Wireless powered drive system for implantable myocardial contractility modulation device

Abstract

The application provides a wireless energy supply driving system of an implantable myocardial contractility adjusting device, and comprises the following steps: fusing multi-source operation parameters of an external unit and an internal unit, quantitatively estimating a coupling state; based on the estimation result and a load condition, obtaining initial driving parameters by inquiring a pre-calibration data model, and dynamically adjusting the initial driving parameters according to real-time deviations to generate final driving parameters; monitoring and correcting the driving parameters and estimation model parameters in real time to inhibit long-term parameter drift; and establishing a quantitative safety evaluation model, calculating a safety level in real time, and triggering graded protection. Through the above method, efficient, stable and safe energy supply for the implantable device is realized.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and more specifically, to a wireless power supply drive system for an implantable myocardial contractility modulation device. Background Technology

[0002] Implantable medical devices, such as implantable cardiac contractility modulators (CCMs), rely on a reliable power supply for long-term stable operation. Wireless power supply technology is the core technology for achieving battery-free operation of such devices. However, existing wireless power supply methods, when applied to deep implantation scenarios such as subclavian implantation, suffer from significant challenges in maintaining stable power supply due to frequent changes in the relative positions of the external transmitter and internal receiver caused by human movement and posture variations, resulting in drastic fluctuations in the coupling coefficient between them.

[0003] To address this problem, existing technologies typically employ simple closed-loop control strategies. For example, the transmit power is coarsely adjusted by monitoring a single parameter, such as the transmitter current or receiver voltage, and comparing it to a fixed threshold. These methods have several drawbacks: First, the coupling state estimation is inaccurate; a single parameter cannot accurately reflect the actual coupling state influenced by multiple factors such as position, attitude, and temperature. Second, power adjustment is not adaptive; simple threshold adjustment or fixed power transmission strategies cannot dynamically match the optimal transmission power based on real-time coupling state and load requirements. This can lead to insufficient power supply when the coupling state is poor, and energy redundancy and waste when the coupling state is good, reducing overall power supply efficiency and increasing the risk of overheating of surrounding tissues due to radio frequency energy. Third, there is a lack of long-term stability assurance; existing methods typically do not consider parameter drift caused by component aging and environmental changes, and lack effective closed-loop correction mechanisms, resulting in significant performance degradation after long-term operation. Finally, the safety protection mechanisms are relatively crude, often employing a "one-size-fits-all" over-temperature or over-voltage shutdown protection, lacking the ability to quantitatively assess and grade risks, and failing to achieve a good balance between ensuring safety and maintaining continuous equipment operation. Therefore, there is an urgent need for a wireless power supply driving method that can accurately estimate dynamic coupling state, achieve adaptive power adjustment, ensure long-term operational stability, and possess refined safety management capabilities. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a wireless power supply drive system for an implantable myocardial contractility modulation device.

[0005] According to the present invention, a wireless power supply drive system for an implantable myocardial contractility modulation device includes an external transmitting unit, an internal implantation unit, a communication module, and a processor. The external transmitting unit transmits power wirelessly to the internal implantation unit via resonant coupling. The communication module is configured to feed back the operating parameters of the implanted unit to the external transmitting unit; The processor is configured to perform the following steps: Coupling state estimation steps: Real-time acquisition and fusion of the operating parameters of the external launch unit, the operating parameters fed back by the internal implantation unit and its load status, and quantitative estimation of the coupling state between the external launch unit and the internal implantation unit through an estimation model containing adjustable model parameters; Adaptive power adjustment step: Based on the estimation results of the coupling state and the load of the implanted unit in vivo, the initial driving parameters are obtained by querying the pre-calibrated data model, and the initial driving parameters are dynamically adjusted according to the deviation between the operating parameters of the external launch unit and its corresponding preset target value, so as to generate the final driving parameters for controlling the external launch unit. Closed-loop feedback correction steps: Real-time monitoring of the adjustment of the final driving parameters and the estimation of the coupling state, and based on the monitoring results, closed-loop correction of the driving parameters of the external emission unit and / or the model parameters of the estimation model used for the coupling state estimation are performed to suppress parameter drift during long-term system operation.

[0006] Furthermore, the processor is also configured to perform the following steps: Quantitative graded security protection steps: Establish a quantitative security assessment model that integrates multiple operating parameters, calculate the security level in real time, and trigger a preset graded protection logic containing at least three levels based on the security level.

[0007] Furthermore, the operating parameters of the external emission unit include the output voltage, output current, external emission power, and external temperature of the external emission unit; the operating parameters fed back by the in vivo implanted unit include the rectified DC voltage and the in vivo temperature.

