Chip-driven token ticket recycling machine electric integrated control method

Abstract

The application discloses a chip-driven token ticket recycling electromechanical integrated control method, and belongs to the technical field of automatic ticket selling and checking equipment control. The method comprises the following steps: acquiring infrared photoelectric sensor data, pressure microswitch data, Hall sensor data and motor current data collected by a motor current detection circuit and performing time sequence analysis and current ripple feature extraction; generating ticket movement trajectory and mechanical component health state; determining a control mode; adjusting PWM duty cycle in combination with a mapping relationship for torque conversion; generating a motor control signal to drive the motor; and executing a ticket recycling operation. The application adopts the means of fusing multi-source sensing data to generate ticket movement trajectory and mechanical component health state, and dynamically determines the control mode and adjusts the motor output torque based on the same, so that the adaptive ability, control precision and long-term operation reliability of the ticket recycling operation can be improved.

Description

Technical Field

[0001] This invention relates to the field of automatic ticketing equipment control technology, and in particular to a chip-driven electromechanical integrated control method for token-based ticket recycling. Background Technology

[0002] Token-based ticket collection machines are core equipment widely used in automated fare collection systems such as urban rail transit, parking lots, and access control systems. Their main function is to receive token-based tickets (such as single-journey tickets and stored-value tickets) inserted by users and transport them to designated collection bins via an internal electromechanical transmission mechanism, completing the ticket collection and counting process. This equipment typically consists of an entrance detection unit, a ticket conveying unit, a drive motor, and a control system. Its reliability and efficiency directly affect the service quality and traffic flow efficiency of the entire automated system.

[0003] Existing token-based card recycling machines mostly employ relatively simple open-loop or closed-loop control schemes based on a few sensor signals. Typically, the control system detects a card insertion via a photoelectric sensor at the entrance and then starts a DC motor to drive a conveyor belt or rollers at a fixed speed or duty cycle. When the card reaches the end and triggers another sensor, the motor stops. The control logic for the entire process is relatively fixed, and the motor's drive parameters are essentially fixed after the equipment leaves the factory, lacking the ability to adapt to complex operating conditions.

[0004] However, the control strategy of token-based ticket recycling machines cannot cope with the diversity of the physical state of the tickets themselves, such as their hardness, age, dryness, wetness, and whether they are bent. These factors can lead to significant changes in friction during transmission. Secondly, traditional control methods ignore the time-varying characteristics of the equipment itself. With prolonged operation, mechanical components such as drive belts and gears will wear down, leading to increased internal resistance, which fixed motor drive parameters cannot effectively compensate for. Finally, its fault detection mechanism is relatively crude, usually relying only on timeout judgment or simple overcurrent protection, making it difficult to accurately diagnose the root cause of the fault, resulting in decreased equipment reliability and a high failure rate. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a chip-driven electromechanical integrated control method for token-based ticket recycling. This method integrates multi-source sensor data to generate the ticket's motion trajectory and the health status of mechanical components. Based on this, the control mode is dynamically determined and the motor output torque is adjusted, thereby improving the adaptability, control accuracy, and long-term reliability of the ticket recycling operation.

[0006] The above objectives can be achieved through the following approach:

[0007] A chip-driven electromechanical control method for token-based ticket recycling includes acquiring data from infrared photoelectric sensors, pressure micro-switches, Hall effect sensors, and motor current data collected by a motor current detection circuit along the ticket delivery path, and fusing them into multi-source sensor data; performing time-series analysis and current ripple feature extraction on the multi-source sensor data to generate a ticket movement trajectory characterizing the real-time movement of the ticket and a health status of mechanical components characterizing the wear and tear of the equipment; determining a control mode using the ticket movement trajectory and the health status of the mechanical components; adjusting the PWM duty cycle based on the control mode and a preset mapping relationship for torque conversion to generate a motor control signal; and using the motor control signal to drive the motor and perform the ticket recycling operation.

[0008] Optionally, the fusion of multi-source sensor data includes: reading an optical obstruction signal collected by a preset infrared photoelectric sensor at the ticket entrance; reading a physical compression signal collected by a preset pressure micro switch at the ticket turning point; reading a rotational speed signal collected by a preset Hall sensor on the motor shaft; reading a current waveform signal collected by a preset motor current detection circuit; and synchronizing the optical obstruction signal, the physical compression signal, the rotational speed signal, and the current waveform signal in time to generate the multi-source sensor data.

[0009] Optionally, generating the ticket movement trajectory representing the real-time movement of the ticket and the health status of mechanical components representing the wear and tear of the equipment includes: preprocessing the multi-source sensor data to obtain preprocessed sensor data; extracting time-series features and current ripple features from the preprocessed sensor data and integrating them to obtain a multi-dimensional feature vector; using the multi-dimensional feature vector to reconstruct the trajectory to obtain the ticket movement trajectory representing the real-time movement of the ticket; and using the multi-dimensional feature vector to assess the wear and tear of the equipment to obtain the health status of the mechanical components.

[0010] Optionally, the integration to obtain the multidimensional feature vector includes: detecting the trigger time point of the optical occlusion signal in the preprocessed sensing data, calculating the signal time interval characteristics between adjacent sensors; analyzing the periodicity and amplitude variation of the current waveform signal, extracting the ripple frequency characteristics and amplitude variation coefficient; and combining the signal time interval characteristics, the ripple frequency characteristics, and the amplitude variation coefficient to form a multidimensional feature vector.

