Simulation test design method and device, terminal and storage medium

Abstract

The application provides a simulation test design method and device, a terminal and a storage medium. The method comprises the following steps: obtaining a plurality of test objects in a test scheme; wherein the test object is an influencing factor of a test index in the test scheme; obtaining a test design model; displaying a parameter acquisition interface corresponding to the test design model, and acquiring a test design parameter input by a user; and combining each test object based on the test design model and the test design parameter to obtain a plurality of test scheme samples for simulation test. After obtaining the test objects, the test objects can be combined and processed according to the test design model and the corresponding test design parameter selected or recommended by the user, and the test scheme samples required by the user can be automatically generated, so that the efficiency is high and the accuracy is high, and the effect of test design is improved.

Description

Technical Field

[0001] This invention relates to the field of experimental design technology, and in particular to a simulation experimental design method, apparatus, terminal and storage medium. Background Technology

[0002] Simulation experiments are a crucial part of scientific research. Before implementation, experimental design is essential, including determining the experimental scenario and sample experimental schemes. Different processing methods are then applied to each sample scheme to verify the impact of various experimental factors on the experimental indicators. To ensure experimental effectiveness, the experimental design needs to consider factors such as the type of problem to be solved, the degree of generality expected of the conclusions, the desired power level for verification, the homogeneity of experimental units, and the cost and time required for each experiment. Appropriate factors and levels are then selected to provide the specific procedures for experimental implementation and the framework for data analysis.

[0003] Currently, experimental design samples usually need to be selected, combined, and designed manually, which is inefficient and the designed experimental design samples are not easy to meet the experimental requirements, resulting in poor simulation results. Summary of the Invention

[0004] This invention provides a simulation experiment design method, device, terminal, and storage medium to address the problem of poor simulation experiment performance.

[0005] In a first aspect, embodiments of the present invention provide a simulation experiment design method, including:

[0006] Obtain multiple subjects in the experimental design; among them, the subjects are the factors influencing the experimental indicators in the experimental design.

[0007] Obtain the experimental design model;

[0008] Display the parameter acquisition interface corresponding to the experimental design model, and obtain the experimental design parameters input by the user;

[0009] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple experimental scheme samples for simulation experiments.

[0010] In one possible implementation, obtaining the experimental design model includes:

[0011] Display the experimental design model selection page and obtain the user's selection action; or,

[0012] The recommended experimental design model is determined based on the number of subjects and the type of each subject; among them, the recommended experimental design model is determined based on machine learning or knowledge graph methods.

[0013] In one possible implementation, the experimental design model includes one or more of the following: a completely randomized design model, a block randomized design model, and an orthogonal design model.

[0014] In one possible implementation, the experimental design model is a completely randomized design model; the experimental design parameters include the number of subjects n in each experimental scheme sample, the number of subjects m in each experimental group, the number of experimental groups q, and the number of trials p;

[0015] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple sample experimental schemes for simulation experiments, including:

[0016] Based on m and q, one level is randomly selected from each subject and combined to obtain q experimental groups;

[0017] Based on p, the experimental order of each experimental group is randomly arranged to obtain p*q experimental scheme samples.

[0018] In one possible implementation, the experimental design model is a block randomized design model; the experimental design parameters include obtaining the number of trials p;

[0019] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple sample experimental schemes for simulation experiments, including:

[0020] The number of blocks is determined based on p, and the number of cells within a block is determined based on the number of experimental treatments;

[0021] Display the subject acquisition interface and acquire the subjects included in each cell as input by the user;

[0022] For each subject in each community, a sample of experimental schemes is randomly selected to form a sample of experimental schemes, resulting in multiple sample experimental schemes.

[0023] In one possible implementation, the experimental design model is an orthogonal design model; the experimental design parameters include the number of subjects n in each experimental scheme sample;

[0024] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple sample experimental schemes for simulation experiments, including:

[0025] The number of trials, p, is determined based on n and the number of levels.

[0026] The levels of each subject are mapped to an orthogonal table with p rows and n columns, resulting in p experimental scheme samples.

