A visual perception training system based on digital twins

Abstract

The present invention relates to the field of computer vision technology, and in particular to a visual perception training system based on digital twins, including a project feature acquisition module; a project evaluation module for determining the complexity of a training project; an environment acquisition module for determining the acquisition of environmental feature data required for the training project; an environment simulation module for building a training simulation environment; a data acquisition module for collecting training feature data during a training simulation process; a matching degree determination module for determining the matching degree of user operations; a proficiency determination module for determining the proficiency of a user's training simulation process; an effect evaluation module for determining an effect evaluation of a training system; and an adjustment module for determining the adjustment of the environment acquisition module or the matching degree determination module according to the growth rate of proficiency. The present invention overcomes the problem of low accuracy in monitoring and controlling the training process in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of computer vision technology, and in particular to a visual perception training system based on digital twins. Background Art

[0002] Digital twins make full use of data such as physical models, sensor updates, and operation history, integrate multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, and complete mapping in virtual space, thereby reflecting the entire life cycle of the corresponding physical equipment. Digital twins are a concept that transcends reality and can be regarded as a digital mapping system of one or more important, interdependent equipment systems. Digital twins are a universally applicable theoretical and technical system that can be applied in many fields, and are widely used in product design, product manufacturing, medical analysis, engineering construction, and other fields.

[0003] Chinese patent application publication number: CN117153011A, discloses a digital twin training method, including the following steps: Step S1: Create a virtual model, the virtual model is used to build a virtual image of the physical system with the key core of the training equipment; Step S2: Construct the twin perception of the virtual model, and transmit the data of the training equipment to the virtual model through the twin perception to realize the twin-level simulation and monitoring of the virtual model; Step S3: Transmit interactive information to the virtual model through the interactive device, and make real-time changes to the virtual model according to the twin perception and interactive information to realize virtual training.

[0004] It can be seen that the digital twin training method described above has the following problems: Although the method realizes virtual training by constructing a virtual model and twin perception of the virtual model, the method does not collect and monitor the characteristic data during the training process, resulting in low accuracy in monitoring and controlling the training process, which leads to poor training results and low working efficiency of the training system. Summary of the invention

[0005] To this end, the present invention provides a visual perception training system based on digital twins to overcome the problem that the prior art does not collect and monitor the characteristic data in the training process, resulting in low accuracy in monitoring and controlling the training process, which leads to poor training results and low working efficiency of the training system.

[0006] To achieve the above objectives, the present invention provides a visual perception training system based on digital twins, comprising:

[0007] Project feature collection module, which is used to collect several influencing factors of the practical training project;

[0008] A project evaluation module, which is connected to the project feature collection module and is used to determine the complexity of the practical training project based on several influencing factors of the practical training project;

[0009] An environment collection module, which is connected to the project evaluation module and is used to determine the collection of environmental characteristic data required for the training project according to the complexity of the training project;

[0010] An environment simulation module, which is connected to the environment acquisition module and is used to input the environment feature data into the digital twin model to build a training simulation environment;

[0011] A data acquisition module, connected to the environment simulation module, for collecting training feature data during the training simulation process;

[0012] A matching degree determination module, which is connected to the data acquisition module and is used to determine the matching degree of the user's operation according to the eye movement trajectory and the hand movement trajectory acquired by the data acquisition module, so as to determine the eligibility of the user's practical training operation according to the matching degree;

[0013] A proficiency determination module, which is connected to the data acquisition module and is used to determine the user's proficiency in the training simulation process;

[0014] An effect evaluation module, which is connected to the proficiency determination module and is used to determine the effect evaluation of the training system according to the overall pass rate of the training operations of several users;

[0015] An adjustment module is connected to the environment acquisition module and the matching degree determination module respectively, and is used to determine the adjustment of the environment acquisition module or the matching degree determination module according to the growth rate of the proficiency.

[0016] Furthermore, the environment collection module determines the collection of environment characteristic data according to the complexity of the training project, including determining to collect the environment characteristic data in a preset collection cycle under the condition that the complexity evaluation value is less than or equal to a preset complexity evaluation value.

