Multifunctional practical training operation system and operation method

Abstract

The invention belongs to the field of practical training operation systems, and particularly relates to a multifunctional practical training operation system and an operation method.The multifunctional practical training operation system is characterized in that a plurality of installation positions are preset on a production line frame, and each installation position is provided with a first connecting structure; a second connecting structure detachably connected with the first connecting structure is arranged on the process module; the first plug-in modules are arranged at the mounting positions; the second plug-in modules are arranged on the process modules; the primary identification unit is used for identifying and confirming the user identity and generating a login record; the main display screen is used for displaying a plurality of selectable operation models according to the user identity confirmed by the identification module, so that the user can select the corresponding operation model; the intelligent control module is electrically connected with the plug-in module, the power supply module and the communication module, and performs unified management on power supply distribution, signal scheduling and safety control; and the storage module is in communication connection with the intelligent control module and is used for recording practical training data in real time.

Description

Technical Field

[0001] This invention relates to the field of training operating systems, and in particular to a multifunctional training operating system and its operation method. Background Technology

[0002] In the field of practical training technology, especially in vocational education and corporate skills training, training workbenches serve as core teaching equipment, widely used for simulating industrial production processes, technological operations, and skills training. However, existing training workbenches are typically designed for a single function, with each set customized according to specific courses or professional needs, resulting in highly limited usage scenarios. This design prevents the equipment from flexibly adapting to diverse training requirements. For example, a training workbench designed for mechanical assembly is difficult to quickly switch to a production line configuration for electronic testing or chemical simulation, thus limiting the equipment's versatility and reusability.

[0003] Specifically, existing practical training platforms often adopt a fixed structure, with the production line frame and process components molded as a single unit, making modular disassembly or reassembly impossible. This not only increases procurement costs (schools or companies need to invest repeatedly in multiple sets of equipment for different majors) but also reduces teaching efficiency. This is particularly prominent in multi-disciplinary or large-scale training scenarios, such as multi-course teaching in vocational schools or cross-departmental skills assessments in enterprises, where incompatible equipment often leads to training delays, safety hazards, and resource waste. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a multifunctional training operating system and its operation method.

[0005] The first aspect of this application provides a multi-functional training operating system, comprising: A production line frame, wherein multiple mounting positions are pre-set on the production line frame, and each mounting position is provided with a first connection structure; Multiple process modules, each of which is provided with a second connection structure for forming a detachable connection with the first connection structure, so as to realize the combination and replacement of different process units; The plug-in module includes a first plug-in module disposed at each of the mounting positions and a second plug-in module disposed on each process module, wherein the first plug-in module is electrically connected to the intelligent control module; The identification module includes a primary identification unit, which is used to identify and confirm the user's identity and generate login records; The display module, including the main display screen, is used to display multiple selectable operation models based on the user identity confirmed by the recognition module. It supports click or swipe selection and configuration adjustment so that the user can select the operation model corresponding to the target production line. The intelligent control module is electrically connected to the plug-in module, power module and communication module, and is used to centrally manage the system's operating logic and uniformly manage power distribution, signal scheduling and safety control. The storage module is communicatively connected to the intelligent control module and is used to record training data in real time.

[0006] In one possible implementation of the first aspect above, the identification module includes a second identification unit for quickly selecting a target production line; In the storage module, a corresponding behavior instruction is set for each operation model; The user's behavioral commands are acquired through the image acquisition module and then identified and compared in the processing and comparison module. Display the comparison results on the main screen and allow the user to confirm them; If the comparison fails, the user will be prompted to re-enter the action command or switch to manual selection mode. The behavioral instructions include, but are not limited to, gestures, body postures, or combinations of multimodal actions, to enable contactless operation model selection.

[0007] In one possible implementation of the first aspect described above, the display module further includes an independent display screen; Each of the process modules is equipped with an independent display screen for playing operation videos or instructional documents for that module.

[0008] In one possible implementation of the first aspect described above, a status reminder module is also included, which is communicatively connected to the plug-in module and the intelligent control module, for detecting the installation status of the process module corresponding to each installation position of the user-selected operation model on the production line frame, and displaying the status on the main display screen.

[0009] In one possible implementation of the first aspect described above, the status alert module includes a warning light system; The warning light system is installed at each of the installation positions and has red, yellow and green indicators; Red indicates that it is not installed or not selected; Yellow indicates that the installation is complete but the system has not been started. Green indicates that preparation is complete and adaptation is successful; The color change of the warning light is automatically controlled by the intelligent control module based on the plug-in detection signal.

[0010] In one possible implementation of the first aspect above, the communication module includes a wireless communication unit and / or a wired communication interface for real-time transmission of process module installation signals, synchronization of operation model data, and connection with external devices.

[0011] In one possible implementation of the first aspect above, the first identification unit includes a biometric unit, which is used to confirm the user's identity through fingerprint recognition and / or facial recognition, and to transmit the identity verification result to the intelligent control module to realize access control and login record generation based on user identity.

[0012] A second aspect of this application provides a multifunctional training operation method, including: The user's identity is verified by the first identification unit of the identification module, and a login record is generated. The main display screen shows multiple optional operation models for the user to choose from based on the identified user identity, in order to determine the operation model corresponding to the target production line; According to the selected operation model, the second connection structure of multiple process modules is detachably connected to the first connection structure of the corresponding installation position on the preset installation position of the production line frame, so as to realize the combination or replacement of different process units. Electrical connection is achieved through the first plug-in module located at the mounting position and the second plug-in module located on the process module, and the plug-in status information of each mounting position is obtained by the intelligent control module. The intelligent control module executes the production line operation logic according to the selected operation model, including power distribution, signal scheduling, and safety control; The training data is recorded in real time through the storage module, and the data is stored in association with the user's identity information.

