A full-life-cycle intelligent management method based on multi-modal neural data fusion and central nervous system life science large model

Abstract

The application discloses a full-life-cycle intelligent management method and system based on multi-modal neural data fusion and central nervous system life science large model, and belongs to the cross field of brain-computer interface, neuroscience and artificial intelligence. In view of the problems of multi-vendor neural equipment heterogeneous access, clinical and research and development data island and lack of full-life-cycle dynamic management, the application adopts an IOT interface method to access and encrypt and authenticate, identify open source code and identify API interface, and access brain-computer interface and neural sensing equipment to obtain multi-modal neural data through three verification; edge-cloud collaborative calculation and feature extraction are carried out through a combination of mixed expert models and reasoning models; distributed task identification is carried out based on a multi-Agent collaborative system to fuse a skill and a process memory library, a long-term scene memory vector library and a short-term working memory; a clinical-research double closed-loop data flow with original data not leaving the local is constructed by cross-fusing the identification result and clinical data, AI diagnosis data and drug research and development data by using federated learning, differential privacy and secure multi-party computation; a central nervous system life science large model including an LLM base, an improved JEPA module, a V-JEPA module, a cross-modal dynamic world model fusion module, a GNN module and a Transformer module is used for deep analysis to generate full-life-cycle neural state prediction and intervention strategy; and finally, regulation and control instructions are issued to brain-computer interface and neural regulation and control equipment through closed-loop feedback.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary technical field of brain-computer interface, neuroscience, artificial intelligence and biopharmaceuticals. Specifically, it relates to a full life cycle intelligent management system and method based on multimodal neural data fusion and a large life science model of the central nervous system. In particular, it relates to a technical solution that utilizes IoT secure interfaces to access brain-computer interfaces and neural sensing devices, performs distributed computing through hybrid expert models and multi-agent collaboration, constructs a dual closed-loop data flow for clinical and R&D based on federated learning and secure multi-party computation, and adopts an improved JEPA and cross-modal dynamic world model fusion architecture to realize digital simulation and state regulation of the central nervous system. Background Technology

[0002] The diagnosis and treatment of central nervous system diseases (including Alzheimer's disease, Parkinson's disease, glioma, refractory epilepsy, amyotrophic lateral sclerosis, etc.) face severe challenges such as fragmented data collection, disconnect between clinical practice and research and development, and lack of dynamic management methods throughout the entire life cycle.

[0003] At the neural data acquisition level, existing brain-computer interface (BCI) devices, central nervous system sensors, and acquisition instruments are typically manufactured by different companies, using their own proprietary communication protocols and data formats. Current technologies fail to provide a unified, highly secure, and highly compatible central control access solution, making it difficult to securely converge and temporally align multimodal neural data (electrophysiology, imaging, chemistry) on a single platform. More critically, traditional BCI systems only use the acquired neural data for a single purpose (such as motor decoding or disease diagnosis), failing to synchronously transmit the data to drug development, resulting in a significant waste of data value.

[0004] At the level of intelligent computing, a single Large Language Model (LLM) or a single Graph Neural Network (GNN) struggles to simultaneously handle the high-dimensional temporal characteristics of neural signals, the structural characteristics of brain networks, and the semantic characteristics of clinical text. While Hybrid Expert Models (MOEs) have shown potential in multimodal processing, current technologies have not effectively combined them with dedicated inference models for the central nervous system, nor have they established standardized data interfaces for multi-agent collaborative systems. Furthermore, current technologies lack the ability to model the temporal evolution of neural data, making it impossible to predict future disease progression trajectories based on historical neural states.

[0005] At the collaborative decision-making level, existing multi-agent frameworks (such as MetaGPT and AutoGen) are mainly geared towards general software development or office automation tasks, lacking dedicated memory systems and task decomposition strategies for the neuroscience field. More importantly, existing technologies have not established a closed-loop feedback mechanism between agent execution results and clinical diagnosis and treatment data, drug development data, and the agent's identification and decision-making results cannot automatically drive downstream drug screening and clinical treatment plan generation.

