Method and system for optimizing data based on living body implantable chip

Abstract

The application discloses a kind of based on living body implantable chip's regulation and control data optimization method and system, belong to data processing technical field.The application utilizes implantable chip to continuously collect physiological data in living body, and generates the relational graph atlas of multiple regulation and control data optimization schemes based on optimization activation feature and optimization stop feature, so that the selection and execution of optimization strategy are more scientific, reasonable, avoid the potential influencing factors and the data fluctuation caused by mistake in optimization process.Finally, the target optimization guide information involved in each spatiotemporal regulation task is generated with historical monitoring information as optimization benchmark, and this highly customized data optimization scheme is issued to living body implantable chip, realizes the accurate preprocessing and optimization of data, significantly improves the quality and availability of data.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, specifically relating to a method and system for optimizing regulatory data based on a live implantable chip. Background Technology

[0002] With the rapid development of modern medical technology, in vivo implantable neuromodulation technology, as an innovative treatment method, is gradually demonstrating its enormous potential. However, due to the complexity and variability of the internal environment of organisms, and the various unavoidable interference factors during the modulation process, the data collected by implantable modulation systems often contains a large amount of irrelevant data. This seriously affects the accuracy and reliability of the data, making it impossible to provide a stable and reliable data source for subsequent data management and analysis (such as data sharing, data storage, data visualization, and data textification). Therefore, effectively optimizing the modulation data of in vivo implantable chips has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method and system for optimizing control data based on implantable medical devices, which can solve or partially solve the technical problems involved in the background art mentioned above.

[0004] This application provides a method for optimizing regulatory data based on an implantable chip, applied to a regulatory data optimization system. The method includes:

[0005] Historical monitoring information of in vivo implantable chips and corresponding multiple spatiotemporal control tasks are acquired, and there is overlap among the multiple spatiotemporal control tasks;

[0006] Based on the historical monitoring information and the multiple spatiotemporal control tasks, multiple control data optimization schemes are generated.

[0007] Based on the optimization activation features and optimization stopping features corresponding to the multiple regulation data optimization schemes, a relationship graph of the multiple regulation data optimization schemes is determined.

[0008] Based on the relationship graph, target optimization guidance information for the multiple spatiotemporal control tasks is generated, wherein the target optimization guidance information uses monitoring information as the optimization benchmark.

[0009] The target optimization guidance information is sent to the in vivo implantable chip so that the in vivo implantable chip can optimize the control data of the multiple spatiotemporal control tasks based on the target optimization guidance information.

[0010] Optionally, based on the historical monitoring information and the multiple spatiotemporal control tasks, multiple control data optimization schemes are generated, including:

[0011] For the multiple spatiotemporal domain control tasks, the expected strategies of the spatiotemporal domain control tasks are mapped to obtain multiple expected task knowledge networks containing optimized activation features and optimized stopping features.

[0012] The historical monitoring information is mapped to obtain a historical task knowledge network containing the same optimization start conditions and optimization stop conditions;

[0013] Based on the multiple expected task knowledge networks and the multiple historical task knowledge networks, the multiple regulation data optimization schemes are determined.

[0014] Optionally, determining the relationship graph of the multiple regulation data optimization schemes based on the optimization activation features and optimization stopping features corresponding to the multiple regulation data optimization schemes respectively includes:

[0015] Obtain the optimization activation features and optimization stopping features corresponding to multiple regulation data optimization schemes;

[0016] By superimposing the optimized activation features and optimized stopping features corresponding to the multiple regulation data optimization schemes, potential defect image guidance information is determined.

[0017] Based on the optimization activation features and optimization stopping features corresponding to the multiple regulation data optimization schemes, the correlation between the first strategy identifier of the multiple regulation data optimization schemes and the second strategy identifier of the remaining regulation data optimization schemes is calculated to obtain the correlation degree.

[0018] The potential defect image guidance information is mapped based on the correlation degree to obtain the relationship map.

[0019] Optionally, the step of acquiring the optimization activation features and optimization stopping features corresponding to the multiple regulation data optimization schemes includes:

[0020] The system queries multiple regulation data optimization schemes and, based on the regulation dimension, determines a target regulation data optimization scheme that is correlated with the historical execution schemes in the historical monitoring information of the implantable chip. Based on the optimization activation features and optimization stopping features of the historical execution schemes, the system calculates the optimization activation features and optimization stopping features corresponding to the target regulation data optimization scheme.

[0021] or,

[0022] Obtain the scheme node variables of multiple regulation data optimization schemes, excluding the topology labels corresponding to the historical monitoring information; determine the optimization activation features and optimization stopping features of the multiple regulation data optimization schemes in the target feature space based on the scheme node variables of the multiple regulation data optimization schemes, wherein the target feature space is determined in combination with the topology labels corresponding to the historical monitoring information.

[0023] Optionally, mapping the potential defect image guidance information based on the correlation degree to obtain the relationship graph includes:

[0024] Based on the potential defect image guidance information, the target optimization activation features and target optimization stopping features are determined;

[0025] If the regulation data optimization schemes corresponding to the target optimization activation feature and the target optimization stopping feature match, then the graph semantic features corresponding to the target optimization activation feature and the target optimization stopping feature are used as preset semantic codes;

[0026] If the regulation data optimization schemes corresponding to the target optimization activation feature and the target optimization stopping feature do not match, then the cosine similarity between the target optimization stopping feature and the target optimization activation feature corresponding to the correlation is determined as the graph semantic feature corresponding to the target optimization activation feature and the target optimization stopping feature, thus obtaining the relation graph.

[0027] Optionally, acquiring historical monitoring information of the implantable chip and corresponding multiple spatiotemporal control tasks includes:

[0028] Obtain historical monitoring information of the implantable chip;

[0029] By optimizing the scheme planning system, historical execution schemes are created based on the historical monitoring information of the implantable chip.

[0030] In the execution schemes corresponding to the monitoring database of the in vivo implantable chip, schemes other than the historical execution schemes are taken as execution schemes to be processed.

[0031] Based on the information association weight of the execution scheme to be processed, the historical monitoring information of the in vivo implantable chip is divided into multiple monitoring and control information sets.

[0032] The multiple spatiotemporal control tasks are determined based on the pending execution schemes within the target monitoring and control information set.

[0033] Optionally, acquiring historical monitoring information of the implantable chip and corresponding multiple spatiotemporal control tasks includes:

[0034] Obtain historical monitoring information of implantable chips in vivo;

[0035] By analyzing the correlation between the historical monitoring information of the implantable chip and the in vivo physiological attribute data, a knowledge graph of in vivo physiological attributes is obtained.

[0036] Based on the in vivo physiological attribute knowledge graph, the target task events in the multiple spatiotemporal domain regulation tasks are determined;

[0037] If the target task event does not have an associated target task event, then the control task corresponding to the target task event is taken as the spatiotemporal control task;

[0038] If there are target task events that do not belong to the same control data optimization scheme, and the correlation between the target task event and the target task event associated with the target task event is less than or equal to a set correlation degree, then the control task corresponding to the target task event is taken as the spatiotemporal control task.

[0039] Optionally, generating target optimization guidance information for the multiple spatiotemporal control tasks based on the relationship graph includes:

[0040] By combining the aforementioned relationship graph and long short-term memory model, the task set characteristics of multiple spatiotemporal control tasks are determined;

[0041] Based on the historical monitoring information in the regulation data optimization scheme, and based on the task set characteristics of the multiple spatiotemporal regulation tasks, target optimization guidance information is generated to ensure that the timeliness weight meets the preset weight conditions.

[0042] The process of combining the relationship graph and the long short-term memory model to determine the task set characteristics of multiple spatiotemporal control tasks includes:

[0043] From multiple spatiotemporal control tasks, the correlation between the optimization benchmark and the historical monitoring information in the control data optimization scheme determined based on the relationship graph meets the preset correlation conditions to achieve the target optimization target characteristics.

[0044] Based on the relationship graph, the target optimization achievement feature with the highest correlation to the optimization benchmark of the derived living information is repeatedly determined from the remaining spatiotemporal domain control tasks other than the target spatiotemporal domain control task, until all spatiotemporal domain control tasks have been traversed.

[0045] Based on the traversal time series labels of multiple target spatiotemporal domain modulation tasks, the task set characteristics of multiple spatiotemporal domain modulation tasks are determined, wherein the spatiotemporal domain modulation tasks are neural signal modulation tasks.

[0046] The step of generating target optimization guidance information based on the task set characteristics of the multiple spatiotemporal control tasks, which satisfies the preset weight conditions for timeliness weights, includes:

[0047] If, based on neural signal modulation identifiers, it is determined that there is a marked modulation task that has been optimized in the historical information of the in vivo implantable chip between two consecutive spatiotemporal modulation tasks, then the target optimization guidance information is generated based on the two consecutive spatiotemporal modulation tasks and the marked modulation task that has been optimized, wherein the neural signal modulation identifiers are determined according to the task set characteristics of multiple spatiotemporal modulation tasks;

[0048] If, based on the neural signal modulation identifier, it is determined that there are no related task items between two consecutive spatiotemporal modulation tasks, then, based on the two consecutive spatiotemporal modulation tasks, a first target spatiotemporal modulation task set and a second target spatiotemporal modulation task set are obtained according to multiple spatiotemporal modulation tasks; based on the first target spatiotemporal modulation task set and the second target spatiotemporal modulation task set, a first alternative optimization guidance information and a second alternative optimization guidance information are determined respectively.

[0049] Optionally, after sending the target optimization guidance information to the in vivo implantable chip, the method further includes:

[0050] Obtain the current device status of the implantable chip;

[0051] If the current device status does not match the target optimization guidance information, and the number of spatiotemporal control tasks to be processed is less than the set number, then the task correlation degree between the spatiotemporal control tasks to be processed is determined, and optimization guidance information is determined based on the task correlation degree.

[0052] If the current device status does not match the target optimization guidance information, and the number of pending spatiotemporal control tasks is greater than the set number, then based on the current device status, the optimization guidance information with the highest timeliness weight involving multiple pending spatiotemporal control tasks will be generated again.

[0053] The optimization instruction information is sent to the in vivo implantable chip to optimize the control data based on the spatiotemporal control task to be processed through the optimization instruction information.

[0054] This application provides a data optimization system, including at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the above-described method.

[0055] This application provides a readable storage medium on which a program or instruction is stored, and when the program or instruction is executed by a processor, it implements the steps of the above method.

[0056] This application utilizes an implantable chip to continuously collect physiological data in vivo, and generates a relationship graph of multiple regulatory data optimization schemes based on optimized activation and cessation features. This makes the selection and execution of optimization strategies more scientific and reasonable, avoiding data fluctuations caused by potential influencing factors and errors during the optimization process. Ultimately, it generates target optimization guidance information covering various spatiotemporal regulatory tasks based on historical monitoring information, and distributes this highly customized data optimization scheme to the implantable chip in vivo, achieving precise data preprocessing and optimization, significantly improving data quality and usability. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a method for optimizing control data based on a live implantable chip, as provided in an embodiment of this application.

[0058] Figure 2 This is a schematic diagram of the structure of a data optimization system for regulation provided in an embodiment of this application. Detailed Implementation

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, in this application, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0061] Figure 1 A method for optimizing regulatory data based on a live implantable chip is shown, which is applied to a regulatory data optimization system. The method includes the following steps 110-150.

[0062] Step 110: Obtain historical monitoring information of the implantable chip and the corresponding multiple spatiotemporal control tasks. There is overlap between the multiple spatiotemporal control tasks.

[0063] The primary task of the above steps is to acquire historical monitoring information from the implanted chip, as well as multiple related spatiotemporal modulation tasks. Historical monitoring information and spatiotemporal modulation tasks form the basis for the system's data optimization processing. The historical monitoring information from the implanted chip is a data set integrating various biological signals and regulatory parameters, recording the physiological state of the target organism and the operational state of its neural regulatory system. Spatiotemporal modulation tasks refer to specific regulatory tasks performed on the target organism under different time and spatial conditions. These tasks often overlap and intersect, forming a complex data association network.

[0064] Step 120: Based on historical monitoring information and multiple spatiotemporal control tasks, generate multiple control data optimization schemes.

[0065] After acquiring historical monitoring information and spatiotemporal control tasks, multiple control data optimization schemes can be generated based on this information. These schemes possess optimization activation and optimization stopping features. Optimization activation features are those that can activate the optimization mechanism, effectively filtering and processing the data. They are typically closely related to the physiological state of the target organism, the nature of the control task, and the noise characteristics of the data. Optimization stopping features, on the other hand, are those features that are unsuitable for optimization under current conditions. Their presence helps prevent errors and data loss during the optimization process.