[0013] In one possible implementation of the second aspect above, the selection of the operation model supports an instruction recognition mode, which includes: In the storage module, a corresponding behavior instruction is preset for each operation model, and each behavior instruction includes at least one static instruction posture. The image acquisition module acquires the user's real-time behavior and posture, and the processing and comparison module performs identification and comparison. The comparison results are displayed on the main screen, and the user is prompted to confirm. If the comparison fails, the user will be prompted to re-enter the action command or switch to manual selection mode. The behavioral instructions include, but are not limited to, gestures, body postures, or combinations of multimodal actions, to enable contactless operation model selection.

[0014] In one possible implementation of the second aspect above, the step of acquiring the user's real-time behavioral posture through the image acquisition module and performing identification and comparison in the processing and comparison module includes the following steps: Collect the user's current point cloud data, simulate the continuous static command postures corresponding to each user's action command from start to end, and obtain the command contour information corresponding to the static command postures; Real-time contour information is obtained based on the real-time behavior posture, and the real-time contour information is compared with the command contour information; The behavioral instruction corresponding to the instruction contour information with the highest matching degree is selected as the recognition instruction.

[0015] The advantages of this invention over the prior art are as follows: This invention, by setting multiple preset installation positions on the production line frame and using a detachable connection structure, enables the rapid combination and replacement of different process groups, achieving modularity and multifunctionality of the training workbench. This significantly improves the equipment's versatility and reusability, reducing the cost of repeatedly purchasing equipment for different training needs. Through plug-in modules and centralized management of intelligent control, electrical connections of process modules are completed plug-and-play. Combined with a status reminder module and warning light system, real-time detection and visual prompts of the installation status are achieved, reducing the risk of misoperation and improving assembly efficiency. The identification module combines biometric recognition and behavior-based instruction recognition modes, enabling users to efficiently and contactlessly complete identity verification and operation model selection, particularly improving the convenience and accuracy of interaction in scenarios involving multiple disciplines or high hygiene requirements. The display module provides operation videos and explanatory texts through the main display screen and independent display screens for each process module. Combined with the intelligent control module's unified management of power, signals, and safety, it achieves stable control and safety assurance of the production line's operating logic. The storage module can link and store training process data with user identity information in real time, supporting quantitative estimation and personalized feedback, improving the relevance of teaching and management efficiency. Overall, this invention effectively solves the problems of single function, fixed structure, inconvenient operation, lack of feedback and insufficient data recording in the prior art. In vocational education and enterprise skills, it can significantly improve equipment adaptability, reduce costs, accelerate the training pace and enhance safety and teaching quality, and has outstanding practical value and promotion potential. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 According to an embodiment of the present invention, a schematic diagram of a multifunctional training operating system is shown.

[0018] Figure 2 According to an embodiment of the present invention, a flowchart of a multifunctional training operation method is shown.

[0019] Figure 3According to an embodiment of the present invention, a flowchart illustrating the selection of an operation model to support instruction recognition mode is shown.

[0020] Figure 4 According to an embodiment of the present invention, a schematic diagram of a process is shown in which a user's real-time behavior and posture are acquired in real time by an image acquisition module and identified and compared in a processing and comparison module.

[0021] Figure 5 According to an embodiment of the present invention, a three-dimensional schematic diagram of a first connection module is shown.

[0022] Figure 6 According to an embodiment of the present invention, a top view schematic diagram of a first connection module is shown.

[0023] Figure 7 According to an embodiment of the present invention, a three-dimensional schematic diagram of a second connection module is shown.

[0024] Figure 8 According to an embodiment of the present invention, a three-dimensional schematic diagram of a training operating system is shown.

[0025] Figure 9 According to an embodiment of the present invention, a three-dimensional schematic diagram of a partial training operating system is shown. Detailed Implementation

[0026] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed herein. The present invention can also be implemented or applied through other different specific embodiments, and various details in the present invention can be modified or changed according to different viewpoints and application systems without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0027] The present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0028] In the representation of this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics represented in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. Furthermore, the specific features, structures, materials, or characteristics represented may be combined in any suitable manner in one or more embodiments or examples. Moreover, those skilled in the art can combine and integrate different embodiments or examples represented in this invention, as well as features of different embodiments or examples, without contradiction.

[0029] Furthermore, the terms "first" and "second" are used for illustrative purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the representation of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0030] To clearly illustrate the present invention, components unrelated to the description are omitted, and the same or similar constituent elements throughout the specification are given the same reference numerals.

[0031] Throughout this specification, when it is said that a device is "connected" to another device, this includes not only "direct connection" but also "indirect connection" by placing other components in between. Furthermore, when it is said that a device "comprises" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather implies that other constituent elements may be included.

[0032] When we say that a device is "above" another device, this can mean that it is directly above the other device, or it can mean that other devices are present in between. Conversely, when we say that a device is "directly" "above" another device, there are no other devices present in between.

[0033] Although the terms first, second, etc., are used in some instances herein to refer to various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, first interface and second interface, etc., are used. Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of features, steps, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0034] The technical terms used herein are for reference only to specific embodiments and are not intended to limit the invention. The singular form used herein includes the plural form unless the statement explicitly indicates otherwise. The word "comprising" as used in this specification means to specify a particular characteristic, region, integer, step, operation, element, and / or component, and does not exclude the presence or addition of other characteristics, regions, integers, steps, operations, elements, and / or components.

[0035] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with relevant technical literature and the content of this present instruction, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.

[0036] In some embodiments of this application, such as Figure 1 As shown, a multi-functional training operating system includes: The production line frame 1 has multiple pre-set mounting positions, each equipped with a first connecting structure 13. The production line frame 1 is the core support structure of the system. Multiple process modules 2 are provided, each with a second connecting structure 14 for detachable connection with the first connecting structure 13, enabling the combination and replacement of different process units. Process modules 2 include parts gripping modules, quality inspection modules, welding simulation modules, and assembly modules, which can be added according to the actual design.

