Simulation training system and method based on intelligent digital twinborn body

Abstract

The invention relates to a simulation training system and method based on intelligent digital twins, and the system comprises a training simulator which is provided with human-computer interaction operation hardware and is configured to simulate a simulation training process based on the specific operation of an operator; the intelligent digital twinning body and the training simulator are in twinning mapping, and the intelligent digital twinning body and the training state of the training simulator are mutually synchronized; the guidance and control evaluation module is configured to configure a training scene and a training mode, record the training process of the operator and evaluate the training level and weak links of the operator; and the distributed platform performs data interaction with the training simulator, the intelligent digital twinborn body and the guidance and control evaluation module.

Description

Technical Field

[0001] The present disclosure generally relates to the field of simulation training technology, and more specifically, to a simulation training system and method based on intelligent digital twins. Background Art

[0002] Simulation training is a method of improving skills and learning knowledge by simulating actual situations. It is generally carried out in a combination of virtual and real methods. Actual operations are carried out on the actual equipment operation subsystem, and all phenomena and behaviors of the equipment are observed in the virtual equipment subsystem. This improves the realism and complete cognition of training to a certain extent, and is beneficial to improving the training effect. Summary of the invention

[0003] Some embodiments of the present disclosure provide a simulation training system based on an intelligent digital twin, the simulation training system comprising:

[0004] A training simulator having human-computer interaction operation hardware configured to simulate a training process based on specific operations of an operator;

[0005] An intelligent digital twin, which is twin-mapped with the training simulator and is configured to be synchronized with the training state of the training simulator;

[0006] A guidance and control evaluation module, configured to configure training scenarios and training modes, record the training process of operators and evaluate the training level and weak links of operators; and

[0007] The distributed platform interacts with the training simulator, the intelligent digital twin, and the guidance and control evaluation module.

[0008] In some embodiments, the guidance and control evaluation module includes:

[0009] A training scene and training mode configuration module, configured to configure the training scene and training mode in the training simulator and the intelligent digital twin, the training scene in the training simulator and the intelligent digital twin are the same, one of the training modes in the training simulator and the intelligent digital twin is an active training mode, and the other is a passive training mode;

[0010] A recording module configured to record a training process of an operator performing active operation training on a training simulator;

[0011] An evaluation module is configured to evaluate the training process to determine the training level of the operator and record the weak links of the operator.

[0012] In some embodiments, the training simulator comprises:

[0013] The operation interactive interface is configured for operators to conduct active operation training and implement the demonstration training process;

[0014] A first model module, configured to store and call training scenarios;

[0015] The first communication module is configured to communicate with the distributed platform and transmit action information, status information and command information.

[0016] In some embodiments, the intelligent digital twin includes:

[0017] An intelligent decision-making module is configured to automatically perform operation training in a repetitive manner or an intelligent manner when the intelligent digital twin is in an active mode. When the repetitive manner is adopted, the intelligent decision-making module automatically performs operation training according to the previously stored operation training process of the operator to achieve the reproduction of the operation training process of the operator. When the intelligent manner is adopted, the intelligent decision-making module automatically performs operation training by adding random disturbances according to the standard operation process;

[0018] A second model module, configured to store and call training scenarios;

[0019] The second communication module is configured to communicate with the distributed platform and transmit action information, status information and command information.

[0020] The present disclosure provides a simulation training method based on an intelligent digital twin, using the simulation training system described in the above embodiment, and the simulation training method includes the following steps:

[0021] The training simulator receives the operator's login information and training condition instructions and sends the login information and training condition instructions to the guidance and control evaluation module;

[0022] The guidance and control evaluation module provides training suggestions based on the login information and training conditions, and configures the training simulator and the training scenarios and training modes in the intelligent digital twin based on the operator's selection;

[0023] The training simulator and the intelligent digital twin synchronously perform operation training under the same training scenario, and one of the training simulator and the intelligent digital twin is in an active training mode, and the other is in a passive training mode.

[0024] In some embodiments, the simulation training method further comprises the following steps:

[0025] The guidance and control evaluation module exchanges the training modes of the training simulator and the intelligent digital twin based on the operator's mode switching instructions or preset mode switching instructions, and transfers the operation training control authority between the training simulator and the intelligent digital twin.

