Electrical stimulation type nerve regeneration method based on calcium signal regulation and control

Abstract

The invention discloses an electrical stimulation type nerve regeneration method based on calcium signal regulation and control, and relates to the technical field of electrical stimulation type nerve regeneration, and the method comprises the following steps: based on a Schwann cell population calcium signal response feature set, identifying whether the calcium ion concentration is in a microliter stage which breaks through a lowest activation threshold and does not reach a stable state; judging whether the target nervous tissue is in a calcium signal activation critical state or not based on the behavior mode change of the Schwann cell population; based on the judgment result of the calcium signal activation critical state, an electrical stimulation adjustment control instruction is generated in real time in combination with the calcium ion concentration fluctuation trend and Schwann cell population response characteristics, and the calcium ion concentration stability is optimized and the activation state is maintained by adjusting the electrical stimulation intensity, the electrical stimulation pulse width, the electrical stimulation waveform type and the electrical stimulation application time sequence. According to the method, the problem that a calcium signal is difficult to recognize and dynamically optimize in a microliter stage is solved, and precise maintenance of Schwann cell activation and improvement of nerve regeneration efficiency are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical stimulation type nerve regeneration, and in particular to an electrical stimulation type nerve regeneration method based on calcium signal regulation. Background Art

[0002] With the increasing incidence of nerve injuries, effectively promoting nerve regeneration and functional recovery has become a research hotspot in medicine and bioengineering. Neuroregeneration refers to the process by which damaged nerve tissue achieves structural and functional repair under certain conditions. Schwann cells, as key supportive cells in peripheral nerve regeneration, play a vital role in nerve repair by promoting axonal growth and guiding the directional growth of nerve fibers. Electrical stimulation, as a physical intervention, can activate a series of intracellular signaling pathways by applying an electric field with specific parameters to nerve tissue, thereby promoting the proliferation, migration, and functional restoration of nerve cells. Calcium ion signals, as core intracellular second messengers, are widely involved in regulating the growth, differentiation, and functional maintenance of nerve cells, playing a key regulatory role in nerve regeneration. Activating calcium signaling pathways through electrical stimulation can effectively elevate the activity of Schwann cells, enhance their pro-regenerative activity, and provide a favorable microenvironment for axonal growth and functional recovery. Therefore, electrical stimulation-based neural regeneration methods, which regulate calcium signaling, can precisely and controllably mobilize endogenous repair mechanisms to promote efficient regeneration and functional restoration of nerve tissue, and have promising application prospects and clinical value.

[0003] Existing electrical stimulation-based neural regeneration technologies, based on calcium signaling regulation, primarily utilize electrical stimulation with specific parameters at the site of nerve injury to regulate changes in calcium ion concentration within nerve cells, particularly Schwann cells. This in turn activates multiple signal transduction pathways within the cells, promoting the initiation and continuation of the neural regeneration process. This type of technology typically involves the following key steps: First, through the design of a rational electrical stimulation device and electrode structure, a suitable electric field is applied directly or indirectly to the target neural tissue. Second, under electrical stimulation, calcium ion channels on the cell membrane are activated, allowing extracellular calcium ions to enter the cell, inducing an increase in calcium signals. Subsequently, calcium signals, acting as key biological signals, further regulate downstream cytokine expression, cytoskeletal remodeling, and growth factor secretion, thereby enhancing Schwann cell proliferation, migration, and myelination, promoting nerve fiber growth and functional recovery. Finally, by continuously or periodically regulating calcium signaling levels, the regenerative environment can be further optimized, maintaining the stability and effectiveness of the neural repair process. Throughout this process, calcium signals serve as a bridge between electrical stimulation and biological responses, playing a core role in precisely regulating neural regeneration behavior, amplifying physiological effects, and improving regeneration efficiency.

[0004] The existing technology has the following deficiencies: In existing technologies, when neural tissue has been unstimulated for a long time or when electrical stimulation therapy is first initiated, the calcium signal within Schwann cells, just after electrical stimulation, crosses the minimum activation threshold and enters a "slightly rising phase" where calcium ion concentration rises slightly but has not yet stabilized. During this critical transition period, the cells are in a critical transition period for functional activation, and their responses are extremely sensitive and unstable. In this specific situation, due to the typical "nonlinear gating" characteristics of calcium signals, if the electrical stimulation intensity is insufficient or the waveform is mismatched, the calcium ion channel may not remain open, resulting in a difficulty in further increasing the calcium signal, or even a rapid drop back to baseline, causing activation failure. Existing electrical stimulation-based neural regeneration technologies based on calcium signal regulation cannot dynamically optimize the electrical stimulation maintenance strategy based on the fluctuation state of the intracellular calcium signal during this "slightly rising phase". Instead, they can only implement a fixed stimulation scheme based on a simple binary judgment of activation / inactivation. This fails to identify and stably maintain the signal state within this critical window, resulting in the failure of Schwann cells to successfully complete functional activation and the initiation of the axon growth chain. If this failure reaction occurs repeatedly, it can also induce a decrease in cell excitability or adaptability, seriously affecting the progress and efficacy of nerve repair, prolonging the patient's recovery period, and even rendering the treatment ineffective.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an electrical stimulation-type nerve regeneration method based on calcium signal regulation to solve the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an electrical stimulation-based neural regeneration method based on calcium signal regulation, specifically comprising the following steps: S01. Acquire continuous data on the change of calcium ion concentration in the target neural tissue over time, and construct a calcium signal dynamic response model based on the change trend and fluctuation characteristics of the calcium ion concentration to predict the response trend of the target neural tissue to electrical stimulation; S02. Based on the calcium signal dynamic response model, extract characteristic parameters for predicting the response state of the target neural tissue, determine the electrical stimulation intensity, pulse width, waveform type, and application timing, and generate electrical stimulation start control instructions; S03, when executing the electrical stimulation start control command, collecting calcium ion concentration change data of several Schwann cells in the target neural tissue in real time, extracting synchronization features, phase consistency features and peak time difference features, and forming a group calcium signal response feature set; S04. Based on the Schwann cell population calcium signal response feature set, identify whether the calcium ion concentration is in a slightly rising stage that has exceeded the minimum activation threshold but has not reached a stable state, and determine whether the target neural tissue is in a critical state of calcium signal activation based on changes in the Schwann cell population behavior pattern; S05. Based on the determination result of the critical state of calcium signal activation, combined with the fluctuation trend of calcium ion concentration and the response characteristics of Schwann cell population, generate electrical stimulation adjustment control instructions in real time, and optimize the stability of calcium ion concentration and maintain the activation state by adjusting the electrical stimulation intensity, electrical stimulation pulse width, electrical stimulation waveform type and electrical stimulation application timing; S06. Based on the calcium ion concentration change data after each electrical stimulation, continuously update the calcium signal dynamic response model and the electrical stimulation adjustment control instructions.

[0008] Preferably, S01 specifically includes: Continuous data on the change of calcium ion concentration in the target nerve tissue over time is obtained by placing a calcium signal detection device in the target nerve tissue injury area; Based on the calcium ion concentration time series data, the calcium ion concentration change trend and fluctuation characteristics are extracted to form a standardized characteristic index set; Based on the changing trend and fluctuation characteristics of calcium ion concentration, a calcium signal dynamic response model is constructed to predict the response of target neural tissue to electrical stimulation; The prediction results of the calcium signal dynamic response model are compared with the real-time monitoring results for consistency, and the calcium signal dynamic response model is continuously optimized based on the comparison results.

[0009] Preferably, S02 specifically includes: The calcium ion concentration change trend data obtained by the calcium signal dynamic response model is input into the feature extraction unit to extract six feature dimensions: calcium ion concentration rising rate, peak stabilization time, fluctuation symmetry, peak width, waveform steepness and time delay; Based on the nonlinear mapping algorithm, the six feature dimensions are weighted and classified to extract a set of core feature parameters that can reflect the response state of the target neural tissue. Based on the core feature parameter set and using a time-evolution-based prediction algorithm, the sensitivity level and response state of the target neural tissue to electrical stimulation are dynamically predicted; Based on the prediction results, the electrical stimulation intensity, pulse width, waveform type, and application timing are determined to form a set of electrical stimulation application parameters that are compatible with the physiological state of the target neural tissue; The electrical stimulation application parameter set is converted into an electrical stimulation start control instruction and sent to the electrical stimulation execution system in real time.

[0010] Preferably, S03 specifically includes: When the electrical stimulation start control command is executed, the multi-point calcium signal sensor array deployed in the target nerve tissue injury area collects the calcium ion concentration change data of multiple Schwann cells in real time; Perform time series alignment and data cleaning on the collected calcium ion concentration change data to remove noise and optimize data continuity; Based on the cleaned data, the calcium ion concentration synchronization characteristics were extracted, and the group synchronization evaluation was optimized through the correlation coefficient matrix and time regularization; Based on synchronization analysis, we further extracted phase consistency features and peak time difference features to quantify group activation coordination; The calcium ion concentration synchronization features, phase consistency features and peak time difference features are fused to form the Schwann cell population calcium signal response feature set.