[0066] Step 130: Based on the optimization activation features and optimization stopping features corresponding to multiple regulation data optimization schemes, determine the relationship graph of multiple regulation data optimization schemes.

[0067] Next, based on the generated regulatory data optimization schemes, a relationship graph can be determined among multiple regulatory data optimization schemes. The relationship graph is a multi-dimensional data model that describes the interactions and dependencies between different execution strategies. Through the relationship graph, it is possible to more intuitively understand which regulatory data optimization schemes are mutually beneficial and which may conflict, which can be used to generate subsequent optimization guidance information.

[0068] Step 140: Generate target optimization guidance information for multiple spatiotemporal control tasks based on the relationship graph. The target optimization guidance information uses the monitoring information as the optimization benchmark.

[0069] Specifically, after determining the relationship graph of the regulation data optimization scheme, it is also possible to generate target optimization guidance information for regulation tasks in various spatiotemporal domains, using historical monitoring information as the optimization benchmark. This target optimization guidance information is a highly targeted and customized data optimization scheme. It details how to select and execute optimization strategies for regulation tasks in different spatiotemporal domains to achieve the best optimization results. The generation of target optimization guidance information signifies that the regulation data optimization system has moved from the macro-level data perspective to the specific operational level.

[0070] Step 150: Send the target optimization guidance information to the in vivo implantable chip so that the in vivo implantable chip can optimize the control data for multiple spatiotemporal control tasks based on the target optimization guidance information.

[0071] In other words, the generated target optimization guidance information can finally be sent to the in vivo implantable chip. This in vivo implantable chip is a crucial component of the implantable control system, responsible for directly preprocessing and optimizing the collected raw data. Upon receiving the target optimization guidance information, the in vivo implantable chip will optimize the control data for multiple spatiotemporal control tasks according to the guidance provided. Through precise optimization processing and data filtering, the accuracy and reliability of the data can be significantly improved, providing a stable and reliable data source for subsequent medical data management operations.

[0072] In detail, optimization activation features refer to specific attributes or conditions that can trigger or activate optimization mechanisms during data optimization, thereby enabling effective filtering and processing of data. These features are usually closely related to the physiological state of the target living organism, the nature of the regulatory task, and the noise characteristics of the data. For example, in implantable neuromodulation, if the heart rate of the target living organism suddenly increases, this change may be considered an optimization activation feature because it may indicate that the living organism is in a state of stress, at which point the noise level in the data may increase accordingly, requiring intervention from optimization mechanisms.

[0073] Optimization stopping features refer to specific attributes or conditions that, under current conditions, are unsuitable for optimization. The existence of these features can prevent errors and data loss during the optimization process. For example, in implantable neuromodulation, if the target organism's body temperature suddenly drops (e.g., below 2°C), this change might be considered an optimization stopping feature. Because a sharp drop in body temperature may indicate an abnormal state, performing optimization at this time could inadvertently delete important physiological information; therefore, the optimization operation should be temporarily stopped.

[0074] Optimization schemes for modulated data are a series of specific operational steps or methods formulated to achieve the best optimization effect, based on optimizing activation and stopping features. These strategies typically include selecting appropriate optimization algorithms, adjusting optimization parameters, and determining the timing of optimization. For example, in implantable neuromodulation, a modulated data optimization scheme might be: when the target living organism's heart rate increases by more than 20% (optimization activation feature), a wavelet transform optimization algorithm is initiated, and the optimization intensity is set to medium (specific parameters such as a threshold of 0.5), and optimization processing continues until the heart rate returns to normal; conversely, when the body temperature drops below 2°C below the normal value (optimization stopping feature), all optimization operations are paused to avoid accidentally deleting important data.

[0075] Furthermore, the relationship graph mentioned above is a multi-dimensional data model used to describe the interactions and dependencies between different data optimization schemes. Through the relationship graph, it's possible to intuitively understand which execution strategies are mutually reinforcing and which may conflict. For example, in implantable neuromodulation, the relationship graph might show that when the target living organism's heart rate increases (optimizing activation features), initiating wavelet transform optimization and increasing sampling frequency are mutually reinforcing because they both improve data accuracy and reliability; while simultaneously initiating overly strong optimization strategies and decreasing sampling frequency may conflict because they may lead to the loss or distortion of important data. Analysis of the relationship graph allows for more scientific formulation and execution of data optimization strategies.

[0076] It is worth mentioning that the technical solutions described in steps 120 to 140 are the core invention of the control data optimization system. They describe in detail how to generate control data optimization schemes with optimization activation features and optimization stopping features based on historical monitoring information and spatiotemporal control tasks, and further generate target optimization guidance information based on these strategies.

[0077] In step 120, multiple regulatory data optimization schemes are generated based on the acquired historical monitoring information and multiple spatiotemporal regulatory tasks. The key to this step is the ability to intelligently identify when the optimization mechanism needs to be activated (optimization activation features) and when optimization processing should be paused (optimization cessation features) based on the target organism's real-time physiological state and the specific requirements of the regulatory task. For example, when the target organism's heart rate or EEG signals show abnormal fluctuations, this may be identified as an optimization activation feature, and a corresponding optimization strategy may be generated; while when the organism is in deep sleep or a resting state, this may be identified as an optimization cessation feature to avoid misprocessing normal physiological signals.

[0078] In step 130, the relationship graph of multiple regulation data optimization schemes is determined based on the optimization activation characteristics and optimization stopping characteristics corresponding to the generated multiple regulation data optimization schemes. The purpose of this step is to reveal the interaction and dependency between different optimization strategies in order to better understand and optimize the entire optimization process. For example, in the actual regulation optimization process, it may be found that some optimization strategies are mutually reinforcing under certain conditions and can be used simultaneously to improve the optimization effect; while other strategies may have conflicting or competitive relationships, requiring trade-offs and choices when using them.

[0079] In step 140, target optimization guidance information for multiple spatiotemporal control tasks is generated based on the relationship graph. This target optimization guidance information uses monitoring information as the optimization benchmark. This step transforms the preceding analysis results into specific, executable optimization instructions. The target optimization guidance information details how to select and execute optimization strategies under different spatiotemporal control tasks to achieve the best optimization results. For example, the text may indicate which optimization algorithm should be prioritized during a specific time period and physiological state, or which optimization parameters need to be adjusted to obtain better data quality. Through such guidance information, clear guidance can be provided for in vivo implantable chips, ensuring the accuracy and effectiveness of the data optimization process.

[0080] For example, the target optimization guidance information sent to a live implantable chip can be as follows.

[0081] 1. Specific optimization strategies and execution conditions

[0082] 1.1 Optimization Strategy 1: Adaptive Optimization Based on Wavelet Transform

[0083] 1.1.1 Execution conditions:

[0084] The target living subject's heart rate exceeds 20% of the normal value for more than 5 minutes.

[0085] Meanwhile, the target living subject is not in a deep sleep state (judged based on EEG signals).

[0086] 1.1.2 Operating Procedures:

[0087] Start the wavelet transform optimization algorithm.

[0088] The intensity is dynamically adjusted and optimized based on real-time heart rate data, with an initial threshold set at 0.5.

[0089] Continue to optimize until the heart rate returns to normal or the optimization effect reaches the preset standard.

[0090] 1.2 Optimization Strategy Two: Targeted Filtering Based on Frequency Domain Characteristics

[0091] 1.2.1 Execution conditions:

[0092] The target living organism's EEG signals showed abnormal fluctuations in specific frequency bands (such as alpha waves and beta waves), with the fluctuation amplitude exceeding 30% of the normal range.

[0093] The target living organism is either awake or in a light sleep state.

[0094] 1.2.2 Operating Procedures:

[0095] Frequency domain analysis of EEG signals was performed to determine the specific frequency bands of abnormal fluctuations.

[0096] Targeted filtering methods were applied to optimize this frequency band.

[0097] Based on the optimization results, adjust the filtering parameters in a timely manner until abnormal fluctuations are eliminated or reduced to the normal range.

[0098] 1.3 Optimization Strategy 3: Optimization Pause Based on Physiological State Judgment

[0099] 1.3.1 Execution conditions:

[0100] The target living organism's body temperature is 2°C below normal and lasts for more than 10 minutes.

[0101] Alternatively, the target living organism is in a deep sleep state, and its physiological signals (such as heart rate and respiration) are stable without abnormal fluctuations.

[0102] 1.3.2 Operating Procedures:

[0103] Immediately pause all optimization operations.

[0104] Record physiological state and optimization progress during pauses.

[0105] Recovery and optimization should be considered after the physiological state returns to normal or after deep sleep ends.

[0106] 2. Strategy relevance and execution order

[0107] 2.1 Strategy Relevance Analysis:

[0108] Optimization strategy one and strategy two may meet the execution conditions at the same time under certain conditions. In this case, priority should be given to judging based on the main physiological abnormalities of the target living organism.

[0109] Optimization strategy three has the highest priority. Once the execution conditions are met, all other optimization operations should be immediately suspended to avoid unnecessary interference or risk to the target organism.

[0110] 2.2 Execution sequence guidance:

[0111] 2.2.1 When both optimization strategies 1 and 2 meet the execution conditions:

[0112] Prioritize strategy one, as abnormal heart rate may more directly reflect changes in the physiological state of the target living organism.

[0113] Once the heart rate returns to normal, assess whether strategy two needs to be implemented.

[0114] 2.2.2 When optimization strategy three meets the execution conditions:

[0115] Immediately suspend the execution of all other optimization strategies.

[0116] Record the current physiological state and optimization progress for reference during subsequent recovery processing.

[0117] 2.3 Strategies for Handling Special Circumstances:

[0118] If the physiological state of the target living organism changes drastically during the execution of the optimization strategy (such as a sudden drop in heart rate or a sharp rise in body temperature), the current optimization operation should be stopped immediately, and emergency medical intervention should be considered.

[0119] If the data quality does not meet the expected standards after implementing the optimization strategy, the selection and implementation conditions of the optimization strategy should be reassessed, and the strategy should be adjusted or professional assistance should be sought if necessary.

[0120] Furthermore, it is important to note that the embodiments of this application do not pertain to disease diagnosis and treatment methods, but rather focus on data optimization at the data level. The core objective of these embodiments is to optimize implantable control information and improve the accuracy and reliability of the data. It primarily focuses on the generation and execution of data optimization strategies, rather than directly diagnosing or treating diseases. Moreover, these embodiments optimize the information of the target living organism through a control data optimization system using algorithms and strategies, without involving the operation of any medical devices or the execution of any medical actions, nor directly affecting the human body. Furthermore, these embodiments are a data processing method, and their output (i.e., the optimized data) serves only as a reference for medical data management, medical analysis, or decision-making, and does not directly constitute a diagnosis or treatment. Finally, these embodiments are widely used in the field of medical data management, particularly in implantable control systems, to improve the accuracy and reliability of data. Its value lies in providing a stable and reliable data source for medical data management.

[0121] In summary, this application addresses the challenges of implantable neuromodulation technology in modern medicine, particularly the data noise problem caused by the complexity and variability of the biological environment and various interfering factors during the modulation process. It proposes an innovative technical solution: by acquiring historical monitoring information and multiple overlapping spatiotemporal modulation tasks as a starting point, it achieves comprehensive optimization of data processing.

[0122] Specifically, this disclosure utilizes an implantable chip to continuously collect physiological data in vivo and generates a relationship graph of multiple regulatory data optimization schemes based on optimized activation and cessation features. This makes the selection and execution of optimization strategies more scientific and reasonable, avoiding data fluctuations caused by potential influencing factors and errors during the optimization process. Ultimately, it generates target optimization guidance information covering various spatiotemporal regulatory tasks based on historical monitoring information. This highly customized data optimization scheme is then distributed to the implantable chip in vivo, achieving precise data preprocessing and optimization, significantly improving data quality and usability. Thus, it not only effectively solves the problem of data noise in implantable neuromodulation technology but also provides a stable and reliable data source for subsequent medical data management and analysis.

[0123] In some optional embodiments, generating multiple control data optimization schemes based on the historical monitoring information and the multiple spatiotemporal control tasks includes: mapping the multiple spatiotemporal control tasks based on the expected strategies of the spatiotemporal control tasks to obtain multiple expected task knowledge networks containing optimization activation features and optimization stopping features; mapping the historical monitoring information to obtain a historical task knowledge network containing the same optimization start conditions and optimization stopping conditions; and determining the multiple control data optimization schemes based on the multiple expected task knowledge networks and the historical task knowledge networks.