[0011] Optionally, determining the control mode using the ticket movement trajectory and the health status of mechanical components includes: collecting historical multi-source sensor data and corresponding control mode labels; extracting historical ticket movement trajectories and historical mechanical component health statuses from the historical multi-source sensor data; using a preset neural network model, training it based on the relationship between the historical ticket movement trajectory, the historical mechanical component health status, and the control mode labels to generate a logic model for state switching; inputting the ticket movement trajectory and the mechanical component health status into the logic model, and outputting the control mode.

[0012] Optionally, the method further includes: calibrating the correspondence between motor load torque and PWM duty cycle under different mechanical component health states to obtain corresponding calibration data; constructing a mapping relationship between motor load torque and PWM duty cycle based on the calibration data of different mechanical component health states to obtain a corresponding torque mapping function; and establishing a mapping relationship library based on the corresponding torque mapping function.

[0013] Optionally, generating the motor control signal includes: determining the target torque according to the control mode; selecting a corresponding torque mapping function from the mapping relationship library based on the health status of the mechanical components, and generating a target duty cycle based on the corresponding torque mapping function and the target torque; generating a PWM modulation signal by combining the target duty cycle and the feedback value of the speed signal, and using the PWM modulation signal as the motor control signal.

[0014] Optionally, the step of using the motor control signal to drive the motor and perform the ticket recycling operation includes: inputting the motor control signal into the motor drive circuit to control the speed and direction of the DC motor; monitoring the motor current data and the speed signal in real time during the driving process; and triggering a protection mechanism and adjusting the control mode when the motor current data or the speed signal exceeds a preset safety threshold.

[0015] Optionally, the method further includes: storing the multi-source sensor data, the ticket movement trajectory, the health status of the mechanical components, and the control mode for each recycling operation, and establishing a status log record; using the status log record to periodically update the logical model and the mapping relationship library.

[0016] Based on the same inventive concept, this invention also provides a chip-driven electromechanical integrated control system for token-based ticket recycling, characterized in that the system includes: a data acquisition module for acquiring data from infrared photoelectric sensors, pressure micro-switches, Hall effect sensors, and motor current data collected by a motor current detection circuit located on the ticket conveying path, and fusing them into multi-source sensor data; a feature extraction module for performing time-series analysis and current ripple feature extraction on the multi-source sensor data to generate a ticket movement trajectory characterizing the real-time movement of the ticket and a mechanical component health status characterizing the wear and tear of the equipment; a mode determination module for determining a control mode using the ticket movement trajectory and the mechanical component health status; a control signal generation module for adjusting the PWM duty cycle based on the control mode and a preset mapping relationship for torque conversion to generate a motor control signal; and a motor drive module for driving the motor using the motor control signal to perform the ticket recycling operation.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention integrates data from multiple heterogeneous sensors, including optical, physical, rotational, and current sensors, to accurately characterize the real-time motion of tickets within the recycling channel and dynamically assess the wear and tear of the equipment's mechanical components. This comprehensive state perception capability enables the control system to go beyond traditional simple logical judgments, accurately identify complex working conditions such as ticket slippage, bending, and jamming, and adjust strategies based on the long-term health of the equipment, thereby greatly improving the system's adaptability to different ticket qualities and equipment aging conditions, as well as the recycling success rate.

[0019] 2. This invention establishes a control parameter model that dynamically correlates with the health status of mechanical components, particularly a mapping relationship library between motor torque and PWM duty cycle. This method can automatically select the most suitable control mapping function based on the assessed degree of equipment wear, and perform feedforward compensation for performance degradation such as increased internal resistance caused by wear, ensuring the accuracy and consistency of motor output torque. This enables the equipment to maintain stable and efficient performance regardless of its stage in the life cycle, improving the reliability of ticket recycling operations.

[0020] 3. By continuously recording and analyzing the status logs during operation, the system can periodically update its logical model for decision-making and its mapping relationship library for execution. This self-learning capability enables the equipment to continuously improve from past experience, continuously optimize its control strategy, and achieve intelligent upgrades from passive response to active prediction and adaptive adjustment, thereby effectively extending the service life of the equipment and providing data support for predictive maintenance.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart of a chip-driven electromechanical integrated control method for token-based ticket recycling according to an embodiment of the present invention.

[0024] Figure 2 This is a comparison diagram of motor current ripple characteristics under different healthy states of mechanical components in an embodiment of the present invention.

[0025] Figure 3 This is a torque-PWM mapping curve related to the health status of mechanical components in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the structure of the chip-driven token-based ticket recycling electromechanical integrated control system according to an embodiment of the present invention.

[0027] Figure 5 This is a timing diagram of multi-source sensor data fusion according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 One embodiment of the present invention proposes a chip-driven electromechanical integrated control method for token-based ticket recycling. It uses multi-source sensor data to generate the ticket movement trajectory and the health status of mechanical components, and dynamically determines the control mode and adjusts the motor output torque based on this. This can improve the adaptability, control accuracy and long-term reliability of ticket recycling operation.