[0017] Further, it is characterized in that the environment acquisition module determines the collection of training feature data based on the complexity of the training project, including determining the collection ratio of the environment feature data based on the comparison result of the difference between the complexity evaluation value and the preset complexity evaluation value and the difference between the preset complexity evaluation values, under the condition that the complexity evaluation value is greater than the preset complexity evaluation value.

[0018] Furthermore, the matching degree determination module determines the eligibility of the user's practical training operation according to the matching degree, including determining that the user's practical training operation is qualified under the condition that the matching degree evaluation value is greater than or equal to a preset matching degree evaluation value.

[0019] Furthermore, the matching degree determination module determines the eligibility of the user's practical training operation according to the matching degree, including determining that the user's practical training operation is unqualified under the condition that the matching degree evaluation value is less than a preset matching degree evaluation value.

[0020] Furthermore, the effect evaluation module determines the effect evaluation of the training system according to the overall pass rate of the training operations of several users, including evaluating the effect of the training system based on the average value of the proficiency evaluation values ​​of several users and determining the effect evaluation value under the condition that the overall pass rate is less than or equal to the preset pass rate.

[0021] Furthermore, the effect evaluation module determines the effect evaluation of the training system according to the overall pass rate of the training operations of several users, including evaluating the effect of the training system based on the proficiency evaluation value of a single user and determining the effect evaluation value under the condition that the overall pass rate is greater than a preset pass rate.

[0022] Furthermore, the adjustment module is used to determine the adjustment of the environment acquisition module or the matching degree determination module according to the proficiency growth rate, including determining whether the training effect meets the standard based on the comparison result of the proportion of users whose proficiency growth rate is greater than the standard growth rate and the preset proportion, and determining the adjustment of the environment acquisition module or the matching degree determination module.

[0023] Furthermore, the adjustment module determines whether the training effect meets the standard according to the comparison result of the proportion of users whose proficiency growth rate is greater than the standard growth rate and the preset proportion, including:

[0024] If the proportion of users whose proficiency growth rate is greater than the standard growth rate is greater than or equal to the preset proportion, it is determined that the training effect has reached the standard;

[0025] Under the condition that the proportion of users whose proficiency growth rate is greater than the standard growth rate is less than the preset proportion, it is determined that the training effect does not meet the standard, and the environment acquisition module or the matching degree determination module is adjusted.

[0026] Furthermore, under the condition that the training effect does not meet the standard, the adjustment module determines the adjustment of the environment acquisition module or the matching degree determination module according to the comparison result between the difference between the proportion of people and the preset proportion and the difference between the preset proportions, including:

[0027] Under the condition that the difference between the proportion of people and the preset proportion is less than or equal to the preset proportion difference, the preset matching degree evaluation value is adjusted according to the adjustment coefficient;

[0028] Under the condition that the difference between the proportion of the number of people and the preset proportion is greater than the preset proportion difference, the preset collection period or collection ratio is adjusted according to the adjustment coefficient.

[0029] Compared with the prior art, the beneficial effect of the present invention lies in that the environment acquisition module is set to select different acquisition methods according to the complexity of the training project. Selecting a smaller amount of data for training projects with low complexity can reduce resource waste and improve system efficiency. The right amount of materials can make the user interface more concise and easy to understand; collecting a larger amount of data for training projects with high complexity can ensure the comprehensiveness and accuracy of the digital twin model when constructing a simulation environment; by selecting different amounts of data to be collected according to the complexity of the training project, greater flexibility and scalability can be provided for system updates and maintenance.

[0030] Furthermore, the present invention, by setting the data acquisition module, collects eye movement trajectories and hand movement trajectories and analyzes their matching degree, and determines the eligibility of user training operations by the matching degree, thereby improving the accuracy and comprehensiveness of system evaluation, and at the same time laying a good foundation for system maintenance, thereby improving the quality and effectiveness of the system.

[0031] Furthermore, the present invention sets an effect evaluation module to determine the evaluation method of the practical training effect of the training system according to the overall pass rate of the practical training operations of several users. By selecting different evaluation methods in different situations, the effect of the system can be evaluated more comprehensively, thereby improving the training effect.

[0032] Furthermore, the present invention determines that the evaluation of the system's effectiveness combines the two factors of user proficiency growth and system stability. It can not only judge the system's ability to improve training results, but also judge whether the system can operate normally under different conditions. It not only helps to have a more comprehensive understanding of the system's performance but also provides strong support for the system's optimization and long-term development.