[0124] It is understood that, in order to generate multiple control data optimization schemes with optimized activation and optimized stopping features based on the historical monitoring information and the multiple spatiotemporal control tasks, it is first necessary to map the multiple spatiotemporal control tasks into multiple expected task knowledge networks containing optimized activation and optimized stopping features, according to the expected strategies of the spatiotemporal control tasks. This step is crucial because it provides the system with a structured framework for understanding and handling the specific needs and constraints in different spatiotemporal control tasks.

[0125] When constructing the knowledge network for desired tasks, the system conducts in-depth analysis of each spatiotemporal control task to identify its unique desired strategies. These patterns may include specific physiological indicators, specific time windows, specific spatial locations, etc. Based on these desired patterns, the system further extracts key features related to optimization, namely optimization activation features and optimization stopping features. Optimization activation features refer to those features that indicate whether the system should activate the optimization mechanism under the current spatiotemporal conditions. They are usually closely related to the physiological state of the target organism, the nature of the control task, and the noise characteristics of the data. Optimization stopping features, on the other hand, refer to those features that are not suitable for optimization under the current conditions. Their existence can avoid errors and data loss during the optimization process.

[0126] After constructing the desired task knowledge network, the system next needs to map the historical monitoring information into a historical task knowledge network containing the same optimization activation and optimization stopping features. The purpose of this step is to align the real-time information of the target living organism with the desired strategy of the spatiotemporal control task, in order to generate a more accurate and effective control data optimization scheme. When constructing the historical task knowledge network, advanced machine learning algorithms and pattern recognition techniques are used to extract key features related to optimization from the historical monitoring information and match and map them with the features in the desired task knowledge network.

[0127] With multiple expected task knowledge networks and historical task knowledge networks in place, multiple data optimization schemes can be generated based on these two graphs. This step is the core of the entire process, involving complex strategy optimization and decision-making processes. The system will perform comprehensive analysis and reasoning based on the expected patterns and optimization characteristics in the expected task knowledge network, as well as the real-time information and optimization characteristics in the historical task knowledge network, to determine which optimization strategies should be adopted under the current spatiotemporal conditions to achieve the best optimization effect.

[0128] Specifically, this method assesses the potential impact of different optimization strategies on improving data accuracy and reliability, considering the feasibility and cost of strategy implementation. In this process, the system may utilize advanced optimization algorithms, such as genetic algorithms and particle swarm optimization, to search for and determine the optimal regulatory data optimization scheme. Simultaneously, the system also considers the interactions and dependencies between different strategies to ensure that the generated regulatory data optimization schemes are mutually reinforcing rather than conflicting.

[0129] When generating optimization schemes for regulatory data, the system pays special attention to the application of optimization activation and deactivation features. For optimization activation features, the system ensures that the optimization mechanism is activated promptly when specific conditions are met to effectively filter and process the data. For optimization deactivation features, the system ensures that optimization operations are paused promptly when current conditions are unsuitable for optimization, thus avoiding accidental operations and data loss.

[0130] When discussing specific examples of expected task knowledge networks and historical task knowledge networks, a scenario can be constructed based on the authorized access data to illustrate these two concepts.

[0131] Expected Task Knowledge Network

[0132] Definition: The expectation task knowledge network is constructed for multiple spatiotemporal control tasks. It includes the expectation patterns of these tasks, key features (including optimization activation features and optimization stopping features), and the relationships between them.

[0133] Example: In the field of neuromodulation, three patients participated in different spatiotemporal modulation tasks.

[0134] Task MA: Real-time assessment of cortical activity in patient A to evaluate responses to specific stimuli. The desired pattern for this task is to capture changes in electrical signals in the cortex before and after stimulation. Optimized activation features may include abrupt changes in signal intensity or the appearance of specific frequency components, while optimized stopping features may be small fluctuations in signal around a stable baseline.

[0135] Task MB: Long-term monitoring of spinal nerve conduction in patient B to study the process of nerve regeneration. The expected monitoring pattern for this task is to track changes in nerve conduction velocity. Optimization of activation characteristics may involve a significant increase or decrease in conduction velocity, while optimization of cessation characteristics may involve transient signal interference caused by the patient's daily activities.

[0136] Task MC: Periodically monitor the autonomic nervous system function of patient C to assess autonomic regulation ability. The expected monitoring pattern for this task is to analyze indicators such as heart rate variability. Optimizing activation characteristics may involve abnormal increases or decreases in heart rate variability, while optimizing cessation characteristics may involve normal physiological rhythm fluctuations.

[0137] Integrating the monitoring expectation patterns, optimization activation characteristics, and optimization stopping characteristics of these tasks, as well as the relationships between them, forms the expected task knowledge network. This expected task knowledge network helps the system understand the needs and constraints of different tasks, providing a foundation for subsequent data optimization schemes.

[0138] Historical Mission Knowledge Network

[0139] Definition: The historical task knowledge network is constructed based on historical monitoring information. It also includes optimized activation features and optimized stopping features, but these features need to match and correspond to the features in the desired task knowledge network.

[0140] Example: Continuing with the medical scenario above, the system is receiving real-time monitoring data of the cerebral cortex activity of patient A. This data constitutes historical monitoring information. To optimize this data, the system needs to construct a historical task knowledge network. The optimized activation and optimized stopping features in this network should match the features of task MA in the desired task knowledge network.

[0141] For example, in a historical mission knowledge network, optimizing activation features might include identifying patterns similar to sudden changes in signal strength or the appearance of specific frequency components as defined in the mission MA; while optimizing stopping features might be used to exclude signal interference that is similar to small fluctuations near a stable baseline defined in the mission MA.

[0142] Through this matching and correspondence process, historical monitoring information can be precisely optimized based on the historical task knowledge network, thereby improving the accuracy and reliability of the data.

[0143] Therefore, the expected task knowledge network and the historical task knowledge network represent different levels of knowledge representation in the medical context: the former focuses on the needs and constraints of the task itself, while the latter focuses on how to apply these needs and constraints to specific historical monitoring information. The collaborative work of these two maps provides strong support for the optimized processing of implantable neuromodulation data.

[0144] It is evident that by constructing the expected task knowledge network and the historical task knowledge network, and using them as the basis for generating and optimizing the regulatory data, the system can more accurately and effectively handle the noise problem in the data collected by the implantable regulatory system, thereby improving the accuracy and reliability of the data and providing a more stable and reliable data source for subsequent medical data management and analysis.

[0145] In some other preferred embodiments, determining the relationship graph of the multiple control data optimization schemes based on the optimization activation features and optimization stopping features corresponding to the multiple control data optimization schemes respectively includes: obtaining the optimization activation features and optimization stopping features corresponding to the multiple control data optimization schemes respectively; superimposing the optimization activation features and optimization stopping features corresponding to the multiple control data optimization schemes respectively to determine potential defect image guidance information; calculating the correlation between the first strategy identifier of the multiple control data optimization schemes and the second strategy identifier of the remaining control data optimization schemes according to the optimization activation features and optimization stopping features corresponding to the multiple control data optimization schemes respectively, to obtain the correlation degree; and mapping the potential defect image guidance information based on the correlation degree to obtain the relationship graph.

[0146] It is understandable that determining the relationship graph corresponding to the multiple regulation data optimization schemes based on their respective optimization activation and stopping features is a complex process involving feature extraction, correlation analysis, and graph construction. This process aims to provide strong support for subsequent regulation data optimization by deeply analyzing the intrinsic connections between the regulation data optimization schemes.

[0147] First, the system needs to acquire the optimization activation features and optimization stopping features corresponding to each regulation data optimization scheme. These features are extracted from the previously constructed expected task knowledge network and historical task knowledge network, representing the unique behavior and constraints of different strategies when dealing with noise. Optimization activation features are usually related to the conditions for activating the optimization mechanism in the strategy, while optimization stopping features are related to the conditions for pausing the optimization operation. By extracting these features, the system can quantify and describe the optimization behavior of different strategies.

[0148] Next, based on the optimization activation and stopping features corresponding to each of the aforementioned control data optimization schemes, the system needs to determine potential defect image guidance information. Potential defect image guidance information is an abstract representation used to describe noise patterns that may occur under a specific strategy. These descriptors are obtained by analyzing the interaction between optimization features in the strategy and historical monitoring information. They can help the system understand the potential effects and limitations of different strategies in handling noise.

[0149] To construct potential defect image guidance information, the system may employ advanced machine learning algorithms, such as deep learning networks or clustering algorithms. These algorithms can learn noise patterns and features from large amounts of data and abstract them into descriptors. In this way, the system can obtain a series of potential defect image guidance information related to different strategies, supporting subsequent analysis and decision-making.

[0150] After obtaining the potential defect image guidance information, the system needs to further determine the correlation between the first strategy identifier of each control data optimization scheme and the second strategy identifier of the remaining control data optimization schemes. Correlation is a quantitative indicator used to measure the similarity and complementarity of different strategies in their optimization behavior. By calculating the correlation, the system can discover which strategies have similar effects in handling noise, which strategies may have complementary relationships, and which strategies may conflict with each other.

[0151] To calculate the correlation, the system may employ similarity metrics such as cosine similarity, Pearson correlation coefficient, or mutual information. These methods can quantitatively compare the optimization features between different strategies and obtain a numerical value representing the similarity. Through this approach, the system can obtain a correlation matrix, where each element represents the degree of similarity and complementarity between a pair of strategies.

[0152] Finally, based on the determined correlation, the system needs to map the potential defect image guidance information into the relationship graph. The relationship graph is a visual representation that shows the inherent connections and interactions between different strategies in their optimization behaviors. In the relationship graph, each node represents a data optimization scheme, and the edges between nodes represent the correlation between strategies. The weight and color of the edges can be used to represent the strength and directionality of the correlation.

[0153] By constructing a relationship graph, the system can visually demonstrate the similarities and complementarities between different strategies, as well as their potential effects and limitations in handling noise. This helps physicians or researchers better understand the behavior and effects of different strategies and provides strong support for subsequent data optimization. For example, they can select strategies with similar effects for combined use based on the relationship graph to improve the efficiency and accuracy of optimization; or they can choose complementary strategies for alternating use to address different types of noise.

[0154] For example, regarding potential defect image guidance information, there are three data optimization schemes A, B, and C, each corresponding to a set of optimization activation features and optimization stopping features. Furthermore, each scheme may have only one primary optimization activation feature and one primary optimization stopping feature, both represented numerically.

[0155] Strategy A: Optimize activation feature = 0.8, optimize stopping feature = 0.2;

[0156] Strategy B: Optimize activation feature = 0.6, optimize stopping feature = 0.4;

[0157] Strategy C: Optimize activation feature = 0.9, optimize stopping feature = 0.1.

[0158] Latent defect image guidance information is the result of further abstraction and quantification of these features. In this exemplary scenario, latent defect image guidance information can be defined as the difference between optimizing activation features and optimizing stopping features, i.e.:

[0159] The potential defect image guidance information for Strategy A = 0.8 - 0.2 = 0.6;

[0160] The potential defect image guidance information for Strategy B = 0.6 - 0.4 = 0.2;

[0161] The potential defect image guidance information for strategy C = 0.9 - 0.1 = 0.8.

[0162] These descriptors reflect the potential effectiveness and tendency of different strategies in handling noise. For example, strategy C has the highest optimization activation feature and the lowest optimization stopping feature, thus it has the highest potential defect image guidance information, indicating that it may have the strongest effect in handling noise.

[0163] Next, based on the image guidance information of these potential defects, the correlation between strategies is calculated, and a relationship graph is constructed. The correlation can be obtained by calculating the similarity between descriptors. In this exemplary scenario, a simple absolute difference is used as the measure of similarity:

[0164] The correlation between strategy A and strategy B is |0.6 - 0.2| = 0.4;

[0165] The correlation between strategy A and strategy C is |0.6 - 0.8| = 0.2;

[0166] The correlation between strategy B and strategy C is |0.2 - 0.8| = 0.6.

[0167] Based on these associations, a relationship graph can be constructed. In this graph, each strategy is a node, and the edges between nodes represent the association between strategies. The weight and color of the edges can be used to represent the strength and direction of the association. For example, different colors can be used to represent different ranges of association, and the thickness of the edges can be used to represent the strength of the association.

[0168] In this exemplary scenario, the relationship graph might look like this:

[0169] There is a medium-thickness blue border between strategy A and strategy B, indicating that their correlation is 0.4;

[0170] There is a thin blue border between strategy A and strategy C, indicating that their correlation is 0.2;

[0171] There is a thick red edge between strategy B and strategy C, indicating that their correlation is 0.6.