[0011] Preferably, S04 specifically includes: S041. Based on the calcium ion concentration change data of Schwann cell populations, a baseline model of the dynamic response trend of calcium signals was constructed to eliminate baseline drift and extract differential dynamic features; S042. Based on the dynamic response trend baseline model, a multi-dimensional indicator cross-analysis method is used to identify whether the calcium signal is in the micro-increase stage; S043. Construct a map of the evolution of Schwann cell population behavior patterns based on the identified population of calcium signal spikes, reflecting the coordinated response relationship between cells in real time. S044. Based on the behavioral pattern evolution map, a mutation recognition algorithm is used to detect group behavioral mutations and determine whether the critical state of calcium signal activation has been reached; S045. Implement cross-validation and continuous tracking based on the mutation identification results, dynamically correct the judgment results and ensure the continuity and safety of electrical stimulation adjustment.

[0012] Preferably, S041 and S042 specifically include: Real-time collection of calcium ion concentration change data from multiple Schwann cells, time alignment and standardization, and construction of a baseline model for the dynamic response trend of calcium signals; The trend characteristics of calcium ion concentration changes were extracted through statistical regression analysis, and the model parameters were dynamically updated using the Bayesian fitting algorithm. Based on the trend baseline model, three features are extracted: immediate growth slope, fluctuation amplitude change, and time persistence; A multi-dimensional indicator cross-analysis method was used to comprehensively judge the three characteristics to identify whether the calcium signal was in the micro-increase stage; When all three characteristics meet the set conditions, it is confirmed that the target neural tissue is in a true slightly elevated state.

[0013] Preferably, S043 and S044 specifically include: Construct a behavioral pattern evolution map based on the synchronization, phase consistency, and waveform similarity of Schwann cell calcium signals; Through the time-synchronized dynamic network analysis algorithm, the Schwann cell collaborative relationship network is updated in real time to extract the evolution characteristics of behavioral patterns; Real-time calculation of three dynamic indicators: synchronization improvement rate, dominant response cell ratio change rate, and waveform consistency change rate; Based on the dynamic threshold system, the three dynamic indicators are jointly identified for mutations to determine whether the activation critical state has been reached; Trend continuity verification and multi-index consistency verification are implemented on the mutation identification results to ensure the reliability and security of the judgment results.

[0014] Preferably, S05 specifically includes: Based on the results of the critical state of calcium signal activation, the fluctuation trend characteristics of calcium ion concentration and the response characteristics of Schwann cell population are extracted in real time as input for the generation of electrical stimulation adjustment control instructions; The calcium ion concentration fluctuation trend characteristics and Schwann cell population response characteristics are input into the multi-objective state evaluation system to evaluate the stability level of the calcium signal activation state and the activation maintenance requirement level in real time; Based on the calcium signal stability score, instability risk level, and optimization adjustment urgency level, electrical stimulation adjustment control instructions are generated in real time to adjust the electrical stimulation intensity, pulse width, waveform type, and application timing; The electrical stimulation adjustment control instructions are sent to the electrical stimulation execution system in real time to implement real-time parameter adjustments. During the execution process, calcium ion concentration change data and Schwann cell population response characteristics are continuously collected to form a feedback data stream. Based on feedback data, the control instruction generation rules are continuously optimized to form an individual self-evolution adjustment mechanism, thereby improving the efficiency of maintaining calcium signal activation and the safety of electrical stimulation control.

[0015] Preferably, the calcium ion concentration fluctuation trend characteristics and the Schwann cell population response characteristics are input into a multi-target state evaluation system to evaluate the stability level of the calcium signal activation state and the activation maintenance demand level in real time. Specifically, based on the calcium ion concentration fluctuation trend, a trend fitting regression algorithm and a volatility index calculation method are used to evaluate in real time whether the calcium signal has an instability tendency; based on the Schwann cell population response characteristics, a population synergy index calculation method and a multidimensional similarity clustering algorithm are used to evaluate in real time whether the cell synergy response tends to weaken; by introducing a weighted decision-making mechanism, a sensitivity weight is assigned to each feature, and the judgment boundary value is adjusted in real time based on a dynamic threshold system; based on the evaluation results, three quantitative indicators, namely, calcium signal stability score, instability risk level, and optimization regulation urgency level, are output as the decision basis for generating electrical stimulation adjustment control instructions.

[0016] Preferably, S06 specifically includes: After each electrical stimulation is completed, the calcium ion concentration change data of multiple Schwann cells in the target nerve tissue are collected in real time to extract the concentration peak, rising rate, fluctuation amplitude, stability and synchronization characteristics; Based on the collected real-time data, time series learning and Bayesian dynamic update algorithms are used to modify the key parameters of the calcium signal dynamic response model in real time, improving the model's adaptability to individual physiological states and its prediction accuracy. Based on the updated calcium signal dynamic response model and current real-time physiological characteristics, the electrical stimulation adjustment control instructions are dynamically optimized, and real-time control instructions are generated through the intelligent rule engine to achieve a closed-loop control process of feedback, update and optimization.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention collects calcium ion concentration change data of multiple Schwann cells in the target neural tissue in real time, and based on the synchronization characteristics, phase consistency characteristics and peak time difference characteristics, it has achieved the first accurate identification of the key activation critical state of "calcium signal micro-rise stage". Different from the existing technology that can only judge by a single peak or threshold, this technology introduces a group behavior pattern evolution map and mutation recognition algorithm, which can dynamically capture the transition trend of Schwann cells from asynchronous reaction to synchronous activation, thereby identifying potential activation failure risks in advance and improving the sensitivity and success rate of calcium signal initiation. This innovative mechanism effectively avoids the traditional method in the early stage of nerve repair, which is prone to activation failure and repair chain interruption due to recognition lag or misjudgment, and significantly enhances the stability and safety of treatment initiation.

[0018] 2. The present invention constructs a dynamic response model of calcium signals and combines it with real-time physiological characteristics, adopts a multi-target state evaluation and intelligent rule engine generation mechanism, and can dynamically adjust the electrical stimulation intensity, pulse width, waveform type and application timing based on the calcium signal stability score, instability risk level and optimization adjustment urgency level. This technical means breaks through the problem that the existing fixed parameter control strategy cannot adapt to individual differences and state fluctuations, and realizes real-time personalized and dynamic optimization and regulation of electrical stimulation parameters. Its advantage is that it can perceive subtle changes in the state of neural tissue in real time and make rapid adjustments, significantly reducing the risk of over-stimulation or under-stimulation, effectively maintaining the stability of Schwann cell calcium signals, improving the sustainability of nerve repair, ensuring the consistency and safety of long-term efficacy, and has the advantages of high intelligence and precise intervention.

[0019] 3. The present invention introduces time series learning and Bayesian dynamic update algorithms. After each electrical stimulation is completed, feedback data is continuously collected and the calcium signal dynamic response model and control instruction generation rules are corrected in real time, forming a complete feedback-update-optimization closed-loop control mechanism. This mechanism is highly adaptive and evolvable. It can dynamically optimize the control strategy according to the individual physiological characteristics of the patient and the changing trends during the treatment process, and gradually improve the calcium signal activation maintenance efficiency and nerve regeneration effect. Compared with traditional isolated and static control methods, this solution has the unique advantages of long-term learning, individual evolution, and continuous optimization. It can not only improve the success rate of short-term treatment, but also effectively prolong the duration of the therapeutic effect and reduce the probability of adverse reactions, and ultimately achieve intelligent, efficient, safe, and lasting nerve regeneration intervention effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0021] Figure 1 This is a flow chart of an electrical stimulation-type nerve regeneration method based on calcium signal regulation according to the present invention. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0023] The present invention provides Figure 1 The electrical stimulation-based neural regeneration method based on calcium signal regulation shown in the figure specifically includes the following steps: S01. Acquire continuous data on the change of calcium ion concentration in the target neural tissue over time, and construct a calcium signal dynamic response model based on the change trend and fluctuation characteristics of the calcium ion concentration to predict the response trend of the target neural tissue to electrical stimulation; In order to obtain continuous data on the change of calcium ion concentration in the target neural tissue over time and build a calcium signal dynamic response model based on the change trend and fluctuation characteristics of calcium ion concentration, so as to effectively predict the response trend of the target neural tissue to electrical stimulation, this can be achieved by the following methods: The calcium signal detection device is placed in the damaged area of ​​the target neural tissue. It uses a combination of highly sensitive optical sensors and electrochemical sensors to continuously acquire raw calcium ion concentration data from Schwann cells within the target neural tissue in real time with millisecond-level time resolution. The instantaneous changes in intracellular calcium ions are acquired through optical path signals, while electrochemical methods are used for synchronous calibration to ensure data accuracy and continuity. The calcium signal detection device collects calcium ion concentration values ​​at each time point at preset time intervals, forming continuous time series data.