[0033] Furthermore, the present invention sets an adjustment module to determine the adjustment of the environment acquisition module or the matching degree determination module according to the growth rate of the proficiency level. When the training effect does not meet the standard, the adjustment module adopts different adjustment methods to achieve more accurate adjustment of the system, thereby improving the training effect to a certain extent. At the same time, a specific adjustment coefficient is set during the adjustment process to avoid excessive adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1It is a structural schematic diagram of a visual perception training system based on digital twins according to an embodiment of the present invention;

[0035] Figure 2 This is a workflow diagram of the environment acquisition module in the visual perception training system based on digital twins according to an embodiment of the present invention;

[0036] Figure 3 This is a workflow diagram of the effect evaluation module in the visual perception training system based on digital twins according to an embodiment of the present invention;

[0037] Figure 4 This is a workflow diagram of the adjustment module in the visual perception training system based on digital twins described in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0040] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0041] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. Example

[0042] See also Figure 1-Figure 4 As shown, Figure 1 It is a structural schematic diagram of a visual perception training system based on digital twins according to an embodiment of the present invention; Figure 2 This is a workflow diagram of the environment acquisition module in the visual perception training system based on digital twins according to an embodiment of the present invention; Figure 3 This is a workflow diagram of the effect evaluation module in the visual perception training system based on digital twins according to an embodiment of the present invention; Figure 4 This is a workflow diagram of the adjustment module in the visual perception training system based on digital twins described in an embodiment of the present invention.

[0043] The embodiment of the present invention provides a visual perception training system based on digital twins, including:

[0044] Project feature collection module, which is used to collect several influencing factors of the practical training project;

[0045] A project evaluation module, which is connected to the project feature collection module and is used to determine the complexity of the practical training project based on several influencing factors of the practical training project;

[0046] An environment collection module, which is connected to the project evaluation module and is used to determine the collection of environmental characteristic data required for the training project according to the complexity of the training project;

[0047] An environment simulation module, which is connected to the environment acquisition module and is used to input the environment feature data into the digital twin model to build a training simulation environment;

[0048] A data acquisition module, connected to the environment simulation module, for collecting training feature data during the training simulation process;

[0049] A matching degree determination module, which is connected to the data acquisition module and is used to determine the matching degree of the user's operation according to the eye movement trajectory and the hand movement trajectory acquired by the data acquisition module, so as to determine the eligibility of the user's practical training operation according to the matching degree;

[0050] A proficiency determination module, which is connected to the data acquisition module and is used to determine the user's proficiency in the training simulation process;

[0051] An effect evaluation module, which is connected to the proficiency determination module and is used to determine the effect evaluation of the training system according to the overall pass rate of the training operations of several users;

[0052] The adjustment module is connected to the environment acquisition module and the matching degree determination module respectively, and is used to determine the adjustment of the environment acquisition module or the matching degree determination module according to the growth rate of the proficiency.

[0053] Specifically, the project feature collection module collects several influencing factors of the training project, including skill variety, technical difficulty, resource requirements, project scale, and project risk.

[0054] Specifically, the project evaluation module determines the complexity of the training project by calculating a complexity evaluation value P of the training project.

[0055] The complexity evaluation value P is calculated according to the following formula:

[0056] Among them, P represents the complexity evaluation value, represents the score of the factor that affects the training project. Represents the weight coefficient corresponding to the i-th factor.

[0057] In an embodiment of the present invention, the factors affecting the complexity of the training project include skill diversity, technical difficulty, resource requirements, project scale, and project risk, and the corresponding score ranges from 0 to 10. The skill diversity score is determined according to the types of skills required to complete the training project, and the value is 7; the technical difficulty score is 8; the resource requirement score is determined according to the manpower and time required to complete the training project, and the value is 6; the project scale score is determined according to the number of tasks and the amount of data collected in the training project, and the value is 7; the project risk score is 6; the weight coefficients corresponding to each project are 0.2, 0.2, 0.2, 0.2, and 0.2, respectively; at this time, the complexity evaluation value is 6.8.