[0006] At the data fusion level, hospital clinical information systems (HIS, PACS, EMR), AI-assisted diagnostic knowledge bases, biopharmaceutical R&D data management systems, wet laboratory raw data storage, and AI drug development platforms are currently scattered across different institutions and security domains, forming serious data silos and business barriers. Clinical departments cannot obtain real-time information on the latest candidate drug molecules and gene therapy vectors from the R&D side, while the R&D side cannot obtain real-time efficacy feedback and patient phenotypic data from the clinical side. This results in long drug development cycles, high costs, high failure rates, and delayed updates to clinical treatment protocols. While existing technologies have proposed privacy-preserving computing concepts such as federated learning, they have not been engineered to integrate these concepts with the multimodal data characteristics of neuroscience, secure IoT access mechanisms, and multi-agent collaborative decision-making.

[0007] At the model architecture level, existing central nervous system analysis models are mostly static predictive models (such as classification models based on single fMRI or EEG scans), lacking dynamic extrapolation capabilities based on world models. They cannot perform counterfactual reasoning on disease progression, nor can they achieve closed-loop management throughout the entire lifecycle from data acquisition to computation, diagnosis, regulation, drug development, and efficacy feedback. This is particularly true for neurodegenerative diseases requiring long-term monitoring, where static models fail to capture the nonlinear dynamics of disease progression.

[0008] Meta's JEPA (Joint Embedding Predictive Architecture) and its visual extension V-JEPA have demonstrated powerful self-supervised representation learning capabilities in general video understanding tasks. However, when applied to the central nervous system, they face challenges such as the non-stationarity of neural signals, low signal-to-noise ratio, high sampling frequency, and complex spectral characteristics. Directly adopting the original JEPA architecture cannot effectively model the temporal evolution of neural signals.

[0009] In summary, the existing technologies have not yet solved the following core problems: (1) secure and unified access and data standardization of neural acquisition devices from multiple vendors; (2) bidirectional closed-loop fusion of multimodal neural data and clinical-R&D data under the premise of privacy protection; (3) construction of an improved dynamic world model and full life cycle state regulation for non-stationary signal characteristics of the central nervous system. Summary of the Invention

[0010] This invention aims to overcome the aforementioned deficiencies of the prior art and provide a technical solution capable of: (a) achieving secure and unified access and data standardization of brain-computer interfaces, neural sensors, and acquisition instruments through IoT interface methods; (b) achieving efficient edge-cloud collaborative computing of multimodal neural data through a combination of MOE and inference models; (c) achieving distributed intelligent recognition and decision-making for neuroscience through a multi-agent system; (d) achieving data cross-mixing and bidirectional enhancement through a clinical-research dual closed loop based on federated learning, differential privacy, and secure multi-party computation, with the original data remaining local; (e) achieving digital simulation, compression, and intelligent analysis of the central nervous system through an improved JEPA, V-JEPA, cross-modal dynamic world model fusion module, and a combined architecture of GNN and Transformer; and (f) ultimately achieving real-time monitoring, risk warning, and state regulation throughout the entire life cycle.

[0011] The technical solution of the present invention is as follows:

[0012] A full-lifecycle intelligent management method based on multimodal neural data fusion and a large-scale life science model of the central nervous system includes the following steps:

[0013] S1. Secure Access Steps for Multimodal Neural Data: An IoT interface is used as the central control system. Through a triple verification process involving encryption authentication, open-source code recognition, and API interface recognition, the system connects to brain-computer interface devices, central nervous system sensors, and central nervous system data acquisition instruments to acquire multimodal neural data. This multimodal neural data includes, but is not limited to: electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), calcium imaging signals, neurotransmitter concentration sensing data, and invasive microelectrode array signals. This triple verification process ensures that only authorized devices that have passed firmware signature verification and API compliance verification can access the system, preventing unauthorized devices from injecting forged neural data.

[0014] S2, Edge-Cloud Collaborative Computing Step: The multimodal neural data is transmitted to a computational architecture combining a hybrid expert model (MOE) and an inference model. The MOE includes dedicated expert sub-networks (temporal experts, imaging experts, graph structure experts) for different neural modalities, dynamically activated by a routing network based on data characteristics. The inference model is responsible for cross-modal temporal alignment and causal inference, generating structured neural representation data. Edge-cloud collaboration refers to: high-frequency, low-latency data (such as EEG and LFP) undergoing real-time preprocessing at the edge, while high-dimensional, complex data (such as fMRI and genomic data) is uploaded to the cloud for deep learning computation.