[0027] In one possible implementation, multiple subjects in the experimental design are obtained, including:

[0028] Multiple subjects are selected in the experimental protocol based on at least one of the following:

[0029] Machine learning, semantic analysis, and knowledge graphs.

[0030] Secondly, embodiments of the present invention provide a simulation experiment design apparatus, comprising:

[0031] The first acquisition module is used to acquire multiple subjects in the experimental design; wherein, the subjects are the influencing factors of the experimental indicators in the experimental design.

[0032] The second acquisition module is used to acquire the experimental design model;

[0033] The display module is used to display the parameter acquisition interface corresponding to the experimental design model and to acquire the experimental design parameters input by the user.

[0034] The sample generation module is used to combine various test subjects based on the experimental design model and experimental design parameters to obtain multiple experimental scheme samples for simulation experiments.

[0035] Thirdly, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the first aspect or any possible implementation of the first aspect.

[0036] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the first aspect or any possible implementation of the first aspect.

[0037] The beneficial effects of the simulation experiment design method, device, terminal, and storage medium provided in the embodiments of the present invention are as follows:

[0038] After obtaining the test subjects, this invention can combine and process the test subjects according to the experimental design model and corresponding experimental design parameters selected or recommended by the user, and automatically generate the experimental scheme sample required by the user. It is highly efficient and accurate, thereby improving the effect of experimental design. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the implementation of a simulation experiment design method according to an embodiment of the present invention;

[0041] Figure 2 This is a flight path diagram of a drone provided in an embodiment of the present invention;

[0042] Figure 3 This is a subject selection page provided in an embodiment of the present invention;

[0043] Figure 4 This is a parameter acquisition page provided in an embodiment of the present invention;

[0044] Figure 5 This is a sample display page of an experimental scheme provided in an embodiment of the present invention;

[0045] Figure 6 This is a schematic diagram of the structure of a simulation experiment design device provided in an embodiment of the present invention;

[0046] Figure 7 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0047] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0049] See Figure 1 The flowchart illustrating the implementation of the simulation experiment design method provided in this embodiment of the invention is shown below:

[0050] Step 101: Obtain multiple subjects in the experimental design; where the subjects are the influencing factors of the experimental indicators in the experimental design.

[0051] In this embodiment, the simulation experiment design method can be implemented based on a simulation experiment design platform. The experiment plan is a specific idea for conducting the experiment determined by the user. It can be uploaded by the user to the simulation experiment design platform, or the user can directly select the various elements of the experiment plan in the simulation experiment design platform to generate the experiment plan required by the user.

[0052] The purpose of an experiment is to elucidate the effects of a treatment factor on the test subjects. Therefore, the test subjects, treatment factor, and experimental effect are the three basic elements of experimental design. The test subjects refer to the objects to which the treatment factor acts, determined by the research objective; their correct selection has a significant impact on the experimental results. The treatment factor, also known as the test factor, is the specific experimental measure administered to the test subjects by the researcher according to the research objective. The experimental effect is the test subjects' response or outcome under the influence of the treatment factor. The experimental effect is manifested through the observation of experimental indicators. The test subject's level refers to the state of the test subject; for example, in an experiment, temperature is the test subject, and 100℃, 110℃, and 120℃ are three temperature levels.

[0053] Step 102: Obtain the experimental design model.

[0054] In this embodiment, for different experimental objectives and test subjects, appropriate experimental design models can be selected based on the characteristics of the test subjects, thereby designing suitable experimental scheme samples. To meet the diverse needs of users, the simulation experimental design platform is equipped with a variety of experimental design models, each corresponding to a variety of experimental design methods for users to choose from.

[0055] Step 103: Display the parameter acquisition interface corresponding to the experimental design model and acquire the experimental design parameters input by the user.

[0056] In this embodiment, parameters such as the number of experiment repetitions, the number of samples in the experimental design, and the number of subjects in the samples need to be set by the user according to the experimental requirements. Furthermore, different experimental design models require different types of parameters from the user. Displaying the parameter acquisition interface corresponding to the experimental design model can provide prompts to the user and guide them to input the correct type of parameters.