[0058] Specifically, the environment collection module determines the collection of environment characteristic data according to the complexity of the training project, including determining the collection mode of the training characteristic data according to the comparison result of the complexity evaluation value P and the preset complexity evaluation value P0,

[0059] If the complexity evaluation value P is less than or equal to the preset complexity evaluation value P0, the environment acquisition module determines to acquire the environment feature data in the first acquisition mode;

[0060] If the complexity evaluation value P is greater than the preset complexity evaluation value P0, the environment acquisition module determines to acquire the environment feature data in the second acquisition mode;

[0061] In the embodiment of the present invention, the preset complexity evaluation value is 5.

[0062] Specifically, the environment acquisition module acquires the environment characteristic data in a preset acquisition period T in the first acquisition mode.

[0063] Specifically, the environment acquisition module determines the acquisition ratio E of the environment feature data in the second acquisition mode according to the comparison result of the difference ΔP between the complexity evaluation value P and the preset complexity evaluation value P0 and the preset complexity evaluation value difference ΔP0,

[0064] If the difference ΔP between the complexity evaluation value P and the preset complexity evaluation value P0 is less than or equal to the preset complexity evaluation value difference ΔP0, the first collection ratio E1 is selected to collect the training feature data;

[0065] If the difference ΔP between the complexity evaluation value P and the preset complexity evaluation value P0 is greater than the preset complexity evaluation value difference ΔP0, the second collection ratio E2 is selected to collect the training feature data.

[0066] In the embodiment of the present invention, based on the different complexities of the training projects, the amount and proportion of data that need to be collected are also different. The higher the complexity, the more data is collected and the higher the proportion of image information. The first collection ratio E1 is less than the second collection ratio E2, and the collection ratio is the proportion of image information to the total amount.

[0067] In the embodiment of the present invention, the first acquisition ratio E1 is 0.3, and the second acquisition ratio E2 is 0.5.

[0068] In an embodiment of the present invention, the environment acquisition module selects different acquisition methods according to the complexity of the training projects. Selecting a smaller amount of data for training projects with low complexity can reduce resource waste and improve system efficiency. An appropriate amount of material can make the user interface more concise and easy to understand; collecting a larger amount of data for training projects with high complexity can ensure the comprehensiveness and accuracy of the digital twin model when constructing a simulation environment; by selecting different amounts of data to be collected according to the complexity of the training projects, greater flexibility and scalability can be provided for system updates and maintenance.

[0069] Specifically, the matching degree determination module determines the matching degree evaluation value Q of the user operation according to the eye movement trajectory and the hand movement trajectory collected by the data collection module, so as to determine the eligibility of the user operation according to the matching degree evaluation value Q.

[0070] The matching degree evaluation value Q is determined according to the following formula:

[0071] Among them, Q represents the matching degree evaluation value, n represents dividing the eye movement trajectory and hand movement trajectory into n two-dimensional coordinate points, ( , ) represents the i-th two-dimensional coordinate point of the eye position, ( , ) represents the i-th two-dimensional coordinate point of the hand position.

[0072] Specifically, the matching degree determination module determines whether the training operation of a single user is qualified according to the comparison result of the matching degree evaluation value Q of the eye movement trajectory and the hand movement trajectory and the preset matching degree evaluation value Q0.

[0073] If the matching degree evaluation value Q is greater than or equal to the preset matching degree evaluation value Q0, the user's practical training operation is qualified;

[0074] If the matching degree evaluation value Q is less than the preset matching degree evaluation value Q0, the user's practical training operation is unqualified.

[0075] In the embodiment of the present invention, the preset matching degree evaluation value Q0 is 0.8, and the matching degree evaluation value Q ranges from 0 to 1.

[0076] Specifically, the proficiency determination module determines the user's proficiency in the training simulation composition by determining a proficiency evaluation value B.

[0077] The proficiency evaluation value B is calculated according to the following formula:

[0078] Among them, B represents the proficiency evaluation value, C represents the time to complete the project, Y represents the operation accuracy, and α and β represent weight coefficients.

[0079] In the embodiment of the present invention, the value range of the proficiency evaluation value B is 0-1; the value range of the operation accuracy is 0-1; and the values ​​of the weight coefficients α and β are 0.5 and 0.5 respectively.