[0026] In some embodiments, the simulation training method further comprises:

[0027] When the training simulator switches from active training mode to passive training mode, and the intelligent digital twin switches from passive training mode to active mode, after the intelligent digital twin obtains the operation training control authority, the intelligent digital twin automatically performs operation training in a replicable or intelligent manner, and synchronizes the action information and status information to the training simulator.

[0028] In some embodiments, the simulation training method includes:

[0029] When the intelligent digital twin switches to the active mode to automatically perform operation training in a reproduction manner, the intelligent digital twin retrieves the previously stored operation training process of the operator to automatically perform operation training, thereby realizing the reproduction of the operator's operation training process.

[0030] In some embodiments, the simulation training method includes:

[0031] When the intelligent digital twin switches to the active mode to automatically perform operation training in an intelligent manner, the intelligent digital twin automatically performs operation training by adding random disturbances according to the standard operation process.

[0032] In some embodiments, the simulation training method includes:

[0033] When the intelligent digital twin switches to active mode to automatically perform operation training in a replicable or intelligent manner, the rate at which the intelligent digital twin performs operation training is adjustable.

[0034] According to the technical solution of the embodiments of the present invention, the training simulator and the intelligent digital twin are mapped, and the training states of the two are synchronized with each other. The intelligent digital twin is used to assist the training process. Training can be carried out link by link according to the operation process to maintain the continuity of the training. The training simulator can also be used to enable operators to actually operate the weak links in the training to ensure targeted training of the weak links. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other features of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0036] Figure 1 A schematic diagram of the structure of a simulation training system based on intelligent digital twins provided in some embodiments of the present disclosure;

[0037] Figure 2 A schematic diagram of the structure of a guidance and control evaluation module provided in some embodiments of the present disclosure;

[0038] Figure 3A schematic diagram of the structure of a training simulator provided in some embodiments of the present disclosure;

[0039] Figure 4 A schematic diagram of the structure of an intelligent digital twin 20 provided in some embodiments of the present disclosure;

[0040] Figure 5 A flowchart of a simulation training method based on intelligent digital twins provided in some embodiments of the present disclosure;

[0041] Figure 6 A switching mode flow chart of a simulation training method based on intelligent digital twins provided for some embodiments of the present disclosure; and

[0042] Figure 7 A flow chart of switching modes of a simulation training method based on intelligent digital twins provided for some embodiments of the present disclosure.

[0043] In the drawings, the same or similar structures are marked with the same or similar reference numerals. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0045] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments, and are not intended to limit the present disclosure. The singular forms "a", "said" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0046] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0047] It should be understood that although the terms first, second, third, etc. may be used to describe structures in the disclosed embodiments, these structures should not be limited to these terms. These terms are only used to distinguish different structures. For example, without departing from the scope of the disclosed embodiments, a first component may also be referred to as a second component, and similarly, a second component may also be referred to as a first component.

[0048] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0049] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0050] In the related technologies, the virtual-real combination simulation training system cannot carry out targeted training for the weak links of different trainees during the whole training process. Specifically, the current operation of large and complex equipment often involves multiple continuous or interrelated links, some of which are relatively simple and easy to master, while other links are difficult to operate and difficult to master. Because each trainee has different learning foundations, knowledge structures, expertise, etc., there are also differences in the difficulty of each link. From the perspective of cognitive science, conducting personalized training for different trainees and targeting their weak links in training is the best way to improve the training level.

[0051] In the related art, there are two main ways for simulation training systems to conduct simulation training: (1) Single-subject decomposition training. The continuous operation training links are broken down into several independent training subjects. For example, field measurement equipment needs to drive the equipment to the point to be measured, and then start a series of measurement operation training. The training process can be broken down into driving training subjects and multiple measurement training subjects. The advantage of single-subject training is that it is highly targeted, but the disadvantage is that the subject scenario is relatively fixed and it is impossible to conduct training based on the differences between different students. Moreover, the subjects cannot be linked, and the training of related subjects before and after is not continuous. The training is prone to feel fragmented, which affects the training effect. (2) Full-process coherent training. Training is carried out step by step according to the operation process. The training has good coherence, but lacks the targeted training of weak links, and the training quality and efficiency are low.