[0030] The method described in this embodiment specifically includes:

[0031] Data from infrared photoelectric sensors, pressure microswitches, Hall sensors, and motor current collected by motor current detection circuits set on the ticket delivery path are acquired and fused into multi-source sensor data.

[0032] The multi-source sensor data is subjected to time-series analysis and current ripple feature extraction to generate a ticket movement trajectory that characterizes the real-time movement of the ticket and a health status of mechanical components that characterizes the wear and tear of the equipment.

[0033] The control mode is determined by using the ticket movement trajectory and the health status of mechanical components;

[0034] The PWM duty cycle is adjusted based on the control mode and the preset mapping relationship for torque conversion to generate a motor control signal;

[0035] The motor is driven by the motor control signal to perform the ticket recycling operation.

[0036] This invention, through a comprehensive assessment of ticket movement and the equipment's own health status, enables the system to effectively distinguish and properly handle various complex operating conditions, including normal, abnormal, and even critical faults, significantly improving the success rate of ticket retrieval and the system's operational robustness. Simultaneously, by incorporating equipment wear into the control closed loop, the system can dynamically compensate for performance degradation caused by aging, ensuring stable and consistent performance throughout the equipment's lifespan and enabling predictive maintenance. This, in turn, helps extend the equipment's service life and reduce maintenance costs.

[0037] Optionally, the fusion into multi-source sensing data includes:

[0038] Read the optical obstruction signal collected by the preset infrared photoelectric sensor at the ticket entrance;

[0039] Read the physical compression signal collected by the preset pressure micro switch at the bend of the ticket;

[0040] Read the speed signal collected by the preset Hall sensor on the motor shaft;

[0041] Read the current waveform signal collected by the preset motor current detection circuit;

[0042] The optical blocking signal, the physical compression signal, the rotation speed signal, and the current waveform signal are time-synchronized and aligned to generate the multi-source sensing data.

[0043] Specifically, the system first monitors the level of the infrared photoelectric sensor located at the ticket inlet in real time via a general-purpose input / output port. When a ticket enters and blocks the light path, the port captures a clear optical obstruction signal, typically manifested as a level transition. Secondly, the system also reads the status of a pressure microswitch located at a bend in the ticket transport path via an input port. When a ticket passes through and presses the switch, a physical pressure signal is generated, indicating that the ticket has reached this critical position. Simultaneously, the system uses the chip's built-in timer or pulse capture unit to count and measure the frequency of the pulse sequence output by the Hall sensor mounted on the motor shaft, thereby accurately calculating the speed signal representing the motor's real-time rotational speed. More importantly, the system drives the chip's built-in high-speed analog-to-digital converter (ADC) to continuously sample the analog voltage signal output by the motor current detection circuit at a fixed high sampling rate, thereby obtaining a current waveform signal that reflects subtle changes in the motor load. The final fusion step involves synchronizing and aligning these four signals from different sources and with varying properties. In practice, the system adds a high-precision timestamp to each sample point of the acquired optical obstruction signal, physical compression signal, rotation speed signal, and current waveform signal. By using this timestamp as a common reference, the data from different sensors at the same moment or within a very small time window are organized into a structured data set, thereby generating multi-source sensor data that can comprehensively describe the electromechanical dynamic characteristics of the ticket recycling process.

[0044] Optionally, the generation of the ticket movement trajectory characterizing the real-time movement of the ticket and the health status of the mechanical components characterizing the wear and tear of the equipment includes:

[0045] The multi-source sensor data is preprocessed to obtain preprocessed sensor data.

[0046] Temporal features and current ripple features are extracted from the preprocessed sensor data and integrated to obtain a multi-dimensional feature vector.

[0047] The multidimensional feature vectors are used to reconstruct the trajectory, thereby obtaining the ticket movement trajectory that represents the real-time movement of the ticket.

[0048] The wear and tear of the equipment is assessed using the multidimensional feature vectors to obtain the health status of the mechanical components.