[0024] The acquired calcium ion concentration time series data is input into the signal feature extraction and processing unit, which performs a multi-dimensional analysis of the calcium ion concentration change trend and fluctuation characteristics. Specifically, seven key characteristic indicators are extracted, including the calcium ion concentration's rise rate, fall rate, peak amplitude, trough depth, fluctuation frequency, duration, and change slope. These characteristic indicators are then standardized to eliminate scale inconsistencies that may exist under different sampling conditions. At the same time, a time-window-based sliding mean method is introduced to dynamically eliminate the impact of high-frequency noise on trend judgment, improving the stability and accuracy of feature extraction.

[0025] Based on the extracted trend and fluctuation characteristics of calcium ion concentration, a dynamic response prediction algorithm based on machine learning was used to construct a calcium signal dynamic response model. This algorithm uses a method based on long-short-term memory neural networks to capture the nonlinear evolution of time series in continuous data streams. It combines a historical calcium signal feature library with actual collected data for real-time online training, thereby achieving high-precision predictions of the target neural tissue's response trend to electrical stimulation. This calcium signal dynamic response model can not only predict the changing trend of calcium ion concentration at future moments, but also provide early warnings for possible abnormal signal fluctuations, delayed reactions, or non-physiological responses, ensuring the accuracy of subsequent electrical stimulation control.

[0026] The calcium signal trend predictions based on the dynamic response model are compared and analyzed with real-time calcium ion concentration monitoring results. When the consistency between the predicted and real-time monitoring results reaches a preset threshold, the calcium signal dynamic response model is deemed valid and used as the core basis for formulating subsequent electrical stimulation control strategies. If the consistency is insufficient, the model's adaptive adjustment mechanism is triggered, automatically optimizing the model parameters until the prediction accuracy reaches the target. This closed-loop optimization method ensures that the calcium signal dynamic response model remains efficient, accurate, and stable, providing reliable data support and decision-making basis for dynamically adjusting electrical stimulation control instructions.

[0027] Through the above, not only can the changing trends and fluctuation characteristics of calcium ion concentration in the target neural tissue be obtained in real time, continuously and accurately, but also the calcium signal response prediction model can be dynamically constructed and optimized, which solves the problem that traditional electrical stimulation technology cannot accurately identify and predict the changing trends and fluctuation behaviors of calcium signals, and significantly improves the intelligent level of stimulation control and individualized adaptation capabilities in the process of nerve regeneration.

[0028] S02. Analyze the trend of calcium ion concentration changes based on a calcium signal dynamic response model, extract characteristic parameters for predicting the response state of the target neural tissue to electrical stimulation, determine the intensity, pulse width, waveform type, and application timing of the electrical stimulation based on the characteristic parameters, and generate an electrical stimulation start control instruction; In order to analyze the trend of calcium ion concentration changes based on the calcium signal dynamic response model, extract characteristic parameters for predicting the response state of target neural tissue to electrical stimulation, determine the intensity, pulse width, waveform type and application timing of electrical stimulation based on the characteristic parameters, and generate electrical stimulation start control instructions, the specific steps include: First, the continuous trend data of calcium ion concentration obtained through the calcium signal dynamic response model is input into the feature extraction unit, which decomposes and analyzes multiple key change dimensions of calcium ion concentration, including six characteristic dimensions: calcium ion concentration rise rate, peak stabilization time, fluctuation symmetry, peak width, waveform steepness, and time delay. This analysis method, through a multi-dimensional feature composite recognition mechanism, can effectively improve the accuracy of identifying weak response states of neural tissue calcium signals.

[0029] Next, based on the six characteristic dimensions, a nonlinear mapping algorithm is used to weight and classify each dimension, extracting a core set of characteristic parameters that directly reflect the target neural tissue's response to electrical stimulation. This core set of characteristic parameters does not rely on a single indicator, but rather forms a dynamic feature configuration through the interaction of multiple features. This eliminates the risk of misleading predictions due to fluctuations in a single parameter, thereby improving prediction robustness and accuracy.

[0030] Third, based on the extracted core feature parameter set, a time-evolution-based prediction algorithm is used to dynamically predict the target neural tissue's sensitivity and response to the impending electrical stimulation. This prediction algorithm, which leverages the temporal characteristics of calcium ion concentration trends, can determine in advance whether the neural tissue is in one of four distinct states: readily activatable, critically activatable, difficult to activate, or inhibited and recovered. This provides a scientific basis for the subsequent formulation of stimulation parameters.

[0031] Fourth, based on the predicted response state of the target neural tissue, the intensity, pulse width, waveform type, and application timing of the electrical stimulation are determined in sequence. The electrical stimulation intensity is calculated based on the response sensitivity; the pulse width is dynamically adjusted based on the peak width and stabilization time; the waveform type is automatically selected based on the symmetry of calcium ion concentration fluctuations; and the application timing is precisely determined by the activation delay time, thus forming a set of electrical stimulation application parameters that are highly compatible with the current physiological state of the neural tissue.

[0032] Finally, the determined set of electrical stimulation application parameters is converted into standardized electrical stimulation start control instructions through the control instruction generation unit and issued in real time to the electrical stimulation execution system. This start control instruction is not only real-time but also dynamically adaptable to the individual, ensuring a high success rate of calcium signal activation and effective triggering of Schwann cell function during the initial electrical stimulation, thus laying a solid foundation for subsequent nerve regeneration.

[0033] Through the above five implementation steps, it is possible to achieve in-depth analysis and feature extraction of the calcium ion concentration change trend based on the calcium signal dynamic response model, accurately predict the response state of the target neural tissue to electrical stimulation, and dynamically determine and generate electrical stimulation start control instructions on this basis. This effectively solves the problem of the inability to achieve accurate prediction and personalized electrical stimulation settings in existing technologies, and significantly improves the intelligence level and biosafety of nerve regeneration treatment.

[0034] S03. When executing the electrical stimulation start control instruction, collecting calcium ion concentration change data of several Schwann cells in the target neural tissue in real time, extracting calcium ion concentration synchronization characteristics, phase consistency characteristics, and peak time difference characteristics, and forming a Schwann cell population calcium signal response feature set; In order to collect the calcium ion concentration change data of several Schwann cells in the target neural tissue in real time when executing the electrical stimulation start control command, and extract the calcium ion concentration synchronization characteristics, phase consistency characteristics, and peak time difference characteristics, and form the Schwann cell population calcium signal response feature set, this is achieved through the following methods: A high-precision multi-point calcium signal sensor array is precisely placed at predetermined monitoring points in the target nerve tissue injury area. Each monitoring point corresponds to the spatial location of one or more Schwann cells, ensuring real-time and synchronous acquisition of continuous changes in calcium ion concentration in multiple Schwann cells during electrical stimulation. This multi-point calcium signal sensor array combines optical calcium imaging with nano-ion electrode technology to achieve continuous dynamic acquisition with millisecond-level time precision, while ensuring consistent data acquisition across multiple cells in a spatially distributed manner, avoiding data drift and signal interference.

[0035] The acquired raw data streams of Schwann cell calcium ion concentration changes were time-aligned and initially cleaned according to the principle of time synchronization. Incidental noise points, signal discontinuities, and spurious signal peaks were removed to ensure that subsequent feature extraction was based on high-quality, valid data. During the data cleaning process, a continuous sliding window mechanism and an adaptive outlier filtering algorithm were employed to enhance the sensitivity of capturing weak signal fluctuations and ensure accurate reproduction of the subtle Schwann cell responses.

[0036] A population feature extraction method was used to extract calcium ion concentration synchronization features from the cleaned and aligned data stream. Specifically, the correlation coefficient matrix of the calcium ion concentration change curves between different Schwann cells was calculated to quantify the consistency of the responses of different cells to the same electrical stimulation command. The offset of the different curves on the time axis was further aligned and optimized using a dynamic time warping algorithm, thereby forming a quantitative feature value that accurately reflects the state of population synchronization. Synchronicity features can effectively reflect the overall coordination of the Schwann cell population in response to electrical stimulation signals and are a key indicator for determining the potential for functional recovery of neural tissue.

[0037] Based on the results of synchronization analysis, the phase consistency and peak time difference characteristics of the Schwann cell population were further extracted. The phase consistency feature calculates the phase concentration index by Fourier transforming the phase angle change sequence of each cell's calcium ion concentration curve to reflect whether different cells respond together under the same physiological rhythm. The peak time difference feature quantifies the level of group activation consistency by accurately extracting the peak moments of each cell's calcium ion concentration curve and calculating the maximum time difference, average time difference, and coefficient of variation between the peak occurrence times. These two features can further overcome the limitations of synchronization characteristics and enhance the multi-dimensional description of the coordination of Schwann cell activation.

[0038] The calcium ion concentration synchronization, phase consistency, and peak time difference features were fused to form a comprehensive set of Schwann cell population calcium signal response features. This feature set, through weighted fusion and nonlinear dimensionality reduction, was used to construct a unified metric system that comprehensively characterizes the dynamic response state, activation level, response latency, and coordination of the Schwann cell population during electrical stimulation. Once generated, the feature set was immediately pushed to the subsequent recognition and judgment steps, serving as the basis for dynamic adjustments to electrical stimulation commands and maintaining Schwann cell functional activity.