[0015] S3. Multi-Agent Intelligent Collaborative Recognition Step: The structured neural representation data is input into the multi-Agent collaborative system. Based on the neural data analysis skill blocks already embedded in the skill and process memory bank, historical case experience in the long-term episodic memory vector bank, and the current session context in the short-term working memory area, the system performs task decomposition, agent routing, and conflict resolution through a distributed Harness cluster to generate collaborative recognition results (including but not limited to: abnormal brain region localization, neural circuit dysfunction assessment, disease staging inference, and identification of neurodegenerative disease biomarkers).

[0016] S4. Multi-source heterogeneous data fusion step: The collaborative recognition results are cross-integrated with hospital clinical data information (electronic medical records, laboratory examinations, and image archives), AI clinical diagnostic data information (AI-assisted diagnostic output based on PubMed / Cochrane / UpToDate literature, evidence-based guidelines, and expert consensus), biopharmaceutical drug development data information, wet laboratory data information, and AI drug development process data information to construct a dual closed-loop data flow between clinical and R&D.

[0017] The clinical diagnosis closed loop extracts clinical data from the hospital information system using the HL7 FHIR R4 standard interface. After anonymization using differential privacy (ε-differential privacy, privacy budget ε=1.0), the data is uploaded to a secure computing environment via a federated learning framework (FedAvg or FedProx). The hospital trains the model locally and only uploads the model gradients to the central server; the raw clinical data remains within the hospital's intranet.

[0018] The drug development closed loop uses a standardized RESTful API interface (OAuth 2.0 authentication) to access R&D data from the biopharmaceutical R&D platform. After processing through secure multi-party computation (based on a privacy-preserving matrix computation protocol with secret sharing), the data is jointly modeled with clinical data in the secure computing environment. The original R&D data does not leave the pharmaceutical company's R&D platform.

[0019] The cross-hybrid closed loop achieves bidirectional data augmentation by aggregating gradient parameters through a federated learning central server: the gradients of disease phenotypic features from the clinical end and the gradients of drug response features from the R&D end are weighted and aggregated on the central server to generate a globally optimized model, which is then distributed to the hospital and R&D ends respectively, achieving bidirectional augmentation of "the model moves while the data remains stationary." The federated learning task is allocated using a consistent hashing algorithm to ensure balanced computational load at both ends.

[0020] S5. Trusted Storage and Backhaul Steps: The dual closed-loop data streams are written to a computer-readable storage medium via the IoT interface method. The storage medium serves as a trusted data node in the central control system, employing the same triple verification and identification method as in the access phase to ensure data access security.

[0021] S6. Large Model Deep Analysis Steps: The trusted data source is read by the large model of the central nervous system life science. The large model adopts a combined architecture:

[0022] LLM Foundation: Based on medical augmented large language models (such as Llama-3-70B medical fine-tuning version), it processes semantic understanding and generation of clinical texts, drug development literature and neuroscience knowledge.

[0023] The JEPA module addresses the non-stationary nature of neural signals. It replaces the spatial masking strategy of the original V-JEPA with a temporal masking strategy. The masking ratio is dynamically adjusted based on the signal non-stationarity index, which is obtained by calculating the rate of change of signal variance within a sliding window. The predictor employs a hybrid LSTM-Attention structure: the LSTM layer captures the long-range temporal dependencies of the neural signal, while the attention layer dynamically focuses on task-related neural oscillation frequency bands (such as the beta band in Parkinson's disease and spike-and-wave complexes in epilepsy). The loss function introduces a frequency domain consistency constraint term on top of the standard JEPA contrastive loss. By calculating the KL divergence between the predicted embedding and the target embedding on the power spectral density, the model maintains the frequency characteristics of the neural signal in the representation space.

[0024] V-JEPA module: Employs a self-supervised video representation learning method, taking fMRI 4D data (3D space + time) or calcium imaging video sequences as input to extract visual features without manual annotation.

[0025] The cross-modal dynamic world model fusion module maps the temporal embeddings output by the JEPA module and the visual embeddings output by the V-JEPA module to a unified representation space through a learnable cross-modal bridging layer (multilayer perceptron + layer normalization); then it fuses them with the brain network topology embeddings output by the GNN module through a graph-sequence attention mechanism, so that the temporal dynamic features and spatial topology features mutually enhance each other; finally, a dynamic world model of the central nervous system is constructed to support counterfactual reasoning and state prediction.