[0008] Furthermore, the quantitative estimation of the coupling state includes:

[0009] The coupling state evaluation value at time t. Let be the equivalent coupling coefficient at time t. Let be the DC voltage after rectification within the body at time t. The target DC voltage within the body. and These are weighting coefficients; Equivalent coupling coefficient at time t A temperature correction factor is used to compensate for the errors in the estimation model caused by temperature drift.

[0010] Furthermore, the closed-loop feedback correction step includes: coordinating real-time correction of the final driving parameter and the temperature correction coefficient based on the adjustment deviation between the final driving parameter and a preset dynamic target value, and the estimation deviation between the estimation result of the coupling state and a preset reference coupling state.

[0011] Furthermore, the adjustment deviation includes voltage deviation, and the estimation deviation includes coupling state deviation; Voltage deviation at time t :

[0012] Coupling state deviation at time t :

[0013] in Let be the DC voltage after rectification within the body at time t. The target DC voltage within the body. The target coupling state evaluation value, This is the coupling state evaluation value at time t.

[0014] Furthermore, the final driving parameters include the final driving voltage at time t. The final driving duty cycle at time t ;

[0015]

[0016] in, Let be the initial driving voltage at time t. Let t be the initial drive duty cycle. and To adjust the gain coefficient.

[0017] Furthermore, the pre-calibrated data model is a lookup table, which is used to match the initial driving parameters based on the estimation results of the coupling state and the load of the implanted unit in vivo.

[0018] Furthermore, the external transmitting unit drives the transmitting coil through a Class E power amplifier to form a resonant coupling with the receiving coil of the internal implanted unit at an operating frequency of 6.78 MHz.

[0019] Furthermore, the external emission unit includes: A power amplifier; A transmitting coil; and A first sensing unit is used to collect the operating parameters of the external emission unit; The implantable unit includes: A receiving coil; and A second sensing unit is used to collect the operating parameters of the implanted unit.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. Significantly improved power supply efficiency: Through adaptive power adjustment based on a preset model and real-time deviation, the optimal transmission power can be matched quickly and accurately, avoiding energy waste and thus significantly improving the transmission efficiency of wireless power supply.

[0021] 2. Significantly improved drive stability: The estimation method using multi-source information fusion provides accurate coupling state input, while the collaborative closed-loop correction mechanism effectively suppresses long-term parameter drift caused by factors such as component aging and environmental changes, ensuring the stability of the internal power supply voltage and enabling stable response to coupling fluctuations in deep implantation.

[0022] 3. Enhanced safety: Through a quantitative and graded safety protection algorithm, it can comprehensively assess and finely manage risks in multiple dimensions such as internal temperature and transmission power, and achieve multi-level response from early warning to emergency shutdown, effectively avoiding tissue overheating and equipment damage, which is far superior to simple threshold shutdown protection.

[0023] 4. Enhanced adaptability: The complete method provided in this application can automatically adapt to coupling changes caused by individual patient differences and posture changes, without the need for manual intervention, thus achieving true adaptive drive. Attached Figure Description

[0024] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the wireless power supply system structure provided in an embodiment of this application; Figure 2 A flowchart illustrating the adaptive wireless power supply driving method provided in an embodiment of this application; Figure 3 The signaling interaction timing diagram between the external unit and the internal unit within one control cycle is provided for the embodiments of this application; Figure 4 This is a block diagram of the control algorithm module inside the external main control MCU provided in the embodiments of this application. Detailed Implementation

[0025] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0026] Example 1 This application provides a complete implementation scheme for an adaptive wireless power supply drive system for implantable devices, aiming to provide efficient, stable and safe wireless power for implantable myocardial contractility regulation devices.

[0027] Reference Figure 1 The diagram illustrates a wireless power supply system according to an embodiment of this application. The system mainly consists of an external transmitting unit 100 and an internal implanted unit 200, which transmit wireless power and communicate data via resonant coupling.

[0028] Specifically, the external transmitter unit 100, placed on the patient's body surface, primarily functions to generate radio frequency energy and transmit it to the implanted unit within the body. In this embodiment, the external transmitter unit 100 includes an external main control MCU 110, a Bluetooth communication module 120, a temperature detection module 130, a Class E power amplifier 140, and an external transmitter coil 150. The external main control MCU 110 is the control core of the entire external unit, responsible for executing the adaptive wireless power supply drive algorithm proposed in this application, processing data from various modules, and generating corresponding control commands. The Bluetooth communication module 120 is responsible for bidirectional data exchange with the implanted unit 200 within the body, such as receiving operating parameters from the body or sending system status information to external monitoring devices. The temperature detection module 130 is used to monitor the temperature of the external transmitter unit 100 in real time, particularly near the external transmitter coil 150, and uses this temperature as an input parameter for safety monitoring and coupling state estimation. The Class E power amplifier 140, as the core component for energy transmission, receives drive signals from the external main control MCU 110 and efficiently converts DC power into high-frequency AC power. As a preferred implementation, to achieve higher conversion efficiency, the operating frequency of the Class E power amplifier in this embodiment can be set to 6.78MHz. The external transmitting coil 150 serves as an energy transmitting antenna, radiating the high-frequency electrical energy output from the Class E power amplifier 140 in the form of a magnetic field.