[0039] Through the above methods, a comprehensive, accurate and dynamic Schwann cell population calcium signal response feature set can be formed based on multi-point real-time acquisition, continuous data cleaning, population synchronization quantification, phase and time difference multi-dimensional feature extraction and feature fusion, which significantly improves the reliability and real-time performance of the identification of the activation state of neural tissue calcium signals, effectively supports the scientific and individual level of subsequent electrical stimulation regulation decisions, and solves the problem that the existing technology cannot accurately quantify and determine the dynamic response behavior of multi-cellular calcium signals.

[0040] S04. Based on the Schwann cell population calcium signal response feature set, identify whether the calcium ion concentration is in a slightly rising stage that has exceeded the minimum activation threshold but has not reached a stable state, and determine whether the target neural tissue is in a critical state of calcium signal activation based on changes in the Schwann cell population behavior pattern; In order to identify whether the calcium ion concentration is in the micro-rising stage that has exceeded the minimum activation threshold but has not reached a stable state based on the calcium signal response feature set of the Schwann cell population, and to determine whether the target neural tissue is in a critical state of calcium signal activation based on the changes in the behavior pattern of the Schwann cell population, the specific steps include: In the target neural tissue, after collecting the calcium ion concentration change data of multiple Schwann cells in real time at multiple points, it is first necessary to construct a dynamic response trend baseline model based on individual cell responses and group status. The specific implementation method is: the calcium ion concentration change curves of different Schwann cells in the same time window are standardized to eliminate the baseline drift caused by physiological differences, sensor micro-differences and external factors, and based on statistical regression analysis and Bayesian fitting algorithm, the differential dynamic baseline model of the group calcium signal in the state without electrical stimulation and with electrical stimulation is extracted. This model not only captures static concentration differences, but also pays more attention to the trend characteristics of dynamic evolution, ensuring that the subsequent identification of the micro-rise stage has a scientific and rigorous benchmark reference. At the same time, in order to ensure that individual differences are taken into account, the model is designed to be in a state of sustainable learning. As the data collected during continuous stimulation becomes richer, the model parameters are dynamically adjusted to make it continuously adaptable to individuals and different pathological states, forming a personalized and accurate baseline support.

[0041] Statistical regression analysis and the Bayesian fitting algorithm are data analysis techniques that combine classical statistical methods with probabilistic inference. They are effective in addressing the complex individual variability, dynamic variability, and data uncertainty inherent in neurobiological signals. In this approach, statistical regression analysis is primarily used to reveal linear or nonlinear trends in calcium concentration data. A basic trend analysis model is formed by modeling the correlation between calcium concentrations of multiple Schwann cells at different time points and multiple variables such as time, stimulation conditions, and environmental parameters. The Bayesian fitting algorithm further incorporates prior knowledge and a dynamic probability update mechanism, incorporating the physiological characteristics, historical response behavior, and real-time data fluctuations of each cell. Using the Bayesian formula, the model parameters are continuously iteratively modified, maintaining high predictive accuracy and stability even with small sample sizes, high volatility, and high noise. The core advantage of the Bayesian fitting algorithm lies in its robustness and adaptability. It can incorporate new observations in real time, continuously optimizing the model's representation of the true state of calcium signaling and avoiding overfitting and the failure of static assumptions.

[0042] The specific implementation method for "Extracting a Dynamic Baseline Model of Population Calcium Signaling Variation Based on Statistical Regression Analysis and Bayesian Fitting" is as follows: First, calcium sensor-based real-time calcium concentration curves of multiple Schwann cells within a duration window are acquired. All curves are time-aligned and normalized to eliminate sampling start differences and amplitude offsets. Subsequently, statistical regression analysis techniques are used to fit individual cell concentration trend curves using polynomial regression and locally weighted regression for both the non-stimulated and stimulated calcium concentration data. Characteristic values ​​such as slope, curvature, and amplitude of each curve are calculated to generate preliminary trend analysis results. After completing the trend regression, a Bayesian fitting algorithm is further introduced to construct a probabilistic model of calcium signal changes for each cell. The specific steps are as follows: First, a prior distribution of calcium signal responses is set. This prior value can be derived from the patient's historical physiological data, literature statistics, or a large-sample learning model. Second, the real-time calcium concentration changes are used as observation data and substituted into a Bayesian formula to calculate the posterior probability, dynamically updating the state confidence of each cell's response to electrical stimulation. Through continuous iterative Bayesian updates, the model can integrate past information and current data in real time to derive the differential dynamic probability distribution of calcium signal changes in each cell under unstimulated and stimulated conditions. Finally, at the population level, the dynamic probability distribution models of each cell are fused through a weighted integration algorithm to form a dynamic baseline model of the overall population calcium signal. This model can clearly display the mean change trend, fluctuation range, and uncertainty interval of population calcium ion concentration under different stimulation conditions, and can adjust the baseline range in real time to adapt to individual differences and physiological fluctuations during treatment, ensuring that the subsequent identification of the micro-rise stage of the calcium signal is highly scientific, accurate, and flexible.

[0043] After constructing a dynamic response baseline model, a multi-dimensional cross-analysis method based on real-time Schwann cell calcium concentration data was used to accurately identify subtle increases in calcium signaling. This identification involves three dimensions: first, calculating the instantaneous slope of the calcium concentration curve for each cell to identify a sustained trend exceeding the activation threshold; second, analyzing whether the fluctuation amplitude is within a subtle fluctuation state, that is, whether it has not yet entered the high-amplitude activation range; and third, assessing the temporal persistence characteristics to determine whether the subtle increase state is stable and sustained for more than a preset physiological time window (e.g., more than 500 milliseconds). All three indicators must be met simultaneously to determine that the cell is in a true subtle increase state. This approach effectively avoids false positives caused by judging based solely on peak value while also enabling early identification of "activation intention states," gaining a time window for subsequent dynamic regulation.

[0044] The multi-dimensional indicator cross-analysis method is an analytical technique that comprehensively determines the target state based on multiple independent but interrelated characteristic parameters. It is particularly suitable for complex physiological data such as Schwann cell calcium ion signals in neurobiology, which are highly dynamic, volatile, and have significant individual differences. In this embodiment, the method simultaneously extracts and analyzes multiple key dynamic indicators of the calcium ion concentration curve, including the immediate growth slope, fluctuation amplitude change, and time persistence characteristics, forming a mutually complementary and mutually verified judgment system. Among them, the immediate growth slope is used to capture whether the calcium ion concentration has a sustained upward trend and is the core dimension for determining energy increase; the fluctuation amplitude change is used to identify whether the signal is in a state of slight fluctuation, thereby avoiding the misidentification of short-term strong fluctuations as valid activation; and the time persistence characteristic focuses on whether the physiological response remains stable, preventing short-term noise from being mistaken for a true response. These three indicators are based on the three dimensions of trend, amplitude, and stability. Through cross-analysis, mutual verification, and logical parallelization, only when all three indicators simultaneously meet the set judgment conditions can the Schwann cell be confirmed to be in a true slight rise state. This method can effectively reduce the risk of misjudgment caused by single feature judgment and significantly improve recognition accuracy and reliability. It is especially suitable for early and accurate identification of calcium signal activation status under electrical stimulation guidance.

[0045] For the identified Schwann cell population with a slight increase in calcium signal, we further constructed an evolutionary map of the group behavior pattern. Specifically, based on the three key relationship indicators of synchronization of calcium signal changes between cells in the group, phase consistency, and response waveform similarity, we used a time-synchronized dynamic network analysis algorithm to form a coordinated response relationship network between cells. On this basis, we further tracked the evolution of group behavior from loose response to synchronous activation in the network based on a continuous time scale, and drew a multi-dimensional group behavior pattern map. This map can clearly reflect whether Schwann cells are entering the transition period of coordinated activation and reveal whether calcium signals are forming a physiological behavior phase transition from disordered to ordered. It has extremely high biological explanatory power and intervention guidance value.

[0046] The time-synchronized dynamic network analysis algorithm is a complex network computational method based on time alignment and dynamic relationship evolution. It is suitable for analyzing the coordinated behavior and pattern evolution of multiple Schwann cells in response to calcium signals. This method first performs high-precision time synchronization on the calcium concentration curves of different Schwann cells to eliminate time offsets caused by cell response delays, signal fluctuations, and sampling errors, ensuring that the dynamic responses of all cells have a unified time reference. Subsequently, a network of intercellular coordinated responses is constructed, with each cell as a node and the similarity, synchrony, and phase consistency of calcium signal trends as edge weights. This network is continuously updated over time, reflecting the dynamic evolution of intercellular coordinated relationships. Furthermore, by calculating multiple network dynamics metrics such as network density, clustering coefficient, and synchronization index, the network's evolution from a loose, low-synchronization state to a highly coordinated, synchronized activation state is tracked in real time, dynamically capturing whether the Schwann cell population is undergoing activation transitions. This algorithm can reflect the temporal, group and pattern characteristics of calcium signal response behavior in a multi-dimensional, full-process and real-time manner. It has extremely high dynamic recognition accuracy and physiological behavior analysis capabilities, and is a key supporting means for realizing calcium signal phase change recognition and intelligent intervention decision-making.