[0049] Specifically, the process of generating the ticket's motion trajectory, representing its real-time movement, and the health status of mechanical components, representing the wear and tear of the equipment, begins with signal preprocessing of the multi-source sensor data generated in the previous steps. This preprocessing aims to eliminate noise and interference in the original signals, providing a high-quality data foundation for subsequent feature extraction. For example, software de-jitter algorithms are used on the switching signals output by the infrared photoelectric sensor and the pressure micro-switch to filter out false triggers caused by mechanical vibration; moving average or Kalman filtering is applied to the speed signal acquired by the Hall sensor to smooth the speed curve and obtain a more stable instantaneous speed value; and a digital bandpass filter is applied to the current waveform signal acquired by the motor current detection circuit to filter out high-frequency electromagnetic noise and separate the current ripple component closely related to changes in mechanical load. After these steps, the preprocessed sensor data is obtained. Next, key temporal features and current ripple features are extracted from the preprocessed sensor data. The temporal features mainly reflect the temporal relationship of the ticket's movement in physical space. For example, by recording the trigger timestamps of the optical obstruction signal and the physical compression signal, the time interval between the ticket passing through two specific monitoring points can be calculated. The current ripple feature is used to deeply mine the motor load information contained in the current waveform signal. Current ripple refers to the periodic fluctuations superimposed on the armature current during the commutation process of a DC motor, and its shape and amplitude are directly affected by the changes in the motor load torque. By performing Fast Fourier Transform (FFT) or Short-Time Fourier Transform (STFT) on the current waveform signal, features such as the dominant frequency, harmonic components, and amplitude changes in its spectrum can be extracted. The extracted time-series features are integrated with the current ripple features, i.e., arranged and combined in a predetermined order, to form a multi-dimensional feature vector. This vector comprehensively quantifies the dynamic information in a single ticket retrieval process. Finally, two core state descriptions are generated using this multi-dimensional feature vector. On the one hand, through a trajectory reconstruction algorithm, the speed, position, and load information contained in the multi-dimensional feature vector are mapped to the real-time movement of the ticket. Here, the ticket movement trajectory is not a simple physical coordinate path, but a dynamic state sequence containing the speed, acceleration, and force conditions of the ticket at each key node on the transmission path. For example, combining time interval characteristics with rotational speed signals can determine whether the ticket card is slipping, while instantaneous changes in current ripple can reveal whether the ticket card is encountering abnormal resistance. On the other hand, a multi-dimensional feature vector is analyzed using a device wear assessment model. This model is typically built based on historical data, comparing the current feature vector with a baseline under normal conditions or different wear stages to determine the wear condition of mechanical components. For example, if the current ripple amplitude continuously increases under no-load or normal load conditions during multiple recycling operations, it indicates that the frictional resistance of the transmission system is increasing, suggesting wear. The final output of this assessment is the health status of the mechanical components, characterizing the degree of device wear, such as "good," "slightly worn," or "severely worn."

[0050] Optionally, the integration to obtain a multidimensional feature vector includes:

[0051] The trigger time point of the optical occlusion signal in the preprocessed sensor data is detected, and the signal time interval characteristics between adjacent sensors are calculated.

[0052] Analyze the periodicity and amplitude variation of the current waveform signal to extract ripple frequency characteristics and amplitude variation coefficient;

[0053] The signal time interval feature, the ripple frequency feature, and the amplitude variation coefficient are combined to form a multidimensional feature vector.

[0054] Specifically, such as Figure 2 As shown, the significant difference in motor current ripple is observed between the "good" and "worn" health states of the equipment. First, the pre-processed sensor data stream is continuously monitored, specifically targeting optical obstruction signals generated by sensors at different locations, such as infrared photoelectric sensors or pressure microswitches. As the ticket passes through these sensors sequentially, the system precisely records the trigger time point of each signal state transition. By calculating the difference between the trigger times of two adjacent sensors, the signal time interval characteristic can be obtained. This characteristic directly reflects the average transit time of the ticket between two points on the transmission path. Its calculation can be expressed as:

[0055] ,

[0056] in, This refers to the signal time interval characteristic. The system timestamp that triggers the upstream sensor for the ticket. The system timestamps for ticket-triggered downstream sensors are provided by a high-precision real-time clock within the chip. Next, a deep analysis is performed on the preprocessed motor current waveform signal to extract the underlying load dynamics. This analysis focuses on the periodicity and amplitude variations of the current waveform signal. By applying frequency domain analysis algorithms such as Fast Fourier Transform (FFT), the main fluctuation frequencies in the current signal can be identified. These frequencies are directly related to the motor's commutation frequency, thus obtaining the ripple frequency characteristics, which reflect the motor's real-time speed. Simultaneously, to quantify the stability of current fluctuations, the system calculates the coefficient of variation of the current waveform signal amplitude within a fixed time window. This coefficient is a relative indicator of the data dispersion. The calculation process for the amplitude variation coefficient is as follows:

[0057] ,

[0058] in, It is the amplitude variation coefficient. It is the standard deviation of the current ripple amplitude within that time window, while It is the average value of the current ripple amplitude. Both of these statistics are obtained by calculation of the separated AC current components. A larger value indicates drastic load fluctuations, while This reflects the magnitude of the average load. Finally, the three physically meaningful values—signal time interval characteristics, ripple frequency characteristics, and amplitude variation coefficient—calculated independently in the above steps, are combined into a vector in a pre-defined order. This vector is the final generated multi-dimensional feature vector, which provides a comprehensive digital description of a single ticket collection process from both time and load dynamic dimensions.

[0059] Optionally, determining the control mode using the ticket's movement trajectory and the health status of mechanical components includes:

[0060] Collect historical multi-source sensor data and corresponding control mode labels;

[0061] Extract historical ticket movement trajectories and historical mechanical component health status from the historical multi-source sensor data;

[0062] Using a preset neural network model, a logical model for state switching is generated by training based on the relationship between the historical ticket movement trajectory, the health status of the historical mechanical components, and the control mode label.

[0063] The ticket movement trajectory and the health status of the mechanical components are input into the logic model, and the control mode is output.

