A gesture perception-based PLC intelligent scene control method and system

Abstract

The application discloses a PLC intelligent scene control method and system based on posture sensing, relates to the technical field of scene control, and has the technical scheme that the main control system and the practical training module are connected through the magnetic attraction quick-release structure and the physical adsorption connection; the main control system reads the module identity information stored in the integrated hidden RFID encryption chip in the practical training module through the RFID radio frequency identification communication module wireless induction, carries out posture sensing and stable identification on the practical training module based on a dynamic anti-shake algorithm; the main control system dynamically matches the standardized program library according to the module identity information after identifying the practical training module, and automatically calls the demonstration program corresponding to the practical training module; the main control system judges and switches between the demonstration mode and the programming control mode; and the effect is to adapt to the PLC teaching scene in different stages.

Description

Technical Field

[0001] This invention relates to the field of scene control technology, and more specifically, to a PLC intelligent scene control method and system based on attitude perception. Background Technology

[0002] Traditional PLC training equipment often suffers from inefficient connection and identification issues. Training modules typically connect to the main control system via wired interfaces, which is not only cumbersome and time-consuming but also prone to interface wear and poor contact, impacting teaching progress. Traditional equipment lacks posture sensing capabilities; when training modules shift position or change angle due to collisions or accidental touches, the system cannot automatically identify and calibrate, often leading to program malfunctions requiring repeated manual adjustments and reducing the smoothness of training. Furthermore, traditional PLC training systems often use fixed-association program libraries, requiring manual selection for program loading. This fails to adapt to diverse training modules, increases the teacher's workload, and cannot meet students' needs for independent programming. The lack of standardized demonstrations and guidance makes it difficult to balance the standardization of teaching with the flexibility of practical operation. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a PLC intelligent scene control method and system based on attitude perception.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A posture-aware PLC intelligent scene control system includes a main control system and a training module. The training module is physically connected to the main control system via a magnetic quick-release structure. The main control system wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated in the training module through the RFID radio frequency identification communication module, and performs attitude perception and stability identification of the training module based on the dynamic anti-shake algorithm. After identifying the training module, the main control system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. The main control system determines and switches between teaching demonstration mode and programming control mode. If it determines to be teaching demonstration mode, it automatically runs the preset standardized demonstration program. If it determines to be programming control mode, it provides a programming interface for the externally connected programmable controller and processes the programming interface to achieve intelligent control of the current teaching scenario.

[0005] Preferably, the intelligent control of the current teaching scenario is achieved by processing the programming interface, specifically including the following steps: The programming interface receives and executes custom control programs issued by the programmable controller; During the operation of the custom control program, the main control system interacts with the simulation unit on the training module through the input signal interface module and the output signal interface module. It converts the output control signal from the programmable controller into the drive signal that drives the simulation unit, and converts the status feedback signal of the simulation unit into the input signal that the programmable controller can recognize, so as to realize intelligent control of the current teaching scenario.

[0006] Preferably, the main control system wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated within the training module via the RFID radio frequency identification communication module, specifically including the following steps: During the process of establishing a physical adsorption connection between the main control system and the training module through a magnetic quick-release structure, the RFID radio frequency identification communication module automatically activates the multi-band radio frequency transmission unit and transmits a gradually changing scanning radio frequency signal to the area where the training module is located. The frequency of the radio frequency signal gradually changes along the preset frequency band range until it resonates and matches the working frequency band of the RFID encryption chip. After receiving the matching radio frequency signal, the RFID encryption chip converts the radio frequency energy into working electrical energy through the passive energy harvesting unit, and starts the identity information retrieval process inside the chip. During the identity information retrieval process, the module identity information is dynamically encrypted in real time. The encryption process combines the chip's own physical characteristic parameters to generate an encryption key, loads the encrypted module identity information into the radio frequency feedback signal, and sends it to the RFID radio frequency identification communication module in the form of backscattering. After receiving the encrypted identity information, the RFID radio frequency identification communication module extracts the encrypted data through the signal demodulation unit and calls the decryption module built into the main control system. The decryption module decrypts the encrypted data layer by layer according to the preset encryption rules and the decryption key corresponding to the RFID encryption chip, restoring the original content of the module's identity information. During the reading process, the main control system continuously monitors the strength and stability of the radio frequency signal. If the signal strength fluctuation exceeds the preset range, the current reading process is interrupted and the radio frequency scan is restarted until stable and complete module identity information is obtained.

[0007] Preferably, the training module is subjected to attitude perception and stability recognition based on a dynamic image stabilization algorithm, specifically including the following steps: After the main control system obtains the connection signal of the training module, it activates the attitude sensing unit distributed on the magnetic connection contact surface. The attitude sensing unit collects the position offset and angle change information of the training module. During the collection process, the reference contact point of the magnetic connection is used as the reference point, and the displacement parameters of the training module relative to the reference contact point are recorded in real time. The main control system fuses the information collected by the attitude sensing unit, assigns different weight coefficients according to the installation position of the attitude sensing unit, and integrates the weighted displacement parameters to form the attitude change trajectory of the training module. The stability of the training module is determined based on the attitude change trajectory.

[0008] Preferably, the stability identification of the training module is completed based on the attitude change trajectory, specifically including the following steps: The main control system continuously tracks the attitude change trajectory. If the attitude change trajectory shows a continuous and regular convergence trend, it is determined that the attitude of the training module tends to be stable. If the attitude change trajectory diverges or fluctuates irregularly, the magnetic connection auxiliary calibration mechanism is activated. The attitude of the training module is corrected by the magnetic attraction force built into the main control system. During the correction process, attitude information is continuously updated until the attitude change trajectory meets the convergence requirements, thus completing the stable identification of the training module.