[0029] Accordingly, the implantable unit 200 is a device surgically implanted into the patient's body (e.g., in the subclavian region). Its main function is to receive energy emitted by the external unit, convert it into stable direct current to supply the load, and monitor its own key operating parameters to provide feedback to the external unit. In this embodiment, the implantable unit 200 includes an in-vivo receiving coil 210, a passive matching network 220, a rectifier and voltage regulator module 230, an in-vivo main control MCU 240, a Bluetooth communication module 250, a temperature detection module 260, and a myocardial stimulation module 270. The in-vivo receiving coil 210 is used to induce high-frequency alternating current in the alternating magnetic field generated by the external transmitting coil 150. The passive matching network 220 is used to optimize the impedance matching between the in-vivo receiving coil 210 and subsequent circuits, thereby maximizing energy reception efficiency. The rectifier and voltage regulator module 230 is responsible for converting the received high-frequency alternating current into stable direct current, for example, providing a stable 3.3V operating voltage for subsequent circuits. The in vivo master control MCU 240, as the control core of the in vivo unit, is responsible for collecting parameters from various in vivo sensors, controlling the Bluetooth communication module 250 for data feedback, and managing the power supply to the myocardial stimulation module 270. The Bluetooth communication module 250 pairs with the Bluetooth communication module 120 of the external unit to form a low-power wireless data link. The temperature detection module 260 is used to monitor the tissue temperature around the implanted unit in real time; this parameter is crucial for assessing system safety. The myocardial stimulation module 270 is the final load of this wireless power supply system. Utilizing the stable power provided by the rectifier and voltage regulator module 230, it generates specific electrical pulses under the control of the in vivo master control MCU 240 to regulate the myocardium. It should be noted that the method and system provided in this application are only responsible for providing reliable power to the myocardial stimulation module 270 and do not involve, control, or interfere with its specific stimulation parameters and treatment logic.

[0030] Based on the above system architecture, the adaptive wireless power supply driving method disclosed in this embodiment is executed by the external main control MCU110, and its detailed process can be found in [reference needed]. Figure 2 and Figure 4 .in, Figure 2 This is a schematic diagram of the overall process of the method, and Figure 4 This shows the block diagram of the control algorithm module implemented inside the external main control MCU 110.

[0031] Understandably, the method operates in a continuous loop, with the duration of each control cycle set to 5 milliseconds in this embodiment.

[0032] Step S101: System Initialization. Upon system startup, the external main control MCU 110 first loads preset configuration parameters. These parameters include, but are not limited to: lookup table data pre-calibrated offline and stored in non-volatile memory, control coefficients for various algorithms (such as filter coefficients and gain adjustment coefficients), safety thresholds (such as maximum safe temperature and maximum transmit power), and communication protocol parameters. Simultaneously, the external main control MCU 110 establishes a Bluetooth connection with the internal main control MCU 240 to ensure uninterrupted data feedback.

[0033] Step S102: Multi-source parameter acquisition. In this step, the external main control MCU 110 acquires system operating status data from multiple sources through its internal parameter acquisition and filtering module 310. The data acquired by the external main control MCU 110 comes from two sources: firstly, the operating parameters of the external transmitting unit 100 itself, which includes the voltage U applied across the external transmitting coil 150 in real time through the sampling circuit. tx-ext (t) and the current I flowing through the coil tx-ext (t), and the external temperature T is obtained through the temperature detection module 130. ext (t); secondly, it refers to data packets received via Bluetooth communication module 120 from the implanted unit 200, such as... Figure 3 The signaling interaction timing diagram is shown. This data packet contains key operating parameters acquired by the onboard main control MCU 240 and transmitted via its Bluetooth communication module 250, primarily including the DC voltage U output by the rectifier and regulator module 230. rect-int (t) Body temperature T measured by temperature detection module 260 int (t), and the load current I of the myocardial stimulation module 270 monitored by the in vivo main control MCU 240. load-int (t). To improve the accuracy and stability of subsequent calculations, the parameter acquisition and filtering module 310 preprocesses the noisy raw acquisition data. In this embodiment, a first-order low-pass filtering algorithm is used to smooth the acquired parameters, and its filtering formula is:

[0034] Among them, Y raw (t) is the raw value collected at the current time, Y filtered (t-1) is the filtered value at the previous time step, and α is the filtering coefficient. In this embodiment, α is set to 0.85 to achieve a balance between response speed and filtering effect.