[0026] GNN module: Uses multi-scale graph neural network to model multi-scale structure-function relationship graph of neurons-brain regions-whole brain, and the edge weights are dynamically adjusted according to the correlation between structural connection strength and functional connection.

[0027] Transformer module: Performs global attention modeling on fused multi-scale temporal neural data to capture long-range temporal dependencies.

[0028] Each module interacts with the graph-sequence attention mechanism through the aforementioned cross-modal bridging layer, achieving deep coupling between multimodal neural data and biomedical knowledge, and generating full-lifecycle neural state prediction and intervention strategies.

[0029] S7. Closed-loop feedback step: Based on the intervention strategy, the central control system sends control commands (such as deep brain stimulation parameter adjustment, closed-loop neurofeedback training program, and drug delivery pump dosage command) to the brain-computer interface device, central nervous system sensor and central nervous system acquisition instrument to realize real-time monitoring and state control of the central nervous system.

[0030] The present invention also provides a corresponding system, including a multimodal neural data secure access module, an edge-cloud collaborative computing module, a multi-agent intelligent collaborative identification module, a multi-source heterogeneous data fusion module, a trusted storage and backhaul module, a central nervous system life science large model module, and a closed-loop feedback module.

[0031] Furthermore, the IoT interface method employs a four-dimensional module: the first-dimensional module verifies the initial ID through public-key encryption / private-key decryption; the second-dimensional module verifies the free open-source ID through a BSD open-source code database; the third-dimensional module verifies the development authorization command code through an API interface database; and the fourth-dimensional module establishes a secure data channel based on the authorization command code. The BSD open-source code database stores various BSD open-source code variants, compatible with customized modifications to the open-source system by different vendors; the API interface database stores various API interface specifications, supporting protocol adaptation for devices from different vendors.

[0032] Furthermore, the multi-agent collaborative system includes a unified collaborative framework layer, a multi-level long-term memory system, N domain agents, and a distributed Harness cluster. The skill and process memory monitors the execution frequency and success rate of subtasks, and automatically solidifies them into parameterized DAG skill blocks when thresholds are met. This solidification is automatically triggered by the system without manual preset. The Harness cluster maintains state consistency through the Raft protocol, and log entries record the execution state changes of each subtask in the task DAG.

[0033] Furthermore, the AI ​​drug development process data includes gene editing tool protein directed evolution optimization data based on multi-scale mutation perception graph networks and adaptive temporal evolution modeling, which is used for virtual screening and activity prediction of gene therapy drugs for the central nervous system.

[0034] The beneficial effects of this invention compared to the prior art are as follows:

[0035] 1. Solves the problem of secure and unified access for neural data acquisition devices from multiple vendors. Through triple verification and identification using the IoT interface method, it achieves encrypted authentication and API-level secure interface for brain-computer interfaces, neural sensors, and acquisition instruments, is compatible with BSD open-source code variants from different vendors, and significantly improves device access compatibility compared to traditional proprietary protocol solutions, while eliminating attacks by counterfeit devices from the data source.

[0036] 2. It achieves efficient collaborative computation of multimodal neural data. The MOE+ inference model combination architecture dynamically activates expert subnetworks for different neural modalities, avoiding computational redundancy caused by using the same network for all modalities. The edge-cloud collaboration mechanism controls the processing latency of high-frequency data to the millisecond level, and the overall computational efficiency is improved by more than 40% compared with a single model.

[0037] 3. A multi-agent intelligent collaborative mechanism for neuroscience was established. Through automatic skill block solidification and three-layer memory fusion, the repetitive decomposition rate of similar neuroanalysis tasks was reduced to 0%, the number of communication rounds between agents was reduced by 40%-50%, and historical successful experiences were continuously injected into the current task through a long-term contextual memory vector library, improving the recognition accuracy by 6-13 percentage points compared to memoryless systems.

[0038] 4. Pioneering a dual-loop data fusion architecture for clinical and R&D based on privacy-preserving computation. Through the combined application of differential privacy, federated learning, and secure multi-party computation, bidirectional data enhancement is achieved without leaving the local environment for raw clinical and R&D data. This breaks down data silos and meets the compliance requirements of the Personal Information Protection Law, the Data Security Law, and cross-border medical data transfer. The drug development cycle is expected to be shortened by more than 50%.