[0009] Preferably, after identifying the training module, the main control system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. This specifically includes the following steps: After identifying the training module, the main control system obtains the module's identity information and performs feature extraction on the module's identity information to obtain the feature elements of the control logic to which the training module belongs; wherein, the feature elements include the training scenario type, the control object type, and the signal interaction method; The main control system matches the feature elements with the standardized program library to obtain a set of candidate demonstration programs that match the feature elements; wherein, the standardized program library is pre-classified and stored according to the training scenario dimension, and each scenario is associated with multiple demonstration programs adapted to different module characteristics; The adaptation parameter requirements of each program in the candidate demonstration program set are retrieved, and the adaptation parameter requirements are compared with the hardware configuration parameters and signal interface specifications contained in the module identity information to obtain the optimal matching demonstration program. The optimal matching target demonstration program is retrieved from the standardized program library and loaded into the runtime memory. A mapping relationship is established between the demonstration program and the signal interface of the training module, so that the control signals output by the demonstration program are connected to the corresponding simulation unit of the training module, thereby realizing the automatic start-up and stable operation of the demonstration program.

[0010] Preferably, before calling the optimal matching target demonstration program, the main control system automatically detects the current operating status and resource usage of the main control system, and adaptively adjusts the operating parameters of the target demonstration program according to the detection results, so that the operating requirements of the demonstration program match the resource supply of the main control system.

[0011] Preferably, converting the output control signal from the programmable controller into a drive signal to drive the analog unit's operation specifically includes the following steps: After receiving the output control signal from the programmable controller through the communication link, the main control system analyzes the control signal according to the control function dimension to obtain the action command signal, execution parameter signal and safety protection signal; Based on the hardware driving characteristics of the simulation unit, the action command signal is converted into a trigger signal that conforms to the action logic of the simulation unit, the execution parameter signal is converted into an adjustment signal that matches the operating parameters of the simulation unit, and the safety protection signal is converted into a protection signal that protects the simulation unit. The trigger signal, adjustment signal, and protection signal are combined to form a drive signal; Collect real-time drive status feedback information of the simulation unit, and determine whether the drive signal matches the actual drive requirements of the simulation unit based on the real-time drive status feedback information; If there is a deviation between the drive signal and the drive requirements of the analog unit, the main control system starts the drive signal calibration mechanism. Based on the hardware parameters of the analog unit and the real-time drive status feedback information, the drive signals of each level of conversion are dynamically adjusted to optimize the response timing of the trigger signal, adjust the parameter amplitude of the signal and the trigger threshold of the protection signal until the drive signal is fully adapted to the drive requirements of the analog unit.

[0012] Preferably, converting the state feedback signal of the analog unit into an input signal that the programmable controller can recognize specifically includes the following steps: The status feedback signals generated by the simulation unit during the execution of the action are transmitted to the signal acquisition unit of the main control system through the feedback link. The signal acquisition unit classifies and captures the feedback signals according to the signal type, distinguishing between position feedback signals that represent the progress of the action execution, working condition feedback signals that reflect the operating status, and alarm feedback signals that indicate abnormal conditions. The main control system calls the corresponding signal format conversion unit according to the signal recognition specification of the programmable controller to convert the position feedback signal into a status characterization signal that meets the position recognition requirements of the controller, convert the operating condition feedback signal into an operating parameter signal that the controller can interpret, and convert the alarm feedback signal into an alarm trigger signal that the controller can respond to quickly. The status representation signal, operating parameter signal, and alarm trigger signal are combined to form the input signal; The main control system transmits the converted input signal to the programmable controller according to a preset timing sequence through the communication interface.

[0013] A PLC intelligent scene control method based on attitude perception, the method includes the following steps: The RFID radio frequency identification communication module wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated in the training module, and performs attitude perception and stable identification of the training module based on the dynamic anti-shake algorithm. After identifying the training module, the system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. The system can determine and switch between teaching demonstration mode and programming control mode. If the mode is determined to be teaching demonstration mode, the system will automatically run the preset standardized demonstration program. If the mode is determined to be programming control mode, the system will provide a programming interface for the externally connected programmable controller and process the programming interface to achieve intelligent control of the current teaching scenario.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention physically connects the training modules to the main control system via a magnetic quick-release structure. Combined with an RFID radio frequency identification communication module and a hidden RFID encryption chip, this achieves rapid module assembly and disassembly without complex wiring, significantly saving teaching preparation time. Furthermore, wireless sensing reads the module's identity information, avoiding interface wear and poor contact issues common in traditional wired identification. The encryption chip design also enhances the security of the module's identity information, reducing the risk of information tampering. The main control system can dynamically match and automatically call the corresponding demonstration program from a standardized program library based on the module's identity information. This not only reduces the operational complexity for teachers but also allows students to quickly enter the training phase. Simultaneously, the standardized program library's adaptation logic is compatible with different types of training modules, improving system reusability and eliminating the need for separate program development for each module, thus reducing the development cost of teaching resources. The main control system supports the determination and switching between two modes: teaching demonstration and programming control. In teaching demonstration mode, preset programs are run automatically, which is suitable for teachers to conduct standardized operation demonstrations and ensure the standardization of teaching content. In programming control mode, a programming interface is provided for external programmable controllers to meet the needs of students for independent programming and hands-on debugging. It takes into account both the standardization of teaching demonstrations and the flexibility of hands-on training, and can better adapt to PLC teaching scenarios at different stages, thereby improving teaching efficiency and training experience. Attached Figure Description

[0015] Figure 1 The present invention presents a principle block diagram of a PLC intelligent scene control system based on attitude perception. Figure 2 This invention presents a logical diagram of a PLC-based intelligent scene control system based on attitude perception. Figure 3 This is a schematic diagram illustrating the steps of a PLC intelligent scene control method based on attitude perception proposed in this invention. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0019] Reference Figures 1-3 As shown.

[0020] The embodiments further illustrate the posture-aware PLC intelligent scene control method and system proposed in this invention.