[0035] Step S103: Coupling State Estimation. This step is performed by the coupling state estimation module 320 and is the foundation for adaptive adjustment. The goal of this module is to accurately quantify the real-time coupling state between the external transmitting coil 150 and the internal receiving coil 210 based on the multi-source information collected in step S102. Unlike existing technologies that rely on a single parameter for rough judgment, this embodiment integrates external electrical parameters, internal electrical and physical parameters, and considers the influence of temperature on system characteristics. First, a temperature correction coefficient K is calculated. temp-corr (t) is used to compensate for the error in the estimation model caused by temperature drift. This coefficient can be based on the external temperature T. ext (t) and body temperature T int (t) is obtained by looking up a table or using an empirical formula. In this embodiment, its calculation method is as follows:

[0036] Where T ref This is a reference temperature (e.g., 25°C), and c1 and c2 are pre-calibrated coefficients. Next, the filtered external transmitting coil voltage U is used... tx-ext-filter (t), DC voltage U after internal rectification rect-int-filter (t) and temperature correction factor K temp-corr (t), calculate the real-time equivalent coupling coefficient k trans (t), its calculation formula is as follows:

[0037] Among them, R match It is the system's matching impedance (e.g., 50Ω), L coil-int and L coil-ext These are the inductance values ​​of the internal receiving coil and the external transmitting coil, respectively, both of which are known design parameters. Finally, to comprehensively evaluate the coupling state and make it more suitable as input for subsequent control algorithms, the coupling state estimation module 320 also calculates a normalized coupling state evaluation value S. trans (t). This value not only considers the coupling coefficient but also incorporates the stability of the bulk voltage. Its calculation formula is as follows:

[0038] Among them, U rect-int-base It is the target DC voltage inside the body (3.3V in this embodiment), w k and w U These are weighting coefficients (e.g., w) k =0.7, w U =0.3), indicating that when evaluating the coupling state, more emphasis is placed on the physical coupling coefficient itself, while also taking into account the actual power supply effect.

[0039] Step S104: Adaptive Power Adjustment. This step is performed by the adaptive power adjustment module 330. Its core task is to dynamically calculate the optimal parameters for driving the Class E power amplifier 140, i.e., the drive voltage U, based on the coupling state estimated in the previous step and the real-time load demand. drv-ext (t) and driving duty cycle D drv-ext (t). This process can be divided into two stages. First, the initial driving parameters are obtained by querying the pre-calibrated data model. In this embodiment, the data model is a two-dimensional lookup table completed through extensive offline experiments and simulation calibration during the product design phase. The two input dimensions of this table are the coupling state evaluation value S. trans (t) and the body load current I load-int (t), the output is the corresponding initial drive voltage U. drv-ext-init (t) and initial drive duty cycle D drv-ext-init (t). The external main control MCU 110 will calculate the current S. trans (t) and received I load-int Using (t) as an index, a set of initial drive parameters closest to the current operating condition is found in the lookup table through linear interpolation or the nearest neighbor method. Next, these initial drive parameters are dynamically fine-tuned based on the real-time deviation. To handle finer dynamic changes and model errors, the adaptive power adjustment module 330 calculates two key deviations: Coupling state deviation:

[0040] Where S trans-target This is an ideal target coupling state evaluation value, for example, 0.85. Voltage deviation:

[0041] Then, using these deviations, the final driving parameters are calculated using the following formula:

[0042]

[0043] Where K S and K U This is a preset adjustment gain coefficient. The physical meaning of this adjustment logic is: when the coupling state deteriorates (ΔS) trans When (t) is positive, the driving voltage and duty cycle should be appropriately increased to improve the transmission power; however, when the internal voltage deviates from the target value (whether it is too high or too low), |ΔU rect-int If (t)| increases, then the driving parameters should be appropriately reduced to prevent overshoot and instability. The final calculated U drv-ext (t) and D drv-ext(t) will be sent to the drive circuit of Class E power amplifier 140 to complete the power regulation of one control cycle.