[0052] In order to overcome the above-mentioned problems, the present disclosure provides a simulation training system based on an intelligent digital twin, wherein the simulation training system comprises: a training simulator having human-computer interaction operation hardware, configured to simulate a training process based on the specific operation of an operator; an intelligent digital twin, which is twin-mapped to the training simulator and configured to synchronize the training status of the training simulator with each other; a guidance and control evaluation module, configured to configure training scenarios and training modes, record the training process of the operator and evaluate the training level and weak links of the operator; and a distributed platform, which interacts with the training simulator, the intelligent digital twin and the guidance and control evaluation module for data.

[0053] In the present disclosure, the training simulator and the intelligent digital twin are mapped, and the training states of the two are synchronized with each other. The intelligent digital twin is used to assist the training process. Training can be carried out link by link according to the operation process to maintain the continuity of the training. The training simulator can also be used to enable operators to actually operate the weak links in the training to ensure targeted training of the weak links.

[0054] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0055] Figure 1 This is a schematic diagram of the structure of a simulation training system based on intelligent digital twins provided in some embodiments of the present disclosure. Figure 1 As shown, some embodiments of the present disclosure provide a simulation training system 100 based on intelligent digital twins. The simulation training system 100 includes a training simulator 10, an intelligent digital twin 20, a guidance and control evaluation module 30, and a distributed platform 40.

[0056] The training simulator 10 has human-machine interactive operation hardware, which is configured to simulate the training process based on the specific operation of the operator. The human-machine interactive operation hardware is basically consistent with the actual equipment, and senses the operation of the operator through various sensors to receive the operation information of the operator. In addition, the training simulator 10 can present specific operation visual effects and audio effects based on the operation of the operator to simulate the specific operation effects of the actual assembly.

[0057] The intelligent digital twin 20 is twin-mapped with the training simulator 10 and is configured to be synchronized with the training state of the training simulator 10. The intelligent digital twin 20 runs in a virtual space and can synchronously follow the training state of the training simulator 10 in real time, and can also actively control the training and synchronize the training state to the training simulator 10 in real time, that is, synchronously operate the training simulator 10.

[0058] The guidance and control evaluation module 30 is configured to configure training scenarios and training modes, record the training process of the operator and evaluate the operator's training level and weak links. The training scenarios include multiple, for example, specific driving conditions may include gravel roads, highways, grasslands, etc. The parking points in multiple training scenarios are different, and the slope, visibility conditions, deviation distance and other conditions of the parking points are also different. The training mode includes a first mode in which the training simulator 10 is dominant and the intelligent digital twin 20 follows synchronously, and a second mode in which the intelligent digital twin 20 is dominant and the training simulator 10 follows synchronously. The two modes can be switched according to specific circumstances.

[0059] The guidance and control evaluation module 30 records the training process of the operator's active operation. There are multiple operators. The guidance and control evaluation module 30 assigns an account to each operator and stores and records the training process of each operator's active operation. The training level of the operator is evaluated according to the training process of each operator's active operation, and whether there is a weak training link for each operator is determined. Each training process of each operator's active operation will be recorded to form a training process set of this operator as the historical training process of this operator. After each training of the operator's leading operation is completed, the guidance and control evaluation module will evaluate the operator's training process, and update the operator's training level level in combination with the previous historical evaluation. And determine whether the operator has a weak link, if so, mark it.

[0060] The distributed platform 40 exchanges data with the training simulator 10, the intelligent digital twin 20, and the guidance and control evaluation module 30. The distributed platform 40 is used for data transmission, and the training simulator 10, the intelligent digital twin 20, and the guidance and control evaluation module 30 exchange data through the distributed platform 40.

[0061] Figure 2 This is a structural diagram of a guidance and control evaluation module provided in some embodiments of the present disclosure. The guidance and control evaluation module 30 includes a training scenario and training mode configuration module 31, a recording module 32 and an evaluation module 33.