[0057] Step 104: Based on the experimental design model and experimental design parameters, combine the various test subjects to obtain multiple experimental scheme samples for simulation experiments.

[0058] In this embodiment, after determining the experimental design model and experimental design parameters, the platform can automatically combine each subject according to the logic of the experimental design model to obtain the experimental scheme sample required by the user. It can achieve true random combination, meet user needs, and is efficient and easy to use.

[0059] In one possible implementation, obtaining the experimental design model includes:

[0060] Display the experimental design model selection page and obtain the user's selection action; or,

[0061] The recommended experimental design model is determined based on the number of subjects and the type of each subject; among them, the recommended experimental design model is determined based on machine learning or knowledge graph methods.

[0062] In this embodiment, the experimental design model can be selected by the user or recommended by the experimental design platform. Specifically, the experimental design platform can store recommendation models. These models take one or more of the following as input: subject type, number of subjects, and types of each subject, and output an experimental design model. They recommend experimental design models based on the type and characteristics of the subjects, helping the user select a suitable model. The recommendation model can be based on machine learning, extracting relationships between subjects to recommend experimental design models, or it can be based on a knowledge graph, searching through pre-stored multivariate relationship groups between subjects and experimental design models to determine the most suitable experimental design model for the user-provided subjects and recommending it to the user.

[0063] In one possible implementation, the experimental design model includes one or more of the following: a completely randomized design model, a block randomized design model, and an orthogonal design model.

[0064] In this embodiment, the preset experimental design model may include at least a completely randomized design model, a block randomized design model, and an orthogonal design model. When selecting an experimental design model, the user can select multiple experimental design models simultaneously to design multiple sets of experimental scheme samples in different ways, and then select a suitable experimental scheme sample for testing, or test all the experimental scheme samples in various different ways.

[0065] In one possible implementation, the experimental design model is a completely randomized design model; the experimental design parameters include the number of subjects n in each experimental scheme sample, the number of subjects m in each experimental group, the number of experimental groups q, and the number of trials p;

[0066] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple sample experimental schemes for simulation experiments, including:

[0067] Based on m and q, one level is randomly selected from each subject and combined to obtain q experimental groups;

[0068] Based on p, the experimental order of each experimental group is randomly arranged to obtain p*q experimental scheme samples.

[0069] In this embodiment, the fully randomized design model uses the design logic of a fully randomized design. A fully randomized design randomly divides all test materials into several groups based on the number of treatments, and then implements different treatments in each group. This design ensures that each sample of test material has an equal chance to receive any treatment, without being influenced by the subjective bias of the experimenter.

[0070] In one possible implementation, the experimental design model is a block randomized design model; the experimental design parameters include obtaining the number of trials p;

[0071] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple sample experimental schemes for simulation experiments, including:

[0072] The number of blocks is determined based on p, and the number of cells within a block is determined based on the number of experimental treatments;

[0073] Display the subject acquisition interface and acquire the subjects included in each cell as input by the user;

[0074] For each subject in each community, a sample of experimental schemes is randomly selected to form a sample of experimental schemes, resulting in multiple sample experimental schemes.

[0075] In this embodiment, the block randomized design model utilizes the design logic of block randomized design. Specifically, the block randomized design involves grouping subjects according to attributes that may affect the experimental results (non-random), such as grouping animals by sex and weight, or patients by age, occupation, and disease condition. Then, subjects within each block are randomly assigned to their respective treatment groups. The design characteristics of a randomized complete block design are that the number of subjects in each block is equal to the number of treatment groups, resulting in more balanced characteristics among subjects within each block. This reduces experimental error and improves the efficiency of statistical hypothesis testing, representing an improvement over a completely randomized design.

[0076] In one possible implementation, the experimental design model is an orthogonal design model; the experimental design parameters include the number of subjects n in each experimental scheme sample;

[0077] Based on the experimental design model and experimental design parameters, various test subjects are combined to obtain multiple sample experimental schemes for simulation experiments, including:

[0078] The number of trials, p, is determined based on n and the number of levels.