[0044] Step S105: Closed-loop feedback correction. This step is performed by the closed-loop correction module 340 to address parameter drift caused by factors such as component aging and long-term environmental changes, ensuring the long-term stability of the system. This module operates on a slower timescale than power regulation, and performs collaborative correction on the parameters of the upstream module by real-time monitoring of the adjustment of the drive parameters and the estimation of the coupling state. Specifically, the closed-loop correction module 340 calculates the adjustment deviation of the drive parameters (e.g., the difference between the final drive parameters and the initial drive parameters) and the estimation deviation of the coupling state (e.g., k...). trans (t) fluctuations within a stable period), and a series of correction coefficients are calculated based on these deviations. For example, when monitoring the final drive voltage U to maintain voltage stability within the body, drv-ext (t) remains higher than the initial value U given by the lookup table. drv-ext-init When (t), it indicates that the system may have some losses that are not fully compensated by the model. At this point, a driving voltage correction coefficient K can be generated. U-corr (t) and correct the driving voltage:

[0045] More importantly, this embodiment achieves the correction of the estimated model parameters. For example, when the system is stable in both physical location and load, the calculated coupling coefficient k... trans (t) still exhibits a slow, unidirectional drift Δk trans (t), which likely means that the response characteristics of the temperature sensor have changed, causing K to... temp-corr The calculation of (t) is no longer accurate. At this point, the closed-loop correction module 340 will fine-tune the temperature correction coefficient itself:

[0046] Here, γ is a very small learning rate, such as 0.05. This online calibration of the estimation model parameters is key to ensuring the long-term estimation accuracy and driving stability of the system.

[0047] Step S106: Safety Protection Monitoring. Simultaneously, the safety protection module 350 performs safety protection monitoring in parallel during step S106, and this step has the highest priority. This module establishes a quantitative safety assessment model integrating multiple key operating parameters to achieve refined and hierarchical risk management. In this embodiment, the safety protection module 350 calculates a safety warning level coefficient W in real time. safe (t), its calculation formula is:

[0048] Among them, w T , w P , w U It is the weight of each risk (e.g., w) T =0.4, w P =0.3, w U =0.3). T int-safe-max This is the highest safe temperature inside the body (e.g., 42°C). P trans-ext (t) is the external emission power, which can be obtained from U tx-ext (t) and I tx-ext (t) is calculated to yield P trans-ext-safe-max This is its safety upper limit (e.g., 1.0W). Based on the calculated W... safe If the value of (t) is within a certain range, the system will trigger level four protection logic: Safety (W) safe (t)<0.3): The system is running normally.

[0049] Minor warning (0.3 ≤ W) safe (t)<0.6): The system enters observation mode, which can slightly reduce the upper limit of transmission power and send warning information to external devices via Bluetooth.

[0050] Severe warning (0.6 ≤ W) safe (t)<0.8): The system forces a power reduction operation, such as limiting the maximum transmit power to 50% of the normal value and issuing a higher level alarm.

[0051] Emergency Risk (W) safe (t) ≥ 0.8): or when any single key indicator (such as body temperature exceeding 42°C) directly exceeds the hard threshold, the system immediately shuts down the Class E power amplifier 140, stops energy emission, and records the fault log.

[0052] After completing all the above steps, the process returns to step S102, thus beginning the next control cycle. Through this integrated control logic of "multi-source estimation, model adjustment, collaborative correction, and quantization protection," this embodiment can achieve efficient, stable, and safe wireless power supply to the implantable device. Experimental results show that the power supply efficiency can be stably maintained above 80%, the rectified voltage fluctuation within the implant can be controlled within ±0.1V, and the internal temperature can be effectively controlled below the safe threshold of 42℃.

[0053] Example 2 As an optional implementation of Embodiment 1, this embodiment aims to illustrate that the "pre-calibrated data model" can take forms other than lookup tables. Specifically, this embodiment replaces the lookup table used in the adaptive power adjustment step S104 of Embodiment 1 with a lightweight neural network regression model. Other parts of the system, including the hardware architecture (such as...), Figure 1 The logic for parameter acquisition, coupling state estimation, closed-loop correction, and safety protection (as shown in the figure) is basically the same as that in Example 1.