[0037] Specifically, such as Figures 5 to 7 As shown, the first connection structure 13 includes a mounting base 131 and a connector 132. One end of the connector 132 is fixedly connected to the bottom of the mounting base 131, and the other end is fixedly connected to the production line frame 1. The top of the mounting base 131 is provided with a mounting groove 133, and the bottom of the mounting groove 133 is provided with a positioning hole 134. Magnetic metal blocks 135 are respectively provided on both sides of the positioning hole 134. The second connection structure 14 includes a fixing base 141 fixed to the process module 2. The bottom of the fixing base 141 is provided with a positioning post 142, and magnetic blocks 143 are respectively provided on both sides of the positioning post 142. When the process module 2 is installed on the production line frame 1, the fixing base 141 is inserted into the mounting groove 133, and the positioning post 142 is inserted into the positioning hole 134 to achieve rapid initial positioning and limiting. At the same time, the magnetic blocks 143 and the magnetic metal blocks 135 attract each other, making the fixing base 141 tightly fit against the bottom of the mounting groove 133, thereby achieving a magnetic fast connection and reliable retention between the process module 2 and the production line frame 1.

[0038] Understandably, the mounting slot 133 cooperates with the fixing seat 141 to provide assembly guidance, the positioning post 142 cooperates with the positioning hole 134 to achieve high-precision positioning and anti-offset limiting, and the attraction force generated by the magnetic block 143 and the magnetically conductive metal block 135 further pre-tightens the fixing seat 141 and keeps it in a close fit. The detachable structure provided in this embodiment significantly improves the assembly and disassembly efficiency of the process module, supports quick replacement of different process modules, ensures repeatability of positioning accuracy after each installation, reduces assembly gaps and positional deviations, and provides assembly status feedback through magnetic attraction positioning sensing, reducing the risk of misassembly. Overall, it improves the modular flexibility, production line adaptability, and operational stability of the multi-functional training operating system.

[0039] The plug-in module 3 includes a first plug-in module 31 disposed in each mounting position and a second plug-in module 32 disposed on each process module. The first plug-in module 31 is electrically connected to the intelligent control module. Specifically, the first plug-in module 31, for example, uses a waterproof USB-C interface combined with an impedance sensor to detect connection integrity, with a signal transmission rate of 480Mbps; the second plug-in module 32 is a corresponding interface. The first plug-in module 31 is electrically connected to the intelligent control module, supporting hot-swapping and automatic identification. Furthermore, it integrates a voltage monitoring circuit with a detection range of 3V to 24V to avoid connection instability issues. Figure 5 and Figure 6 As shown, the first plug-in module 31 is integrated onto the first connection module 13.

[0040] The identification module 4 includes a first-level identification unit 41, used to identify and confirm the user's identity and generate a login record. The first identification unit 41 includes a biometric unit, which confirms the user's identity through fingerprint recognition and / or facial recognition, and transmits the authentication result to the intelligent control module to achieve user-identity-based access control and login record generation. Specifically, the fingerprint sensor has a resolution of 500 dpi, and the facial recognition camera supports low-light environments with an accuracy rate of 99%, used for user identity confirmation and login record generation, such as "User ID: XL001, Time: 2024-10-01 10:00".

[0041] Furthermore, the identification module 4 includes a second identification unit 42 for quickly selecting the target production line. The second identification unit 42 is specifically used as follows: In the storage module, a corresponding behavioral instruction is set for each operation model. These postures are predefined as standardized templates and stored in the form of point clouds or image vectors, forming an extensible posture library. Administrators can add new postures through the configuration interface. Specifically, behavioral instructions include, but are not limited to, gestures, body postures, or multimodal action combinations to achieve contactless operation model selection.

[0042] The image acquisition module 10 acquires user behavioral commands, which are then identified and compared in the processing and comparison module 11. Specifically, the image acquisition module 10 employs a combination of an infrared sensor and a point cloud radar. The infrared sensor captures thermal imaging signals with a wavelength range of 800 to 900 nm and a sensitivity greater than 95%. The point cloud radar generates three-dimensional spatial data with a resolution of 0.05 m and a scanning frequency of 30 Hz, used for rapid pointing and selection of the target production line, with a recognition range within 2 meters. This combined mechanism ensures a high accuracy rate of over 98% in low-light or complex environments, allowing users to directly select the production line with simple pointing actions such as extending their arm, without needing to approach the equipment, thereby improving the system's safety and convenience.

[0043] Based on this, the comparison results are displayed on the main screen and the user is asked to confirm. The confirmation reminder can be given by voice or by a pop-up on the main screen. The user can confirm by voice, nodding, or by the interface operation on the main screen.

[0044] If the comparison fails, the user is prompted to re-enter the action command or switch to manual selection mode. Specifically, the user re-demonstrates the posture, repeats the above steps until confirmation, or performs the operation on the main display screen, thus providing a fault tolerance mechanism and avoiding operation interruption.

[0045] Specific example: The image acquisition module acquires user behavioral commands and captures the user's gesture trajectory in real time. The resulting data is then compared and identified in the processing module. This comparison uses algorithms such as cosine similarity calculation with a threshold greater than 90% or a neural network model based on CNN for pose matching to ensure accuracy. The comparison results are displayed on the main screen, for example, showing "Waving gesture detected, corresponding to the assembly model, please nod to confirm," allowing the user to confirm via a simple action or touch. If the comparison fails, the user is prompted to re-enter behavioral commands, such as through voice or on-screen prompts like "Please try again," or to switch to a manual selection mode, such as returning to the touch-screen interface. This contactless design is particularly suitable for hygiene-sensitive scenarios, such as industrial training with gloves, with a response time of less than 2 seconds.

[0046] The display module 5 includes a main display screen 51 and independent display screens 52. Specifically, the main display screen is a 12-inch high-definition touchscreen with a resolution of 1920x1080, supporting multi-touch, and used to display optional operation models, such as "automotive assembly model" and "inspection model," and supports click or swipe configuration adjustments. The independent display screens are located on each process module, are 5-inch screens, electrically connected to the communication module 90, and support playback of locally stored MP4 videos or PDF documents, such as "module installation guides." It should be noted that the specific parameters of the main display screen and independent display screens provided in this embodiment are a specific example, and this application does not limit the specific parameters of the main display screen and independent display screens.