[0047] After obtaining the behavioral pattern evolution map, a mutation recognition algorithm is used to identify the key transition behaviors in the group behavior pattern. Specifically, it includes: real-time calculation of three key dynamic indicators in the behavioral map: the rate of increase in inter-cell synchronization, the rate of change in the proportion of dominant response cells, and the rate of change of the waveform consistency mean, and based on the set dynamic threshold system, it is determined whether the mutation threshold is reached. When the system detects a mutation in the group behavior pattern from asynchronous to synchronous transition, and the duration of the mutation exceeds the set physiological threshold, it can be determined that the target neural tissue as a whole has entered the critical state of calcium signal activation. Compared with the traditional fixed threshold method, this mutation recognition method is highly sensitive and robust, and can effectively capture the window of transformation from the slightly rising state to the activated state, significantly improving the real-time and effectiveness of the control strategy.

[0048] The mutation identification algorithm is an intelligent identification method based on the detection of mutations in dynamic behavioral characteristics. It aims to accurately capture key turning points in the transition from asynchronous to synchronized activation within the evolutionary map of Schwann cell calcium signaling behavior. The algorithm first analyzes the evolutionary map of the population's behavioral pattern over a continuous time window to extract three dynamic metrics in real time: the rate of increase in intercellular synchrony, the rate of change in the proportion of dominant responding cells, and the rate of change in mean waveform consistency. These metrics reflect the changing trends in Schwann cell calcium signaling responses across three different dimensions: the degree of synergy, the proportion of active cells, and response consistency. Subsequently, based on a multi-threshold dynamic system, mutation thresholds are set that are tailored to the individual's baseline, physiological state, and historical behavioral patterns. These dynamic metrics are then jointly compared against the real-time calculated dynamic metrics. A mutation is considered to have occurred when the rate of change of any metric exceeds its corresponding threshold and the duration of the mutation exceeds the set physiological timeframe. To improve the accuracy of the identification, the mutation identification algorithm also employs trend continuity verification and multi-metric consistency validation to ensure the stability and biological plausibility of the identification results. Compared with traditional judgment methods based on fixed numerical thresholds, this algorithm can perceive tiny but continuous behavioral pattern transitions in real time, and has high sensitivity, dynamic adaptability and anti-interference capabilities. It is the core technical support for achieving accurate judgment and intelligent dynamic adjustment of the critical state of calcium signal activation.

[0049] To further enhance the reliability and security of identification results, a cross-validation mechanism is implemented: the results of group behavioral mutation identification are cross-compared with single-cell calcium ion concentration trends, micro-rise state persistence, and historical behavioral maps. Only when the consistency of multidimensional data reaches a preset confidence level is the critical activation state determination result output. Furthermore, to avoid omissions or delayed responses, the system implements continuous state tracking for tissues identified as critical, updating behavioral pattern evolution maps and determination results in real time to ensure dynamic correction and continuous optimization during subsequent electrical stimulation adjustments, effectively preventing activation failures or misjudgments and ensuring the continuity and safety of the neural regeneration process.

[0050] S05. Based on the determination result of the critical state of calcium signal activation, combined with the fluctuation trend of calcium ion concentration and the response characteristics of the Schwann cell population, generate electrical stimulation adjustment control instructions in real time, and optimize the stability of calcium ion concentration and maintain the activation state by adjusting the intensity, pulse width, waveform type or application timing of the electrical stimulation; Based on the results of the critical state of calcium signal activation, combined with the calcium ion concentration fluctuation trend and the response characteristics of the Schwann cell population, electrical stimulation adjustment control instructions are generated in real time. By adjusting the intensity, pulse width, waveform type or application timing of electrical stimulation, the stability of calcium ion concentration is optimized and the activation state is maintained. This is achieved specifically through the following methods: After identifying that the target neural tissue is in a critical state of calcium signal activation, the calcium ion concentration fluctuation trend and Schwann cell population response characteristics are extracted in real time as the basic input data for subsequent electrical stimulation adjustments. Among them, the calcium ion concentration fluctuation trend characteristics include the real-time change slope of the calcium ion concentration, the difference between the peak and trough, the duration of the continuous rise, the short-term fluctuation amplitude and the stability score; the Schwann cell population response characteristics include the intercellular synchronization index, the phase consistency index, the waveform similarity score, the dominant cell ratio and the population activation delay time. These characteristics comprehensively reflect the true state of the current calcium signal behavior in the neural tissue from multiple dimensions such as time, amplitude, synergy, and persistence, and ensure high data quality and strong accuracy through standardization, denoising and continuity verification methods. Through this step, it can be ensured that subsequent electrical stimulation adjustments are based on comprehensive, real-time and accurate physiological state perception.

[0051] After identifying that the target neural tissue is in a critical state of calcium signal activation, in order to achieve accurate dynamic generation of electrical stimulation adjustment control instructions, it is necessary to extract the calcium ion concentration fluctuation trend characteristics and Schwann cell population response characteristics in real time as the basic input data for subsequent regulatory decisions. Specifically, the acquisition of calcium ion concentration fluctuation trend characteristics includes: first, based on the real-time continuously collected calcium ion concentration change curve, the sliding window differential algorithm is used to calculate the concentration change slope to determine the signal rise or fall rate; second, the dynamic peak detection method is used to identify the peak and trough difference and quantify the signal fluctuation amplitude; third, the time window integration method is used to calculate the continuous rise duration to reflect whether the signal has a continuous activation trend; fourth, the short-term fluctuation amplitude is evaluated based on the short-time Fourier transform method to accurately eliminate the influence of high-frequency noise; fifth, the stability score is calculated by trend line fitting and residual analysis to reflect whether the signal has sufficient stability to support continuous activation. These five characteristics together constitute a multidimensional indicator system for calcium ion concentration fluctuation trends. At the same time, the acquisition of Schwann cell population response characteristics includes: first, calculating the synchronization index through the correlation matrix of intercellular calcium signal curves to quantify the level of coordination; second, calculating the phase consistency index using the phase lock value method to reflect the consistency of the response rhythm; third, calculating the waveform similarity score based on the dynamic time warping method to reflect the consistency of the population activation morphology; fourth, calculating the proportion of dominant responding cells through dominant cell activity analysis to reflect the influence of key cell groups; and fifth, calculating the population activation delay time based on timestamp comparison to reflect the response lag. All characteristic parameters are uniformly standardized and denoised using wavelet transform and adaptive filtering to eliminate occasional abnormal fluctuations. A time continuity verification algorithm is used to ensure that the characteristic values ​​are stable and reliable over a sustained period. The resulting multidimensional feature set not only comprehensively covers the four core dimensions of time, amplitude, coordination, and continuity, but also provides scientific, accurate, and real-time physiological state perception support for individualized and dynamic decision-making of subsequent electrical stimulation control instructions, significantly improving the effectiveness and safety of neuroregenerative interventions.

[0052] The acquired calcium ion concentration fluctuation trend characteristics and Schwann cell population response characteristics are input into the multi-objective state assessment system to evaluate the stability level of the calcium signal activation state and the activation maintenance requirement level in real time. Specifically, based on the calcium ion concentration fluctuation trend, it is evaluated whether the current signal has an instability tendency; based on the Schwann cell population response characteristics, it is evaluated whether the cell coordinated response tends to weaken. The multi-objective state assessment system adopts a weighted decision-making mechanism, assigning different sensitivity weights to each feature, and combining it with a dynamic threshold system to achieve scientific quantification of state stability and optimization needs. The state assessment output results include: calcium signal stability score, instability risk level, and optimization adjustment urgency level. Through this method, potential instability risks can be discovered in advance, providing accurate decision-making support for the real-time generation of electrical stimulation adjustment control instructions.