[0054] The key difference in this embodiment lies in the implementation method of the adaptive power adjustment module 330: 1. Offline Model Training: During the product development phase, a large amount of training data was collected by building an experimental platform. Specifically, different coupling states were simulated by physically changing the relative positions and angles of the external transmitting coil 150 and the internal receiving coil 210, while simultaneously changing the operating mode of the myocardial stimulation module 270 (e.g., standby, low-power stimulation, high-power stimulation) to simulate different load conditions. For each combined operating condition (i.e., a specific set of coupling state evaluation values ​​S),... trans and load current I load-int By manually fine-tuning or using optimization algorithms, the "optimal" initial drive parameter pair (U) that achieves the most stable voltage and highest transmission efficiency within the body is found. drv-ext-init D drv-ext-init The tens of thousands or even hundreds of thousands of groups {S} collected trans , I load-int} ->{U drv-ext-init D drv-ext-init The data pairs were used as the training set to train a small feedforward neural network model. The input layer of this model has two neurons (corresponding to S...). trans and I load-int The output layer has two neurons (corresponding to U). drv-ext-init and D drv-ext-init The model can contain one or two hidden layers, each with a number of neurons. The goal of training is to minimize the mean squared error between the model's predicted output and the true "optimal" output.

[0055] 2. Model Deployment: After training, the structure and weight parameters of the neural network model are extracted and embedded into the firmware of the external main control MCU 110 in a compact format. Considering the limited computing resources of the external main control MCU 110, the selected neural network model must be lightweight to ensure that the latency of online inference is low enough to meet the requirement of a 5-millisecond control cycle.

[0056] 3. Online Prediction: During actual system operation, when the adaptive power adjustment module 330 needs to obtain initial drive parameters, it no longer queries the lookup table, but instead uses the S calculated by the coupling state estimation module 320. trans (t) and I feedback from the body load-int (t) is used as input and passed to the neural network model embedded in the firmware. The model performs one forward propagation calculation and directly infers the corresponding initial driving parameters U. drv-ext-init (t) and D drv-ext-init (t).

[0057] The subsequent steps are the same as in Example 1: based on the initial parameters predicted by the neural network model, and combined with the real-time coupling state deviation ΔS trans (t) and voltage deviation ΔU rect-int (t), the final driving parameter U is calculated using the same adjustment formula. drv-ext (t) and D drv-ext (t).

[0058] Compared to the lookup table and linear interpolation method in Example 1, the neural network model used in this example can learn and express more complex nonlinear relationships. Therefore, when dealing with edge cases or parameter combinations not densely covered by the lookup table, it may provide smoother and more accurate initial parameter matching. Thus, in some complex dynamic scenarios, its adjustment smoothness and final energy efficiency may be slightly better than the interpolation-based scheme. The successful implementation of this example demonstrates that the concept of a "pre-calibrated data model" can encompass more advanced technologies such as machine learning models.

[0059] Example 3 This embodiment provides another variation of Embodiment 1, mainly offering a feasible alternative implementation for "quantitatively graded security protection". In this embodiment, the security warning level coefficient W in Embodiment 1 is calculated based on a single weighting formula. safe The safety protection module 350 of (t) is replaced by a finite state machine based on a combination of multi-parameter thresholds. The system's hardware architecture and the preceding coupled state estimation, adaptive power adjustment, closed-loop correction, and other algorithm modules can remain consistent with Embodiment 1.

[0060] In this embodiment, the security protection module 350 internally implements a finite state machine, which defines at least four core states and explicit transition conditions between states.

[0061] State definition: 1. Safe State: The system's default initial state and normal operating state. In this state, all operating parameters are within the preset safe range, and the system operates at maximum performance.

[0062] 2. Observation Status: The system enters this status when one or more parameters begin to approach but have not yet reached a dangerous threshold. This is a warning level designed to alert the system or user to potential risks.

[0063] 3. Reduced power state: When the risk escalates further, the system enters this state and takes proactive measures to limit energy output in order to reduce the risk.

[0064] 4. Shutdown State: When a clear and urgent danger is detected, or when the risk cannot be controlled even under reduced power conditions, the system enters this state and immediately stops energy transmission to ensure absolute safety.

[0065] State transition conditions (example): Conditions for transitioning from a safe state to an observation state may include: body temperature T. int (t) First exceeds a lower warning threshold (e.g., 40°C), or external emission power P trans-ext (t) remains above a high power level (e.g., 0.9W) for a period of time (e.g., 30 seconds).

[0066] The transition conditions from the observation state to the safe state may include: in the observation state, all parameters that triggered the entry into the state are restored to the safe range and remain so for a period of time (e.g., 60 seconds).

[0067] The transition conditions from the observation state to the power reduction state may include: in the observation state, monitoring the internal temperature T. int The rate of increase of (t) exceeds a certain threshold (indicating that heat is accumulating rapidly), or the body temperature T int (t) directly exceeds a higher threshold (e.g., 41°C).

[0068] The transition conditions from the power reduction state to the observation state may include: in the power reduction state, the power reduction measures taken are effective, resulting in key risk parameters (such as T) being reduced. int (t) falls back to the threshold range of the observed state and remains stable.