[0172] This relationship graph visually illustrates the similarities and complementarities between different strategies. For example, the high correlation between strategy B and strategy C suggests they may have similar effects or complementary behaviors when dealing with noise. Conversely, the low correlation between strategy A and strategy C indicates they may have significant differences in noise handling.

[0173] Therefore, this embodiment can provide in-depth analysis of the intrinsic connections and interactions between different strategies, and provide strong support for subsequent optimization of regulatory data.

[0174] In some alternative embodiments, obtaining the optimization activation features and optimization stopping features corresponding to the multiple regulation data optimization schemes respectively includes: querying the multiple regulation data optimization schemes, determining the target regulation data optimization scheme that is related to the historical execution scheme in the historical monitoring information of the in vivo implantable chip based on the regulation dimension; and calculating the optimization activation features and optimization stopping features corresponding to the target regulation data optimization scheme based on the optimization activation features and optimization stopping features of the historical execution scheme.

[0175] In practical applications, this embodiment aims to identify target optimization schemes that are related to specific historical control data optimization schemes in terms of control dimension from numerous control data optimization schemes, and further determine the optimization activation characteristics and optimization stopping characteristics of these target optimization schemes.

[0176] Among them, historical regulation data optimization schemes refer to strategies that have been implemented in past regulation tasks, which include experience and knowledge accumulated in practical applications; regulation dimensions refer to multiple aspects used to describe and evaluate regulation tasks, such as the nature of the target, environmental conditions, and data characteristics; target regulation data optimization schemes refer to strategies that have some connection or similarity with historical regulation data optimization schemes in terms of regulation dimensions, and these may be strategies that need to be considered or selected in the current task.

[0177] Based on this, from multiple control data optimization schemes, the control data optimization system needs to screen out target control data optimization schemes that are related to historical control data optimization schemes in the historical monitoring information of the implanted chip in terms of control dimensions. This process is a strategy screening process, which requires the control data optimization system to conduct in-depth analysis and comparison of existing control data optimization schemes to identify those strategies that are similar to or related to historical control data optimization schemes in specific dimensions.

[0178] To achieve this goal, regulatory data optimization systems can employ various methods. For example, they can utilize classification or clustering algorithms from machine learning to automatically classify or cluster regulatory data optimization schemes, identifying strategies with similar characteristics. Specifically, the system can use historical regulatory data optimization schemes as training samples, extracting features from their regulatory dimensions and training a classifier or clusterer. Then, the system can use the remaining regulatory data optimization schemes as input, employing the trained model for prediction or clustering to identify strategies similar to the target historical regulatory data optimization scheme.

[0179] Alternatively, the regulatory data optimization system can also employ rule-based methods for strategy selection. For example, the system can define a set of rules or conditions to describe the characteristics or constraints of historical regulatory data optimization schemes across the regulatory dimension. Then, the system can iterate through all regulatory data optimization schemes, checking whether they satisfy these rules or conditions, thereby selecting strategies that are related to the target historical regulatory data optimization scheme.

[0180] Once the regulatory data optimization system has identified the target regulatory data optimization schemes, the next step is to determine the corresponding optimization activation and stopping features of these target optimization schemes based on the optimization activation and stopping features of historical regulatory data optimization schemes. This process is a feature mapping and feature determination process, requiring the regulatory data optimization system to apply the feature knowledge of historical regulatory data optimization schemes to the target optimization schemes.

[0181] To achieve this goal, the regulatory data optimization system can employ various methods. One direct approach is that if the historical regulatory data optimization scheme and the target optimization scheme are very similar or almost identical in terms of regulatory dimensions, the regulatory data optimization system can directly assign the optimization activation and stopping features of the historical regulatory data optimization scheme to the target optimization scheme. This method is suitable for historical strategies that are very close to the target optimization scheme and can ensure the accuracy and effectiveness of feature mapping.

[0182] However, in many cases, there may be differences or changes between the historical control data optimization scheme and the target optimization scheme. In such cases, the control data optimization system needs to employ more complex methods for feature mapping and determination. For example, the control data optimization system can utilize regression or interpolation algorithms from machine learning to predict or calculate the features of the target optimization scheme based on the characteristics of the historical control data optimization scheme and the differences between the target optimization scheme and the target optimization scheme. Specifically, the control data optimization system can use the features of the historical control data optimization scheme as input and the differences in the target optimization scheme as output to train a regression model or interpolation model. Then, the control data optimization system can use this model to predict or calculate the optimization activation features and optimization stopping features of the target optimization scheme.

[0183] Alternatively, the data optimization system can employ rule-based or template-based methods for feature mapping and determination. For example, the system can define a set of rules or templates to describe how to derive the characteristics of the target optimization scheme from the characteristics of historical data optimization schemes. These rules or templates can be developed based on domain knowledge, experience, or expert opinions. The system can then apply these rules or templates to derive the activation and termination characteristics of the target optimization scheme.

[0184] Therefore, obtaining the activation and termination features corresponding to each regulation data optimization scheme is a comprehensive process involving strategy selection, feature mapping, and feature determination. This process requires the regulation data optimization system to conduct in-depth analysis and comparison of existing regulation data optimization schemes to identify strategies similar to historical regulation data optimization schemes, and further apply the feature knowledge of historical strategies to the target optimization scheme. In this way, the regulation data optimization system can provide accurate and effective strategy feature information for subsequent regulation data optimization, thereby supporting more precise regulation data optimization.

[0185] In other possible embodiments, obtaining the optimization activation features and optimization stopping features corresponding to the multiple regulation data optimization schemes may further include: obtaining the scheme node variables of the multiple regulation data optimization schemes other than the topology labels corresponding to the historical monitoring information; determining the optimization activation features and optimization stopping features of the multiple regulation data optimization schemes in the target feature space based on the scheme node variables of the multiple regulation data optimization schemes, wherein the target feature space is determined in combination with the topology labels corresponding to the historical monitoring information.

[0186] It is important to note that the process of obtaining the optimization activation features and optimization stopping features corresponding to each regulation data optimization scheme is a comprehensive process involving topological label reference, target feature space determination, scheme node variable acquisition, and feature determination. The aim is to extract features related to historical monitoring information from numerous regulation data optimization schemes, thereby providing accurate strategy feature information for subsequent regulation data optimization.

[0187] In this embodiment, the topology label is an identifier used to describe and distinguish different regulation data optimization schemes. It reflects the position or attribute of the strategy in a certain topology or relational network. The target feature space refers to the space composed of features related to historical monitoring information. It is the space in which the regulation data optimization system wants to extract and determine the optimization activation features and optimization stopping features. The scheme node variable refers to the variable that describes the regulation data optimization scheme in a specific dimension or aspect. It can be the strategy's parameters, configuration, status, etc.

[0188] Furthermore, using the topological labels corresponding to historical monitoring information in multiple regulation data optimization schemes as a reference, the regulation data optimization system first needs to determine the target feature space. This process is a feature space localization process, which requires the regulation data optimization system to find features related to historical monitoring information from numerous regulation data optimization schemes and determine the space constituted by these features.

[0189] To achieve this goal, the regulatory data optimization system can employ various methods. One possible approach is to utilize feature selection algorithms from machine learning to extract features relevant to historical monitoring information from all regulatory data optimization schemes. Specifically, the system can use historical monitoring information as labels or response variables and the features of the regulatory data optimization schemes as input variables to train a feature selection model. Then, the system can use this model to select the features most relevant to the historical monitoring information, thereby determining the target feature space.

[0190] Another possible approach is that the regulatory data optimization system can determine the target feature space based on domain knowledge or expert experience. For example, the regulatory data optimization system can select features that have a significant impact or relevance on historical monitoring information based on its nature and needs, thereby constructing the target feature space.

[0191] Once the regulatory data optimization system has determined the target feature space, the next step is to obtain the scheme node variables for each regulatory data optimization scheme, excluding the topological labels corresponding to historical monitoring information. This process is a strategy variable extraction process, which requires the regulatory data optimization system to extract variables describing the strategy in specific dimensions or aspects from the remaining regulatory data optimization schemes.

[0192] To achieve this goal, the regulatory data optimization system can employ various methods. One possible approach is for the system to directly access the storage or representation of the regulatory data optimization scheme and extract the scheme node variables. These variables can be policy parameters, configurations, states, etc., describing the policy's characteristics or attributes in specific aspects.

[0193] Another possible approach is for the regulatory data optimization system to utilize data mining or information extraction techniques to extract scheme node variables from relevant data sources. For example, the regulatory data optimization system can extract variables describing strategy characteristics from documents, logs, or metadata related to the regulatory data optimization scheme.

[0194] Once the regulatory data optimization system has acquired the scheme node variables for each regulatory data optimization scheme, the next step is to determine the optimization activation and stopping features of each scheme in the target feature space based on these variables. This process is a feature mapping and determination process, requiring the regulatory data optimization system to map the scheme node variables to the target feature space and determine the corresponding optimization activation and stopping features.

[0195] To achieve this goal, the data optimization system can employ various methods. One possible approach is to utilize regression or classification algorithms from machine learning to predict or classify the optimization activation and stopping features in the target feature space based on the scheme node variables. Specifically, the data optimization system can use the scheme node variables as input and the optimization activation and stopping features as outputs to train a regression or classification model. Then, the system can use this model to predict or classify the optimization activation and stopping features of each data optimization scheme in the target feature space.

[0196] Another possible approach is for the data optimization system to determine activation and stopping characteristics based on rules or templates. For example, the system could define a set of rules or templates describing how to calculate or derive activation and stopping characteristics of the target feature space based on the scheme's node variables. These rules or templates could be developed based on domain knowledge, experience, or expert opinions. The system could then apply these rules or templates to determine the activation and stopping characteristics of each data optimization scheme in the target feature space.

[0197] To illustrate this process more concretely, consider a data optimization system with three optimization schemes, A, B, and C, each corresponding to different topological labels and scheme node variables. The goal of the data optimization system is to determine the activation and stopping features of these three strategies in the target feature space.

[0198] First, the data optimization system uses the topological labels corresponding to historical monitoring information as a reference to determine the target feature space as {X1, X2, X3}, where X1, X2, and X3 are features related to historical monitoring information.

[0199] Then, the data optimization system obtains the node variables for strategies A, B, and C. Assume the node variables for strategy A are {V1=0.5, V2=0.7}, for strategy B are {V1=0.6, V2=0.8}, and for strategy C are {V1=0.4, V2=0.6}.

[0200] Finally, the data optimization system determined the optimization activation features and optimization stopping features of each strategy in the target feature space based on the scheme node variables. Assuming the data optimization system uses a regression algorithm for feature mapping and determination, the following results were obtained: Strategy A's optimization activation features in the target feature space are {X1=0.6, X2=0.7, X3=0.5}, and its optimization stopping features are {X1=0.4, X2=0.5, X3=0.3}; Strategy B's optimization activation features in the target feature space are {X1=0.7, X2=0.8, X3=0.6}, and its optimization stopping features are {X1=0.5, X2=0.6, X3=0.4}; Strategy C's optimization activation features in the target feature space are {X1=0.5, X2=0.6, X3=0.4}, and its optimization stopping features are {X1=0.3, X2=0.4, X3=0.2}.

[0201] This demonstrates that different regulatory data optimization schemes exhibit varying activation and termination characteristics in the target feature space. These characteristics reflect the potential effectiveness and tendency of the strategy in handling noise, providing accurate strategy feature information for subsequent regulatory data optimization.

[0202] It is understandable that the process of obtaining the activation and deactivation features corresponding to each regulation data optimization scheme is a comprehensive process involving topological label reference, target feature space determination, scheme node variable acquisition, and feature mapping and determination. This process requires the regulation data optimization system to extract features related to historical monitoring information from numerous regulation data optimization schemes and determine the activation and deactivation features of these features in the target feature space. In this way, the regulation data optimization system can provide accurate and effective strategy feature information for subsequent regulation data optimization, thereby supporting more precise regulation data optimization.

[0203] In some alternative design approaches, mapping the potential defect image guidance information based on the correlation degree to obtain the relationship graph includes: determining target optimization activation features and target optimization stopping features based on the potential defect image guidance information; if the regulation data optimization schemes corresponding to the target optimization activation features and the target optimization stopping features match, then the graph semantic features corresponding to the target optimization activation features and the target optimization stopping features are used as preset semantic codes; if the regulation data optimization schemes corresponding to the target optimization activation features and the target optimization stopping features do not match, then the cosine similarity between the target optimization stopping features corresponding to the correlation degree and the target optimization activation features is determined as the graph semantic features corresponding to the target optimization activation features and the target optimization stopping features, thus obtaining the relationship graph.