[0053] After obtaining the calcium ion concentration fluctuation trend characteristics and Schwann cell population response characteristics, the above characteristics are introduced as input data into the multi-objective state assessment system to achieve real-time and accurate assessment of the stability level of the calcium signal activation state and the activation maintenance demand level. Specifically, first, based on the calcium ion concentration fluctuation trend, the trend fitting regression algorithm and the volatility index calculation method are used to comprehensively evaluate the characteristic indicators such as the real-time concentration change slope, fluctuation amplitude, and continuity stability to determine whether the current calcium signal has a clear tendency to destabilize, that is, whether it shows a downward trend, abnormal fluctuations, or short-term reversal risks. Secondly, based on the Schwann cell population response characteristics, the population synergy index calculation method and the multidimensional similarity clustering algorithm are used to dynamically analyze the intercellular synchronization index, phase consistency index, and waveform similarity change trends to determine whether the Schwann cell synergy response is weakening, whether there is a delay or desynchronization phenomenon in the response of some cells, and then infer whether the neural tissue is facing the risk of synergy failure. The multi-objective state assessment system incorporates the above two assessment dimensions into decision-making at the same time. By introducing a weighted decision-making mechanism, a sensitivity weight is set for each characteristic indicator. The weight is automatically optimized based on historical data learning, and a dynamic threshold system is used to adjust the judgment boundary value in real time to improve the adaptability to different individuals and different physiological states. In specific implementation, the output of the assessment system includes three quantitative indicators: one is the calcium signal stability score, which is calculated based on the weighted calculation of multiple trend characteristics; the second is the instability risk level, which is determined based on the volatility index and the synergistic consistency index; the third is the optimization adjustment urgency level, which is given in real time based on the mutation trend, fluctuation rate and physiological safety boundary distance. These three indicators serve as the direct decision-making basis for the subsequent generation of electrical stimulation adjustment control instructions. They can effectively warn of potential instability risks of calcium signals in advance, ensure that electrical stimulation adjustments are scientific, forward-looking and real-time, and significantly improve the individualization and intelligence level of nerve regeneration treatment.

[0054] Based on the calcium signal stability score, instability risk level, and optimization and adjustment urgency level, electrical stimulation adjustment control instructions are generated in real time. The generation of this instruction includes the following four adjustment dimensions: first, electrical stimulation intensity adjustment, which increases or decreases the intensity in real time based on the change in the slope of the calcium ion concentration; second, electrical stimulation pulse width adjustment, which dynamically fine-tunes the pulse width based on the peak duration and stability; third, electrical stimulation waveform type adjustment, which automatically selects different waveform types such as square wave, step wave, and biphasic wave based on the similarity and phase consistency of the Schwann cell population response waveform; fourth, electrical stimulation application timing adjustment, which determines the optimal application time node and application rhythm based on the activation delay time and response window width. All adjustment actions are driven by real-time data and generated by an intelligent rule engine to form highly personalized and dynamic control instructions, effectively avoiding the risk of activation failure or overexcitation caused by traditional fixed parameter stimulation.

[0055] After obtaining the calcium signal stability score, instability risk level, and optimization and regulation urgency level, electrical stimulation adjustment control instructions are generated in real time based on the three status evaluation results to accurately optimize the stability of calcium ion concentration and maintain the activation state of Schwann cells. Specifically, the generation of electrical stimulation adjustment control instructions includes four key adjustment dimensions. First, electrical stimulation intensity adjustment, by continuously monitoring the slope of calcium ion concentration change, the deviation value between the concentration rise rate and the target stable range is calculated in real time. When the slope is lower than the preset threshold and the stability score is low, the stimulation intensity is automatically increased, otherwise it is appropriately reduced to achieve precise control of the activation threshold; second, electrical stimulation pulse width adjustment, based on the calcium ion concentration peak duration and stability score, a progressive width fine-tuning algorithm is used to dynamically lengthen or shorten the pulse width. When the peak duration is insufficient, the width is automatically extended, and vice versa, when the fluctuation is too strong, it is shortened to avoid over-activation or signal dissipation; third, electrical stimulation Waveform type adjustment: By analyzing the similarity and phase consistency index of Schwann cell population response waveforms in real time, a waveform pattern matching algorithm is used to automatically select the waveform type that best matches the current population behavior characteristics from a preset library of waveforms, including square waves, step waves, and biphasic waves. This waveform type is automatically switched during critical phase transitions to maximize the success rate of cell activation. Fourth, the timing of electrical stimulation is adjusted. Based on the current activation delay and response window width, a time window prediction algorithm is used to accurately calculate the optimal application time node. The application frequency and interval are then adjusted using a rhythm optimization algorithm to achieve precise control of cell excitability. All of these adjustments are driven by real-time physiological data and automatically generate control instructions through a pre-built intelligent rules engine. This rules engine is continuously optimized based on Bayesian probability models, time series prediction models, and evolutionary learning mechanisms to ensure highly personalized and dynamically adaptive control instructions. This control strategy effectively avoids the risks of activation failure, overreaction, or neural adaptive failure that can occur with traditional fixed-parameter stimulation methods due to individual differences, signal fluctuations, or changes in physiological state, significantly improving the safety, stability, and long-term effectiveness of neuroregenerative therapy.

[0056] The dynamically generated electrical stimulation adjustment control instructions are sent to the electrical stimulation execution system in real time to adjust the electrical stimulation intensity, electrical stimulation pulse width, electrical stimulation waveform type or electrical stimulation application timing in real time. During the electrical stimulation execution process, calcium ion concentration change data and Schwann cell population response characteristics are continuously collected in real time to form an instant feedback data stream. This feedback data is automatically transmitted back to the state evaluation system to form a closed-loop feedback control mechanism. Through this closed-loop mechanism, the physiological feedback results after each electrical stimulation execution can be used in real time to adjust the next round of control instructions to ensure that the stability of calcium ion concentration is continuously optimized and the continuous activation state of Schwann cells is dynamically maintained, thereby realizing adaptive, precise and continuous electrical stimulation intelligent control.

[0057] After generating electrical stimulation adjustment control instructions in real time, these instructions are sent to the electrical stimulation execution system in real time, directly driving the electrical stimulation execution unit to precisely adjust electrical stimulation parameters in the target neural tissue. This includes real-time adjustments to four dimensions: stimulation intensity, pulse width, waveform type, and stimulation application timing. Specifically, the electrical stimulation execution system automatically adjusts the output voltage amplitude to achieve changes in stimulation intensity based on the parameter values ​​in the control instructions; adjusts the pulse duration to achieve changes in stimulation pulse width; and automatically switches the stimulation waveform type through the waveform generator to ensure that it matches the current response pattern of the Schwann cell population. Furthermore, the timing controller precisely sets the stimulation start time, cycle, and interval to achieve intelligent scheduling of application timing. During the stimulation process, the system simultaneously and continuously collects real-time calcium ion concentration change data and Schwann cell population response characteristics, including multi-dimensional physiological parameters such as calcium ion concentration curves, peak duration, cell synchrony, phase consistency, and population activation delay, generating an instant feedback data stream. This feedback data is transmitted in real time to the state assessment system via a high-speed data bus, triggering real-time status updates and serving as direct input for the generation of the next round of electrical stimulation adjustment control instructions. To implement a closed-loop feedback control mechanism, a real-time data processing unit based on an adaptive Kalman filter algorithm is embedded within the system to ensure that the feedback data remains stable and credible even when subject to noise interference or physiological abnormalities. At the same time, by introducing an intelligent optimization engine based on a reinforcement learning mechanism, the system can continuously learn the calcium ion concentration response effect after each round of electrical stimulation, thereby automatically adjusting the control rules and optimizing the adjustment strategy to achieve an adaptive, precise, and continuous electrical stimulation intelligent control closed loop. Through this closed-loop mechanism, it is possible to ensure that the physiological feedback after each electrical stimulation is fully utilized, dynamically optimize the effect of electrical stimulation, and effectively improve the stability of calcium ion concentration and the maintenance of the continuous activation state of Schwann cells, thereby significantly improving the safety, accuracy, and long-term effectiveness of nerve regeneration therapy.

[0058] During multiple rounds of electrical stimulation adjustment and feedback execution, the control instruction generation rules are dynamically optimized based on the real-time accumulated calcium ion concentration fluctuation trend data, Schwann cell population response characteristic data, and the results of the control instruction execution effect, forming an individual self-evolution adjustment mechanism. Specifically, this includes: optimizing and adjusting weight coefficients, fine-tuning dynamic thresholds, and upgrading control instruction generation paths, gradually improving the efficiency of maintaining calcium signal activation and the safety of electrical stimulation control. Through this continuous optimization process, it is possible to achieve individualized differential adaptation, long-term efficacy improvement, and intelligent upgrade iterations, ultimately building an efficient, precise, and intelligent electrical stimulation dynamic control system driven by the real-time status of calcium signals and guided by individual physiological characteristics.

[0059] During the execution of multiple rounds of continuous electrical stimulation adjustment and feedback, the system implements dynamic optimization control instruction generation rules based on the real-time accumulated calcium ion concentration fluctuation trend data, Schwann cell population response characteristic data, and the execution effect results of each round of electrical stimulation control instructions, and gradually builds an individual self-evolution adjustment mechanism. Specifically, the system first introduces a weighted multi-objective optimization algorithm to dynamically analyze historical feedback data. Based on the importance of different calcium ion concentration fluctuation trend characteristics and Schwann cell response characteristics, it optimizes and adjusts the weight coefficients in real time, making the influence of each characteristic in the control command generation more consistent with the current individual physiological characteristics, thereby improving the rationality of decision-making. Second, a dynamic threshold-based adaptive control algorithm is used to continuously fine-tune key indicators involved in control decisions, such as the calcium signal activation threshold, response consistency threshold, and fluctuation risk threshold. This allows the control boundary to flexibly adjust with patient status fluctuations, avoiding misjudgment or over-adjustment caused by static thresholds. Third, based on reinforcement learning methods and evolutionary path optimization mechanisms, the electrical stimulation adjustment control command generation path is dynamically upgraded, specifically including adjustment rule chains, decision priorities, and feedback response strategies. This ensures that each adjustment not only optimizes the current state, but also enables progressive optimization based on long-term effect trends. In addition, this individual self-evolutionary adjustment mechanism uses real-time iterative learning and adaptive convergence control strategies to avoid overfitting and frequent ineffective adjustments, improving stability and safety. Through multiple rounds of continuous optimization, the system gradually forms an electrical stimulation control model that fully matches the individual characteristics of each patient, significantly improving the efficiency of maintaining calcium signal activation, maintaining the long-term stability of Schwann cell activation, and continuously enhancing the efficacy of nerve regeneration. Ultimately, a highly efficient, precise, and intelligent electrical stimulation dynamic control system driven by the real-time state of calcium signals, guided by individual physiological characteristics, and capable of dynamic optimization and intelligent upgrades has been constructed, realizing a truly personalized, long-term, and self-evolving nerve regeneration treatment plan.