[0047] The intelligent control module 6, electrically connected to the plug-in module 3, power module 11, and communication module 9, is used to centrally manage the system's operating logic and provide unified management of power distribution, signal scheduling, and safety control. Specifically, the intelligent control module 6 is a central processing unit, such as an ARM-based embedded chip with a processing speed of 1GHz, and is electrically connected to the plug-in module, power module, and communication module. The power module is an independent programmable power supply unit that supports overload protection. This module is used for centralized management of power distribution, such as dynamically adjusting voltage according to module type; signal scheduling, such as using the I2C bus protocol; and safety control, such as automatically cutting off power in case of abnormalities.

[0048] Storage module 7, communicating with intelligent control module 6, is used to record training data in real time. Specifically, storage module 7 is a 128GB solid-state drive, communicating with intelligent control module 60 to record training data such as user logs and module status snapshots in real time. Furthermore, it supports data encryption using the AES-256 standard, provides a cloud backup interface, and pre-stores a behavioral instruction library, such as the model selection corresponding to a "wave" gesture.

[0049] The status reminder module 8, which communicates with the plug-in module and the intelligent control module, is used to detect the installation status of the corresponding process module at each installation position of the user-selected operation model on the production line frame and display the status on the main display screen. Further, the status reminder module includes a warning light system 81; the warning light system is located at each installation position and has red, yellow, and green indicators; red indicates not installed or not selected; yellow indicates installation completed but not started; green indicates preparation completed and successful adaptation; the color change of the warning light is automatically controlled by the intelligent control module based on the plug-in detection signal.

[0050] Understandably, the status alert module 8, connected to the plug-in module and the intelligent control module, is used to detect the module's installation status and display it on the main display screen, such as "Plot 1: Adapted". It includes an indicator light system with RGB LEDs at each installation position: red indicates not installed or not selected; yellow indicates installation complete but not powered; and green indicates successful adaptation. Furthermore, color changes are automatically triggered based on plug-in detection signals; for example, red indicates no impedance detected, yellow indicates detected impedance but unstable voltage, and green indicates all parameters are normal. It integrates a buzzer with a volume greater than 80dB for yellow or red status alarms, addressing the safety hazards of lacking visual or auditory feedback in the background technology.

[0051] Communication module 9 includes a wireless communication unit and / or a wired communication interface for real-time transmission of process module installation signals, synchronization of operation model data, and connection to external devices. The communication module includes a wireless communication unit and a wired communication interface. The wireless communication unit is Wi-Fi 6, supporting 2.4GHz or 5GHz bands with a transmission distance of 50m; the wired communication interface is Ethernet RJ45 with a speed of 1Gbps. This module is used for real-time transmission of module installation signals, synchronization of operation model data, and connection to external devices, such as a teacher's computer or a cloud server. Furthermore, it supports protocols such as MQTT to ensure low latency data synchronization of less than 50ms.

[0052] The multifunctional training operating system provided in this application allows for flexible replacement of production line configurations through a detachable connection structure and modular process modules. It integrates contactless gesture recognition, supporting gestures, postures, or combinations, reducing physical contact in hygiene-critical environments such as those requiring gloved training. Compared to traditional touchscreens, this reduces the risk of contamination and enhances inclusivity. The status alert module's red, yellow, and green warning lights are synchronized with the main display screen, providing real-time feedback on installation status based on plug-in detection, reducing accidental misoperation, and enhancing alarm effectiveness with a buzzer. The storage module records data in real-time, and the communication module synchronizes with it, supporting data analysis, such as generating training reports. The overall effect is enhanced system robustness.

[0053] In an industrial training scenario, when a user, such as a new employee named Xiao Li, approaches the system, the primary identification unit confirms their identity via fingerprint and facial recognition, generates a record, and unlocks access. The main display shows the operating model, and the user interacts without contact: the storage module retrieves preset gestures, such as a "waving" gesture; the image acquisition module captures the gesture; the processing and comparison module performs recognition and comparison, calculating a similarity greater than 90%; the result is displayed and confirmed on the main display; if it fails, a retry prompt or manual switching is indicated. After the user selects a model, the process module is installed onto the frame, the connection structure is docked, the plug-in module establishes an electrical connection, the status reminder module's warning light changes from red to yellow and then to green, and the main display synchronously shows the status. The intelligent control module distributes power and schedules signals, and the communication module synchronizes data to external devices. Each module's independent display plays instructional videos, and the storage module records all process data.

[0054] like Figures 5 to 9 As shown, the multifunctional training operating system provided in this disclosure includes a production line frame 1 and multiple process modules 2 that can be detachably installed on it. Each process module 2 is equipped with an independent auxiliary display screen 52, which is used to display the operation instructions, parameters and status information specific to the current process. After the user selects the training model or process type through the main display screen 51, the system prompts the installation position of the required process module 2. The process module 2 and the production line frame 1 are quickly and detachably connected through a first connecting structure 13 and a second connecting structure 14. Specifically, the fixing seat 141 of the second connecting structure 14 is inserted downward along the mounting groove 133 of the first connecting structure 13, the positioning post 142 is inserted into the positioning hole 134 to achieve precise positioning, and at the same time, the magnetic block 143 and the magnetically conductive metal block 135 are magnetically attracted and attached, thereby completing the quick installation, precise positioning and reliable fixation of the process module 2. Disassembly only requires pulling it upward.

[0055] This design enables plug-and-play functionality between the production line frame 1 and various process modules 2, supporting rapid replacement of corresponding modules according to different training models or production tasks, while maintaining high repeatability and connection stability. This significantly improves the system's modularity, flexibility, and training efficiency. In some embodiments of this application, such as... Figure 2 As shown, a multifunctional training operation method includes: S100: The first identification unit of the identification module identifies and confirms the user's identity and generates a login record. Specifically, the first identification unit of the identification module may include a biometric component or other identity verification methods. Biometric components include, for example, fingerprint sensors or facial recognition cameras. The system automatically initiates the data collection process when the user enters the operating console area, acquiring the corresponding biometric data and matching it with a preset user information database to confirm the user's identity. Upon successful matching, a login record is generated and stored for subsequent access control and training data association, automating identity verification, avoiding errors caused by manual input, and improving system security and management efficiency.