[0069] The transition conditions from a reduced-power state to a shutdown state may include: in the reduced-power state (e.g., the transmit power has been limited to 80% of its normal value), the internal temperature T int (t) continues to rise and exceeds a dangerous threshold (e.g., 41.5°C).

[0070] The transition conditions from any state to the shutdown state may include: the occurrence of any event defined as "absolute danger", such as the body temperature T. int (t) Exceeding the ultimate safety threshold (e.g., 42°C), or exhibiting a large and unexplained deviation in the body voltage (e.g., |ΔU)rect-int (t)|>0.5V), which may indicate a hardware failure or a serious system malfunction.

[0071] Operating Process: In each control cycle, the safety protection module 350 in the external main control MCU 110 evaluates whether any state transition conditions are met based on the latest sensor data. If met, it updates the current state of the state machine and executes the action associated with entering the new state. For example, entering the power reduction state involves modifying the internal parameters of the adaptive power regulation module 330, forcibly reducing its output drive voltage and duty cycle upper limit.

[0072] Compared to the weighted formula-based method in Example 1, the state machine method in this example does not rely on the setting of specific weights, and its risk judgment logic is more explicit and structured. Each risk scenario and response strategy is defined through clear states and transition conditions, making the design, verification, and certification process of security logic more intuitive and reliable, especially in situations where strict compliance with certain medical device safety regulations is required. This example provides a logically rule-based, equally effective implementation method for "quantitatively graded security protection."

[0073] Example 4 This embodiment is another variation of Embodiment 1, and its purpose is to provide a more classic and simplified implementation of the "closed-loop feedback correction" technical feature. In this embodiment, the closed-loop correction module 340 that performs "co-correction" of the driving parameters and the estimated model parameters in Embodiment 1 is replaced by a classic proportional-integral controller that only corrects the driving parameters.

[0074] The system's hardware architecture, as well as the coupling state estimation, adaptive power regulation, and safety protection modules, are similar to those in Embodiment 1. The key difference lies in the algorithm implementation of the closed-loop correction module 340.

[0075] In this embodiment, the closed-loop correction module no longer calculates the drive parameter deviation and coupling estimation deviation, nor does it adjust the temperature correction coefficient K. temp-corr (t) is corrected. Its sole objective is to eliminate steady-state errors that may occur in the system during long-term operation and that the adaptive power regulation module fails to fully compensate for. Its core idea is to correct the deviation ΔU of the rectified DC voltage within the system. rect-int (t) serves as the input to the proportional-integral controller. Through the action of the controller, a correction is added to the adaptive adjustment, thereby driving the long-term steady-state error to zero.

[0076] Algorithm flow: 1. Error Input: The input error e(t) of the proportional-integral controller is directly taken from the voltage deviation calculated in the adaptive power regulation module.

[0077] 2. Proportional-Integral Control Calculation: The controller calculates a correction amount ΔU based on the current error e(t) and the accumulation of historical errors. corr (t). This calculation consists of two parts: one is the proportional term, i.e. It responds quickly to the current error; the second is the integral term, i.e. Its function is to accumulate all past errors. As long as a steady-state error exists (i.e., e(t) is not zero for a long time), the integral term will continuously increase or decrease, thus generating a continuously increasing correction force until the error is eliminated. In discrete digital control systems, the formula for a proportional-integral controller can be expressed as:

[0078]

[0079] 3. Among them, K p and K i These are the proportional and integral gain coefficients of the proportional-integral controller, which need to be tuned according to the system's response characteristics. s This is the control period (5ms). Understandably, to prevent integral saturation, upper and lower limits are usually set for IntegralTerm(t).

[0080] 4. Application of correction amount: The calculated correction amount ΔU corr (t) is used to correct the drive voltage U calculated by the adaptive power regulation module 330. drv-ext (t). The final voltage applied to the power amplifier drive circuit is:

[0081] 5. Correspondingly, a similar proportional-integral controller can also be designed to correct the drive duty cycle D. drv-ext (t).

[0082] Operating Process: Assume that due to slight aging of a component, the overall system transmission efficiency decreases by 1%. Although the adaptive power regulation module 330 is still operating, its lookup table or neural network model is calibrated based on the system characteristics before aging. This may result in the voltage remaining stable at 3.28V, 0.02V lower than the target value of 3.3V, even with the regulation module working at its best, creating a steady-state error of -0.02V. At this point, the proportional-integral controller comes into play. The proportional term immediately produces a correction proportional to -0.02V, but this may not be sufficient to completely eliminate the error. The key is the integral term, which begins to accumulate this -0.02V error. Over time, the output of the integral term becomes increasingly larger (or smaller, depending on the sign), resulting in a continuously increasing positive correction ΔU. corr (t). This correction is added to U. drv-ext (t) causes the final drive voltage to be slightly increased. This process continues until the additional energy generated by the increased drive voltage just compensates for the 1% efficiency loss, resulting in a decrease in the bulk voltage U. rect-int-filter When the voltage rises back to 3.3V, the steady-state error e(t) becomes zero. At this point, the integral term stops accumulating, and the system reaches a new steady state at a new, slightly elevated drive voltage level.