[0062] The training scene and training mode configuration module 31 is configured to configure the training scene and training mode in the training simulator 10 and the intelligent digital twin 20. The training scenes in the training simulator 10 and the intelligent digital twin 20 are the same. One of the training modes in the training simulator 10 and the intelligent digital twin 20 is an active training mode, and the other is a slave training mode. Specifically, the training scene and training mode configuration module 31 configures the training scenes in the training simulator 10 and the intelligent digital twin 20 according to the instructions. The training scenes in the training simulator 10 and the intelligent digital twin 20 are exactly the same and the two are synchronized. In addition, in the training modes configured in the training simulator 10 and the intelligent digital twin 20, when the training simulator 10 is in the active training mode, the training operation is manually completed by the operator, and the intelligent digital twin 20 is in the slave training mode, and its training process synchronously follows the training process on the training simulator 10. When the intelligent digital twin 20 is in the active training mode, the training operation is automatically completed by the intelligent digital twin 20, and the training simulator 10 is in the passive training mode, and its training process synchronously follows the training process on the intelligent digital twin 20.

[0063] The recording module 32 is configured to record the training process of the operator performing active operation training on the training simulator 10. Specifically, the recording module 32 records each active operation training of each operator, and records the specific operation steps, operation completion degree, and the corresponding presentation results of the operation. For example, the actual operation parking point position, the deviation from the standard parking point, etc. are recorded. Each training process of each operator's active operation will be recorded to form a training process set of this operator as the historical training process of this operator.

[0064] The evaluation module 33 is configured to evaluate the training process to determine the training level of the operator and record the weak links of the operator. Specifically, the evaluation module 33 evaluates the training level of the operator according to the training process of each operator's active operation, and determines whether there is a weak training link for each operator. After each training of the operator's leading operation is completed, the guidance and control evaluation module will evaluate the operator's training process, and update the operator's training level level in combination with the previous historical evaluation. And determine whether the operator has a weak link, if so, record it.

[0065] Figure 3 A schematic diagram of the structure of a training simulator provided in some embodiments of the present disclosure. Figure 3 As shown, the training simulator 10 includes an operation interaction interface 11 , a first model module 12 and a first communication module 13 .

[0066] The operation interaction interface 11 is configured for operators to conduct active operation training and implement the demonstration training process. The human-machine interaction operation hardware of the operation interaction interface 11 is basically consistent with the actual equipment, and various sensors are used to sense the operation of the operator and receive the operation information of the operator. The presentation interface of the operation interaction interface 11 includes, for example, a display screen, an audio system, etc., which are used to present the scenes and operation effects during the training process.

[0067] The first model module 12 is configured to store and call training scenarios, which specifically include a three-dimensional model, an environmental model, and a mechanism model. The first model module 12 receives the operation information of the operator, and uses the three-dimensional model, the environmental model, and the mechanism model for settlement to generate audio and video effects and dynamic responses that are the same as the actual equipment.

[0068] The first communication module 13 is configured to communicate with the distributed platform 40 to transmit action information, state information and command information. The first communication module 13 communicates with other parts of the simulation training system 100 through the distributed platform 40. It mainly includes action information, state information and command information.

[0069] Action information mainly includes the actual operation information performed by the operator, such as driving the vehicle, powering on the equipment, raising the antenna, setting up the jack, etc. This information can be expressed in the form of a quaternion <E id ,P id ,Op i ,T>description, where E id Represents the equipment number, P id The part number representing the operation, Op i represents the operation code executed on the component, and T represents the time information of the operation. It should be noted that this time is the relative change time of the simulation start time.

[0070] The status information is used to describe the status of the current simulation equipment as a whole or its main components related to training, such as the movement information of the equipment. It is mainly divided into two categories: whole equipment information and important component information. Whole equipment information can be used <E id ,S i ,T>description, where E id Represents the equipment number, S i Represents status information, which is a composite structure, usually including position information such as latitude and longitude, attitude information such as direction and pitch, and health status information of the equipment itself. T represents the time information relative to the start time of the simulation. Important component information can be used <E id ,P id , S i ,T>Description, compared with the whole package, the part number information is added. S i The specific status information varies depending on the component.

[0071] The command information is used to configure and adjust the training scenarios and training methods, and mainly includes the scenario configuration information and the current simulator working mode information sent by the guidance and control evaluation module, as well as the instruction information issued by the operator on the training simulator 10.

[0072] Figure 4 This is a schematic diagram of the structure of the intelligent digital twin 20 provided in some embodiments of the present disclosure. Figure 4 As shown, the intelligent digital twin 20 includes: an intelligent decision-making module 21, a second model module 22 and a second communication module 23.