[0204] Based on this design concept, the process of mapping potential defect image guidance information into a relationship graph according to the determined correlation is a comprehensive task involving feature mining, semantic unit determination, and graph construction. This process aims to extract target optimization activation features and target optimization stopping features from the potential defect image guidance information through analysis and processing, and then map them into a relationship graph that reflects the interrelationships between these features, based on the correlation between them.

[0205] First, the data optimization system needs to extract target optimization activation and stopping features from the guidance information of potential defective images. This is a feature extraction process that requires the data optimization system to identify features related to optimization activation and stopping from complex image descriptors. These features can be image texture, color, shape, etc., and they play an important indicative role in subsequent noise processing and image optimization.

[0206] After identifying the target optimization activation feature and the target optimization stopping feature, the regulatory data optimization system needs to further determine whether these two features correspond to the same regulatory data optimization scheme. If they correspond to the same regulatory data optimization scheme, then the graph semantic features corresponding to these two features can be determined as preset semantic codes. In this embodiment, the preset semantic code is a predefined code used to represent the specific relationship between the target optimization activation feature and the target optimization stopping feature under the same regulatory data optimization scheme. By assigning the same preset semantic code to these two features, the regulatory data optimization system can clearly represent their association in the relationship graph.

[0207] However, if the target optimization activation feature and the target optimization stopping feature correspond to different regulatory data optimization schemes, then the regulatory data optimization system cannot simply assign them the same preset semantic code. In this case, the regulatory data optimization system needs to calculate the cosine similarity between the target optimization stopping feature and the target optimization activation feature from the determined correlation. Cosine similarity is an index that measures the degree of similarity between two vectors; the closer its value is to 1, the more similar the two vectors are. By calculating the cosine similarity between the target optimization stopping feature and the target optimization activation feature, the regulatory data optimization system can obtain a numerical value that reflects the degree of similarity between them.

[0208] After obtaining the cosine similarity, the data optimization system can use this value as the graph semantic feature corresponding to the target optimization activation feature and target optimization stopping feature. Thus, in the relationship graph, the data optimization system can represent the degree of similarity between these two features using graph semantic features. When the data optimization system plots these two features in the relationship graph, it can adjust their positions in the graph based on their cosine similarity, placing similar features closer together and dissimilar features further apart.

[0209] To illustrate this process more specifically, consider two potential defect image guidance messages, Ua and Ub, from which the data optimization system extracts target optimization activation features a1 and b1, and target optimization stopping features a2 and b2, respectively. Further analysis reveals that features a1 and a2 correspond to the same data optimization scheme, while features b1 and b2 correspond to different data optimization schemes.

[0210] For features a1 and a2, since they correspond to the same regulatory data optimization scheme, the regulatory data optimization system can assign them the same preset semantic code, such as "001". In the relationship graph, the regulatory data optimization system can plot features a1 and a2 in the same or similar positions and label them with "001".

[0211] For features b1 and b2, since they correspond to different regulatory data optimization schemes, the regulatory data optimization system needs to calculate their cosine similarity. Suppose the regulatory data optimization system calculates a cosine similarity of 0.7 for b1 and b2. Then, in the relationship graph, the regulatory data optimization system can plot features b1 and b2 at relatively far positions and label the connection or relationship between them with "0.7". In this way, by observing the relationship graph, the regulatory data optimization system can clearly see the degree of similarity between features b1 and b2 and their differences from features a1 and a2.

[0212] Through the above process, the data optimization system can map the target optimization activation features and target optimization stopping features in the potential defect image guidance information into a relationship graph. This graph not only helps the data optimization system intuitively understand the relationships between features, but also provides important reference information for subsequent noise processing and image optimization. For example, by observing the relationship graph, the data optimization system can identify which features have high similarity, thus allowing it to consider merging them or performing similar processing; conversely, it can also identify which features have low similarity, thus allowing it to consider differentiated processing or optimization.

[0213] In summary, mapping potential defect image guidance information into a relational graph based on the determined correlation is a crucial process. It requires the regulatory data optimization system to extract useful feature information from complex image descriptors and construct a relational graph reflecting the relationships between these features. This process not only helps the regulatory data optimization system better understand the feature information in the image descriptors but also provides strong support for subsequent noise processing and image optimization.

[0214] In some other possible embodiments, acquiring the historical monitoring information of the implantable chip and the corresponding multiple spatiotemporal control tasks includes: acquiring the historical monitoring information of the implantable chip; creating historical execution plans based on the historical monitoring information of the implantable chip through an optimization plan planning system; in the execution plans corresponding to the monitoring database of the implantable chip, taking plans other than the historical execution plans as pending execution plans; dividing the historical monitoring information of the implantable chip into multiple monitoring and control information sets according to the information association weight of the pending execution plans; and determining the multiple spatiotemporal control tasks according to the pending execution plans in the target monitoring and control information set.

[0215] It is understandable that acquiring historical monitoring information from the historical monitoring data of an implanted chip, as well as multiple spatiotemporal control tasks subordinate to that historical monitoring data, is a comprehensive process involving data processing and task planning. This process aims to extract useful information from the complex historical monitoring data of the implanted chip and, based on this information, plan a series of specific spatiotemporal control tasks.

[0216] First, the regulatory data optimization system needs to acquire historical regulatory data optimization schemes created by the optimization strategy planning system from the historical monitoring information of the implanted chip. This process is a data retrieval process, requiring the regulatory data optimization system to extract data that has been optimized and recognized by the system as historical regulatory data optimization schemes from the historical monitoring information of the implanted chip. These historical regulatory data optimization schemes are important information that the regulatory data optimization system needs to refer to and rely on in subsequent processing; they represent the system's past experience and knowledge in processing implanted regulatory information.

[0217] After acquiring historical control data optimization schemes, the control data optimization system needs to remove the execution strategies of these historical control data optimization schemes from the historical monitoring information of the implanted chip, and retain the execution strategies that have not yet been processed or require further processing. These execution strategies awaiting processing are the focus of the control data optimization system in subsequent processing.

[0218] Next, the control data optimization system needs to decompose the historical monitoring information of the implanted chip into multiple monitoring and control information sets based on the information involvement weight of the execution strategy to be processed. This process is a data decomposition and classification process, requiring the control data optimization system to decompose the data in the historical monitoring information of the implanted chip into multiple relatively independent but somewhat related monitoring and control information sets according to the information involvement weight of the execution strategy to be processed. The information involvement weight is an indicator that measures the degree of involvement or importance of the execution strategy in the information pool. The larger the value, the higher the degree of involvement or importance of the execution strategy in the information pool. By considering the information involvement weight, the control data optimization system can ensure that, during the decomposition process, execution strategies with high involvement or importance are assigned to the same control information set for subsequent processing and analysis.

[0219] After breaking down the information into multiple monitoring and control sets, the control data optimization system needs to determine multiple spatiotemporal control tasks based on the pending execution strategies within the target monitoring and control information sets. This process is a task planning and generation process, requiring the control data optimization system to plan a series of specific spatiotemporal control tasks based on the pending execution strategies within the target monitoring and control information sets. These tasks are the specific operations or instructions that need to be executed in subsequent actual operations, aiming to achieve the effective acquisition and processing of historical monitoring information. When determining spatiotemporal control tasks, the control data optimization system needs to consider the spatiotemporal characteristics of the tasks, that is, the tasks need to be executed within a specific time and space range. By considering the spatiotemporal characteristics, the control data optimization system can ensure that the planned tasks can be effectively executed in actual operations.

[0220] Finally, the regulatory data optimization system needs to utilize the target monitoring and regulatory information set to determine historical monitoring information. This process is one of information extraction and determination, requiring the regulatory data optimization system to extract historical monitoring information based on the data and information in the target monitoring and regulatory information set. Historical monitoring information is the final result that the regulatory data optimization system needs to obtain in this process; it represents useful information related to the target living organism from the historical monitoring information of the implanted chip. By extracting historical monitoring information, the regulatory data optimization system can achieve effective utilization and processing of implanted regulatory information, providing strong support for subsequent medical diagnosis, treatment, or health management.

[0221] For example, the historical monitoring information of an implantable chip contains 1000 execution strategies. Of these, 500 are historical control data optimization schemes created by an optimization strategy planning system, and the remaining 500 are execution strategies to be processed. The control data optimization system can decompose the historical monitoring information of the implantable chip into 10 monitoring and control information sets, each containing 50 execution strategies, based on the information involvement weights of these 500 pending execution strategies. Then, the control data optimization system can plan 10 spatiotemporal control tasks based on the pending execution strategies within these 10 monitoring and control information sets, with each task processing a specific monitoring and control information set. Finally, the control data optimization system can extract information related to the target living organism from these 10 monitoring and control information sets as the final historical monitoring information.

[0222] Through the above process, the control data optimization system can effectively acquire and process historical monitoring information and multiple spatiotemporal control tasks from the historical monitoring data of the implanted chip. This process not only considers the involvement weight and spatiotemporal characteristics of the information, but also ensures that the planned tasks can be effectively executed in actual operation. Simultaneously, by extracting historical monitoring information, the control data optimization system can provide strong support for subsequent data optimization.

[0223] In some other possible embodiments, acquiring the historical monitoring information of the in vivo implantable chip and the corresponding multiple spatiotemporal control tasks includes: acquiring the historical monitoring information of the in vivo implantable chip; analyzing the correlation of the in vivo physiological attribute data corresponding to the historical monitoring information of the in vivo implantable chip to obtain an in vivo physiological attribute knowledge graph; determining the target task event among the multiple spatiotemporal control tasks based on the in vivo physiological attribute knowledge graph; if the target task event does not have any associated target task events, then the control task corresponding to the target task event is taken as the spatiotemporal control task; if the target task event has target task events that do not belong to the same control data optimization scheme, and the correlation degree between the target task event and the target task events associated with the target task event is less than or equal to a set correlation degree, then the control task corresponding to the target task event is taken as the spatiotemporal control task.

[0224] It is understood that this embodiment requires the data optimization system to not only deeply understand the structure and content of the historical monitoring information of the implanted chip, but also to accurately identify and extract key information to support subsequent data optimization processing. The following is a detailed description of this technical solution:

[0225] First, the regulatory data optimization system needs to perform knowledge mining on the in vivo physiological attribute data corresponding to the historical monitoring information of the implanted chip to obtain an in vivo physiological attribute knowledge graph. The in vivo physiological attribute knowledge graph is a complex knowledge network containing various knowledge about in vivo physiological attributes and the relationships between them. By performing knowledge mining on the in vivo physiological attribute data, the regulatory data optimization system can reveal the implicit knowledge and patterns behind this data, providing strong support for subsequent regulatory tasks. In this process, the regulatory data optimization system may use various data mining and knowledge discovery techniques and methods, such as association rule mining, cluster analysis, and classification prediction, to extract useful information and knowledge from the in vivo physiological attribute data.

[0226] Secondly, based on a living physiological attribute knowledge graph, the regulatory data optimization system can identify target task events among the various task events of the regulatory task within the historical monitoring information of the implantable chip. Target task events refer to events with high priority and importance in the regulatory task. These events typically require priority processing and execution to ensure the smooth progress and completion of the regulatory task. In identifying target task events, the regulatory data optimization system needs to comprehensively consider multiple factors, such as the urgency, importance, and correlation with other events. By comprehensively considering these factors, the regulatory data optimization system can accurately identify target task events in the historical monitoring information of the implantable chip.

[0227] Then, the data optimization system needs to further assess and process the target task event. If the target task event has no associated target task events, the data optimization system can directly treat the corresponding control task as a spatiotemporal domain control task. This is because if the target task event is not associated with other target task events, then its corresponding control task is a relatively independent task that can be directly executed as a spatiotemporal domain control task.

[0228] However, if a target task event has associated target task events that do not belong to the same control data optimization scheme, and the correlation between the target task event and the associated target task events is less than or equal to a set correlation degree, then the control data optimization system also needs to treat the control task corresponding to the target task event as a spatiotemporal domain control task. This is because, although the target task event is related to other target task events, this relationship is not strong, or they do not belong to the same control data optimization scheme. Therefore, the control data optimization system can execute the control task corresponding to the target task event as an independent spatiotemporal domain control task.

[0229] In this process, setting the correlation degree is an important parameter, used to measure the degree of correlation between the target task event and its associated target task events. The specific value of the correlation degree can be set and adjusted according to the actual application scenario and requirements. For example, in some application scenarios, the data optimization system can set the correlation degree to 0.5, meaning that if the correlation degree between the target task event and its associated target task events is less than or equal to 0.5, then the control task corresponding to the target task event will be regarded as a spatiotemporal domain control task.