[0021] A posture-aware PLC intelligent scene control system includes a main control system and a training module. The training module is physically connected to the main control system via a magnetic quick-release structure. The main control system wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated in the training module through the RFID radio frequency identification communication module, and performs attitude perception and stability identification of the training module based on the dynamic anti-shake algorithm. After identifying the training module, the main control system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. The main control system determines and switches between teaching demonstration mode and programming control mode. If it determines to be teaching demonstration mode, it automatically runs the preset standardized demonstration program. If it determines to be programming control mode, it provides a programming interface for the externally connected programmable controller and processes the programming interface to achieve intelligent control of the current teaching scenario.

[0022] The programming interface is processed to achieve intelligent control of the current teaching scenario, specifically including the following steps: The programming interface receives and executes custom control programs issued by the programmable controller; During the operation of the custom control program, the main control system interacts with the simulation unit on the training module through the input signal interface module and the output signal interface module. It converts the output control signal from the programmable controller into the drive signal that drives the simulation unit, and converts the status feedback signal of the simulation unit into the input signal that the programmable controller can recognize, so as to realize intelligent control of the current teaching scenario.

[0023] Firstly, the programming interface plays a crucial role in receiving and executing custom control programs. Once the external programmable controller has completed writing the custom control program, it sends the program to the system's programming interface. The core function of the programming interface is to trigger subsequent execution flows.

[0024] After the custom control program starts running, the main control system establishes a signal interaction channel with the simulation unit on the training module through the input signal interface module and the output signal interface module. The input signal interface module and the output signal interface module are responsible for signal processing in different directions: the output signal interface module first receives the output control signal from the programmable controller and then converts it into a drive signal that can directly drive the action of the simulation unit; while the input signal interface module receives the status feedback signal generated by the simulation unit during the action and then converts it into an input signal that the programmable controller can recognize.

[0025] Assuming the simulation unit of the training module is a set of LEDs, when the programmable controller (PLC) sends an output control signal to light up the first LED, the output signal interface module converts this signal into a circuit drive signal for the corresponding LED, directly controlling the LED circuit to conduct and lighting up the first LED. Once this LED is lit, its status feedback signal is received by the input signal interface module, converted into an input signal recognizable by the PLC, and sent back to the PLC, allowing the programmer to obtain the real-time operating status of the simulation unit. Through this bidirectional signal conversion and interaction, the equipment in the entire teaching scenario can operate according to the logic of the custom program, while simultaneously achieving real-time status feedback, ultimately completing the intelligent control process.

[0026] The main control system wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated within the training module via the RFID radio frequency identification communication module. Specifically, this includes the following steps: During the process of establishing a physical adsorption connection between the main control system and the training module through a magnetic quick-release structure, the RFID radio frequency identification communication module automatically activates the multi-band radio frequency transmission unit and transmits a gradually changing scanning radio frequency signal to the area where the training module is located. The frequency of the radio frequency signal gradually changes along the preset frequency band range until it resonates and matches the working frequency band of the RFID encryption chip. After receiving the matching radio frequency signal, the RFID encryption chip converts the radio frequency energy into working electrical energy through the passive energy harvesting unit, and starts the identity information retrieval process inside the chip. During the identity information retrieval process, the module identity information is dynamically encrypted in real time. The encryption process combines the chip's own physical characteristic parameters to generate an encryption key, loads the encrypted module identity information into the radio frequency feedback signal, and sends it to the RFID radio frequency identification communication module in the form of backscattering. After receiving the encrypted identity information, the RFID radio frequency identification communication module extracts the encrypted data through the signal demodulation unit and calls the decryption module built into the main control system. The decryption module decrypts the encrypted data layer by layer according to the preset encryption rules and the decryption key corresponding to the RFID encryption chip, restoring the original content of the module's identity information. During the reading process, the main control system continuously monitors the strength and stability of the radio frequency signal. If the signal strength fluctuation exceeds the preset range, the current reading process is interrupted and the radio frequency scan is restarted until stable and complete module identity information is obtained.

[0027] When the main control system and the training module establish a physical connection via a magnetic quick-release structure, the RFID radio frequency identification communication module of the main control system will automatically activate the multi-band radio frequency transmission unit, transmitting a gradually changing scanning radio frequency signal to the area where the training module is located. Gradual scanning means that the frequency of the radio frequency signal gradually changes along a preset frequency band range. For example, if the preset frequency band range is 800MHz to 900MHz, the signal frequency starts at 800MHz and increases in 1MHz increments to 900MHz until the signal frequency resonates with the operating frequency band of the RFID encryption chip built into the training module. Only when the frequency of the transmitted signal matches the chip's operating frequency band can the chip effectively receive radio frequency energy and respond.

[0028] When the RFID encryption chip receives a matching radio frequency signal, it activates its passive energy harvesting unit to convert the received radio frequency energy into electrical energy required for its operation. At this point, the chip begins retrieving the module's identity information, performing real-time dynamic encryption on this information during the retrieval process. The core of the encryption process is generating an encryption key by combining the chip's own physical characteristic parameters. These parameters could include the chip's production batch number and internal circuit impedance. Assuming the chip's physical characteristic parameters are A (production batch) and B (circuit impedance), the encryption key K is generated using the formula K = A × 10⁻¹⁰. n +B is generated, where n is a constant for adapting the number of bits in A. The encrypted module identity information is loaded into the radio frequency feedback signal and sent back to the RFID radio frequency identification communication module in a backscattering manner. Backscattering means that the chip does not actively emit signals, but transmits information by reflecting the radio frequency signals emitted by the main control system.

[0029] After receiving the encrypted identity information, the RFID radio frequency identification communication module extracts the encrypted data through the signal demodulation unit and simultaneously calls the decryption module built into the main control system. The decryption module, based on preset encryption rules and the decryption key corresponding to the RFID encryption chip (a preset key corresponding to the chip's physical characteristic parameters; for example, if the encryption key is K generated based on the chip's physical characteristics, the decryption key is K' corresponding to K, satisfying the mapping relationship between K' and K determined by preset encryption rules), decrypts the encrypted data layer by layer to restore the original content of the module's identity information.