[0073] The intelligent decision module 21 is configured to automatically perform operation training in a reproduction manner or an intelligent manner when the intelligent digital twin is in active mode. Specifically, when the operation training is automatically performed in a reproduction manner, the intelligent decision module 21 automatically performs the operation training according to the previously stored operation training process of the corresponding operator, realizes the reproduction of the operation training process of the corresponding operator, reproduces the typical problem scenarios that appear in the training, and synchronizes the typical problem scenarios with the training simulator 10. After the vortex simulator 10 switches to the active training mode, the operator can perform operation training in the typical problem scenario, thereby improving the targeted training effect.

[0074] When the intelligent method is adopted, the intelligent decision module 21 automatically performs operation training by adding random disturbances according to the standard operation process. Specifically, the intelligent decision module 21 automatically executes a given training link using methods such as rule trees or deep reinforcement learning. The intelligent decision module 21 can guide the training process according to the standard process of the whole process operation, and can also appropriately add random disturbances during the automatic execution process to improve the richness of the training conditions, so that the trainees can understand the pre-process of the target training link and prepare the training pre-conditions for it. After the vortex simulator 10 is switched to the active training mode, the operator can perform operation training under the pre-conditions to improve the training effect.

[0075] The second model module 22 is configured to store and call the training scene, which specifically includes a three-dimensional model, an environmental model and a mechanism model. The second model module 22 uses the three-dimensional model, the environmental model and the mechanism model for settlement to generate audio and video effects and dynamic responses that are the same as the actual equipment, so that the intelligent digital twin 20 is synchronized with the training simulator 10.

[0076] The second communication module 23 is configured to communicate with the distributed platform to transmit action information, state information and command information. The third communication module 23 communicates with other parts of the simulation training system 100 through the distributed platform 40. It mainly includes action information, state information and command information.

[0077] Figure 5A flowchart of a simulation training method based on an intelligent digital twin provided in some embodiments of the present disclosure, such as Figure 5 As shown, the present disclosure provides a simulation training method based on an intelligent digital twin, using the simulation training system 100 described in the above embodiment, and the simulation training method includes the following steps:

[0078] S100: The training simulator receives the operator's login information and training condition instructions and sends the login information and training condition instructions to the guidance and control evaluation module.

[0079] Specifically, the operator's login information is used by the simulation training system to identify the operator, call the operator's historical training information, historical training level rating, etc. The training condition instruction includes the training conditions actively selected by the operator, such as training scenes, active training links, etc.

[0080] S200: The guidance and control evaluation module provides training suggestions based on the login information and training conditions, and configures the training simulator and the training scenarios and training modes in the intelligent digital twin based on the operator's selection.

[0081] Specifically, the guidance and control evaluation module 30 provides training suggestions based on the operator's historical training information, historical training level rating, and the input training condition instructions, such as which training link needs to be actively completed by the operator and which training link is actively and intelligently completed by the intelligent digital twin 20. For example, it is recommended that the operator actively complete the weak links of the operator, and it is recommended that the intelligent digital twin 20 actively and intelligently complete the training links that the operator is proficient in.

[0082] The guidance and control evaluation module 30 configures the training scene and training mode in the training simulator and the intelligent digital twin according to the operator's selection feedback. The training scene in the training simulator 10 and the intelligent digital twin 20 is the same, and one of the training modes in the training simulator 10 and the intelligent digital twin 20 is configured as an active training mode and the other is configured as a passive training mode.

[0083] S300: The training simulator and the intelligent digital twin synchronously perform operation training under the same training scenario, one of the training simulator and the intelligent digital twin is in an active training mode, and the other is in a passive training mode.

[0084] Specifically, the training simulator 10 and the intelligent digital twin 20 synchronously perform operation training in the same training scenario. When the training simulator 10 is in the active training mode, the training operation is manually completed by the operator, and the intelligent digital twin 20 is in the slave training mode, and its training process synchronously follows the training process on the training simulator 10. When the intelligent digital twin 20 is in the active training mode, the training operation is automatically completed by the intelligent digital twin 20, and the training simulator 10 is in the slave training mode, and its training process synchronously follows the training process on the intelligent digital twin 20.