[0064] Specifically, the method of determining the control mode using ticket movement trajectory and mechanical component health status is based on building an intelligent logic model capable of autonomously making decisions according to the current comprehensive state. This is implemented in two phases: offline training and online application. The offline training phase is the foundation for building this logic model. First, a large amount of historical data needs to be collected, continuously recording multi-source sensor data under various operating conditions of the token-based ticket recycling machine. These operating conditions should comprehensively cover normal recycling, different types of abnormal tickets (such as bent or damp tickets), and operation under mechanical components at different wear stages. When collecting this historical multi-source sensor data, professionals or pre-defined rules need to label each recycling operation or event with a clear control mode tag, such as "normal feed," "strong push," "slight reversal ticket return," or "emergency stop." Next, for each tagged historical multi-source sensor data point, the aforementioned technical methods are applied to extract its corresponding historical ticket movement trajectory and historical mechanical component health status. This creates a training dataset containing a large number of samples. Each sample consists of a pair of inputs: the historical ticket movement trajectory and the historical mechanical component health status, and a corresponding output: the control mode label. Finally, a pre-defined neural network model, such as a multilayer perceptron or recurrent neural network, is selected and trained using this labeled training dataset. The training process continuously adjusts the weights and bias parameters within the network through backpropagation, aiming to minimize the difference between the control mode predicted by the model based on the inputs and the actual control mode label. After training, the solidified neural network model becomes the logic model used for state switching, containing complex nonlinear relationships learned from massive amounts of data. The online application phase involves the real-time deployment of this logic model. During actual equipment operation, when a ticket retrieval operation is triggered, the system generates the ticket movement trajectory and the current mechanical component health status in real time. The system then immediately feeds these two real-time state descriptions as inputs to the logic model deployed on the chip. This logic model performs a forward computation and, based on the decision rules learned in the offline phase, outputs a control mode best suited to the current overall situation. The control mode of this output serves as the direct basis for generating the motor control signal in the next stage.

[0065] Optionally, the method further includes:

[0066] The relationship between motor load torque and PWM duty cycle was calibrated under different mechanical component health conditions to obtain the corresponding calibration data;

[0067] Based on the calibration data of the health status of different mechanical components, a mapping relationship between motor load torque and PWM duty cycle is constructed to obtain the corresponding torque mapping function;

[0068] A mapping relationship library is established based on the corresponding torque mapping function.

[0069] Specifically, the first step is to calibrate the recycling machines in different health states of their mechanical components. For example, a brand-new machine can be selected to represent a "good" state, while a machine whose performance has deteriorated after long-term operation can represent a "worn-out" state. By actively changing its mechanical components or adding additional frictional loads, various intermediate health states between the two can be simulated. For each defined health state of the mechanical components, the following calibration procedure is performed: Connect the recycling machine's motor to a dynamometer that can accurately apply and measure the load, or use a high-precision current sensor to indirectly estimate the torque. Systematically iterate through a series of PWM duty cycle settings, from low to high. At each fixed PWM duty cycle, measure the motor load torque that the motor can output during stable operation. This torque value can be read directly from the dynamometer, or it can be obtained by measuring the motor current at this time and converting it using the motor's torque constant. By repeating this process, a series of data point pairs can be collected. Each data pair contains a PWM duty cycle value and a corresponding motor load torque value. These data together constitute the calibration data for that health state. Next, based on the acquired calibration data, a unique torque mapping function is constructed for the health status of each mechanical component. This step typically employs curve fitting or table lookup methods. Using mathematical methods such as polynomial regression, a function can be found that best describes the relationship between the motor load torque and the PWM duty cycle. More commonly, since the control system needs to determine the PWM duty cycle based on the target torque, an inverse mapping relationship is constructed, meaning the PWM duty cycle is a function of the target torque. This torque mapping function can be expressed as:

[0070] ,

[0071] in, It is the PWM duty cycle that needs to be set. It is the target motor load torque, and It is a health status specific to a particular mechanical component. Mapping functions. For example... Figure 3 As shown, this modeling process is repeated for all preset health states, such as "good", "slight wear", and "severe wear", to obtain a set of different torque mapping functions. Finally, all these torque mapping functions established for the health states of different mechanical components are integrated into a structured database, namely the mapping relationship library. In practical applications, this library can be implemented as a lookup table, a set of function pointers, or a series of conditional judgment logic, enabling the control system to quickly and accurately select and call the corresponding torque mapping function based on the currently assessed health state of the mechanical components.

[0072] Optionally, the generation of the motor control signal includes:

[0073] The target torque is determined based on the control mode.

[0074] Based on the health status of the mechanical components, a corresponding torque mapping function is selected from the mapping relationship library, and a target duty cycle is generated based on the corresponding torque mapping function and the target torque;

[0075] A PWM modulation signal is generated by combining the target duty cycle and the feedback value of the speed signal, and the PWM modulation signal is used as the motor control signal.

[0076] Specifically, the system first determines a target torque based on the current control mode output by the mode determination module. This target torque is pre-set for each control mode and represents the ideal drive torque expected from the motor in that mode. For example, the "normal feed" mode might correspond to a standard target torque, while the "power push" mode would correspond to a larger target torque value to overcome potential obstacles. Next, the system uses the currently assessed health status of the mechanical components as an index to query and select the torque mapping function that perfectly matches the current mapping relationship library. This step is crucial for adaptive control, ensuring that the selected function accurately reflects the actual physical relationship between the PWM duty cycle and the motor load torque under the current equipment conditions. Subsequently, the system uses the target torque determined in the previous step as input and substitutes it into the selected torque mapping function for calculation. The result of the calculation is a theoretical target duty cycle. This target duty cycle is the feedforward setting value of the PWM duty cycle required to generate the target torque, without considering the motor's dynamic response error. However, feedforward control alone is insufficient to cope with instantaneous load changes and model inaccuracies. Therefore, this method introduces a feedback adjustment mechanism for the speed signal, forming a closed-loop control system. The system acquires the speed signal provided by the Hall sensor in real time and uses it as a feedback value. This feedback value is compared with the target speed desired in the current control mode or the desired speed calculated based on the target torque, forming a speed error. This error is input to a controller, typically a proportional-integral-derivative (PID) controller. This PID controller calculates an adjustment amount based on the speed error, which is used to dynamically correct the target duty cycle calculated by the feedforward. The corrected duty cycle can be expressed as:

[0077] ,

[0078] in, It is the duty cycle ultimately used to generate the PWM waveform. The target duty cycle is calculated from the torque mapping function, and The PID controller calculates the compensation duty cycle based on the speed error. Finally, the chip's built-in PWM generator uses this final determined duty cycle. This generates a PWM modulation signal with a corresponding pulse width. This PWM modulation signal is the final motor control signal, which is sent to the motor drive circuit to precisely control the average voltage applied across the motor, thereby achieving fine-grained and adaptive control of the motor's output torque and speed.

[0079] Optionally, the step of using the motor control signal to drive the motor and perform the ticket recycling operation includes:

[0080] The motor control signal is input into the motor drive circuit to control the speed and direction of the DC motor;

[0081] The motor current data and the speed signal are monitored in real time during the driving process;

[0082] When the motor current data or the speed signal is detected to exceed the preset safety threshold, the protection mechanism is triggered and the control mode is adjusted.

[0083] Specifically, the PWM modulation signal generated in the previous step is first input into the motor drive circuit as the motor control signal. This motor drive circuit is typically an H-bridge circuit, which controls the switching devices supplying power to the DC motor based on the input PWM modulation signal, thereby precisely adjusting the magnitude and polarity of the average voltage applied to the motor armature. By changing the voltage magnitude, the motor speed and output torque can be controlled; by changing the voltage polarity, the motor direction can be controlled, enabling the forward transmission or reverse withdrawal of tickets. Throughout the entire process of the motor drive circuit driving the motor to rotate and the transmission mechanism moving the tickets, the control system does not merely issue commands unidirectionally, but performs continuous real-time monitoring, forming a tight monitoring closed loop. The system continuously collects motor current data through a motor current detection circuit and acquires speed signals through a Hall sensor. These two key physical quantities can reflect the motor's load and motion status in real time. The system internally presets a series of safety thresholds. These thresholds are determined based on a deep understanding of the equipment's normal operating range and potential failure modes, including upper and lower limits for motor current data and speed signals. For example, the upper current limit prevents the motor from burning out due to overload, while the lower current limit detects load loss faults such as broken drive belts; the upper speed limit prevents the motor from running wild, while the lower speed limit detects motor stall. When the system detects in real-time monitoring that the motor current data or speed signal exceeds its corresponding preset safety threshold range at any given moment, the system will immediately trigger the protection mechanism. The primary task of this protection mechanism is to ensure equipment and operational safety; it may immediately cut off the power supply to the motor and execute an emergency shutdown. More importantly, this over-limit event will be regarded as an important status feedback. The system will dynamically adjust the current control mode based on the specific circumstances of this over-limit event, such as whether the current is too high or the speed is too low. If the current overload occurs in the current "forceful push" mode, the system may determine that it has encountered an insurmountable hard blockage and thus adjust the control mode to "reverse ticket refund" to attempt to eject the ticket. If the speed is consistently lower than expected in the "normal feed" mode, the system may adjust to the "forceful push" mode to overcome soft obstacles. This adjustment enables the control system to dynamically modify its behavior strategy based on real-time feedback during execution, forming a higher-level adaptive closed loop.

[0084] Optionally, the method further includes:

[0085] Store the multi-source sensor data, the ticket movement trajectory, the health status of the mechanical components, and the control mode for each recycling operation, and establish a status log record;

[0086] The logical model and the mapping relationship library are periodically updated using the status log records.

[0087] Specifically, after each ticket recycling operation is completed, the system encapsulates and solidifies the entire process information of that operation. Specifically, it stores the raw multi-source sensor data collected during the operation, the ticket movement trajectory generated based on this data analysis, the assessed health status of mechanical components, and the final decided and executed control mode as a complete data unit. This data unit constitutes a status log record. These records are continuously written to the chip's non-volatile memory, such as flash memory, forming a vast status log database over time that records all the equipment's historical experience. The next core step is periodic updates, which are typically triggered automatically when the equipment is idle or after a preset number of recycling cycles. The update process consists of two parallel tasks. One task is updating the logic model. The system extracts a large amount of historical data from the status log database, especially those containing successful handling cases from anomalies to normal recovery, or cases of success after manual intervention. Using the ticket movement trajectory and mechanical component health status from these records as input features, and the final successful control mode as the target label, a new incremental training set is constructed. The system uses this incremental training set to learn or fine-tune the original neural network logic model online. This process, by adjusting the network weights, allows the model to learn from new experiences and optimize its decision-making ability when facing similar situations in the future. The second task is updating the mapping database, which the system also utilizes a state log database. It groups the logs according to the health status of mechanical components, and within each group, extracts motor current data (usually its mean or torque-related features) and the actual applied PWM duty cycle. Through regression analysis of these massive amounts of real-world data points, the system can refit a more accurate torque mapping function between the motor load torque and the PWM duty cycle under the current actual wear conditions. This newly fitted function more realistically reflects the current physical characteristics of the equipment. Subsequently, the system replaces the old function in the mapping database corresponding to the health status with this new function, thus completing the dynamic calibration of the mapping database.