[0083] While this method does not correct the estimation model itself as effectively as the collaborative correction in Example 1, it is a mature, reliable, and widely used control strategy that can effectively eliminate steady-state errors in the system output and suppress the impact of parameter drift on the final energy supply effect. Therefore, this embodiment provides a powerful and industrially applicable implementation scheme for realizing the technical feature of "closed-loop feedback correction".

[0084] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0085] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A wireless power supply drive system for an implantable myocardial contractility modulation device, characterized in that, It includes an external transmitting unit, an internal implantation unit, a communication module, and a processor. The external transmitting unit wirelessly transmits energy to the internal implantation unit via resonant coupling. The communication module is configured to feed back the operating parameters of the implanted unit to the external transmitting unit; The processor is configured to perform the following steps: Coupling state estimation steps: Real-time acquisition and fusion of the operating parameters of the external launch unit, the operating parameters fed back by the internal implantation unit and its load status, and quantitative estimation of the coupling state between the external launch unit and the internal implantation unit through an estimation model containing adjustable model parameters; Adaptive power adjustment step: Based on the estimation results of the coupling state and the load of the implanted unit in vivo, the initial driving parameters are obtained by querying the pre-calibrated data model, and the initial driving parameters are dynamically adjusted according to the deviation between the operating parameters of the external launch unit and its corresponding preset target value, so as to generate the final driving parameters for controlling the external launch unit. Closed-loop feedback correction steps: Real-time monitoring of the adjustment of the final driving parameters and the estimation of the coupling state, and based on the monitoring results, closed-loop correction of the driving parameters of the external emission unit and / or the model parameters of the estimation model used for the coupling state estimation are performed to suppress parameter drift during long-term system operation.

2. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 1, characterized in that, The processor is also configured to perform the following steps: Quantitative graded security protection steps: Establish a quantitative security assessment model that integrates multiple operating parameters, calculate the security level in real time, and trigger a preset graded protection logic containing at least three levels based on the security level.

3. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 1, characterized in that, The operating parameters of the external emission unit include the output voltage, output current, external emission power, and external temperature of the external emission unit; the operating parameters fed back by the in vivo implanted unit include the rectified DC voltage and in vivo temperature.

4. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 3, characterized in that, Quantitative estimation of coupling states includes: The coupling state evaluation value at time t. Let be the equivalent coupling coefficient at time t. Let be the DC voltage after rectification within the body at time t. The target DC voltage within the body. and These are weighting coefficients; Equivalent coupling coefficient at time t A temperature correction factor is used to compensate for the errors in the estimation model caused by temperature drift.

5. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 4, characterized in that, The closed-loop feedback correction step includes: coordinating real-time correction of the final driving parameter and the temperature correction coefficient based on the adjustment deviation between the final driving parameter and a preset dynamic target value, and the estimation deviation between the estimation result of the coupling state and a preset reference coupling state.

6. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 5, characterized in that, The adjustment deviation includes voltage deviation, and the estimation deviation includes coupling state deviation; Voltage deviation at time t : Coupling state deviation at time t : in Let be the DC voltage after rectification within the body at time t. The target DC voltage within the body. The target coupling state evaluation value, This is the coupling state evaluation value at time t.

7. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 6, characterized in that, The final driving parameters include the final driving voltage at time t. The final driving duty cycle at time t ; in, Let be the initial driving voltage at time t. Let t be the initial drive duty cycle. and To adjust the gain coefficient.

8. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 1, characterized in that, The pre-calibrated data model is a lookup table, which is used to match the initial driving parameters based on the estimation results of the coupling state and the load of the implanted unit in vivo.

9. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 1, characterized in that, The external transmitting unit drives the transmitting coil through a Class E power amplifier, forming a resonant coupling with the receiving coil of the internal implanted unit at an operating frequency of 6.78 MHz.

10. The wireless power supply drive system for the implantable myocardial contractility modulation device according to claim 1, characterized in that, The external emission unit includes: A power amplifier; A transmitting coil; and A first sensing unit is used to collect the operating parameters of the external emission unit; The implantable unit includes: A receiving coil; and A second sensing unit is used to collect the operating parameters of the implanted unit.

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