[0039] 5. An improved dynamic world model for non-stationary neural signals was constructed. The JEPA model was improved by modifying temporal masking, LSTM-Attention predictors, and frequency-domain constraint loss to suit the characteristics of neural signals. Furthermore, a deep fusion of temporal-visual-topological triple embedding was achieved through a cross-modal bridging layer and a graph-sequence attention mechanism. This significantly improved the accuracy of neural state prediction compared to traditional static models and the general JEPA model, and enabled the construction of personalized neural digital twins for each patient.

[0040] 6. A complete closed loop from data acquisition to state regulation has been achieved. By issuing instructions to the neuromodulation devices through the central control system, a complete closed loop of "perception-computation-decision-regulation-re-perception" is formed, providing an engineered solution for the full life cycle management of chronic neurological diseases such as Parkinson's disease, Alzheimer's disease, and intractable epilepsy. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall architecture of the intelligent management system for the entire life cycle based on multimodal neural data fusion and a large-scale life science model of the central nervous system, as described in this invention.

[0042] Figure 2 This is a flowchart of the four-fold verification and identification process of the IOT interface method in this invention.

[0043] Figure 3 This is a schematic diagram of the MOE+ inference model combination architecture of the edge-cloud collaborative computing module in this invention.

[0044] Figure 4 This is a schematic diagram of the three-layer memory intervention task decomposition logic of the multi-Agent intelligent collaborative identification module in this invention.

[0045] Figure 5 This is a schematic diagram of the cross-hybrid fusion of clinical and R&D dual closed-loop data flows in this invention, including the engineering implementation of federated learning, differential privacy, and secure multi-party computation.

[0046] Figure 6 This is a schematic diagram of the combined architecture of the central nervous system life science big model of the present invention, showing the interaction between the improved JEPA, V-JEPA, cross-modal dynamic world model fusion module, GNN and Transformer.

[0047] Figure 7 This is a flowchart illustrating the data generation process for the protein-directed evolution optimization of gene editing tools based on a multi-scale mutation-aware graph network in this invention.

[0048] Figure 8 This is a flowchart illustrating the closed-loop intervention process for the full life-cycle management of Parkinson's disease in a specific embodiment of the present invention. Detailed Implementation

[0049] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0050] Example 1: Deployment of an Intelligent Management System for the Entire Life Cycle of Parkinson's Disease

[0051] 1. System Architecture Deployment

[0052] like Figure 1 As shown, the system of this invention is deployed in the neurology department of a hospital and the R&D center of a cooperating pharmaceutical company.

[0053] The multimodal neural data security access module (central control system) is deployed at the hospital's intranet edge gateway. It connects to the following devices via an IoT interface: - Medtronic Percept™ PC neurostimulator (brain-computer interface / neuromodulation device), acquiring local field potential (LFP) signals from the subthalamic nucleus (STN) at a sampling frequency of 250Hz; - Wearable EEG headband (central nervous system sensor), acquiring cortical EEG signals with 32 channels and a sampling frequency of 500Hz; - Hospital 3.0T MRI scanner (central nervous system acquisition instrument), acquiring structural images (T1-weighted) and resting-state functional images (rs-fMRI) at a TR=2000ms.

[0054] The access process strictly follows four-factor authentication (FSA). Figure 2 The process is as follows: First dimension: The central control system receives the device access request. The device uses an RSA-2048 public key to encrypt the access request, and the central control system decrypts and verifies the device's unique initial ID using the corresponding private key. Second dimension: Based on the initial ID, the system calls a pre-stored BSD open-source code database to verify the device firmware open-source code signature (such as FreeBSD or OpenBSD variants) to prevent firmware tampering or unauthorized firmware flashing. Third dimension: Based on the free open-source code ID, the system calls an API interface database to verify the compliance of the device data reporting API (such as HL7 FHIR R4 or a custom neural data protocol) and generates a development authorization command code. Fourth dimension: Based on the authorization command code, a TLS 1.3 encrypted channel is established, and the transmission of multimodal neural data begins.

[0055] 2. Edge-Cloud Collaborative Computing

[0056] like Figure 3As shown, the MOE+inference model combination is deployed on the hospital's private cloud. For continuously collected EEG, LFP, and fMRI data from Parkinson's disease patients: - A temporal expert subnetwork (based on LSTM-Attention) processes the β-band (13-30Hz) oscillation characteristics of LFP, extracting the duration and amplitude of β bursts; - An imaging expert subnetwork (based on 3D-CNN) processes the basal ganglia-cortical circuit activation map from fMRI, extracting the functional connectivity strength matrix; - A graph structure expert subnetwork (based on GNN) processes the brain functional connectivity matrix, identifying key hub brain regions; - A routing network dynamically activates the above experts based on the data quality of the day, gating and fusing them to generate structured neural representation data, including the motor symptom severity index (UPDRS-III prediction score) and the probability of frozen gait risk.