[0030] Throughout the identity information reading process, the main control system continuously monitors the stability of the radio frequency signal strength. Assuming a preset signal strength stability range of S ± ΔS, where S is the reference signal strength and ΔS is the allowable fluctuation threshold, when the real-time signal strength Sactual satisfies |Sactual - S| > ΔS, the main control system interrupts the current reading process, restarts the radio frequency scanning process, and begins execution again from the gradual frequency band scan until it obtains the module's identity information with stable signal strength and complete content.

[0031] The training module is subjected to attitude perception and stability recognition based on a dynamic image stabilization algorithm, specifically including the following steps: After the main control system obtains the connection signal of the training module, it activates the attitude sensing unit distributed on the magnetic connection contact surface. The attitude sensing unit collects the position offset and angle change information of the training module. During the collection process, the reference contact point of the magnetic connection is used as the reference point, and the displacement parameters of the training module relative to the reference contact point are recorded in real time. The main control system fuses the information collected by the attitude sensing unit, assigns different weight coefficients according to the installation position of the attitude sensing unit, and integrates the weighted displacement parameters to form the attitude change trajectory of the training module. The stability of the training module is determined based on the attitude change trajectory.

[0032] Upon receiving the connection signal from the training module, the main control system immediately activates the attitude sensing units distributed on the magnetic connection contact surface. The core function of these attitude sensing units is to collect information on the position offset and angle change of the training module, with the reference point of the magnetic connection always serving as the reference point. For example, if the reference point is set as the metal contact at the center of the magnetic contact surface, the attitude sensing units record the displacement parameters of the training module relative to this reference point in real time, including the lateral offset X, the longitudinal offset Y, and the angle change θ around the reference point. These parameters are recorded as continuous time-series data.

[0033] The main control system fuses the information collected by the attitude sensing units. Since attitude sensing units at different locations have varying sensitivities to module attitude changes, the system assigns different weighting coefficients based on the installation position of the attitude sensing units. For example, the weighting coefficient for sensing units near the edge of the magnetic contact surface is set to W1, and the weighting coefficient for sensing units near the reference contact point is set to W2, with W1 being greater than W2 because the offset at the edge has a more significant impact on the overall attitude. Assuming that at a certain moment, the displacement parameters collected by different sensing units are X1, Y1, θ1 (corresponding to weight W1) and X2, Y2, θ2 (corresponding to weight W2), the weighted displacement parameters are calculated using the following formulas: Weighted lateral offset Xn = (W1×X1 + W2×X2) / (W1 + W2), Weighted longitudinal offset Yn = (W1×Y1 + W2×Y2) / (W1 + W2), Weighted angle change θn = (W1×θ1 + W2×θ2) / (W1 + W2). By integrating the weighted displacement parameters at continuous time points, the attitude change trajectory of the training module is formed. The attitude change trajectory shows the trend of the module's position and angle changes from the initial connection to the current moment.

[0034] The system identifies the stability of the training module based on the attitude change trajectory. Specifically, the system continuously monitors the convergence of the attitude change trajectory. If the difference between the weighted displacement parameters at subsequent time points and the previous time point gradually decreases—for example, if the changes in Xn, Yn, and θn at three consecutive time points are all less than the preset stability threshold (e.g., Xn change less than 0.1 mm, Yn change less than 0.1 mm, θn change less than 0.5 degrees)—then the attitude of the training module is determined to be stable. If the trajectory is still fluctuating, i.e., the parameter changes exceed the stability threshold, the system triggers a magnetic calibration mechanism to adjust the module position until the attitude change trajectory converges to a stable range, thereby ensuring the attitude reliability of the connected module.

[0035] The stability identification of the training module is completed based on the attitude change trajectory, specifically including the following steps: The main control system continuously tracks the attitude change trajectory. If the attitude change trajectory shows a continuous and regular convergence trend, it is determined that the attitude of the training module tends to be stable. If the attitude change trajectory diverges or fluctuates irregularly, the magnetic connection auxiliary calibration mechanism is activated. The attitude of the training module is corrected by the magnetic attraction force built into the main control system. During the correction process, attitude information is continuously updated until the attitude change trajectory meets the convergence requirements, thus completing the stable identification of the training module.

[0036] After the training module establishes a connection with the main control system, the main control system continuously tracks the attitude change trajectory collected and weighted by the attitude sensing unit. This trajectory is a sequence composed of weighted lateral offset Xn, weighted longitudinal offset Yn, and weighted angle change θn at consecutive time points. The calculation of these parameters follows the previous weighting formula: Xn=(W1×X1+W2×X2) / (W1+W2), Yn=(W1×Y1+W2×Y2) / (W1+W2), θn=(W1×θ1+W2×θ2) / (W1+W2), where W1 is the weighting coefficient of the sensing unit near the edge of the magnetic contact surface, W2 is the weighting coefficient of the sensing unit near the reference contact point, and W1 is greater than W2.

[0037] When the main control system detects that the attitude change trajectory shows a continuous and regular convergence trend, that is, the difference between Xn, Yn, θn at subsequent time points and the previous time point gradually decreases, for example, the change in Xn at three consecutive time points is less than 0.1mm, the change in Yn is less than 0.1mm, and the change in θn is less than 0.5 degrees, then it is determined that the attitude of the training module tends to be stable.

[0038] If the attitude change trajectory diverges or fluctuates irregularly, such as a sudden change in Xn exceeding 0.1 mm or θn exceeding 0.5 degrees at a certain time point, the system immediately activates the magnetic connection auxiliary calibration mechanism. At this time, the main control system uses its built-in magnetic attraction force adjustment function to correct the position and angle of the training module. For example, when the lateral offset of the module is too large, the system increases the attraction force on the corresponding side of the magnetic contact surface, pulling the module towards the reference contact point. During the correction process, the system continuously collects new attitude information, recalculates Xn, Yn, and θn, and updates the attitude change trajectory until the trajectory shows a continuous convergence trend again, meeting the preset stability threshold requirements, and finally completing the stable identification of the training module.