[0085] In some embodiments, the simulation training method further comprises the following steps:

[0086] S400: The guidance and control evaluation module exchanges the training modes of the training simulator and the intelligent digital twin based on the operator's mode switching instructions or preset mode switching instructions, and transfers the operation training control authority between the training simulator and the intelligent digital twin.

[0087] Specifically, the operator can issue a mode switching instruction according to his own situation. The guidance and control evaluation module 30 can also preset the mode switching instruction according to the training progress of different operators based on the degree of their mastery of each training link. The guidance and control evaluation module 30 exchanges the training modes of the training simulator and the intelligent digital twin according to the mode switching instruction, and transfers the operation training control authority between the training simulator and the intelligent digital twin.

[0088] Figure 6 A switching mode flow chart of a simulation training method based on intelligent digital twins provided in some embodiments of the present disclosure, such as Figure 6 As shown, the active training mode of the training simulator 10 is switched to the passive training mode, the passive training mode of the intelligent digital twin 20 is switched to the active training mode, and at the same time, the control authority of the manipulation training is transferred from the training simulator 10 to the intelligent digital twin 20.

[0089] Figure 7 A switching mode flow chart of a simulation training method based on intelligent digital twins provided in some embodiments of the present disclosure, such as Figure 7 As shown, the passive training mode of the training simulator 10 is switched to the active training mode, and the active training mode of the intelligent digital twin 20 is switched to the passive training mode, and at the same time, the control authority of the manipulation training is transferred from the intelligent digital twin 20 to the training simulator 10, as shown in FIG. Figure 7 shown.

[0090] In some embodiments, the simulation training method further comprises:

[0091] When the training simulator switches from active training mode to passive training mode, and the intelligent digital twin switches from passive training mode to active mode, after the intelligent digital twin obtains the operation training control authority, the intelligent digital twin automatically performs operation training in a replicable or intelligent manner, and synchronizes the action information and status information to the training simulator.

[0092] In some embodiments, the simulation training method includes:

[0093] When the intelligent digital twin switches to the active mode to automatically perform operation training in a reproduction manner, the intelligent digital twin retrieves the previously stored operation training process of the operator to automatically perform operation training, thereby reproducing the operation training process of the operator. The intelligent decision-making module in the intelligent digital twin automatically performs operation training according to the previously stored operation training process of the corresponding operator, thereby reproducing the operation training process of the corresponding operator, reproducing the typical problem scenarios that occur during the training, and synchronizing the typical problem scenarios with the training simulator. After the vortex simulator switches to the active training mode, the operator can perform operation training in the typical problem scenario, thereby improving the targeted training effect.

[0094] In some embodiments, the simulation training method includes:

[0095] When the intelligent digital twin switches to the active mode to automatically perform operation training in an intelligent manner, the intelligent digital twin automatically performs operation training by adding random disturbances according to the standard operation process. The intelligent decision-making module in the intelligent digital twin uses methods such as rule trees or deep reinforcement learning to automatically execute a given training link. The intelligent decision-making module can guide the training process according to the standard process of the whole process operation, and can also appropriately increase random disturbances during the automatic execution process to improve the richness of the training conditions, so that the trainees can understand the pre-process of the target training link and prepare the training pre-conditions for it. After the vortex simulator switches to the active training mode, the operator can perform operation training under the pre-conditions to improve the training effect.

[0096] In some embodiments, the simulation training method includes:

[0097] When the intelligent digital twin switches to active mode to automatically perform operation training in a replicable or intelligent manner, the rate at which the intelligent digital twin performs operation training is adjustable.

[0098] Specifically, for training sessions where operators are proficient, the intelligent digital twin can automatically perform operation training at a speed higher than the normal speed, saving time. For training sessions where operators need to observe carefully, the intelligent digital twin can automatically perform operation training at a speed lower than the normal speed, making it easier for operators to observe.

[0099] By adopting the simulation training system and method based on the intelligent digital twin in the above-mentioned embodiment of the present disclosure, the training simulator and the intelligent digital twin are mapped, and the training states of the two are synchronized with each other. The intelligent digital twin is used to assist the training process. It is possible to carry out training step by step according to the operation process to maintain the continuity of the training, and the training simulator can be used to enable operators to actually operate the weak links in the training to ensure targeted training of the weak links.