[0057] 3. Multi-Agent Intelligent Collaborative Recognition

[0058] Structured neural representation data is fed into a multi-agent system. The system includes the following pre-configured agents: - Agent-A (Motor Symptom Analysis Agent): Specializes in UPDRS scale association analysis; its toolset includes a kinematic analysis library. - Agent-B (Sleep Disorder Analysis Agent): Specializes in REM sleep EEG feature analysis; its toolset includes sleep staging algorithms. - Agent-C (Cognitive Assessment Agent): Specializes in MoCA scale and brain network topology association; its toolset includes a cognitive assessment knowledge graph.

[0059] The system queries the skill and process memory bank and finds a solidified "Parkinson's Disease Daytime Symptom Assessment" skill block (automatically solidified after 12 executions of a historical task with a 92% success rate). This skill block's DAG includes: LFP feature extraction → motor symptom scoring → sleep disturbance correction → cognitive function cross-validation. The skill block is directly matched, skipping LLM intent recognition. The Harness cluster schedules the subtask to the corresponding Agent via consistent hashing. Before Agent execution, three layers of memory are fused: short-term working memory provides the patient's medication record from yesterday (levodopa 125mg, taken at 07:30); the long-term episodic memory vector bank retrieves treatment response experiences from 3 similar cases (Top-K=3); and the skill block provides the standard DAG process. The outputs of each Agent are weighted and voted on by the conflict resolution module to generate a collaborative recognition result: the patient is currently in Hoehn-Yahr stage 2.5, with a 68% risk of frozen gait and mild cognitive decline (MoCA prediction of 22 points), suggesting adjustment of stimulus parameters.

[0060] 4. Closed loop between clinical trials and research and development

[0061] The collaborative identification results are fed into the multi-source heterogeneous data fusion module. Figure 5 ):

[0062] Clinical diagnostic closed loop: The hospital deploys an HL7 FHIR R4 standard interface adapter to extract patient historical images from the PACS system and medication records from the EMR system. After ε-differential privacy anonymization (ε=1.0, injecting calibrated Laplace noise into the image feature vectors), the data is used to train a clinical diagnostic sub-model on local edge nodes. Only the model gradients (not the original images or medical records) are uploaded to the federated learning central server.

[0063] Closed-loop drug development: Collaborating pharmaceutical companies deploy RESTful API gateways (OAuth 2.0 certified) on their R&D platforms to upload preclinical data of investigational AAV vector gene therapy drugs. After processing via a secure multi-party computation protocol (based on Shamir's secret-sharing matrix multiplication protocol), the data is jointly modeled with clinical gradient data in a secure computing environment (based on Intel SGX's trusted execution environment).

[0064] Cross-loop hybrid approach: The central server of the federated learning uses the FedAvg algorithm to aggregate model gradients from both the hospital and pharmaceutical companies, generating a globally optimized model. After the global model is distributed to both ends, the hospital uses the updated model to predict the probability of patient response to candidate drugs; the pharmaceutical company uses the updated model to optimize drug structure design. In the next federated learning cycle, the two ends only exchange updated gradients, and the original data never leaves their local machines.

[0065] 5. In-depth analysis and intervention of large models

[0066] The dual closed-loop data streams are encrypted and transmitted back via the IoT interface, then written to the trusted storage node of the central control system. Figure 6 Read data:

[0067] The LLM platform (based on Llama-3-70B Medical Enhanced Edition) analyzes clinical texts and literature to extract relevant evidence on "STN-DBS frequency adjustment for improving frozen gait".

[0068] The JEPA module processes the patient's LFP time-series data from the past 30 days. Specific improvements include: windowing the LFP signal (window length 2s, step size 0.5s), and calculating the variance of the β-band power spectrum within each window as a non-stationarity index. The masking ratio is dynamically adjusted based on the non-stationarity index: when the index is above a threshold of 0.3, the masking ratio decreases to 15% (retaining more signal for prediction); when it is below the threshold, the masking ratio increases to 40%. The predictor uses a 2-layer LSTM (256-dimensional hidden layers) + 4-head attention structure, with a loss function of contrast loss + 0.1 × power spectral density KL divergence. The learned neural state embedding predicts the fluctuation curve of motor symptoms for the next 7 days.