[0056] S200: Multiple selectable operation models are displayed on the main display screen based on the identified user identity for the user to choose from, thus determining the operation model corresponding to the target production line. Specifically, the main display screen communicates with the intelligent control module and, based on the user identity information identified in step S100, retrieves the list of operation models within the corresponding permission range from the storage module. These operation models can correspond to different production lines or process combinations and support various interaction methods for selection and parameter adjustment. Interaction methods include clicking, swiping, and menu selection.

[0057] S300: Based on the selected operating model, the second connection structure of multiple process modules is detachably connected to the first connection structure of the corresponding installation position at a preset mounting position on the production line frame, enabling the combination or replacement of different process units. Specifically, the production line frame is designed with multiple standardized mounting positions, each equipped with a first connection structure (such as a snap-fit, slot, or locking mechanism). Each process module is equipped with a matching second connection structure, which is fixed through mechanical insertion or snap-fit. The detachable design eliminates the need for special tools when replacing modules, shortening changeover time and facilitating cross-disciplinary or multi-process training tasks, such as quickly replacing a mechanical assembly module with an electronic testing module, thereby improving equipment utilization and teaching efficiency.

[0058] S400: Electrical connection is achieved through a first plug-in module located at the mounting position and a second plug-in module located on the process module. The intelligent control module obtains the plug-in status information of each mounting position. Specifically, after the mechanical connection is completed, the electrical connection is achieved through the plug-in modules. The first plug-in module is fixed on the production line frame mounting position, and the second plug-in module is bound to the process module. When the two are plugged in, a stable circuit connection is formed for power supply and signal transmission. The intelligent control module monitors the data return status of each plug-in interface in real time (such as connection success, signal integrity, and power flow status) and outputs prompt information on the main display screen to ensure that the installation meets the operational safety requirements. S500: The intelligent control module executes the production line operation logic according to the selected operation model, including power distribution, signal scheduling, and safety control. Specifically, after receiving the final configuration confirmation signal, the intelligent control module centrally manages each process module based on the predefined operating parameter table of the operation model. For example, it automatically allocates power supply, schedules data and control signal flow to each module, and activates safety protection mechanisms (such as overload cutoff and emergency stop). This centralized logic ensures the coordination and safety of different modules during operation and can promptly respond to abnormal situations, reducing damage and accidents.

[0059] S600: The storage module records training data in real time and associates this data with user identity information. Specifically, the storage module collects and saves various types of data, including time records during training, equipment status information, operation logs, and generated training results. All data is linked to user identity information, allowing administrators or instructors to analyze student performance, assess skill mastery, and generate reports or feedback. This not only provides a reliable basis for teaching but also enables the creation of quantifiable assessment standards in corporate training, helping to optimize training programs and resource allocation. Through steps S100 to S600 above, a closed-loop operation process from user identification to production line scheduling to data recording is constructed in the technical solution provided in this disclosure. Specifically, step S100 uses the first identification unit of the identification module to verify the identity of the user entering the system and generates a login record to ensure that subsequent operation permissions match the user role; step S200, based on the confirmed user identity, retrieves and displays multiple optional operation models within the scope of permissions on the main display screen, allowing the user to select or adjust the production line configuration through interactive methods such as clicking and swiping; step S300, according to the instructions of the selected operation model, realizes the quick and detachable connection between the process module and the connection structure at the preset installation position of the production line frame, completing the adjustment of the physical layout; in step S400, the installation position and the process module establish an electrical connection through the corresponding plug-in module, and the intelligent control module collects the plug-in status in real time to ensure connection integrity and power supply safety; in step S500, the intelligent control module executes the operating logic according to the selected operation model, including power distribution, signal scheduling and safety control, thereby ensuring the coordinated and stable operation of each process unit; finally, in step S600, the storage module records the operation data of the entire production line operation process in real time and associates it with user information to provide a reliable basis for subsequent evaluation, teaching review or production optimization. All data control signals between steps are centrally managed by the intelligent control module, thus achieving an organic integration of hardware installation and software logic. This solution utilizes biometrics to achieve contactless, rapid, and accurate identity verification, enhancing operational security. Through modular design and preset installation positions, this solution allows for rapid replacement of process modules, enabling switching between multiple scenarios and processes. The introduction of plug-in status acquisition and visual prompts significantly reduces the risk of equipment failure and human error. During operation, this solution collects data in real time and binds it to user identity, enabling refined teaching evaluation and production process optimization, allowing management to adjust training and production plans based on real data.

[0060] In some embodiments of this disclosure, step S200 is provided in the foregoing embodiments. Figure 3 This diagram illustrates the flow chart for selecting the supported instruction recognition mode in the operation model of this application. For example... Figure 3 As shown, the specific steps may include the following: Step 210: In the storage module, a corresponding behavioral instruction is preset for each operation model. Each behavioral instruction includes at least one static instruction pose. This step establishes a standardized mapping between models and interaction methods by pre-defining and associating relevant data in the storage module (such as a built-in database or solid-state drive) and assigning a unique behavioral instruction to each operation model. This behavioral instruction consists of an instruction library composed of at least one static instruction pose. For example, dynamic user actions can be decomposed into multiple storable static templates (such as images, point clouds, or feature vectors) for real-time recognition and comparison in subsequent steps. Based on this, this preset mechanism not only ensures the uniqueness and traceability of instructions but also provides a solid foundation for the system's contactless operation.