[0088] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a chip-driven electromechanical integrated control system for token-based ticket recycling, characterized in that the system includes:

[0089] The data acquisition module is used to acquire data from infrared photoelectric sensors, pressure microswitches, Hall sensors, and motor current data collected by the motor current detection circuit set on the ticket delivery path, and integrate them into multi-source sensor data.

[0090] The feature extraction module is used to perform time-series analysis and current ripple feature extraction on the multi-source sensor data to generate a ticket movement trajectory that characterizes the real-time movement of the ticket and a health status of mechanical components that characterizes the wear and tear of the equipment.

[0091] The mode determination module is used to determine the control mode by utilizing the ticket movement trajectory and the health status of mechanical components;

[0092] The control signal generation module is used to adjust the PWM duty cycle based on the control mode and a preset mapping relationship for torque conversion to generate a motor control signal;

[0093] The motor drive module is used to drive the motor using the motor control signal to perform the ticket recycling operation.

[0094] To verify the feasibility of this invention in practice, it was applied to a high-volume ticket collection machine in a city's rail transit system. This rail transit system experiences a huge daily passenger flow, and its automatic fare collection system's token-based ticket collection machines face problems such as a wide variety of ticket types, widespread ticket damage and bending, and mechanical wear caused by long-term high-intensity operation. These issues result in a high failure rate for traditional collection machines, frequent ticket jams, severely impacting passenger flow efficiency and increasing maintenance costs.

[0095] To verify the effectiveness of the method of this invention, the rail transit system selected two high-passenger-flow stations and deployed ticket recycling machines using the control method of this invention as the experimental group, and used a recycling machine using traditional fixed logic control as the control group, conducting a comparative test for 6 months. During the test, the system recorded in detail the sensor data, success rate, fault type, and handling method for each ticket recycling operation.

[0096] In this embodiment, the present invention first acquires multi-source sensor data through a data acquisition module. For example, such as... Figure 5 As shown, at 9:30 AM during the morning rush hour, a slightly damp ticket was inserted into recycling machine number 3 in the experimental group. The system's built-in chip concurrently acquired the optical obstruction signal triggered by the infrared photoelectric sensor at the ticket inlet; the physical signal generated by the pressure microswitch being squeezed when the ticket reached a turn; the rotational speed signal output by the Hall sensor on the motor shaft; and the real-time current waveform signal collected by the motor current detection circuit. These signals, after being appended with high-precision timestamps, were fused into a multi-source sensor data set.

[0097] Next, the system extracted features from the multi-source sensor data to generate the ticket movement trajectory and the health status of mechanical components. Analysis revealed that the time interval between triggering the infrared sensor and the pressure microswitch on the damp ticket was 30% longer than that of a normal ticket. Simultaneously, the amplitude variation coefficient of the motor current waveform signal increased, indicating aggravated motor load fluctuations. Based on this, the system generated the ticket movement trajectory for this operation and determined it to be "slight slippage." At the same time, the system retrieved historical data from the No. 3 recycling machine and found that its average no-load current ripple amplitude when processing standard tickets had increased by 8% compared to the factory setting. Therefore, the health status of the machine's mechanical components was assessed as "mild wear."

[0098] Based on the above conditions, the system determines the control mode. The pre-trained neural network logic model stored in the chip receives input: the ticket's movement trajectory of "slight slippage" and the health status of the mechanical parts of "mild wear". After calculation, the model does not select the "forceful push" mode to avoid causing excessive impact to the worn parts, but instead outputs the control mode of "slight reversal followed by secondary feed".

[0099] Subsequently, the system generates motor control signals. Based on the "slight reverse followed by secondary feed" mode, the system first determines a small target reverse torque. Then, according to the "mild wear" health status, it selects the corresponding torque mapping function from the mapping library. This function considers the additional friction caused by wear and generates the target PWM duty cycle. After driving the motor to reverse a short distance, it switches to normal feed mode, ultimately successfully recovering the damp ticket. Throughout the process, the system monitors the motor current and speed in real time to ensure they do not exceed preset safety thresholds, avoiding stalling or overload.

[0100] After three months of operation, the system analyzed the status logs accumulated by all machines in the experimental group during off-peak hours at night. By relearning from tens of thousands of logs showing successful handling of anomalies such as "slight slippage" and "ticket bending," the system periodically updated its neural network logic model and mapping database. For example, the system discovered that for devices with "severe wear and tear," a combination of reversing and then pushing at low speed had a higher success rate, and incorporated this strategy into the logic model.

[0101] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or parameters that can be superimposed in the same dimension. This eliminates the interference of different dimensions on the computational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. These are conventional technical methods and will not be elaborated further. The electrical connections between the various units described above do not necessarily represent direct or indirect connections; any indirect connection method is applicable to the embodiments of this invention as long as it achieves the purpose of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of this invention.