[0230] Through the above process, the regulatory data optimization system can obtain historical monitoring information and multiple spatiotemporal regulatory tasks from the historical monitoring information of the implanted chip. These spatiotemporal regulatory tasks are obtained based on the in vivo physiological attribute knowledge graph and the judgment and processing of target task events. They have clear spatiotemporal characteristics and priorities, which can provide strong support and guidance for subsequent regulatory data optimization processing.

[0231] In other possible embodiments, generating target optimization guidance information for the multiple spatiotemporal control tasks based on the relationship graph includes: combining the relationship graph and the long short-term memory model to determine the task set characteristics of the multiple spatiotemporal control tasks; and generating target optimization guidance information based on the task set characteristics of the multiple spatiotemporal control tasks, according to historical monitoring information in the control data optimization scheme, and based on the timeliness weights satisfying preset weight conditions.

[0232] In this embodiment, it is necessary to determine the task set features of multiple spatiotemporal control tasks based on relational graphs and long short-term memory (LSM) models. This process is a feature extraction process, requiring the control data optimization system to extract key information related to each spatiotemporal control task from the relational graph and to model and analyze this information using LSM models. LSM models are a special type of recurrent neural network that can effectively process sequential data and capture long-term dependencies within the data. By utilizing LSM models, the control data optimization system can accurately extract and represent the task chain features of each spatiotemporal control task.

[0233] When extracting task chain features, the regulatory data optimization system needs to consider the correlation and temporal sequence between various spatiotemporal regulatory tasks. Correlation refers to the potential mutual influence and dependency between different tasks, while temporal sequence refers to the possible order and time intervals between tasks during execution. By considering correlation and temporal sequence, the regulatory data optimization system can ensure that the extracted task chain features can comprehensively and accurately reflect the characteristics and relationships of various spatiotemporal regulatory tasks.

[0234] Secondly, the regulatory data optimization system needs to use historical monitoring information as an optimization benchmark and, based on the task chain characteristics of each spatiotemporal regulatory task, generate target optimization guidance information with the highest timeliness weight. This process is a text generation process, requiring the regulatory data optimization system to use historical monitoring information as a benchmark and, based on the task chain characteristics of each spatiotemporal regulatory task, generate target optimization guidance information with the highest timeliness weight. Timeliness weight is an indicator that measures the timeliness of text; the higher its value, the higher the timeliness of the text.

[0235] When generating target optimization guidance information, the regulatory data optimization system needs to consider the timeliness and accuracy of the text. Timeliness refers to whether the information contained in the text is up-to-date and relevant, while accuracy refers to whether the information contained in the text is accurate and reliable. To ensure the timeliness and accuracy of the text, the regulatory data optimization system needs to conduct in-depth analysis and processing of the task chain characteristics of each spatiotemporal regulatory task, extract the most timely and accurate information, and integrate it into the generation process of target optimization guidance information.

[0236] For example, the data optimization system for regulation has three spatiotemporal regulation tasks: task MA, task MB, and task MC. Through analysis based on relational graphs and long short-term memory models, the system can extract the task chain features of these three tasks, namely features FA, FB, and FC. Simultaneously, the system also obtains historical monitoring information, which includes the latest data on the target organism.

[0237] Next, the regulatory data optimization system needs to use historical monitoring information as an optimization benchmark and generate target optimization guidance information based on features FA, FB, and FC. In this process, the regulatory data optimization system considers the timeliness and accuracy of the features, as well as their correlation with historical monitoring information. Through comprehensive analysis and processing, the regulatory data optimization system can generate target optimization guidance information with the highest timeliness weight, which contains the latest instructions and suggestions on how to optimize the target live organism.

[0238] The target optimization guidance information can be recorded as follows: "Based on the latest historical monitoring information, the control data optimization system has found that the feature FA of task MA has the greatest impact on the optimization processing of the target liveness. Therefore, the control data optimization system recommends prioritizing the execution of task MA and paying special attention to changes in feature FA. At the same time, the features FB of task MB and FC of task MC also have some impact on the optimization processing, but their timeliness is relatively low. When executing tasks, please be sure to use historical monitoring information as a benchmark to ensure the accuracy and effectiveness of the optimization processing."

[0239] Through this process, the regulatory data optimization system can generate target optimization guidance information based on historical monitoring information, covering various spatiotemporal regulatory tasks. This text not only has the highest weight for timeliness but also contains the latest instructions and suggestions on how to optimize the target data. It helps the regulatory data optimization system better understand and execute various spatiotemporal regulatory tasks, ensuring that the optimization of regulatory data achieves the best results.

[0240] In some alternative embodiments, the step of combining the relationship graph and the long short-term memory model to determine the task set features of multiple spatiotemporal control tasks includes: determining, based on the relationship graph, target optimization achievement features from multiple spatiotemporal control tasks whose correlation with the optimization benchmark of historical monitoring information in the control data optimization scheme meets a preset correlation condition; repeatedly determining, based on the relationship graph, target optimization achievement features with the highest correlation with the optimization benchmark of derived liveness information from the remaining spatiotemporal control tasks other than the target spatiotemporal control task, until all spatiotemporal control tasks have been traversed; and determining the task set features of multiple spatiotemporal control tasks based on the traversal time sequence labels of multiple target spatiotemporal control tasks.

[0241] In this embodiment, determining the task chain features of each spatiotemporal control task based on the relationship graph and long short-term memory model aims to extract key features that can effectively characterize the task chain by deeply analyzing and learning the intrinsic connections between tasks. This process not only relies on the direct correlation between tasks but also involves iteratively mining deeper-level association patterns, ultimately forming a feature set that can comprehensively reflect the dynamic characteristics of the task chain.

[0242] To reiterate, a relational graph is a data structure used to represent the relationships between different entities (in this case, spatiotemporal control tasks). In the graph, nodes represent individual tasks, while edges represent the strength of the correlation between tasks. This structure allows control data optimization systems to intuitively understand and analyze the complex relationships between tasks, providing a foundation for subsequent feature extraction.

[0243] Next, one of the core steps of this embodiment is to identify the target optimization achievement features most relevant to the optimization benchmark in the relationship graph. This historical monitoring information may represent a specific goal or indicator, while the optimization benchmark is a standard for evaluating data quality. Through the relationship graph, the data optimization system can quantify the degree of relevance of each task to this optimization benchmark and select the task with the highest relevance as the initial target optimization achievement feature.

[0244] Subsequently, the technical solution employs an iterative approach, treating the currently selected target optimization feature as a new "optimization benchmark for derived liveness monitoring information," and searching among the remaining tasks for the next target optimization feature most relevant to this new benchmark. This process is repeated continuously, with each iteration updating the optimization benchmark and selecting the most relevant task from the remaining tasks, until all tasks have been traversed. This iterative method simulates the propagation and accumulation effects of information in the task chain, enabling the finally extracted features to capture the dynamic changes and long-term dependencies of the task chain.

[0245] In this application, the application of the Long Short-Term Memory (LSTM) model is particularly important. LSTM is a special type of recurrent neural network, especially suitable for processing and predicting long-term dependencies in time series data. In this technical solution, the LSTM model is used to assist in analyzing and predicting the time series correlations between various tasks in a task chain. By using the LSTM model, the data optimization system can better understand and simulate the evolution of task chain characteristics over time, which is crucial for capturing the dynamic characteristics of the task chain.

[0246] Specifically, when processing task chain data, the LSTM model considers the historical information of each task and their interactions, thereby generating a series of feature vectors that reflect the dynamic state of the task chain. These feature vectors not only contain the direct correlations between tasks but also implicitly include indirect association patterns formed over time. This feature representation, which integrates direct and indirect associations, provides a richer description of the task chain characteristics for regulating and optimizing data optimization systems.

[0247] Finally, the traversal time sequence label of each target spatiotemporal domain regulation task is used as its task chain feature. In this embodiment, the "traversal time sequence label" records the order and time points at which tasks are selected as target optimization achievement features during the iteration process. Through these labels, the regulation data optimization system can clearly see how the importance and influence of each task in the task chain changes over time, which is of great significance for understanding the dynamic behavior of the task chain and optimizing regulation strategies.

[0248] For example, a data optimization system for regulation has a task chain containing five spatiotemporal regulation tasks, labeled T1, T2, T3, T4, and T5. Based on the analysis of the relationship graph, the system finds that T1 has the highest correlation with the initial optimization benchmark (such as a specific environmental indicator), therefore T1 is selected as the first target optimization achievement feature. Next, the system uses T1 as the new optimization benchmark and finds that T3 has the highest correlation with T1, thus T3 becomes the second target optimization achievement feature. Following this logic, the system continues to iterate, eventually obtaining a task chain with the traversal order T1→T3→T5→T2→T4. This traversal order constitutes the task chain feature for each task, reflecting their importance and dynamic relationships within the task chain.

[0249] Thus, by combining relationship graphs and long short-term memory models, a deep understanding of the characteristics of spatiotemporal control task chains is achieved. This method not only considers the direct correlations between tasks but also captures the dynamic changes and long-term dependencies in the task chain through an iterative approach, providing a powerful tool for task chain analysis and optimization.

[0250] In a preferred embodiment, the spatiotemporal modulation task is a neural signal modulation task; the step of generating the target optimization guidance information with the highest timeliness weight based on the task chain characteristics of each spatiotemporal modulation task includes: if, based on the neural signal modulation identifier, it is determined that there is a marked modulation task that has been optimized in the historical information of the in vivo implantable chip between two consecutive spatiotemporal modulation tasks, then the target optimization guidance information is generated based on the two consecutive spatiotemporal modulation tasks and the marked modulation task that has been optimized, wherein the neural signal modulation identifier is determined according to the task set characteristics of multiple spatiotemporal modulation tasks; if, based on the neural signal modulation identifier, it is determined that there are no related task items between two consecutive spatiotemporal modulation tasks, then a first target spatiotemporal modulation task set and a second target spatiotemporal modulation task set are obtained based on the two consecutive spatiotemporal modulation tasks and multiple spatiotemporal modulation tasks; a first alternative optimization guidance information and a second alternative optimization guidance information are determined based on the first target spatiotemporal modulation task set and the second target spatiotemporal modulation task set, respectively.

[0251] In this application scenario, the focus is on a specific spatiotemporal manipulation task: neural signal manipulation. The core of this task lies in the precise monitoring and manipulation of neural signals to achieve a deep understanding and effective intervention of nervous system activity. Based on this, this embodiment aims to generate target optimization guidance information with the highest timeliness weight based on the task chain characteristics of each neural signal manipulation task. This process not only requires the manipulation data optimization system to have a deep understanding of the task chain characteristics, but also requires the system to accurately identify and utilize these characteristics to guide the generation of optimization guidance information.

[0252] In the embodiments of this application, neural signal modulation elements refer to a series of key parameters and features that constitute the basis of a task, which together determine the nature and execution method of the task. These elements may include the target region, the type of signal, the acquisition frequency, the processing algorithm, etc., which together constitute a complete task description.

[0253] Next, the core step of this embodiment is to determine the neural signal regulation elements between two consecutive tasks based on the task chain characteristics of each neural signal regulation task. The key to this step is identifying the correlations and differences between tasks, thereby extracting feature elements that can characterize the relationships between tasks. For example, if two consecutive tasks monitor neural signals in the same brain region but use different processing algorithms, then the "processing algorithm" is an important regulatory element between these two tasks.

[0254] Once the neural signal modulation elements between tasks are identified, the modulation data optimization system can further analyze the information contained in these elements. If the elements represent a labeled modulation task that has been optimized in historical monitoring information from an implanted chip between two consecutive tasks, this means that there is a task between these two tasks that has already been optimized with high-quality optimization results. In this case, the modulation data optimization system can directly generate target optimization guidance information based on these two tasks and the optimized task. This text will contain methods and strategies on how to replicate or learn from the optimized task to guide the execution of subsequent tasks.

[0255] For example, tasks R1 and R2 are two consecutive neural signal modulation tasks, and the modulation elements between them indicate the existence of an optimized task R_opt. In this case, the modulation data optimization system can generate target optimization guidance information such as: "When performing tasks R1 and R2, it is recommended to refer to the methods and strategies of the optimized task R_opt to ensure the accuracy and reliability of the data." Such text directly provides specific guidance on how to perform effective optimization.