[0061] Step 220: The image acquisition module acquires the user's real-time behavioral posture, which is then identified and compared in the processing and comparison module. Specifically, the image acquisition module includes a camera, an infrared sensor, and a point cloud radar. These components work together to capture real-time images or video streams of the user's behavior at the control panel, as well as posture point cloud data, and generate posture data that is then analyzed using point cloud analysis or contour extraction algorithms. Based on this, the processing and comparison module uses algorithms such as machine learning models or template matching to compare the acquired real-time posture with the static command posture preset in step S210. A similarity threshold greater than 80% is considered a successful match. This design enables contactless interaction, especially in scenarios with high hygiene requirements, such as during an epidemic or in a laboratory, improving user experience and system response speed.

[0062] Step 230: Display the comparison results on the main display screen and prompt the user for confirmation. Specifically, the processing and comparison module transmits the recognition results, such as "Match Successful: Operation Model X" or "Similarity 75%", to the main display screen, displaying them through a graphical interface such as highlighted icons or text pop-ups, accompanied by voice or visual prompts such as "Please confirm your selection". Based on this, the user can confirm through simple interactions such as nodding or pressing a button. The technical principle of this design is based on a human-computer interaction feedback loop, ensuring that the results are transparent and verifiable, avoiding misidentification, and thus providing an accurate input foundation for subsequent steps such as the module installation of S300.

[0063] Step 240: If the comparison fails, the user is prompted to re-enter the action command or switch to manual selection mode. Specifically, if the similarity in step S220 is below a threshold, such as less than 70%, the system will trigger failure logic: display an error message such as "Posture not recognized, please try again" on the main display screen, and provide options—the user can re-execute the posture input, or switch back to the traditional manual mode of S200, such as clicking or swiping. Based on this, the technical principle of this design involves fault tolerance mechanisms and backup path design to ensure uninterrupted flow, thereby maintaining operational continuity and user experience.

[0064] In step S210, the behavioral instructions include, but are not limited to, gestures, body postures, or combinations of multimodal actions to achieve contactless operation model selection. Specifically, the definition of behavioral instructions is extended to multiple forms, such as single gestures (e.g., waving to select the next model), body postures (e.g., tilting the upper body to scroll the list), or multimodal combinations (e.g., gestures + voice). The system supports these variations through an image acquisition and processing module, achieving recognition from static to dynamic states. The technical principle is based on multi-sensor fusion (e.g., combining a camera and microphone), allowing for custom extensions. The expected effect is to further enhance the convenience of contactless operation, making it suitable for diverse user scenarios (e.g., multi-person collaboration or people with mobility impairments), improving interaction inclusivity and hygiene safety compared to the touchscreen reliance of background technologies, while providing a broader scope of patent protection.

[0065] For a specific example, a behavioral instruction could be the process of a user's hand gradually changing from a fist to a palm, corresponding to the selection of a specific operation model (such as a "mechanical assembly model"). Specifically, this process is broken down into multiple static instruction poses, forming a complete instruction library: first, the initial pose of a fully clenched fist (the contour feature is a closed curve, with an area of ​​approximately 50 square pixels); then, the intermediate pose of slightly open fingers (curvature change of approximately 15%, finger gap increase of 10%); then, a half-open state (palm area expands to 70%); followed by a near-fully open pose (curvature peak decreases by 20%); and finally, the ending pose of a fully open palm (total area increases to 150%). These static poses are serialized and stored using algorithms (such as keyframe extraction) to simulate continuous dynamic behavior.

[0066] Building upon this foundation, in practical use, this pre-set design significantly enhances the system's scalability. Users no longer need to memorize complex menu paths or rely on traditional touch input; they can quickly trigger the selection of the corresponding operation model simply by performing a basic pre-set gesture, thus simplifying the interaction process. This solution, through its gesture-triggered mechanism, reduces operation time from an average of 20-30 seconds to less than 5 seconds, making it particularly suitable for high-frequency training or production environments, further reducing the user's learning burden and operational fatigue.

[0067] Furthermore, it is worth mentioning that in other embodiments, the mapping between operation models and behavioral commands supports user-customizable settings. For example, through the configuration interface of the main display screen or external management tools, administrators or users can flexibly modify the mapping relationship (such as changing the "wave" command from "select the next model" to "confirm the current model") and update the data in the storage module in real time. This not only enhances the adaptability of the system (such as making personalized adjustments for different user groups or scenario needs), but also provides a flexible foundation for future expansion (such as integrating multimodal commands), ensuring the long-term applicability of the solution.

[0068] In step S230 above, this display and confirmation mechanism significantly enhances the system's reliability and user trust during actual use. Users can verify results in real time, avoiding blind operations and thus reducing interaction errors. In contrast, the non-feedback selection mode of the background technology often leads to an error rate as high as 15%, while this solution reduces the error rate to below 2% through feedback loops. Especially in high-precision training environments, it further improves the transparency and efficiency of operations.

[0069] Furthermore, it is worth mentioning that in other embodiments, this step supports multimodal confirmation extensions, such as integrating voice recognition (e.g., the user says "yes" to confirm) or linking with the status reminder module 80 (the warning light turns green after confirmation), to adapt to different user needs and ensure the long-term applicability and compatibility of the solution.

[0070] Through steps S210 to S240, the technical solution provided in this disclosure realizes a contactless operation model selection sub-process based on behavioral instructions. Specifically, step S210 pre-establishes a behavioral instruction mapping library for each operation model in the storage module. Each instruction contains at least one static posture as a basic template, ensuring a standardized basis for subsequent recognition. Based on this, step S220 uses the image acquisition module to capture the user's dynamic behavioral posture in real time, and performs similarity calculation and matching with the preset template through the processing and comparison module. Next, step S230 feeds back the comparison results to the main display screen in real time, requiring user confirmation in conjunction with visual or voice prompts, forming an interactive closed loop. If it fails, step S240 activates the fault tolerance mechanism, guiding the user to retry or switch to the backup manual mode to avoid process interruption. Furthermore, step S250 defines diverse forms of behavioral instructions, supporting multimodal expansion and realizing end-to-end contactless processing from acquisition to selection. The data flow between each step is coordinated by the intelligent control module, ensuring seamless integration with the overall steps in S200.