[0100] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the system or device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0101] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 disclosure.

Claims

1. A simulation training system based on intelligent digital twins, characterized in that: The simulation training system comprises: A training simulator having human-computer interaction operation hardware configured to simulate a training process based on specific operations of an operator; An intelligent digital twin, which is twin-mapped with the training simulator and is configured to be synchronized with the training state of the training simulator; A guidance and control evaluation module, configured to configure training scenarios and training modes, record the training process of operators and evaluate the training level and weak links of operators; and The distributed platform interacts with the training simulator, the intelligent digital twin, and the guidance and control evaluation module.

2. The simulation training system based on intelligent digital twin according to claim 1, characterized in that: The guidance and control evaluation module includes: A training scene and training mode configuration module, configured to configure the training scene and training mode in the training simulator and the intelligent digital twin, the training scene in the training simulator and the intelligent digital twin are the same, one of the training modes in the training simulator and the intelligent digital twin is an active training mode, and the other is a passive training mode; A recording module configured to record a training process of an operator performing active operation training on a training simulator; An evaluation module is configured to evaluate the training process to determine the training level of the operator and record the weak links of the operator.

3. The simulation training system based on intelligent digital twin according to claim 1 is characterized in that: The training simulator comprises: The operation interactive interface is configured for operators to conduct active operation training and implement the demonstration training process; A first model module, configured to store and call training scenarios; The first communication module is configured to communicate with the distributed platform and transmit action information, status information and command information.

4. The simulation training system based on intelligent digital twin according to claim 2 is characterized in that: The intelligent digital twin comprises: An intelligent decision-making module is configured to automatically perform operation training in a repetitive manner or an intelligent manner when the intelligent digital twin is in an active mode. When the repetitive manner is adopted, the intelligent decision-making module automatically performs operation training according to the previously stored operation training process of the operator to achieve the reproduction of the operation training process of the operator. When the intelligent manner is adopted, the intelligent decision-making module automatically performs operation training by adding random disturbances according to the standard operation process; A second model module, configured to store and call training scenarios; The second communication module is configured to communicate with the distributed platform and transmit action information, status information and command information.

5. A simulation training method based on intelligent digital twins, using the simulation training system according to any one of claims 1 to 4, characterized in that: The simulation training method comprises the following steps: The training simulator receives the operator's login information and training condition instructions and sends the login information and training condition instructions to the guidance and control evaluation module; The guidance and control evaluation module provides training suggestions based on the login information and training conditions, and configures the training simulator and the training scenarios and training modes in the intelligent digital twin based on the operator's selection; The training simulator and the intelligent digital twin synchronously perform operation training under the same training scenario, and one of the training simulator and the intelligent digital twin is in an active training mode, and the other is in a passive training mode.

6. The simulation training method according to claim 5, characterized in that: The simulation training method also includes the following steps: The guidance and control evaluation module exchanges the training modes of the training simulator and the intelligent digital twin based on the operator's mode switching instructions or preset mode switching instructions, and transfers the operation training control authority between the training simulator and the intelligent digital twin.

7. The simulation training method according to claim 6, characterized in that: The simulation training method also includes: When the training simulator switches from active training mode to passive training mode, and the intelligent digital twin switches from passive training mode to active mode, after the intelligent digital twin obtains the operation training control authority, the intelligent digital twin automatically performs operation training in a replicable or intelligent manner, and synchronizes the action information and status information to the training simulator.

8. The simulation training method according to claim 7, characterized in that: The simulation training method comprises: When the intelligent digital twin switches to the active mode to automatically perform operation training in a reproduction manner, the intelligent digital twin retrieves the previously stored operation training process of the operator to automatically perform operation training, thereby realizing the reproduction of the operator's operation training process.

9. The simulation training method according to claim 7, characterized in that: The simulation training method comprises: When the intelligent digital twin switches to the active mode to automatically perform operation training in an intelligent manner, the intelligent digital twin automatically performs operation training by adding random disturbances according to the standard operation process.

10. The simulation training method according to claim 7, characterized in that: The simulation training method comprises: When the intelligent digital twin switches to active mode to automatically perform operation training in a replicable or intelligent manner, the rate at which the intelligent digital twin performs operation training is adjustable.

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