[0079] The levels of each subject are mapped to an orthogonal table with p rows and n columns, resulting in p experimental scheme samples.

[0080] In this embodiment, the orthogonal design model utilizes the design logic of orthogonal experimental design. Orthogonal experimental design refers to an experimental design method for studying multiple factors and levels. Based on orthogonality, representative points are selected from a comprehensive experiment for further testing. These representative points are characterized by uniform distribution and comparable consistency. Orthogonal experimental design is a primary method of fractional factorial design. When the experiment involves three or more factors, and there may be interactions between these factors, the workload becomes substantial, even difficult to implement. Orthogonal experimental design is undoubtedly a better choice to address this challenge. The main tool of orthogonal experimental design is the orthogonal array. Experimenters can find the appropriate orthogonal array based on the number of factors, the number of levels, and whether there are interactions. Then, relying on the orthogonality of the array, representative points are selected from the comprehensive experiment for further testing. This allows for achieving results equivalent to a large number of comprehensive experiments with the fewest possible trials. Therefore, using orthogonal arrays to design experiments is an efficient, fast, and economical method for multi-factor experimental design.

[0081] In one possible implementation, multiple subjects in the experimental design are obtained, including:

[0082] Multiple subjects are selected in the experimental protocol based on at least one of the following:

[0083] Machine learning, semantic analysis, and knowledge graphs.

[0084] In this embodiment, the user-provided test plan typically includes a test scenario, test indicators, and test process. For example, to test the communication effect of a drone during flight, the test scenario can be extracted as the flight route, and the test indicator as the communication quality, through semantic analysis. Then, through machine learning or knowledge graphs, the factors affecting the test indicators can be associated with environmental factors based on the test scenario and test indicators. Further subdivision can identify influencing factors including wind, rain, snow, fog, clouds, civilian radiation sources (such as signal towers), regional noise, and other hostile radiation sources, thereby determining the test subjects.

[0085] In a specific embodiment, the detailed steps for designing an experiment based on the simulation experiment design method and simulation experiment design platform provided by the present invention are as follows:

[0086] 1. Develop an experimental plan. The experimental plan involves a certain type of UAV taking off from an airport and flying to the mission area to conduct reconnaissance of suspicious targets. During the reconnaissance, the detected target intelligence will be transmitted back to the ground via a data link. The UAV's flight path is as follows: Figure 2 As shown.

[0087] 2. Extract the main factors affecting UAV link communication, including: wind, rain, snow, fog, clouds, civilian radiation sources (signal towers, etc.), regional noise, and other hostile radiation sources, etc., to obtain the following results: Figure 3 The list of influencing factors is shown, and five of these eight factors—wind, rain, snow, fog, and clouds—are selected as test subjects to study their impact on the UAV data link. Each test subject is categorized as either present or absent.

[0088] 3. Obtain the experimental design model and experimental design parameters. In this embodiment, it is assumed that the experimental design model selected by the user is a completely randomized design model. The experimental design parameters include the number of subjects in each experimental protocol sample n=5, the number of subjects in each experimental group m=3, the number of experimental groups q=10, and the number of trials p=1. Each subject is numbered, and... Figure 4 The parameter acquisition page shows the number of each subject at the top, and the user inputs the number of grouped subjects (3) and the number of groups (10).

[0089] 4. Generate experimental design samples based on a completely randomized design model. Randomly select 3 subjects to form 10 groups, resulting in a total of 10 groups. Then, assign each group to one of the 10 experimental design samples, as shown below. Figure 5 The final 10 test protocol samples are shown. Tests can be conducted based on these test protocol samples.

[0090] The beneficial effects of the simulation experiment design method provided in this embodiment of the invention are as follows:

[0091] After obtaining the test subjects, this invention can combine and process the test subjects according to the experimental design model and corresponding experimental design parameters selected or recommended by the user, and automatically generate the experimental scheme sample required by the user. It is highly efficient and accurate, thereby improving the effect of experimental design.