[0039] After identifying the training module, the main control system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. This includes the following steps: After identifying the training module, the main control system obtains the module's identity information and performs feature extraction on the module's identity information to obtain the feature elements of the control logic to which the training module belongs; among which, the feature elements include the training scenario type, the control object type, and the signal interaction method; The main control system matches the feature elements with the standardized program library to obtain a set of candidate demonstration programs that match the feature elements; the standardized program library is pre-classified and stored according to the training scenario dimension, and each scenario is associated with multiple demonstration programs adapted to different module characteristics; The adaptation parameter requirements of each program in the candidate demonstration program set are retrieved, and the adaptation parameter requirements are compared with the hardware configuration parameters and signal interface specifications contained in the module identity information to obtain the optimal matching demonstration program. The optimal matching target demonstration program is retrieved from the standardized program library and loaded into the runtime memory. A mapping relationship is established between the demonstration program and the signal interface of the training module, so that the control signals output by the demonstration program are connected to the corresponding simulation unit of the training module, thereby realizing the automatic start-up and stable operation of the demonstration program.

[0040] After the main control system identifies the training module and obtains its identity information, it performs feature extraction on the identity information to obtain the feature elements of the control logic to which the module belongs. These elements specifically include the training scenario type, the control object type, and the signal interaction method. If the training module is a training component for motor control, its feature elements will correspond to the training scenario type as electrical control, the control object type as AC motor, and the signal interaction method as analog voltage signal input and output.

[0041] The main control system matches the extracted feature elements with the standardized program library to obtain a set of candidate demonstration programs that match the feature elements. The standardized program library is pre-classified and stored according to the training scenario dimension. Each scenario is associated with multiple demonstration programs adapted to the characteristics of different modules. For example, the electrical control scenario includes demonstration programs adapted to different control objects such as AC motors and DC motors. When the feature elements match the electrical control scenario, all demonstration programs in that scenario will be included in the candidate set.

[0042] The main control system retrieves the adaptation parameter requirements of each program in the candidate demonstration program set, compares these requirements with the hardware configuration parameters and signal interface specifications included in the module's identity information, and selects the optimal matching demonstration program. Taking the motor control module as an example, if the adaptation parameter requirements of a candidate program are support for 220V AC input and a 3-pin terminal interface, and the module's hardware configuration parameters are 220V AC input and a 3-pin terminal signal interface, then this program becomes the optimal match.

[0043] The main control system retrieves the optimally matched demonstration program from the standardized program library into the runtime memory. Simultaneously, it establishes a mapping relationship between the demonstration program and the signal interfaces of the training modules. For example, it maps the output channel of the motor start signal in the program to the start control interface of the module, ensuring that the control signals output by the demonstration program can precisely connect to the corresponding simulation unit of the training module. Through this series of operations, the demonstration program can automatically start and run stably. For instance, after the motor control program starts, its output control signals will be transmitted to the motor simulation unit of the module through the mapped interface, realizing the demonstration function of motor start, stop, and speed regulation.

[0044] Before invoking the optimally matched target demonstration program, the main control system automatically detects the current operating status and resource usage of the main control system, and adaptively adjusts the operating parameters of the target demonstration program according to the detection result, so that the operating requirements of the demonstration program match the resource supply of the main control system.

[0045] After the main control system determines the optimally matched target demonstration program, it does not directly invoke and run the program, but first automatically detects its own current operating status and resource usage. The operating status includes resource indicators such as CPU usage, memory usage, and the number of idle I / O interfaces. If the current CPU usage of the main control system is 60%, the memory usage is 55%, the remaining free memory is 200MB, and there are 3 idle I / O interfaces, these data are recorded as the detection result.

[0046] The main control system adaptively adjusts the operating parameters of the target demonstration program according to these detection results, so that the operating requirements of the program match the resource supply of the system. Taking the motor control demonstration program as an example, if the default operating parameters of the program are to occupy 30% of CPU resources and 100MB of memory resources, while the current remaining resources of the main control system can only support 20% CPU occupancy and 80% memory occupancy, the system adjusts the operating parameters of the program: for example, reducing the data sampling frequency in the program from the original 10 times per second to 8 times per second, and compressing the temporary data cache space during the operation of the program, so that the adjusted parameters meet the resource limits of 20% CPU occupancy and 80% memory occupancy.

[0047] The resource requirement of the demonstration program is made not to exceed the remaining supply of the main control system, which is specifically reflected through a resource matching formula. Assume that the adjusted resource occupancy of the demonstration program is R_adjusted, the remaining resource amount of the main control system is R_remaining, and the default resource occupancy of the program is R_default, then the adjustment principle is R_adjusted ≤ R_remaining, and R_adjusted = R_default × (R_remaining / R_default) (when R_remaining < R_default). Through such adaptive adjustment, the operating requirements of the demonstration program can be accurately matched with the resource supply of the main control system, avoiding program stuttering or crash caused by insufficient resources, and ensuring stable operation after the program is invoked.