[0069] The V-JEPA module processes resting-state fMRI 4D data (dividing the 3D space + time series into 3D spatiotemporal blocks, with a block size of 32×32×32×4), performs self-supervised visual representation learning, and outputs visual embeddings.

[0070] A cross-modal dynamic world model fusion module: JEPA temporal embeddings (256-dimensional) and V-JEPA visual embeddings (256-dimensional) are mapped to a unified representation space (512-dimensional) via a two-layer MLP cross-modal bridging layer (512-dimensional hidden layers, layer normalization). The GNN module outputs a brain network topological embedding (128-dimensional, based on the substantia nigra-striatum-cortex functional connectivity map). Through a graph-sequence attention mechanism: the unified representation is treated as a sequence query, and the topological embedding as a graph key value, multi-head attention (8 heads, 64 dimensions / head) is calculated, enabling the temporal dynamic features and spatial topological features to mutually enhance each other. The fused representation is globally modeled using a 6-layer Transformer (512-dimensional hidden layers, 8-head attention) to generate a 90-day intervention strategy: the deep brain stimulation frequency is adjusted from 130Hz to 145Hz, and the pulse width is increased from 60μs to 90μs; combined with a compassionate use application for investigational gene therapy drugs.

[0071] 6. Closed-loop feedback

[0072] The central control system sends parameter adjustment commands to the Percept™ PC stimulator via an IoT secure interface (transmitted after triple verification of device identity) and sends a neurofeedback training schedule (twice daily, 20 minutes each time) to the patient's wearable device. The system then enters the next real-time monitoring cycle, with LFP data continuously transmitted at a frequency of one frame every 250ms.

[0073] Example 2: AI-assisted directed evolution of gene therapy drugs for Alzheimer's disease

[0074] This embodiment illustrates the specific generation method of data information for the entire AI drug development process in the closed loop of drug development, especially involving the directed evolution optimization data of gene editing tool proteins based on multi-scale mutation-aware graph networks and adaptive temporal evolution modeling.

[0075] 1. Data Preparation

[0076] Targeting the CRISPR-Cas9 gene editing system for treating Alzheimer's disease, we conducted directed evolution on SpCas9 variants targeting the Swedish mutation site (KM670 / 671NL) of the APP gene. We obtained 5000 variant sequence-functional data pairs from previous wet experiments, distributed across 10 rounds of directed evolution experiments (500 variants per round). Functional data included: target site editing efficiency (%), number of off-target sites across the entire genome (determined by GUIDE-seq), and specificity score (0-100).

[0077] 2. Multi-scale Seq2Graph Transformation

[0078] The SpCas9 variant sequence was converted into multi-layer graph structure data using the multi-scale Seq2Graph method: - Short-range contact layer (<5 Å): capturing hydrogen bonding and salt bridge interactions between Cas9 and PAM sequences and target DNA; - Mid-range contact layer (5-8 Å): capturing allosteric regulatory effects of the REC2 / REC3 domain; - Long-range contact layer (8-12 Å): capturing long-range conformational coupling of the HNH / RuvC nuclease domain.

[0079] The initial node features are a concatenated vector (1283-dimensional) of 1280-dimensional residue embeddings extracted by ESM-2 (version esm2_t33_650M_UR50D) and one-hot encoded 3-dimensional mutation types. The 3D structure is derived from AlphaFold2 (version 2.3) predictions.

[0080] 3. MSMA-GAT Feature Extraction and Bi-objective Prediction

[0081] A 3-layer MSMA-GAT is used, with 12 attention heads per layer, divided into a short-range head group (4 heads), a mid-range head group (4 heads), and a long-range head group (4 heads). The first layer has 1283-dimensional input and 1024-dimensional output; the second layer has 1024-dimensional input and 1024-dimensional output; and the third layer has 1024-dimensional input and 1024-dimensional output.

[0082] An initial gain bias of 0.3 is applied to the mutation site and its first-order structural neighbors (directly connected by proximal contact edges), which is then fine-tuned as a learnable parameter during training via backpropagation.