[0092] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0093] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0094] Figure 6 A schematic diagram of the simulation experiment design device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0095] like Figure 6 As shown, the simulation experiment design device 6 includes:

[0096] The first acquisition module 61 is used to acquire multiple test subjects in the experimental plan; wherein, the test subjects are the influencing factors of the experimental indicators in the experimental plan;

[0097] The second acquisition module 62 is used to acquire the experimental design model;

[0098] Display module 63 is used to display the parameter acquisition interface corresponding to the experimental design model and to acquire the experimental design parameters input by the user.

[0099] The sample generation module 64 is used to combine various test subjects based on the experimental design model and experimental design parameters to obtain multiple experimental scheme samples for simulation experiments.

[0100] In one possible implementation, the second acquisition module 62 is specifically used for:

[0101] Display the experimental design model selection page and obtain the user's selection action; or,

[0102] The recommended experimental design model is determined based on the number of subjects and the type of each subject; among them, the recommended experimental design model is determined based on machine learning or knowledge graph methods.

[0103] In one possible implementation, the experimental design model includes one or more of the following: a completely randomized design model, a block randomized design model, and an orthogonal design model.

[0104] In one possible implementation, the experimental design model is a completely randomized design model; the experimental design parameters include the number of subjects n in each experimental scheme sample, the number of subjects m in each experimental group, the number of experimental groups q, and the number of trials p;

[0105] The sample generation module 64 is specifically used for:

[0106] Based on m and q, one level is randomly selected from each subject and combined to obtain q experimental groups;

[0107] Based on p, the experimental order of each experimental group is randomly arranged to obtain p*q experimental scheme samples.

[0108] In one possible implementation, the experimental design model is a block randomized design model; the experimental design parameters include obtaining the number of trials p;

[0109] The sample generation module 64 is specifically used for:

[0110] The number of blocks is determined based on p, and the number of cells within a block is determined based on the number of experimental treatments;

[0111] Display the subject acquisition interface and obtain the subjects included in each cell as input by the user;

[0112] For each subject in each community, a sample of experimental schemes is randomly selected to form a sample of experimental schemes, resulting in multiple sample experimental schemes.

[0113] In one possible implementation, the experimental design model is an orthogonal design model; the experimental design parameters include the number of subjects n in each experimental scheme sample;

[0114] The sample generation module 64 is specifically used for:

[0115] The number of trials, p, is determined based on n and the number of levels.

[0116] The levels of each subject are mapped to an orthogonal table with p rows and n columns, resulting in p experimental scheme samples.

[0117] In one possible implementation, the first acquisition module 61 is specifically used for:

[0118] Multiple subjects are selected in the experimental protocol based on at least one of the following:

[0119] Machine learning, semantic analysis, and knowledge graphs.

[0120] The beneficial effects of the simulation experiment design device provided in this embodiment of the invention are as follows:

[0121] After obtaining the test subjects, this invention can combine and process the test subjects according to the experimental design model and corresponding experimental design parameters selected or recommended by the user, and automatically generate the experimental scheme sample required by the user. It is highly efficient and accurate, thereby improving the effect of experimental design.

[0122] Figure 7 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 7 As shown, the terminal 7 in this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. When the processor 70 executes the computer program 72, it implements the steps in the various simulation experiment design method embodiments described above, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when the processor 70 executes the computer program 72, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 6 The functions of modules / units 61 to 64 shown.

[0123] For example, the computer program 72 can be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 72 in the terminal 7. For example, the computer program 72 can be divided into... Figure 6 Modules / units 61 to 64 are shown.