[0256] However, if the neural signal modulation elements represent two consecutive tasks without any related task items, it means that these two tasks are not directly connected in terms of objectives, methods, or strategies. In this case, the modulation data optimization system needs to adopt a different strategy to generate optimization guidance information. Specifically, the modulation data optimization system can use these two consecutive tasks as reference points to decompose the entire neural signal modulation task set into two subsets: a first target spatiotemporal domain modulation task set and a second target spatiotemporal domain modulation task set. These two subsets respectively contain other tasks related to or similar to the first and second tasks.

[0257] After obtaining these two subsets, the data optimization system can generate first and second alternative optimization guidance information based on them, respectively. These two texts will provide suggestions and guidance on how to optimize, tailored to the task characteristics of each subset. For example, if the tasks in the first target spatiotemporal domain modulation task set primarily focus on neural signals in a specific brain region, the first alternative optimization guidance information might emphasize optimization techniques and strategies specific to that brain region; while if the tasks in the second target spatiotemporal domain modulation task set involve different types of neural signals, the second alternative optimization guidance information might provide optimization suggestions on how to process these different types of signals.

[0258] Thus, by deeply analyzing the task chain characteristics of neural signal modulation tasks, effective generation of optimization guidance information for targets with the highest timeliness weight was achieved. This process not only considers the direct correlation between tasks but also addresses situations where tasks are not directly related by decomposing the task set and generating alternative texts. This method provides a flexible and comprehensive strategy for optimizing modulation data systems to guide optimization work in neural signal modulation tasks, thereby improving the accuracy and reliability of the data.

[0259] In some preferred embodiments, after the target optimization guidance information is sent to the in vivo implantable chip, the method further includes: obtaining the current device status of the in vivo implantable chip; if the current device status does not match the target optimization guidance information, and the number of spatiotemporal control tasks to be processed is less than a set number, then determining the task correlation degree between the spatiotemporal control tasks to be processed, and determining optimization guidance information based on the task correlation degree; if the current device status does not match the target optimization guidance information, and the number of spatiotemporal control tasks to be processed is greater than a set number, then generating optimization guidance information with the highest timeliness weight involving multiple spatiotemporal control tasks to be processed again based on the current device status; sending the optimization guidance information to the in vivo implantable chip to optimize the control data based on the spatiotemporal control tasks to be processed through the optimization guidance information.

[0260] Understandably, after the target optimization guidance information was sent to the implantable chip, a series of follow-up operations were performed to ensure that the optimization guidance information matched the current state of the device and to make corresponding adjustments for different situations. This process not only demonstrates the flexibility and adaptability of the technology but also further improves the effectiveness and efficiency of data optimization.

[0261] In the application scenario of in vivo implantable chips, the current device state characteristics refer to a set of parameters such as the device's operating status, resource usage, and processing capacity at a certain moment. These characteristics collectively describe the overall state of the device at the current moment and are an important basis for determining whether the device can perform a specific task.

[0262] Next, the technical solution considers the situation where the current equipment status characteristics do not match the target optimization guidance information. When this mismatch occurs, if the number of pending spatiotemporal control tasks is less than the set number, the system will adopt a strategy based on a cross-modal decision tree to determine the optimization guidance information.

[0263] Furthermore, cross-modal decision trees are a special type of decision tree model capable of handling data from different modalities (i.e., different sources or types) and establishing effective correlations between these data. In this technical solution, cross-modal decision trees are used to identify the task correlation between the spatiotemporal control tasks to be processed. This task correlation may be based on factors such as the similarity between tasks, their spatiotemporal proximity, or their degree of sharing of equipment resources. Through cross-modal decision tree analysis, the control data optimization system can obtain a quantitative index of task correlation, which will serve as an important basis for subsequently generating optimization instruction information.

[0264] After determining the correlation between tasks, optimization instructions are generated based on the current device status characteristics. This update text will contain specific guidance on how to adjust optimization strategies according to the device's current status and task correlation. For example, if the device's current processing capacity is low, but the tasks are highly correlated, the update text might suggest merging highly correlated tasks to reduce the device's load and improve processing efficiency.

[0265] On the other hand, if the current device status characteristics do not match the target optimization guidance information, and the number of pending spatiotemporal control tasks exceeds the set number, then the technical solution will adopt another strategy. In this case, the system will generate optimization guidance information based on the current device status characteristics, with the highest timeliness weight among all pending spatiotemporal control tasks.

[0266] In this application's embodiments, timeliness weight is a key concept. It refers to a weight value assigned to each task based on its importance and urgency. Tasks with higher timeliness weights mean they have a greater impact on the overall system performance or data accuracy, and therefore require more attention in resource allocation and processing priority.

[0267] The process of generating this optimization instruction takes into account the current state of the device, the correlation between tasks, and the timeliness weight of each task. Through an optimized algorithm, the system can calculate how to allocate resources and prioritize processing under a given device state to maximize overall data optimization. This update text will contain detailed instructions on how to execute the optimization operations according to the calculated optimal strategy.

[0268] Finally, both the optimization instructions generated through cross-modal decision trees and those regenerated based on time-sensitivity weights are sent to the implantable chip. Upon receiving this update text, the device optimizes the spatiotemporal control task according to the instructions within the text. In this way, by continuously adjusting the optimization strategy based on device status and task characteristics, the control data optimization system ensures the efficiency and accuracy of the data optimization process, thereby improving the overall system performance and stability.

[0269] For example, the current processing capacity of the implantable chip is low, while there are five spatiotemporal manipulation tasks to be processed, and these tasks are highly correlated. In this case, the system may use cross-modal decision tree analysis to determine the correlation between the tasks and generate an optimization instruction based on the current state of the device. This instruction might suggest merging highly correlated tasks or prioritizing tasks with high timeliness weights to reduce the device's burden and improve processing efficiency. If the number of tasks to be processed exceeds a set limit, the system may recalculate the timeliness weight of each task and generate a new optimization instruction to ensure optimal data optimization results with limited resources.

[0270] Based on the above, in some independent embodiments, after the target optimization guidance information is sent to the in vivo implantable chip so that the in vivo implantable chip can optimize the control data of the multiple spatiotemporal domain control tasks through the target optimization guidance information, the method further includes: receiving the optimized neural control data returned by the in vivo implantable chip; and setting access permissions for the optimized neural control data.

[0271] It is understandable that the above-mentioned access permission settings not only ensure data security and privacy, but also make it possible to achieve more refined data management.

[0272] Optimized neuromodulation data refers to neuromodulation-related data that has been processed using specific algorithms or methods to remove noise or interference signals. This data may include neuronal electrical activity recordings, electroencephalogram (EEG) signals, or response data generated through certain neuromodulation techniques (such as deep brain stimulation or transcranial magnetic stimulation). The purpose of optimization is to improve the signal-to-noise ratio of the data, making subsequent analysis or processing more accurate and reliable.

[0273] After the implanted chip optimizes the spatiotemporal modulation data for the task, it sends the optimized neural modulation data back to the central server or a designated data processing node. This process is achieved through network transmission and may involve data encryption and compression to ensure the security and efficiency of the transmission.

[0274] After receiving the optimized neuromodulation data, the next crucial step is to set access permissions for this data. Access permission settings are a data security management measure that defines the access capabilities and operational permissions of different users or user groups to data resources. By setting appropriate access permissions, the leakage and misuse of sensitive data can be prevented, ensuring data security and privacy.

[0275] When setting access permissions, the first step is to determine the sensitivity and importance of the data. For highly sensitive or important data, stricter access permissions should be set, such as allowing access only to specific users or user groups, with detailed access logging and auditing. For relatively less sensitive or important data, more lenient access permissions can be set to facilitate data analysis and processing by a wider range of users.

[0276] Specifically, access permission settings may include the following aspects:

[0277] (1) User authentication: Ensure that the user accessing the data is legitimate and has the appropriate access rights. This is usually achieved through username and password, two-factor authentication, or other authentication mechanisms;

[0278] (2) Access Control List (ACL): Defines the access permissions of different users or user groups to data resources. ACL can specify the types of operations that users can perform (such as read, write, execute, etc.) and the objects of the operations (such as specific data files, database tables, etc.);

[0279] (3) Role-Based Access Control (RBAC): Users are assigned to different roles, and corresponding access permissions are defined for each role. In this way, users gain access to data resources through their assigned roles. RBAC simplifies access management, making it easier to allocate and adjust permissions when there are many users.

[0280] (4) Data encryption: For highly sensitive data, encryption can be performed during storage or transmission to ensure that even if the data is accessed by an unauthorized user, its true content cannot be easily obtained;

[0281] (5) Access auditing and monitoring: Record user access behavior to data resources and monitor it in real time. This helps to discover potential security threats or violations and take appropriate measures in a timely manner.

[0282] In practical applications, setting access permissions may involve multiple considerations and trade-offs. For example, in the medical field, neuromodulation data may contain patients' personal privacy information, thus requiring strict access permissions to protect patients' privacy rights. At the same time, it is necessary to ensure the availability and shareability of the data while protecting patient privacy.

[0283] To achieve this goal, a role-based access control strategy can be adopted. First, define different roles, such as data administrators, researchers, and doctors, and assign corresponding access permissions to each role. For example, a data administrator might have the highest level of access, managing data storage, backup, and recovery; a researcher might have permission to read and analyze data, but not modify or delete it; and a doctor might have permission to view specific patient data to develop personalized treatment plans.

[0284] Secondly, an authentication mechanism ensures that users accessing the data are legitimate. Users must log in to the system using a username and password or other authentication credentials and be authenticated before accessing the data. This prevents unauthorized users from accessing sensitive data.

[0285] Then, access control lists (ACLs) are used to further refine the data access permissions for each role. For example, an ACL can be set up for researchers, allowing them to access specific datasets but not sensitive data containing patients' personal privacy information. Meanwhile, a different ACL can be set up for physicians, allowing them to view specific patients' neuromodulation data for diagnosis and treatment.

[0286] Finally, user access behavior is tracked through access auditing and monitoring. The system can record information such as the time of each access, user identity, accessed data, and performed operations, and monitor this information in real time. This helps to identify potential security threats or violations and allows for timely measures to protect data security and privacy.

[0287] Therefore, setting access permissions is a crucial step after receiving optimized neuromodulation data from an implantable chip. Appropriate access permissions ensure data security and privacy while enabling data sharing and utilization. This not only helps protect patients' privacy but also promotes research and development in the medical field, providing better data support and safeguards for the application of neuromodulation technologies.

[0288] In other, standalone embodiments, the method further includes: performing data sharing processing on the optimized neuromodulation data in response to a data sharing request.

[0289] It is understood that, for implantable neuromodulation applications, the embodiments of this application are not limited to data optimization, access control settings, and other aspects, but also extend to the important aspect of data sharing. This means that, while ensuring data security, privacy, and compliance, the method can actively respond to data sharing requests and properly process the optimized neuromodulation data for sharing.

[0290] In implantable neuromodulation applications, data sharing is of paramount importance. Implantable neuromodulation, as a cutting-edge medical technique, interacts directly with target neural tissue through implanted electrodes and other devices to record, stimulate, or modulate neural signals. However, due to the complexity and specialized nature of this technology, a single research institution or medical institution often cannot independently complete all related research and application work. Therefore, data sharing has become a crucial pathway to advance implantable neuromodulation technology. By sharing data, different research institutions can collaboratively analyze the effects of neuromodulation, optimize algorithms, and improve the accuracy and effectiveness of the technology. Medical institutions can also share data to compare the effects of different implantable neuromodulation devices, providing patients with more personalized treatment plans.

[0291] However, data sharing is not without risk. Because implantable neuromodulation data involves patient privacy and medical information, strict adherence to relevant laws, regulations, and privacy policies is essential when sharing it. This necessitates that data optimization systems implement a series of measures during data sharing to ensure data security and privacy.

[0292] In response to data sharing requests, the data optimization system first conducts rigorous identity verification and access control audits on the requesting party. This process aims to ensure that only legitimate and compliant requesters can obtain data access permissions, thereby preventing data leakage or misuse. Identity verification may include verifying the requester's identity information, organizational qualifications, and legitimate authorizations related to research or business. Access control audits involve assessing the requester's purpose, scope, method of data use, and security measures to ensure that data sharing complies with relevant laws, regulations, and privacy policies. This step is crucial because it lays a solid foundation for the security of data sharing.

[0293] Once the requester passes authentication and authorization verification, the data optimization system further refines the optimized neuromodulation data to meet data sharing needs. This may include data format conversion, anonymization, encrypted transmission, and access control. Data format conversion ensures that the shared data can be easily read and used by the requester, regardless of the data processing or analysis tools they use. Anonymization protects patient privacy by removing or replacing personal identifiers, preventing direct association of shared data with specific individuals and reducing the risk of privacy breaches. Encrypted transmission ensures data security during sharing, preventing theft, tampering, or malicious use. Access control restricts access to the shared data, allowing only authorized users to access and use it, further enhancing data security.