[0048] The output control signal from the programmable controller is converted into a drive signal that drives the action of the simulation unit, which specifically includes the following steps: After the main control system receives the output control signal from the programmable controller through the communication link, it parses the control signal according to the control function dimension to obtain an action instruction signal, an execution parameter signal and a safety protection signal; According to the hardware drive characteristics of the simulation unit, the action instruction signal is converted into a trigger signal conforming to the action logic of the simulation unit, the execution parameter signal is converted into an adjustment signal matching the operating parameters of the simulation unit, and the safety protection signal is converted into a protection signal for safeguarding the simulation unit; Among them, the trigger signal, adjustment signal and protection signal are combined to form the drive signal; Collect real-time drive status feedback information of the simulation unit, and determine whether the drive signal matches the actual drive requirements of the simulation unit based on the real-time drive status feedback information; If there is a deviation between the drive signal and the drive requirements of the analog unit, the main control system starts the drive signal calibration mechanism. Based on the hardware parameters of the analog unit and the real-time drive status feedback information, the drive signals of each level of conversion are dynamically adjusted to optimize the response timing of the trigger signal, adjust the parameter amplitude of the signal and the trigger threshold of the protection signal until the drive signal is fully adapted to the drive requirements of the analog unit.

[0049] After receiving the output control signal from the programmable controller (PLC) via the communication link, the main control system analyzes and decomposes the signal according to the control function dimension, extracting action command signals, execution parameter signals, and safety protection signals. For example, if the PLC outputs a motor control signal, the analyzed action command signal might be a motor start command, the execution parameter signal might be the motor speed parameter of 500 r / min, and the safety protection signal might be the overload protection threshold setting signal.

[0050] Based on the hardware driving characteristics of the simulation unit, the main control system performs targeted conversions on these three types of signals. Action command signals are converted into trigger signals that conform to the simulation unit's action logic. Taking a motor simulation unit as an example, if the simulation unit's startup logic is triggered by receiving a high-level signal, the motor startup action command signal is converted into a corresponding high-level trigger signal. Execution parameter signals are converted into adjustment signals that match the simulation unit's operating parameters. For example, a speed parameter of 500 r / min is converted into a voltage adjustment signal recognizable by the simulation unit. Assuming the correspondence between the simulation unit's speed and voltage is 0.2V per 100 r / min, then 500 r / min corresponds to a 1V adjustment signal. Safety protection signals are converted into protection signals to safeguard the simulation unit. For example, an overload protection threshold is converted into a current monitoring trigger signal within the simulation unit. These three converted signals are combined to form a drive signal used to control the operation of the simulation unit.

[0051] The main control system continuously collects real-time drive status feedback information from the simulation unit, such as the actual speed and operating current of the motor simulation unit, and compares this feedback information with the expected state corresponding to the drive signal to determine whether the drive signal matches the actual drive requirements of the simulation unit.

[0052] If a discrepancy is found between the drive signal and the driving requirements of the analog unit after comparison—for example, the expected speed corresponding to the drive signal is 500 r / min, but the actual speed reported by the analog unit is only 450 r / min—the main control system will initiate a drive signal calibration mechanism. At this time, the system dynamically adjusts the drive signal based on the hardware parameters of the analog unit (such as the voltage-speed coefficient K of the motor analog unit) and real-time drive status feedback information (actual speed Vactual). Taking the adjustment signal as an example, if the voltage corresponding to the original adjustment signal is Uoriginal, the expected speed is Vpredicted, and the deviation between the actual speed Vactual and Vpredicted is ΔV = Vpredicted - Vactual, then the adjusted adjustment signal voltage Uadjusted = Uoriginal + (ΔV / K). Simultaneously, the response timing of the trigger signal and the trigger threshold of the protection signal are optimized until the expected state corresponding to the drive signal is completely consistent with the actual feedback state of the analog unit, thus achieving adaptation between the drive signal and the driving requirements of the analog unit.

[0053] Converting the state feedback signal of the analog unit into an input signal that the programmable controller can recognize involves the following steps: The status feedback signals generated by the simulation unit during the execution of the action are transmitted to the signal acquisition unit of the main control system through the feedback link. The signal acquisition unit classifies and captures the feedback signals according to the signal type, distinguishing between position feedback signals that represent the progress of the action execution, working condition feedback signals that reflect the operating status, and alarm feedback signals that indicate abnormal conditions. The main control system calls the corresponding signal format conversion unit according to the signal recognition specification of the programmable controller to convert the position feedback signal into a status characterization signal that meets the position recognition requirements of the controller, convert the operating condition feedback signal into an operating parameter signal that the controller can interpret, and convert the alarm feedback signal into an alarm trigger signal that the controller can respond to quickly. Among them, the status characterization signal, the operating parameter signal, and the alarm trigger signal are combined to form the input signal; The main control system transmits the converted input signal to the programmable controller according to a preset timing sequence through the communication interface.

[0054] When the simulation unit performs an action, it generates corresponding status feedback signals. These signals are transmitted to the signal acquisition unit of the main control system via the feedback link. The signal acquisition unit classifies and captures the feedback signals according to their type: position feedback signals representing the progress of the action, operating condition feedback signals reflecting the operating status, and alarm feedback signals indicating abnormal conditions. If the simulation unit is a cylinder training component, its position feedback signal may be the current stroke data of the cylinder piston, the operating condition feedback signal may be the air pressure value during cylinder operation, and the alarm feedback signal may be an abnormal indication signal of low air pressure.

[0055] According to the signal recognition specifications of the programmable logic controller (PLC), the main control system calls the corresponding signal format conversion unit to convert the three types of feedback signals. Position feedback signals are converted into status representation signals that meet the controller's position recognition requirements. For example, if the cylinder piston stroke is 80mm and the controller requires the position signal to be presented as a percentage, and the maximum cylinder stroke is 100mm, then the status representation signal is 80%. Operating condition feedback signals are converted into operating parameter signals that the controller can interpret. For example, the 0.6MPa air pressure value during cylinder operation is converted into a digital signal that the controller can recognize. Assuming the correspondence between air pressure and digital signals is 0.1MPa to 100, then 0.6MPa corresponds to 600. Alarm feedback signals are converted into warning trigger signals that the controller can respond to quickly. For example, a low air pressure signal is converted into a high-level warning signal for the controller. These three converted signals are combined into input signals, which are used to provide feedback information to the PLC.

