental scheme result design information; S54, processing the experimental scheme result design information to obtain a comprehensive evaluation result.

8. A device for designing a scenario simulation experiment, characterized in that: The device comprises: The simulation scenario module is used to process the experimental requirement information and obtain the simulation scenario; An experimental design module is used to process the simulation scenario using an experimental design model to obtain simulation experiment samples and experimental sample space description information; An integrated operation module, used to process the simulation experiment samples and the experimental sample space description information using the integrated operation model to obtain the experimental simulation results; A situation monitoring module, using a situation monitoring model to monitor the situation of the simulation scenario and obtain simulation process data; The comprehensive evaluation module is used to comprehensively evaluate the simulation process data and the experimental simulation results to obtain a comprehensive evaluation result.

9. A device for designing a scenario simulation experiment, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the hypothetical simulation experiment design method as described in any one of claims 1-7.

10. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called, they are used to execute the hypothetical simulation experiment design method as described in any one of claims 1-7.

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