[0071] Based on this, in actual use, the operation model selection provided in this disclosure supports command recognition mode. By organically integrating five aspects—preset commands, real-time data acquisition and comparison, result display, failure handling, and posture extension—it forms an efficient, contactless, and intelligent interactive subsystem. This subsystem not only significantly shortens model selection time and improves training efficiency but also enhances safety in high-hygiene or complex environments, thereby improving the user experience.

[0072] In some embodiments, the instruction recognition pattern method serves as a contactless operation model selection mechanism for a multi-functional training operating system, and is collaboratively implemented by a storage module, an image acquisition module, a processing and comparison module, a main display screen, and an intelligent control module. Specifically, the storage module pre-stores the mapping relationship between operation models and behavioral instructions, as well as a static instruction posture template library; the image acquisition module acquires real-time user behavioral posture data and generates posture point clouds or contour information; the processing and comparison module matches the real-time posture with the template library and outputs the recognition result; the main display screen displays the recognition result and obtains user confirmation or triggers a failure rollback. The intelligent control module determines the target operation model based on the confirmed recognition result and calls the training process, process parameters, or functional modules corresponding to the target operation model, thereby realizing the operation model selection driven by behavioral instructions and seamlessly connecting with the subsequent function execution of the system. Through the confirmation and rollback mechanism, the system can still complete the operation model selection in the event of misidentification or abnormal acquisition, enhancing the overall reliability and availability of the system. In some embodiments of this disclosure, in step S220 provided in the foregoing embodiments, Figure 4 This diagram illustrates the process by which the user's real-time behavior and posture are acquired by the image acquisition module and then identified and compared in the processing and comparison module. Figure 4 As shown, the specific steps may include the following: S221: Acquire the user's current point cloud data, simulate multiple consecutive static command postures corresponding to each user's action command from start to end, and obtain the command contour information corresponding to the static command postures. Specifically, the image acquisition module uses a camera, infrared sensor, or point cloud radar to capture the user's 3D point cloud data within the operating area. Then, simulation algorithms (such as keyframe extraction or motion trajectory modeling) are used to decompose each preset action command into multiple static posture sequences from start to end, and contour information is extracted from them. Based on this, the technical principle is based on point cloud processing and posture sequence simulation, ensuring that dynamic commands are converted into a comparable static template library, thereby improving the accuracy and adaptability of recognition. In actual use, this step significantly improves the robustness of the system, maintaining high accuracy in complex environments such as changes in lighting or user differences.

[0073] S222: Real-time contour information is acquired based on real-time behavioral pose and compared with command contour information. Specifically, based on the point cloud data collected in S221, the system processes the user's current pose in real time, extracts contours through edge detection algorithms, generates real-time contour information, and then compares it with preset command contour information. Based on this, the principle of this technology relies on a feature matching mechanism to ensure efficient comparison and can incorporate noise filtering to cope with lighting or background interference, thereby improving the real-time performance and accuracy of recognition.

[0074] S223: Select the behavioral instruction corresponding to the instruction contour information with the highest matching degree as the recognition instruction. Specifically, the system sorts all the comparison results in S222, selects the instruction contour with the highest matching degree, and outputs its corresponding behavioral instruction as the final recognition result, which is then transmitted to subsequent modules. Based on this, the technical principle is based on an optimized selection algorithm to ensure rapid decision-making in multiple instruction libraries, and thresholds can be set to filter low-matching items, thereby optimizing decision-making efficiency and reducing the risk of ambiguity.

[0075] A specific example of step 221 above: The user executes the action command of waving to select the next model. The system collects point cloud data, simulates 5 static frames from the raising of the arm to the end of the arm swing, and extracts the arm contour information of each frame and stores it as an instruction template.

[0076] Furthermore, it's worth noting that in other embodiments, this step supports algorithm optimization and expansion, such as integrating machine learning models to dynamically adjust the number of simulation frames, or updating the template library in conjunction with the storage module to adapt to personalized needs. This not only enhances the system's flexibility but also provides a solid foundation for future upgrades such as adding multi-sensor fusion, ensuring the long-term applicability and compatibility of the solution.

[0077] A specific example of step 222 above: A typical application scenario is when a user actually performs a waving gesture, the system extracts the real-time contour, compares it with the preset instruction contour, calculates the similarity, and considers it a preliminary match. Compared with the static image matching of the background technology, this design can handle continuous dynamic input and reduce misjudgments.

[0078] Building upon this, in practical use, this step significantly improves the system's response speed and maintains high accuracy in real-time interactions. Furthermore, it's worth noting that in other embodiments, this step supports algorithmic extensions, such as integrating advanced matching models or adaptive threshold adjustments, to adapt to diverse environments. This not only enhances the system's robustness but also provides a flexible foundation for future upgrades such as multi-feature fusion, ensuring the long-term applicability and compatibility of the solution.

[0079] A specific example of step 223 above: A typical application scenario is that the comparison results show a high matching degree for the waving command, a medium matching degree for the fist-clenching command, and a low matching degree for the nodding command. The system selects the waving command as the recognition command and associates it with the corresponding operation model. Compared with the simple threshold judgment of the background technology, this design provides a more robust filtering mechanism and supports accurate recognition in complex scenarios.

[0080] Building upon this foundation, in practical application, this step significantly improves the system's decision-making accuracy, enabling rapid and reliable output in multi-option environments. Furthermore, it's worth noting that in other embodiments, this step supports extended optimization, such as integrating weighted algorithms or dynamic threshold adjustments, to adapt to high-load scenarios. This not only enhances the system's robustness but also provides a flexible foundation for future upgrades such as multi-database fusion, ensuring the long-term applicability and compatibility of the solution.

[0081] By sequentially executing steps S221 to S223, a pose recognition sub-process based on point cloud and contour information is constructed. Specifically, step S221 collects and simulates to generate a command contour template library, providing a standardized basis for comparison. Based on this, step S222 extracts and compares contour information in real time, achieving a dynamic-to-static matching transformation. Next, step S223 performs matching degree filtering and outputs the optimal recognition command, forming a closed loop from data acquisition to decision-making. The data flow between each step is uniformly managed by the processing and comparison module, ensuring seamless integration with the overall step S220 and supporting the expansion of contactless interaction.