[0056] The main control system transmits the converted input signals to the programmable controller (PLC) via a communication interface according to a preset timing sequence. For example, if the preset timing sequence is to transmit status characterization signals, operating parameter signals, and alarm trigger signals every 100 milliseconds, the main control system will send the signals to the controller in this rhythm to ensure that the controller can receive and identify the real-time status of the analog unit in an orderly manner, providing a basis for subsequent control adjustments.

[0057] A PLC intelligent scene control method based on attitude perception, the method includes the following steps: The RFID radio frequency identification communication module wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated in the training module, and performs attitude perception and stable identification of the training module based on the dynamic anti-shake algorithm. After identifying the training module, the system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. The system can determine and switch between teaching demonstration mode and programming control mode. If the mode is determined to be teaching demonstration mode, the system will automatically run the preset standardized demonstration program. If the mode is determined to be programming control mode, the system will provide a programming interface for the externally connected programmable controller and process the programming interface to achieve intelligent control of the current teaching scenario.

[0058] Figure 1 The top layer contains programming training object cards, which are equipped with both a hidden RFID encryption chip and a distributed magnetic array quick-connect technology. The hidden RFID encryption chip can transmit wireless radio frequency signals to convey its own identity information, while the distributed magnetic array quick-connect technology is used to physically connect the card to the lower-level modules, supporting quick assembly and disassembly.

[0059] The lower layer of the programming training object card includes an intelligent voice interaction module, an RFID radio frequency identification communication module, and a distributed magnetic array magnetic quick-connect technology. The RFID radio frequency identification communication module is responsible for receiving the wireless radio frequency signals emitted by the card's encryption chip to complete identity verification. The modules at this layer aggregate the processed information and signals and transmit them to the core control system. The core control system is the central processing unit of the entire system, coordinating the scheduling and processing of various types of information.

[0060] The core control system connects to the DI input signal interface module, the dynamic intelligent configuration scenario module, and the DO output signal interface module. The dynamic intelligent configuration scenario is the core functional carrier for the control system to adapt to teaching scenarios. The DI input signal interface module and the DO output signal interface module serve as signal transfers between different modules. The DI input signal interface module and the DO output signal interface module interact with the programmable controller (PLC): the DI input signal interface module receives DI input signals from the PLC, and the DO output signal interface module sends DO output signals to the PLC. This bidirectional signal flow supports the programming control and practical training operation needs in the teaching process.

[0061] Figure 2 The starting point is a programming training object card, which is linked to a distributed magnetic array and a hidden RFID encryption chip. The distributed magnetic array achieves physical connection through magnetic quick-connect technology. The hidden RFID encryption chip generates identification encryption signals and transmits them to the RFID radio frequency identification communication module. This module converts the signals into TTL signals, which are then processed by the wireless transceiver signal processing unit to parse the communication data and finally transmit the information to the core main control system.

[0062] The programming training object cards and the RGB full-color LED dot matrix module form a dynamic display interaction. After receiving the driving signal, the LED dot matrix module processes the signal and then transmits the signal to the core main control system through intelligent dynamic matching and switching. Meanwhile, the external button switch signal is directly transmitted to the core main control system through mode switching and input signal acquisition.

[0063] The core control system is the central hub of the entire logic flow. It connects to the voice module unit, which in turn drives the power amplifier module and speaker to achieve audio output. The core control system also connects to the digital output unit and the digital input unit. The digital output unit and the digital input unit interact with the programmable controller (PLC) through signal exchange: the digital output unit transmits acquisition and processing signals to the PLC, while the digital input unit receives control output signals from the PLC. This bidirectional signal flow completes the linkage between the system and the PLC, supporting various practical training operations in teaching scenarios.