[0080] Specifically, the effect evaluation module determines the evaluation method of the training effect of the training system according to the overall pass rate L of the training operations of several users.

[0081] If the overall pass rate L is less than or equal to the preset pass rate L0, the effect evaluation module evaluates the effect of the training system in a first evaluation method;

[0082] If the overall pass rate L is greater than the preset pass rate L0, the effect evaluation module evaluates the effect of the training system in a second evaluation manner.

[0083] Specifically, the effect evaluation module evaluates the effect of the training system based on the average value of the proficiency evaluation values ​​B of several users in the first evaluation mode, and determines the effect evaluation value R;

[0084] Specifically, the effect evaluation module evaluates the effect of the training system based on the proficiency evaluation value B of a single user under the second evaluation mode, and determines the effect evaluation value R.

[0085] The effect evaluation value is calculated according to the following formula:

[0086] Among them, R represents the effect evaluation value, It represents the difference between the proficiency evaluation values ​​of a single user at different times or the difference between the average values ​​of the proficiency evaluation values ​​of multiple users. N represents the system stability, and its value is obtained by analyzing whether the system fails within a fixed time. , Represents the corresponding weight coefficient.

[0087] In the embodiment of the present invention, the weight coefficient The values ​​are 0.5 and 0.5 respectively.

[0088] In the embodiment of the present invention, the formula for determining the effect evaluation value combines the two factors of the growth of user proficiency and system stability. It can not only judge the system's ability to improve the training effect, but also judge whether the system can operate normally under different conditions. It not only helps to have a more comprehensive understanding of the system's performance, but also provides strong support for the optimization and long-term development of the system.

[0089] Specifically, the adjustment module is used to determine the adjustment of the environment acquisition module or the matching degree determination module according to the proficiency growth rate M, including determining whether the training effect meets the standard according to the comparison result of the proportion Z of the number of users whose proficiency growth rate is greater than the standard growth rate M0 and the preset proportion Z0,

[0090] If the proportion Z of users whose proficiency growth rate M is greater than the standard growth rate M0 is greater than or equal to the preset proportion Z0, the training effect reaches the standard;

[0091] If the user percentage Z whose proficiency growth rate M is greater than the standard growth rate M0 is less than the preset percentage Z0, the training effect does not meet the standard.

[0092] Specifically, the proficiency growth rate M is determined according to the following formula:

[0093] Among them, M represents the proficiency growth rate, m means that a single user has conducted m training sessions, represents the j-th proficiency evaluation value.

[0094] In the embodiment of the present invention, the value of the standard growth rate M0 is 0.05.

[0095] Specifically, when the training effect does not meet the standard, the adjustment module determines the adjustment method according to the comparison result of the difference ΔZ between the number of people proportion Z and the preset proportion Z0 and the preset proportion difference ΔZ0.

[0096] If the difference ΔZ between the proportion Z of the number of people and the preset proportion Z0 is less than or equal to the preset proportion difference ΔZ0, the adjustment module adjusts the preset matching degree evaluation value Q0 according to the adjustment coefficient X1;

[0097] If the difference ΔZ between the proportion Z of the number of people and the preset proportion Z0 is greater than the preset proportion difference ΔZ0, the adjustment module adjusts the preset collection period T or the collection ratio E according to the adjustment coefficient X2.

[0098] In the embodiment of the present invention, the preset proportion Z0 is set to 90%, and the preset proportion difference ΔZ0 is set to 10%.

[0099] Specifically, the adjustment coefficient X1 is , where ΔZ is the difference between the proportion of people Z and the preset proportion Z0, and the adjusted preset matching degree evaluation value is Qq= ×Q0.

[0100] Specifically, the adjustment coefficient X2 is , the adjusted preset collection period T or collection ratio E is Tt=X2×T or Ee=X2×E.