[0124] The terminal 7 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal 7 may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of terminal 7 and does not constitute a limitation on terminal 7. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0125] The processor 70 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0126] The memory 71 can be an internal storage unit of the terminal 7, such as a hard disk or memory of the terminal 7. The memory 71 can also be an external storage device of the terminal 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal 7. Furthermore, the memory 71 can include both internal storage units and external storage devices of the terminal 7. The memory 71 is used to store the computer program and other programs and data required by the terminal. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0128] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0129] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0130] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0131] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0133] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various simulation experiment design method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0134] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A simulation experiment design method, characterized in that, include: Multiple subjects are obtained in the experimental design; wherein, the subjects are the influencing factors of the experimental indicators in the experimental design. Obtain the experimental design model; Display the parameter acquisition interface corresponding to the experimental design model, and acquire the experimental design parameters input by the user; Based on the experimental design model and the experimental design parameters, the various test subjects are combined to obtain multiple experimental scheme samples for simulation experiments; The acquisition of the experimental design model includes: The recommended experimental design model is determined based on the number of subjects and the type of each subject; wherein the recommended experimental design model is determined based on machine learning or knowledge graph methods. The experimental design model includes one or more of the following: a completely randomized design model, a block randomized design model, and an orthogonal design model; When the experimental design model is a completely randomized design model, the experimental design parameters include the number of subjects n in each experimental scheme sample, the number of subjects m in each experimental group, the number of experimental groups q, and the number of trials p; When the experimental design model is a block randomized design model, the experimental design parameters include obtaining the number of trials p; When the experimental design model is an orthogonal design model, the experimental design parameters include the number of subjects n in each experimental scheme sample.

2. The simulation experiment design method according to claim 1, characterized in that, When the experimental design model is a completely randomized design model, the experiment, based on the experimental design model and the experimental design parameters, combines various test subjects to obtain multiple experimental scheme samples for simulation experiments, including: Based on m and q, one level is randomly selected from each subject and combined to obtain q experimental groups; Based on p, the experimental order of each experimental group is randomly arranged to obtain p*q experimental scheme samples.

3. The simulation experiment design method according to claim 1, characterized in that, When the experimental design model is a block randomized design model, the experiment involves combining the subjects based on the experimental design model and the experimental design parameters to obtain multiple experimental scheme samples for simulation experiments, including: The number of blocks is determined based on p, and the number of cells within a block is determined based on the number of experimental treatments; Display the subject acquisition interface and acquire the subjects included in each cell as input by the user; For each subject in each community, a sample of experimental schemes is randomly selected to form a sample of experimental schemes, resulting in multiple sample experimental schemes.

4. The simulation experiment design method according to claim 1, characterized in that, When the experimental design model is an orthogonal design model, the experiment involves combining various test subjects based on the experimental design model and the experimental design parameters to obtain multiple experimental scheme samples for simulation experiments, including: The number of trials, p, is determined based on n and the number of levels. The levels of each subject are mapped to an orthogonal table with p rows and n columns, resulting in p experimental scheme samples.

5. The simulation experiment design method according to any one of claims 1 to 4, characterized in that, The acquisition of multiple subjects in the experimental protocol includes: Multiple subjects were extracted based on at least one of the following criteria in the experimental protocol: Machine learning, semantic analysis, and knowledge graphs.

6. A simulation experiment design device, characterized in that, include: The first acquisition module is used to acquire multiple test subjects in the experimental plan; wherein, the test subjects are the influencing factors of the experimental indicators in the experimental plan; The second acquisition module is used to acquire the experimental design model; The display module is used to display the parameter acquisition interface corresponding to the experimental design model and to acquire the experimental design parameters input by the user. The sample generation module is used to combine various test subjects based on the experimental design model and the experimental design parameters to obtain multiple experimental scheme samples for simulation experiments. The second acquisition module is specifically used for: The recommended experimental design model is determined based on the number of subjects and the type of each subject; wherein the recommended experimental design model is determined based on machine learning or knowledge graph methods. The experimental design model includes one or more of the following: a completely randomized design model, a block randomized design model, and an orthogonal design model; When the experimental design model is a completely randomized design model, the experimental design parameters include the number of subjects n in each experimental scheme sample, the number of subjects m in each experimental group, the number of experimental groups q, and the number of trials p; When the experimental design model is a block randomized design model, the experimental design parameters include obtaining the number of trials p; When the experimental design model is an orthogonal design model, the experimental design parameters include the number of subjects n in each experimental scheme sample.

7. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5 above.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5 above.

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