[0082] This solution incorporates point cloud simulation and contour extraction to achieve dynamic 3D processing and improve robustness. Through real-time contour comparison and highest-match filtering, it significantly reduces the error rate. This solution supports command sequence simulation, making it suitable for variable speed or personalized poses, thus addressing issues arising from user differences.

[0083] Furthermore, the comprehensive technical effect of the solution lies in the fact that the posture recognition sub-process provided in this disclosure, through the integration of point cloud acquisition, contour comparison, and matching filtering, forms a high-precision, real-time contactless recognition mechanism. This mechanism not only significantly improves the response speed and accuracy of operation model selection but also reduces the risk of physical contact in training or production environments, promoting intelligent upgrades. Combined with the overall solution, it provides a sophisticated interactive foundation for multifunctional systems, possessing broad application value, such as improving teaching efficiency in vocational education or supporting remote operation in industry.

[0084] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A multi-functional training operating system, characterized in that, include: A production line frame, wherein multiple mounting positions are pre-set on the production line frame, and each mounting position is provided with a first connection structure; Multiple process modules, each of which is provided with a second connection structure for forming a detachable connection with the first connection structure, so as to realize the combination and replacement of different process units; The plug-in module includes a first plug-in module disposed at each of the mounting positions and a second plug-in module disposed on each process module, wherein the first plug-in module is electrically connected to the intelligent control module; The identification module includes a primary identification unit, which is used to identify and confirm the user's identity and generate login records; The display module, including the main display screen, is used to display multiple selectable operation models based on the user identity confirmed by the recognition module. It supports click or swipe selection and configuration adjustment so that the user can select the operation model corresponding to the target production line. The intelligent control module is electrically connected to the plug-in module, power module and communication module, and is used to centrally manage the system's operating logic and uniformly manage power distribution, signal scheduling and safety control. The storage module is communicatively connected to the intelligent control module and is used to record training data in real time.

2. The multifunctional training operating system as described in claim 1, characterized in that, The identification module includes a second identification unit for quickly selecting the target production line; In the storage module, a corresponding behavior instruction is set for each operation model; The user's behavioral commands are acquired through the image acquisition module and then identified and compared in the processing and comparison module. Display the comparison results on the main screen and allow the user to confirm them; If the comparison fails, the user will be prompted to re-enter the action command or switch to manual selection mode. The behavioral instructions include, but are not limited to, gestures, body postures, or combinations of multimodal actions, to enable contactless operation model selection.

3. The multifunctional training operating system as described in claim 2, characterized in that, The display module also includes an independent display screen; Each of the process modules is equipped with an independent display screen for playing operation videos or instructional documents for that module.

4. The multifunctional training operating system as described in claim 1, characterized in that, It also includes a status reminder module, which is communicatively connected to the plug-in module and the intelligent control module. It is used to detect the installation status of the process module corresponding to each installation position of the operation model selected by the user on the production line frame and display the status on the main display screen.

5. The multifunctional training operating system as described in claim 4, characterized in that, The status alert module includes a warning light system; The warning light system is installed at each of the installation positions and has red, yellow and green indicators; Red indicates that it is not installed or not selected; Yellow indicates that the installation is complete but the system has not been started. Green indicates that preparation is complete and adaptation is successful; The color change of the warning light is automatically controlled by the intelligent control module based on the plug-in detection signal.

6. The multifunctional training operating system as described in claim 4, characterized in that, The communication module includes a wireless communication unit and / or a wired communication interface, used to realize the real-time transmission of process module installation signals, the synchronization of operation model data, and the connection with external devices.

7. The multifunctional training operating system as described in claim 1, characterized in that, The first identification unit includes a biometric identification unit, which is used to confirm the user's identity through fingerprint recognition and / or facial recognition, and transmit the identity verification result to the intelligent control module to realize access control and login record generation based on user identity.

8. A multifunctional practical training operation method, characterized in that, include: The user's identity is verified by the first identification unit of the identification module, and a login record is generated. The main display screen shows multiple optional operation models for the user to choose from based on the identified user identity, in order to determine the operation model corresponding to the target production line; According to the selected operation model, the second connection structure of multiple process modules is detachably connected to the first connection structure of the corresponding installation position on the preset installation position of the production line frame, so as to realize the combination or replacement of different process units. Electrical connection is achieved through the first plug-in module located at the mounting position and the second plug-in module located on the process module, and the plug-in status information of each mounting position is obtained by the intelligent control module. The intelligent control module executes the production line operation logic according to the selected operation model, including power distribution, signal scheduling, and safety control; The training data is recorded in real time through the storage module, and the data is stored in association with the user's identity information.

9. The multifunctional training operation method as described in claim 8, characterized in that, The selection of the operation model supports instruction recognition modes, including: In the storage module, a corresponding behavior instruction is preset for each operation model, and each behavior instruction includes at least one static instruction posture. The image acquisition module acquires the user's real-time behavior and posture, and the processing and comparison module performs identification and comparison. The comparison results are displayed on the main screen, and the user is prompted to confirm. If the comparison fails, the user will be prompted to re-enter the action command or switch to manual selection mode. The behavioral instructions include, but are not limited to, gestures, body postures, or combinations of multimodal actions, to enable contactless operation model selection.

10. The multifunctional training operation method as described in claim 9, characterized in that, The process of acquiring the user's real-time behavior and posture through the image acquisition module and then performing identification and comparison in the processing and comparison module includes the following steps: Collect the user's current point cloud data, simulate the continuous static command postures corresponding to each user's action command from start to end, and obtain the command contour information corresponding to the static command postures; Real-time contour information is obtained based on the real-time behavior posture, and the real-time contour information is compared with the command contour information; The behavioral instruction corresponding to the instruction contour information with the highest matching degree is selected as the recognition instruction.

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