[0101] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A visual perception training system based on digital twins, characterized in that: include: Project feature collection module, which is used to collect several influencing factors of the practical training project; A project evaluation module, which is connected to the project feature collection module and is used to determine the complexity of the practical training project based on several influencing factors of the practical training project; An environment collection module, which is connected to the project evaluation module and is used to determine the collection of environmental characteristic data required for the training project according to the complexity of the training project; An environment simulation module, which is connected to the environment acquisition module and is used to input the environment feature data into the digital twin model to build a training simulation environment; A data acquisition module, connected to the environment simulation module, for collecting training feature data during the training simulation process; A matching degree determination module, which is connected to the data acquisition module and is used to determine the matching degree of the user's operation according to the eye movement trajectory and the hand movement trajectory acquired by the data acquisition module, so as to determine the eligibility of the user's practical training operation according to the matching degree; A proficiency determination module, which is connected to the data acquisition module and is used to determine the user's proficiency in the training simulation process; An effect evaluation module, which is connected to the proficiency determination module and is used to determine the effect evaluation of the training system according to the overall pass rate of the training operations of several users; An adjustment module, which is connected to the environment acquisition module and the matching degree determination module respectively, for determining the adjustment of the environment acquisition module or the matching degree determination module according to the growth rate of the proficiency; The environment collection module determines the collection of training feature data based on the complexity of the training project, including determining the collection ratio of the environment feature data according to the comparison result of the difference between the complexity evaluation value and the preset complexity evaluation value and the difference between the preset complexity evaluation values ​​under the condition that the complexity evaluation value is greater than the preset complexity evaluation value; The environment collection module determines the collection of environment characteristic data according to the complexity of the training project, including determining to collect the environment characteristic data at a preset collection cycle under the condition that the complexity evaluation value is less than or equal to the preset complexity evaluation value; The matching degree determination module determines the eligibility of the user's practical training operation according to the matching degree, including determining that the user's practical training operation is qualified under the condition that the matching degree evaluation value is greater than or equal to the preset matching degree evaluation value.

2. The visual perception training system based on digital twin according to claim 1 is characterized in that: The matching degree determination module determines the eligibility of the user's practical training operation according to the matching degree, including determining that the user's practical training operation is unqualified under the condition that the matching degree evaluation value is less than a preset matching degree evaluation value.

3. The visual perception training system based on digital twin according to claim 1 is characterized in that: The effect evaluation module determines the effect evaluation of the training system according to the overall pass rate of the training operations of several users, including evaluating the effect of the training system based on the average value of the proficiency evaluation values ​​of several users and determining the effect evaluation value under the condition that the overall pass rate is less than or equal to the preset pass rate.

4. The visual perception training system based on digital twin according to claim 3 is characterized in that: The effect evaluation module determines the effect evaluation of the training system according to the overall pass rate of the training operations of several users, including evaluating the effect of the training system based on the proficiency evaluation value of a single user and determining the effect evaluation value under the condition that the overall pass rate is greater than the preset pass rate.

5. The visual perception training system based on digital twin according to claim 4 is characterized in that: The adjustment module is used to determine the adjustment of the environment acquisition module or the matching degree determination module according to the growth rate of proficiency, including determining whether the training effect meets the standard based on the comparison result of the proportion of users whose proficiency growth rate is greater than the standard growth rate and the preset proportion, and determining the adjustment of the environment acquisition module or the matching degree determination module.

6. The visual perception training system based on digital twin according to claim 5 is characterized in that: The adjustment module determines whether the training effect meets the standard according to the comparison result of the proportion of users whose proficiency growth rate is greater than the standard growth rate and the preset proportion, including: If the proportion of users whose proficiency growth rate is greater than the standard growth rate is greater than or equal to the preset proportion, it is determined that the training effect has reached the standard; Under the condition that the proportion of users whose proficiency growth rate is greater than the standard growth rate is less than the preset proportion, it is determined that the training effect does not meet the standard, and the environment acquisition module or the matching degree determination module is adjusted.

7. The visual perception training system based on digital twin according to claim 6 is characterized in that: Under the condition that the training effect does not meet the standard, the adjustment module determines the adjustment of the environment acquisition module or the matching degree determination module according to the comparison result of the difference between the number of people and the preset ratio and the preset ratio difference, including: Under the condition that the difference between the proportion of people and the preset proportion is less than or equal to the preset proportion difference, the preset matching degree evaluation value is adjusted according to the adjustment coefficient X1; Under the condition that the difference between the proportion of the number of people and the preset proportion is greater than the preset proportion difference, the preset collection period or collection ratio is adjusted according to the adjustment coefficient X2.

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