[0060] S06. Based on the calcium ion concentration change data obtained after each electrical stimulation, the calcium signal dynamic response model and electrical stimulation adjustment control instructions are continuously updated to form a closed-loop control process with continuous adaptive optimization to achieve continuous activation of Schwann cell function and regeneration of target neural tissue.

[0061] After each electrical stimulation is completed, the calcium ion concentration change data of multiple Schwann cells in the target neural tissue are collected in real time, including multi-dimensional key features such as concentration peak, rising rate, fluctuation amplitude, stability and synchronization. Based on the collected real-time data, a time series learning and Bayesian dynamic update algorithm are used to make real-time corrections to the key parameters of the calcium signal dynamic response model, continuously improving the model's adaptability and prediction accuracy to the current individual's physiological state. This model update not only focuses on the static value changes of calcium ion concentration, but also focuses on the dynamic trend of concentration changes and the evolution of response patterns, effectively improving the scientificity and pertinence of subsequent control instruction generation.

[0062] After each electrical stimulation is completed, the system continuously obtains calcium ion concentration change data of multiple Schwann cells in the target neural tissue through a high-precision real-time acquisition device. Specifically, it includes: peak calcium ion concentration, that is, the highest concentration value of calcium ions reached in the cell under stimulation; calcium ion concentration rise rate, which refers to the rate of change per unit time during the process of concentration rising from the baseline value to the peak value; calcium ion concentration fluctuation amplitude, that is, the maximum and minimum difference in concentration within a certain time window, reflecting signal stability; overall stability score of calcium ion concentration changes, and the level of synchronization of calcium signal responses between multiple Schwann cells. These key features are automatically extracted through real-time data streams and input into the calcium signal dynamic response model in a standardized format. To improve the model's adaptability to individual physiological differences and state fluctuations, the system analyzes the continuity, trend and periodicity characteristics of the calcium signal change process based on a time series learning algorithm, and dynamically identifies the law of signal evolution. At the same time, a Bayesian dynamic update algorithm is introduced, and each newly collected real-time data is used as observational evidence, integrated with the original prior distribution of the model, and key parameters in the model are corrected in real time, including response threshold, fluctuation sensitivity, trend response curve, etc., so as to continuously optimize the model's accuracy in depicting the real changes in calcium signals. Bayesian dynamic update has the ability to continuously learn and adjust confidence, and can maintain robustness in the face of small samples, strong noise or sudden anomalies, avoiding model drift or distortion. In addition, the model not only focuses on the static absolute value of calcium ion concentration, but also pays more attention to its dynamic trend, rate of change, response mode and abnormal fluctuation behavior, thereby providing a scientific, accurate and real-time prediction basis for the subsequent generation of electrical stimulation adjustment control instructions, effectively supporting the continuity of Schwann cell functional activation and the efficiency of neural tissue regeneration.

[0063] After the calcium signal dynamic response model is updated, the electrical stimulation control instructions are dynamically optimized based on the latest model output and current real-time physiological characteristics. Specifically, these include adjusting the electrical stimulation intensity based on changes in calcium signal trends; adjusting the pulse width based on Schwann cell synergy; selecting the optimal waveform type based on the stability of calcium signal fluctuations; and adjusting the application timing based on the Schwann cell activation delay characteristics. All control instructions are generated in real time by an intelligent rule engine, and an optimization weight and threshold adaptive mechanism is introduced to achieve personalized, continuous, and more precise electrical stimulation adjustments.

[0064] After the calcium signal dynamic response model is updated in real time, the system dynamically optimizes and generates electrical stimulation adjustment control instructions based on the model's latest output and the currently collected individual physiological characteristic data, thereby achieving precise control of the calcium signal state of the target neural tissue. Specifically, based on the real-time analysis of calcium signal trends, an intensity adjustment algorithm based on change slope and trend prediction is used to automatically increase or decrease the electrical stimulation intensity to ensure that the calcium ion concentration remains within the physiological activation window, thereby improving the success rate of Schwann cell activation. Secondly, based on the level of coordination and consistency among Schwann cell populations, pulse width is optimized in real time based on the calculation results of dynamic time warping and synchronization index. When cell coordination is low, the pulse width is appropriately extended to enhance the population response, while when it is low, the pulse is shortened to avoid overreaction. Thirdly, based on the calcium signal fluctuation stability index and combined with the results of multi-scale wavelet analysis, the current calcium signal stability is assessed in real time. Then, a waveform pattern matching algorithm automatically selects and switches the most appropriate waveform type from a preset waveform library, including square wave, biphasic wave, or step wave, to maximize cell response consistency and physiological compatibility. Fourth, based on the delayed activation characteristics of Schwann cells and the statistical results of the reaction time within a continuous time window, a timing optimization algorithm is applied to automatically adjust the stimulation start time and interval period to ensure that the stimulation rhythm is highly matched with physiological feedback. The above four adjustment dimensions are uniformly generated by an intelligent rule engine to generate control instructions. The rule engine integrates Bayesian reasoning, fuzzy logic decision-making and genetic algorithm optimization technology. It can automatically adjust the optimization weights according to real-time feedback after each execution, and dynamically adjust the decision boundary based on the threshold adaptive mechanism, thereby forming a highly individualized, continuously optimized and more accurate electrical stimulation adjustment control system. Through this method, the system can not only respond to changes in physiological state in real time, but also has the ability to self-evolve in the long term, significantly improving the safety, effectiveness and sustainability of nerve regeneration intervention.

[0065] After each electrical stimulation is executed and the instruction optimization is completed, a closed-loop control process of feedback-update-optimization is formed. Specifically, the effect of each round of execution is immediately fed back, the dynamic response model of the calcium signal is driven to update in real time, and then the next round of control instructions is optimized based on the updated results. This cycle is repeated to form a continuous adaptive optimization mechanism. Through this closed-loop mechanism, the function of Schwann cells can be continuously activated, the stability of calcium signals and the efficiency of nerve tissue regeneration can be significantly improved, while the risk of overstimulation and treatment volatility can be effectively reduced, ensuring the efficient, safe and continuous progress of the nerve regeneration process.

[0066] After each electrical stimulation cycle and control command optimization, the system forms a closed-loop control process called feedback-update-optimization based on real-time data on calcium ion concentration changes and Schwann cell population response characteristics, ensuring dynamic adaptability and sustainability throughout the entire neuroregenerative treatment process. Specifically, after each electrical stimulation cycle, the system immediately collects real-time feedback on the calcium signal activation effect, including multiple key physiological characteristics such as peak calcium ion concentration, rise rate, fluctuation stability, population synchronization, and actual activation delay time. All feedback data is time-aligned, normalized, and denoised to form a high-quality feedback data stream. Based on this feedback data, the system automatically calls a calcium signal dynamic response model based on a time series recursive algorithm and a Bayesian update mechanism, and makes real-time corrections to key model parameters to ensure that the model accurately reflects the individual's current physiological state and stimulus response trends. Subsequently, based on the output of the newly updated calcium signal dynamic response model, the system calls on a multi-objective optimization engine and intelligent rule-based decision-making system in real time to dynamically optimize the electrical stimulation adjustment control instructions. This includes comprehensive optimization of four adjustment dimensions: intensity, pulse width, waveform type, and application timing, achieving precise control of Schwann cell activation. In this closed-loop process, feedback data not only drives model updates but also forms an individualized learning curve through continuous accumulation, optimizing weight adjustment, threshold adaptation, and decision path evolution, thereby establishing a continuous adaptive optimization mechanism. Through repeated cycles and iterations, the system can effectively improve calcium signal stability, enhance Schwann cell activation, and continuously maintain the process of neural tissue regeneration. At the same time, closed-loop control automatically detects overreactions and failure risks, dynamically adjusts stimulation protocols, and effectively reduces treatment volatility and physiological safety risks, thereby achieving efficient, safe, and intelligent dynamic control of the entire process of neural regeneration treatment.