[0102] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A chip-driven electromechanical integrated control method for token-based ticket recycling, characterized in that, The method includes: Data from infrared photoelectric sensors, pressure microswitches, Hall sensors, and motor current collected by motor current detection circuits set on the ticket delivery path are acquired and fused into multi-source sensor data. The multi-source sensor data is subjected to time-series analysis and current ripple feature extraction to generate a ticket movement trajectory that characterizes the real-time movement of the ticket and a health status of mechanical components that characterizes the wear and tear of the equipment. The control mode is determined by using the ticket movement trajectory and the health status of mechanical components; The PWM duty cycle is adjusted based on the control mode and the preset mapping relationship for torque conversion to generate a motor control signal; The motor is driven by the motor control signal to perform the ticket recycling operation.

2. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 1, characterized in that, The fusion into multi-source sensor data includes: Read the optical obstruction signal collected by the preset infrared photoelectric sensor at the ticket entrance; Read the physical compression signal collected by the preset pressure micro switch at the bend of the ticket; Read the speed signal collected by the preset Hall sensor on the motor shaft; Read the current waveform signal collected by the preset motor current detection circuit; The optical blocking signal, the physical compression signal, the rotation speed signal, and the current waveform signal are time-synchronized and aligned to generate the multi-source sensing data.

3. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 2, characterized in that, The generation of the ticket movement trajectory, which characterizes the real-time movement of the ticket, and the health status of the mechanical components, which characterizes the wear and tear of the equipment, include: The multi-source sensor data is preprocessed to obtain preprocessed sensor data. Temporal features and current ripple features are extracted from the preprocessed sensor data and integrated to obtain a multi-dimensional feature vector. The multidimensional feature vectors are used to reconstruct the trajectory, thereby obtaining the ticket movement trajectory that represents the real-time movement of the ticket. The wear and tear of the equipment is assessed using the multidimensional feature vectors to obtain the health status of the mechanical components.

4. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 3, characterized in that, The integration yields a multidimensional feature vector including: The trigger time point of the optical occlusion signal in the preprocessed sensor data is detected, and the signal time interval characteristics between adjacent sensors are calculated. Analyze the periodicity and amplitude variation of the current waveform signal to extract ripple frequency characteristics and amplitude variation coefficient; The signal time interval feature, the ripple frequency feature, and the amplitude variation coefficient are combined to form a multidimensional feature vector.

5. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 3, characterized in that, The process of determining the control mode using the ticket's movement trajectory and the health status of mechanical components includes: Collect historical multi-source sensor data and corresponding control mode labels; Extract historical ticket movement trajectories and historical mechanical component health status from the historical multi-source sensor data; Using a preset neural network model, a logical model for state switching is generated by training based on the relationship between the historical ticket movement trajectory, the health status of the historical mechanical components, and the control mode label. The ticket movement trajectory and the health status of the mechanical components are input into the logic model, and the control mode is output.

6. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 5, characterized in that, The method further includes: The relationship between motor load torque and PWM duty cycle was calibrated under different mechanical component health conditions to obtain the corresponding calibration data; Based on the calibration data of the health status of different mechanical components, a mapping relationship between motor load torque and PWM duty cycle is constructed to obtain the corresponding torque mapping function; A mapping relationship library is established based on the corresponding torque mapping function.

7. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 6, characterized in that, The generated motor control signal includes: The target torque is determined based on the control mode. Based on the health status of the mechanical components, a corresponding torque mapping function is selected from the mapping relationship library, and a target duty cycle is generated based on the corresponding torque mapping function and the target torque; A PWM modulation signal is generated by combining the target duty cycle and the feedback value of the speed signal, and the PWM modulation signal is used as the motor control signal.

8. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 7, characterized in that, The step of using the motor control signal to drive the motor and perform the ticket recycling operation includes: The motor control signal is input into the motor drive circuit to control the speed and direction of the DC motor; The motor current data and the speed signal are monitored in real time during the driving process; When the motor current data or the speed signal is detected to exceed the preset safety threshold, the protection mechanism is triggered and the control mode is adjusted.

9. The chip-driven electromechanical integrated control method for token-based ticket recycling according to claim 6, characterized in that, The method further includes: Store the multi-source sensor data, the ticket movement trajectory, the health status of the mechanical components, and the control mode for each recycling operation, and establish a status log record; The logical model and the mapping relationship library are periodically updated using the status log records.

10. A chip-driven electromechanical integrated control system for token-based ticket recycling, characterized in that, The system includes: The data acquisition module is used to acquire data from infrared photoelectric sensors, pressure microswitches, Hall sensors, and motor current data collected by the motor current detection circuit set on the ticket delivery path, and integrate them into multi-source sensor data. The feature extraction module is used to perform time-series analysis and current ripple feature extraction on the multi-source sensor data to generate a ticket movement trajectory that characterizes the real-time movement of the ticket and a health status of mechanical components that characterizes the wear and tear of the equipment. The mode determination module is used to determine the control mode by utilizing the ticket movement trajectory and the health status of mechanical components; The control signal generation module is used to adjust the PWM duty cycle based on the control mode and a preset mapping relationship for torque conversion to generate a motor control signal; The motor drive module is used to drive the motor using the motor control signal to perform the ticket recycling operation.

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