[0294] During data sharing and processing, the data optimization system places particular emphasis on data traceability and auditability. This means that all data sharing activities are meticulously recorded, including the sharing time, sharing recipients, shared content, and usage methods. This information is crucial for monitoring data usage, preventing data misuse, and meeting relevant legal and regulatory requirements. By maintaining these data sharing records, the data optimization system can trace the flow and usage of data when necessary, ensuring the legal and compliant use of data.

[0295] Furthermore, the data optimization system offers flexible and diverse data sharing strategy configuration options. Administrators can easily configure different data sharing strategies based on various data sharing scenarios and needs, such as setting data sharing periods, limiting the number of times data can be shared, and defining the scope of data sharing. These strategy configurations aim to balance the needs of data sharing with the protection of data security and privacy, ensuring that data sharing meets research or business requirements without disclosing patients' personal privacy or sensitive information.

[0296] In practical applications, the data-sharing function of neuromodulation optimization systems can be widely used in medical, scientific research, and commercial fields. In the medical field, multiple medical institutions can share optimized neuromodulation data to jointly research treatment methods for neurological diseases, improving diagnostic accuracy and treatment effectiveness. Research institutions can also utilize shared data to validate and improve neuromodulation algorithms, driving progress in neuroscience and technology. In the commercial field, companies can share data to develop new neuromodulation products or services, meeting market demands and driving business growth. Simultaneously, data sharing can also promote cooperation and exchange between different institutions, jointly advancing the development and application of implantable neuromodulation technology.

[0297] However, data sharing is not without challenges. One of the biggest challenges is achieving effective data sharing while protecting personal privacy and data security. To address this challenge, the data optimization system employs various technical means and legal measures to ensure the security and compliance of data sharing. In addition to the aforementioned technical means such as identity verification, access control, anonymization, encrypted transmission, and access control, the system strictly adheres to relevant laws, regulations, and privacy policies to ensure the legality and compliance of data sharing. Furthermore, the system actively cooperates and communicates with relevant regulatory agencies to ensure that data sharing activities comply with regulatory requirements and obtain the necessary authorizations.

[0298] This demonstrates that by responding to data sharing requests and performing refined data sharing processing on optimized neuromodulation data, the modulation data optimization system can achieve effective data sharing and utilization while protecting personal privacy and data security.

[0299] Furthermore, Figure 2 This is a schematic diagram of the structure of a control data optimization system 200 provided in an embodiment of this application. Figure 2 The control data optimization system 200 shown includes a processor 210, which can call and run computer programs from memory to implement the methods in the embodiments of this application.

[0300] Optionally, such as Figure 2 As shown, the data optimization system 200 may further include a memory 230. The processor 210 can retrieve and run computer programs from the memory 230 to implement the methods described in this embodiment.

[0301] The memory 230 can be a separate device independent of the processor 210, or it can be integrated into the processor 210.

[0302] Optionally, such as Figure 2As shown, the data optimization system 200 may also include a transceiver 220, and the processor 210 may control the transceiver 220 to interact with other devices. Specifically, it may send information or data to other devices or receive information or data sent by other devices.

[0303] Optionally, the data optimization system 200 can implement the corresponding processes of the storage engine or components (such as processing modules) in the storage engine or the device with the storage engine deployed in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0304] It should be understood that the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities.

[0305] It is understood that the memory in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, suitable types of memory.

[0306] Based on the above, a readable storage medium is provided, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the above method are implemented.

[0307] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0308] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes multiple instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0309] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of protection of this application, and all of these forms are within the protection scope of this application.

Claims

1. A method for regulating data optimization based on a living body implantable chip, characterized in that, The method comprises the following steps: acquiring historical monitoring information of a living body implantable chip and a plurality of spatiotemporal regulation tasks corresponding to the historical monitoring information, wherein the plurality of spatiotemporal regulation tasks have intersections; generating a plurality of regulation data optimization schemes based on the historical monitoring information and the plurality of spatiotemporal regulation tasks; determining a relationship graph of the plurality of regulation data optimization schemes based on optimization activation features and optimization stop features corresponding to the plurality of regulation data optimization schemes respectively; generating target optimization guide information of the plurality of spatiotemporal regulation tasks based on the relationship graph, wherein the target optimization guide information takes the monitoring information as an optimization benchmark; downloading the target optimization guide information to the living body implantable chip, so that the living body implantable chip performs regulation data optimization on the plurality of spatiotemporal regulation tasks based on the target optimization guide information; wherein the optimization activation feature refers to a feature capable of activating an optimization mechanism to effectively filter and process data, and the optimization stop feature refers to a feature that is not suitable for optimization processing under current conditions; determining the relationship graph of the plurality of regulation data optimization schemes based on the optimization activation features and the optimization stop features corresponding to the plurality of regulation data optimization schemes respectively, comprises: acquiring the optimization activation features and the optimization stop features corresponding to the plurality of regulation data optimization schemes respectively; superimposing the optimization activation features and the optimization stop features corresponding to the plurality of regulation data optimization schemes respectively to determine potential defect image guide information; calculating the correlation between a first strategy identifier of the plurality of regulation data optimization schemes and a second strategy identifier of the remaining regulation data optimization schemes according to the optimization activation features and the optimization stop features corresponding to the plurality of regulation data optimization schemes respectively to obtain a correlation degree; and mapping the potential defect image guide information based on the correlation degree to obtain the relationship graph.

2. The method of claim 1, wherein, generating the plurality of regulation data optimization schemes based on the historical monitoring information and the plurality of spatiotemporal regulation tasks, comprises: mapping the plurality of spatiotemporal regulation tasks based on expected strategies of the spatiotemporal regulation tasks to obtain a plurality of expected task knowledge networks respectively containing optimization activation features and optimization stop features; mapping the historical monitoring information to obtain a historical task knowledge network containing the same optimization start condition and optimization stop condition; determining the plurality of regulation data optimization schemes according to the plurality of expected task knowledge networks and the historical task knowledge network.

3. The method of claim 1, wherein, acquiring the optimization activation features and the optimization stop features corresponding to the plurality of regulation data optimization schemes respectively, comprises: querying in the plurality of regulation data optimization schemes, determining a target regulation data optimization scheme having relevance to a historical execution scheme in the historical monitoring information of the living body implantable chip based on regulation dimensions; and calculating the optimization activation features and the optimization stop features corresponding to the target regulation data optimization scheme based on the optimization activation features and the optimization stop features of the historical execution scheme; or Obtaining scheme node variables of a plurality of regulation data optimization schemes except for a topology label corresponding to the historical monitoring information; determining optimization activation features and optimization stop features of the plurality of regulation data optimization schemes in a target feature space according to the scheme node variables of the plurality of regulation data optimization schemes, wherein the target feature space is determined in combination with the topology label corresponding to the historical monitoring information.

4. The method of claim 1, wherein, The mapping of the potential defect image guidance information based on the correlation degree comprises: Based on the potential defect image guidance information, target optimization activation features and target optimization stop features are determined; If the regulation data optimization schemes corresponding to the target optimization activation features and the target optimization stop features match, the graph semantic features corresponding to the target optimization activation features and the target optimization stop features are taken as preset semantic encodings; If the regulation data optimization schemes corresponding to the target optimization activation features and the target optimization stop features do not match, the cosine similarity between the target optimization stop features corresponding to the correlation degree and the target optimization activation features is determined as the graph semantic features corresponding to the target optimization activation features and the target optimization stop features, and the relationship graph is obtained.

5. The method of claim 1, wherein, The obtaining of the historical monitoring information of the living body implantable chip and the plurality of spatiotemporal regulation tasks comprises: Obtaining the historical monitoring information of the living body implantable chip; Creating a historical execution scheme based on the historical monitoring information of the living body implantable chip through an optimization scheme planning system; In the execution scheme corresponding to the monitoring database of the living body implantable chip, schemes other than the historical execution scheme are taken as to-be-processed execution schemes; According to the information correlation weight of the to-be-processed execution scheme, the historical monitoring information of the living body implantable chip is divided into a plurality of monitoring regulation information sets; According to the to-be-processed execution scheme in the target monitoring regulation information set, the plurality of spatiotemporal regulation tasks are determined.

6. The method of claim 1, wherein, The obtaining of the historical monitoring information of the living body implantable chip and the plurality of spatiotemporal regulation tasks comprises: Obtaining the historical monitoring information of the living body implantable chip; Analyzing the correlation of the living body physiological attribute data corresponding to the historical monitoring information of the living body implantable chip to obtain a living body physiological attribute knowledge graph; Based on the living body physiological attribute knowledge graph, a target task event in the plurality of spatiotemporal regulation tasks is determined; If the target task event does not exist in the associated target task event, the regulation task corresponding to the target task event is taken as the spatiotemporal regulation task; If the target task event exists in the target task event that does not belong to the same regulation data optimization scheme and the correlation degree between the target task event and the target task event associated with the target task event is less than or equal to a set correlation degree, the regulation task corresponding to the target task event is taken as the spatiotemporal regulation task.

7. The method according to any one of claims 1 to 6, characterized in that, The generation of the target optimization guidance information of the plurality of spatiotemporal regulation tasks based on the relationship graph comprises: In combination with the relationship graph and a long short-term memory model, task set features of the plurality of spatiotemporal regulation tasks are determined; According to the historical monitoring information in the regulation data optimization scheme, based on the task set characteristics of the plurality of spatio-temporal regulation tasks, target optimization guide information is generated, which meets the preset weight condition of time effectiveness weight; The relationship graph and the long short-term memory model are combined to determine the task set characteristics of the plurality of spatio-temporal regulation tasks, including: From the plurality of spatio-temporal regulation tasks, based on the relationship graph, the target optimization compliance characteristics that meet the preset correlation degree condition of the correlation degree of the optimization benchmark of the historical monitoring information in the regulation data optimization scheme are determined; Based on the relationship graph, the target optimization compliance characteristics that meet the preset correlation degree condition of the correlation degree of the optimization benchmark of the living body information are repeatedly determined from the remaining spatio-temporal regulation tasks except the target spatio-temporal regulation task until the spatio-temporal regulation tasks are traversed; Based on the traversal time sequence label of the plurality of target spatio-temporal regulation tasks, the task set characteristics of the plurality of spatio-temporal regulation tasks are determined, wherein the spatio-temporal regulation task is a neural signal regulation task; The target optimization guide information is generated based on the task set characteristics of the plurality of spatio-temporal regulation tasks, which meets the preset weight condition of time effectiveness weight, including: If it is determined based on the neural signal regulation identifier that there is a marker regulation task that completes optimization in the historical information of the living body implantable chip between the two consecutive spatio-temporal regulation tasks, the target optimization guide information is generated based on the two consecutive spatio-temporal regulation tasks and the marker regulation task that completes optimization, wherein the neural signal regulation identifier is determined according to the task set characteristics of the plurality of spatio-temporal regulation tasks; If it is determined based on the neural signal regulation identifier that there is no associated task item between the two consecutive spatio-temporal regulation tasks, the first target spatio-temporal regulation task set and the second target spatio-temporal regulation task set are obtained based on the plurality of spatio-temporal regulation tasks; the first alternative optimization guide information and the second alternative optimization guide information are determined based on the first target spatio-temporal regulation task set and the second target spatio-temporal regulation task set, respectively.

8. The method according to any one of claims 1 to 6, wherein, After the target optimization guide information is sent to the living body implantable chip, the method further includes: Obtaining the current device state of the living body implantable chip; If the current device state does not match the target optimization guide information, and the number of spatio-temporal regulation tasks to be processed is less than the set number, the task correlation degree between the spatio-temporal regulation tasks to be processed is determined, and the optimization instruction information is determined based on the task correlation degree; If the current device state does not match the target optimization guide information, and the number of spatio-temporal regulation tasks to be processed is greater than the set number, the optimization instruction information involving the plurality of spatio-temporal regulation tasks to be processed is generated again according to the current device state, which has the maximum time effectiveness weight; The optimization instruction information is sent to the living body implantable chip to optimize the spatio-temporal regulation task to be processed based on the optimization instruction information.

9. A regulatory data optimization system, comprising: A computer program product comprising at least one processor and a memory; said memory storing computer-executable instructions; said at least one processor executing the computer-executable instructions stored by the memory causing the at least one processor to perform the method of any one of claims 1-8.

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