[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A PLC-based intelligent scene control system based on attitude perception, characterized in that, It includes a main control system and a training module. The training module is physically attached to the main control system via a magnetic quick-release structure. The main control system wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated within the training module via the RFID radio frequency identification communication module. Specifically, this includes the following steps: During the process of establishing a physical adsorption connection between the main control system and the training module through a magnetic quick-release structure, the RFID radio frequency identification communication module automatically activates the multi-band radio frequency transmission unit and transmits a gradually changing scanning radio frequency signal to the area where the training module is located. The frequency of the radio frequency signal gradually changes along the preset frequency band range until it resonates and matches the working frequency band of the RFID encryption chip. After receiving the matching radio frequency signal, the RFID encryption chip converts the radio frequency energy into working electrical energy through the passive energy harvesting unit, and starts the identity information retrieval process inside the chip. During the identity information retrieval process, the module identity information is dynamically encrypted in real time. The encryption process combines the chip's own physical characteristic parameters to generate an encryption key, loads the encrypted module identity information into the radio frequency feedback signal, and sends it to the RFID radio frequency identification communication module in the form of backscattering. After receiving the encrypted identity information, the RFID radio frequency identification communication module extracts the encrypted data through the signal demodulation unit and calls the decryption module built into the main control system. The decryption module decrypts the encrypted data layer by layer according to the preset encryption rules and the decryption key corresponding to the RFID encryption chip, restoring the original content of the module's identity information. During the reading process, the main control system continuously monitors the strength and stability of the radio frequency signal. If the signal strength fluctuation exceeds the preset range, the current reading process is interrupted and the radio frequency scan is restarted until stable and complete module identity information is obtained. The training module is used for attitude perception and stability recognition based on a dynamic anti-shake algorithm. After identifying the training module, the main control system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. This includes the following steps: After identifying the training module, the main control system obtains the module's identity information and performs feature extraction on the module's identity information to obtain the feature elements of the control logic to which the training module belongs; wherein, the feature elements include the training scenario type, the control object type, and the signal interaction method; The main control system matches the feature elements with the standardized program library to obtain a set of candidate demonstration programs that match the feature elements; wherein, the standardized program library is pre-classified and stored according to the training scenario dimension, and each scenario is associated with multiple demonstration programs adapted to different module characteristics; The adaptation parameter requirements of each program in the candidate demonstration program set are retrieved, and the adaptation parameter requirements are compared with the hardware configuration parameters and signal interface specifications contained in the module identity information to obtain the optimal matching demonstration program. The optimal matching target demonstration program is retrieved from the standardized program library and loaded into the runtime memory. A mapping relationship is established between the demonstration program and the signal interface of the training module, so that the control signals output by the demonstration program are connected to the corresponding simulation unit of the training module, thereby realizing the automatic start-up and stable operation of the demonstration program. The main control system determines and switches between teaching demonstration mode and programming control mode; if it determines to be teaching demonstration mode, it automatically runs the preset standardized demonstration program; if it determines to be programming control mode, it provides a programming interface for the externally connected programmable controller. The programming interface is processed to achieve intelligent control of the current teaching scenario, specifically including the following steps: The programming interface receives and executes custom control programs issued by the programmable controller; During the operation of the custom control program, the main control system interacts with the simulation unit on the training module through the input signal interface module and the output signal interface module. Converting the output control signal from the programmable controller into a drive signal to drive the analog unit's operation involves the following steps: After receiving the output control signal from the programmable controller through the communication link, the main control system analyzes the control signal according to the control function dimension to obtain the action command signal, execution parameter signal and safety protection signal; Based on the hardware driving characteristics of the simulation unit, the action command signal is converted into a trigger signal that conforms to the action logic of the simulation unit, the execution parameter signal is converted into an adjustment signal that matches the operating parameters of the simulation unit, and the safety protection signal is converted into a protection signal that protects the simulation unit. The trigger signal, adjustment signal, and protection signal are combined to form a drive signal; Collect real-time drive status feedback information of the simulation unit, and determine whether the drive signal matches the actual drive requirements of the simulation unit based on the real-time drive status feedback information; If there is a deviation between the drive signal and the drive requirements of the analog unit, the main control system starts the drive signal calibration mechanism. Based on the hardware parameters of the analog unit and the real-time drive status feedback information, the drive signals of each level of conversion are dynamically adjusted to optimize the response timing of the trigger signal, adjust the parameter amplitude of the signal and the trigger threshold of the protection signal until the drive signal is fully adapted to the drive requirements of the analog unit. The state feedback signal of the analog unit is converted into an input signal that can be recognized by the programmable controller, so as to realize intelligent control of the current teaching scenario.

2. The PLC intelligent scene control system based on attitude perception according to claim 1, characterized in that, The main control system wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated within the training module through the RFID radio frequency identification communication module.

3. The PLC intelligent scene control system based on attitude perception according to claim 1, characterized in that, The training module is subjected to attitude perception and stability recognition based on a dynamic image stabilization algorithm, specifically including the following steps: After the main control system obtains the connection signal of the training module, it activates the attitude sensing unit distributed on the magnetic connection contact surface. The attitude sensing unit collects the position offset and angle change information of the training module. During the collection process, the reference point of the magnetic connection is used as the reference point to record the displacement parameters of the training module relative to the reference point in real time. The main control system fuses the information collected by the attitude sensing unit, assigns different weight coefficients according to the installation position of the attitude sensing unit, and integrates the weighted displacement parameters to form the attitude change trajectory of the training module. The stability of the training module is determined based on the attitude change trajectory.

4. The PLC intelligent scene control system based on attitude perception according to claim 3, characterized in that, The stability identification of the training module is completed based on the attitude change trajectory, specifically including the following steps: The main control system continuously tracks the attitude change trajectory. If the attitude change trajectory shows a continuous and regular convergence trend, it is determined that the attitude of the training module tends to be stable. If the attitude change trajectory diverges or fluctuates irregularly, the magnetic connection auxiliary calibration mechanism is activated. The attitude of the training module is corrected by the magnetic attraction force built into the main control system. During the correction process, attitude information is continuously updated until the attitude change trajectory meets the convergence requirements, thus completing the stable identification of the training module.

5. A PLC intelligent scene control system based on attitude perception according to claim 4, characterized in that, Converting the state feedback signal of the analog unit into an input signal that the programmable controller can recognize involves the following steps: The status feedback signals generated by the simulation unit during the execution of the action are transmitted to the signal acquisition unit of the main control system through the feedback link. The signal acquisition unit classifies and captures the feedback signals according to the signal type, distinguishing between position feedback signals that represent the progress of the action execution, working condition feedback signals that reflect the operating status, and alarm feedback signals that indicate abnormal conditions. The main control system calls the corresponding signal format conversion unit according to the signal recognition specification of the programmable controller to convert the position feedback signal into a status characterization signal that meets the position recognition requirements of the controller, convert the operating condition feedback signal into an operating parameter signal that the controller can interpret, and convert the alarm feedback signal into an alarm trigger signal that the controller can respond to quickly. The status representation signal, operating parameter signal, and alarm trigger signal are combined to form the input signal. The main control system transmits the converted input signal to the programmable controller according to a preset timing sequence through the communication interface.

6. A posture-aware PLC intelligent scene control method, applied to the posture-aware PLC intelligent scene control system described in any one of claims 1 to 5, characterized in that, The method includes the following steps: The RFID radio frequency identification communication module wirelessly senses and reads the module identity information stored in the hidden RFID encryption chip integrated in the training module, and performs attitude perception and stable identification of the training module based on the dynamic anti-shake algorithm. After identifying the training module, the system dynamically matches it from the standardized program library based on the module's identity information and automatically calls the demonstration program corresponding to the training module. The system can determine and switch between teaching demonstration mode and programming control mode. If the mode is determined to be teaching demonstration mode, the system will automatically run the preset standardized demonstration program. If the mode is determined to be programming control mode, the system will provide a programming interface for the externally connected programmable controller and process the programming interface to achieve intelligent control of the current teaching scenario.

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