[0067] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0068] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0069] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0071] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0073] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for electrical stimulation of neural regeneration based on calcium signal regulation, characterized in that: The specific steps include: S01. Acquire continuous data on the change of calcium ion concentration in the target neural tissue over time, and construct a calcium signal dynamic response model based on the change trend and fluctuation characteristics of the calcium ion concentration to predict the response trend of the target neural tissue to electrical stimulation; S02. Based on the calcium signal dynamic response model, extract characteristic parameters for predicting the response state of the target neural tissue, determine the electrical stimulation intensity, pulse width, waveform type, and application timing, and generate electrical stimulation start control instructions; S03, when executing the electrical stimulation start control command, collecting calcium ion concentration change data of several Schwann cells in the target neural tissue in real time, extracting synchronization features, phase consistency features and peak time difference features, and forming a group calcium signal response feature set; S04. Based on the Schwann cell population calcium signal response feature set, identify whether the calcium ion concentration is in a slightly rising stage that has exceeded the minimum activation threshold but has not reached a stable state, and determine whether the target neural tissue is in a critical state of calcium signal activation based on changes in the Schwann cell population behavior pattern; S05. Based on the determination result of the critical state of calcium signal activation, combined with the fluctuation trend of calcium ion concentration and the response characteristics of Schwann cell population, generate electrical stimulation adjustment control instructions in real time, and optimize the stability of calcium ion concentration and maintain the activation state by adjusting the electrical stimulation intensity, electrical stimulation pulse width, electrical stimulation waveform type and electrical stimulation application timing; S06. Based on the calcium ion concentration change data after each electrical stimulation, continuously update the calcium signal dynamic response model and the electrical stimulation adjustment control instructions.

2. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 1, characterized in that: S01 specifically includes: Continuous data on the change of calcium ion concentration in the target nerve tissue over time is obtained by placing a calcium signal detection device in the target nerve tissue injury area; Based on the calcium ion concentration time series data, the calcium ion concentration change trend and fluctuation characteristics are extracted to form a standardized characteristic index set; Based on the changing trend and fluctuation characteristics of calcium ion concentration, a calcium signal dynamic response model is constructed to predict the response of target neural tissue to electrical stimulation; The prediction results of the calcium signal dynamic response model are compared with the real-time monitoring results for consistency, and the calcium signal dynamic response model is continuously optimized based on the comparison results.

3. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 1, characterized in that: S02 specifically includes: The calcium ion concentration change trend data obtained by the calcium signal dynamic response model is input into the feature extraction unit to extract six feature dimensions: calcium ion concentration rising rate, peak stabilization time, fluctuation symmetry, peak width, waveform steepness and time delay; Based on the nonlinear mapping algorithm, the six feature dimensions are weighted and classified to extract a set of core feature parameters that can reflect the response state of the target neural tissue. Based on the core feature parameter set and using a time-evolution-based prediction algorithm, the sensitivity level and response state of the target neural tissue to electrical stimulation are dynamically predicted; Based on the prediction results, the electrical stimulation intensity, pulse width, waveform type, and application timing are determined to form a set of electrical stimulation application parameters that are compatible with the physiological state of the target neural tissue; The electrical stimulation application parameter set is converted into an electrical stimulation start control instruction and sent to the electrical stimulation execution system in real time.

4. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 1, characterized in that: S03 specifically includes: When the electrical stimulation start control command is executed, the multi-point calcium signal sensor array deployed in the target nerve tissue injury area collects the calcium ion concentration change data of multiple Schwann cells in real time; Perform time series alignment and data cleaning on the collected calcium ion concentration change data to remove noise and optimize data continuity; Based on the cleaned data, the calcium ion concentration synchronization characteristics were extracted, and the group synchronization evaluation was optimized through the correlation coefficient matrix and time regularization; Based on synchronization analysis, we further extracted phase consistency features and peak time difference features to quantify group activation coordination; The calcium ion concentration synchronization features, phase consistency features and peak time difference features are fused to form the Schwann cell population calcium signal response feature set.

5. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 1, characterized in that: S04 specifically includes: S041. Based on the calcium ion concentration change data of Schwann cell populations, a baseline model of the dynamic response trend of calcium signals was constructed to eliminate baseline drift and extract differential dynamic features. S042. Based on the dynamic response trend baseline model, a multi-dimensional indicator cross-analysis method is used to identify whether the calcium signal is in the micro-increase stage; S043. Construct a map of the evolution of Schwann cell population behavior patterns based on the identified population of calcium signal spikes, reflecting the coordinated response relationship between cells in real time. S044. Based on the behavioral pattern evolution map, a mutation recognition algorithm is used to detect group behavioral mutations and determine whether the critical state of calcium signal activation has been reached; S045. Implement cross-validation and continuous tracking based on the mutation identification results, dynamically correct the judgment results and ensure the continuity and safety of electrical stimulation adjustment.

6. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 5, characterized in that: S041 and S042 specifically include: Real-time collection of calcium ion concentration change data from multiple Schwann cells, time alignment and standardization, and construction of a baseline model for the dynamic response trend of calcium signals; The trend characteristics of calcium ion concentration changes were extracted through statistical regression analysis, and the model parameters were dynamically updated using the Bayesian fitting algorithm. Based on the trend baseline model, three features are extracted: immediate growth slope, fluctuation amplitude change, and time persistence; A multi-dimensional indicator cross-analysis method was used to comprehensively judge the three characteristics to identify whether the calcium signal was in the micro-increase stage; When all three characteristics meet the set conditions, it is confirmed that the target neural tissue is in a true slightly elevated state.

7. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 6, characterized in that: S043 and S044 specifically include: Construct a behavioral pattern evolution map based on the synchronization, phase consistency, and waveform similarity of Schwann cell calcium signals; Through the time-synchronized dynamic network analysis algorithm, the Schwann cell collaborative relationship network is updated in real time to extract the evolution characteristics of behavioral patterns; Real-time calculation of three dynamic indicators: synchronization improvement rate, dominant response cell ratio change rate, and waveform consistency change rate; Based on the dynamic threshold system, the three dynamic indicators are jointly identified for mutations to determine whether the activation critical state has been reached; Trend continuity verification and multi-index consistency verification are implemented on the mutation identification results to ensure the reliability and security of the judgment results.

8. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 1, characterized in that: S05 specifically includes: Based on the results of the critical state of calcium signal activation, the fluctuation trend characteristics of calcium ion concentration and the response characteristics of Schwann cell population are extracted in real time as input for the generation of electrical stimulation adjustment control instructions; The calcium ion concentration fluctuation trend characteristics and Schwann cell population response characteristics are input into the multi-objective state evaluation system to evaluate the stability level of the calcium signal activation state and the activation maintenance requirement level in real time; Based on the calcium signal stability score, instability risk level, and optimization adjustment urgency level, electrical stimulation adjustment control instructions are generated in real time to adjust the electrical stimulation intensity, pulse width, waveform type, and application timing; The electrical stimulation adjustment control instructions are sent to the electrical stimulation execution system in real time to implement real-time parameter adjustments. During the execution process, calcium ion concentration change data and Schwann cell population response characteristics are continuously collected to form a feedback data stream. Based on feedback data, the control instruction generation rules are continuously optimized to form an individual self-evolution adjustment mechanism, thereby improving the efficiency of maintaining calcium signal activation and the safety of electrical stimulation control.

9. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 8, characterized in that: The calcium ion concentration fluctuation trend characteristics and Schwann cell population response characteristics are input into a multi-objective state assessment system to evaluate the stability level of the calcium signal activation state and the activation maintenance requirement level in real time. Specifically, based on the calcium ion concentration fluctuation trend, a trend fitting regression algorithm and a volatility index calculation method are used to evaluate in real time whether the calcium signal has a tendency to become unstable. Based on the Schwann cell population response characteristics, a population synergy index calculation method and a multidimensional similarity clustering algorithm are used to evaluate in real time whether the cell synergy response tends to weaken. By introducing a weighted decision-making mechanism, each feature is assigned a sensitivity weight, and the judgment boundary value is adjusted in real time based on a dynamic threshold system; based on the evaluation results, three quantitative indicators, namely calcium signal stability score, instability risk level and optimization regulation urgency level, are output as the decision basis for generating electrical stimulation adjustment control instructions.

10. The method of electrical stimulation-based neural regeneration based on calcium signal regulation according to claim 1, characterized in that: S06 specifically includes: After each electrical stimulation is completed, the calcium ion concentration change data of multiple Schwann cells in the target nerve tissue are collected in real time to extract the concentration peak, rising rate, fluctuation amplitude, stability and synchronization characteristics; Based on the collected real-time data, time series learning and Bayesian dynamic update algorithms are used to modify the key parameters of the calcium signal dynamic response model in real time, improving the model's adaptability to individual physiological states and its prediction accuracy. Based on the updated calcium signal dynamic response model and current real-time physiological characteristics, the electrical stimulation adjustment control instructions are dynamically optimized, and real-time control instructions are generated through the intelligent rule engine to achieve a closed-loop control process of feedback, update and optimization.

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