[0083] After each head group independently calculates its attention coefficient, it is then weighted and fused using learnable scale-gated weights (three scalars, normalized by Softmax). After three layers of message passing, the node features of each layer are weighted, aggregated, and globally averaged to obtain a 1024-dimensional overall protein sequence representation vector.

[0084] A two-branch fully connected network was used to simultaneously predict editing efficiency (%) and off-target specificity scores (0-100). Shared layers: Layer 1 1024→512, Layer 2 512→256, activation function ReLU, dropout rate 0.1. Independent branch layers: editing efficiency branch 256→1, specificity branch 256→1. Loss function was MSE (efficiency) + MSE (specificity), optimizer Adam, learning rate 0.001, batch size 64.

[0085] 4. Adaptive temporal evolution modeling

[0086] The training baseline was 10 rounds of historical directed evolution experiments.

[0087] Calculate the kernel MMD (maximum mean difference) of the variant sequence distribution between adjacent rounds: Based on the ESM-2 embedding of the variant sequences in each round, the MMD value between rounds 5 and 6 is 0.12, and between rounds 6 and 7 is 0.08, indicating that the sequence distribution drift slows down after round 5.

[0088] Calculate the cumulative mutation density: Estimate the three-dimensional spatial kernel density (bandwidth 3 Å) of newly added mutation sites in each round. The peak growth of kernel density slows down after round 5, indicating that mutation exploration is approaching saturation.

[0089] Iterate through N=3 to N=8, calculating the Spearman correlation coefficient ρ and the training-validation MMD value for each N value. The weighted sum is optimal when N=5 (ρ=0.91, MMD=0.15); when N=6, ρ slightly increases to 0.912 but MMD increases to 0.18. Determine the optimal split point N*=5, i.e., training on data in rounds 1-5 and validation on data in rounds 6-10.

[0090] Ranking stability verification: K=5 independent training-validation trials were performed with N=5. The Spearman correlation coefficients were 0.905, 0.912, 0.908, 0.914, and 0.910, respectively, and the variance σ²=0.0011, which is lower than the threshold of 0.005, indicating that the model is stable.

[0091] 5. Pareto dynamic tradeoffs and confidence calibration

[0092] Predictions were made on the SpCas9 full-site saturated mutation library (approximately 3200 single-point mutation variants), and a two-dimensional prediction scatter plot of editing efficiency-specificity score was constructed to extract the Pareto front non-dominated solution set (47 variants in total).

[0093] Frontier sparsity was calculated: the average spacing for the efficiency dimension was 0.18, and the average spacing for the specificity dimension was 0.12. The higher sparsity in the efficiency dimension indicates that there is still room for exploration in high-efficiency directions. The weights were dynamically adjusted: α=0.7, β=0.3 (α+β=1). The top 20 candidate variants were recommended from the non-dominated solution set, ranked by their comprehensive scores.

[0094] After wet testing, prediction bias was calculated. The prediction efficiency of the candidate variant SpCas9-M7 was 85.2%, compared to 58.1% in the experimental results. The bias exceeded the 95% confidence interval and was therefore marked as an anomaly. Its representation vector and bias value were input into a confidence calibration sub-network (a 2-layer residual network with 128 hidden layers) to learn the bias correction pattern. The calibrated model parameters were then used to update the main network.

[0095] After three rounds of iterations, the optimal variant achieved an editing efficiency of 90.1%, a specificity score of 95, and an off-target rate of 0.19% (the wild-type SpCas9 achieved an editing efficiency of 45.3%, a specificity score of 62, and an off-target rate of 0.56%).

[0096] 6. Closed-loop fusion

[0097] The aforementioned directed evolutionary optimization data serves as the core input to the drug development closed loop. It is cross-fused with clinical data from the clinical diagnosis closed loop, including APOE genotype (ε4 / ε4), cerebrospinal fluid Aβ42 / Tau ratio (0.52), and fMRI default mode network (DMN) connectivity strength of Alzheimer's patients, through a federated learning framework. The fusion process employs secure multi-party computation: the hospital provides an encrypted share of patient genotype and biomarker data, while the pharmaceutical company provides an encrypted share of the drug response model. Both parties jointly calculate personalized response scores in a trusted execution environment, and the results are fed back to both ends after differential privacy processing. The fusion results correct the target selection strategy for SpCas9 variants: prioritizing variants with higher affinity for APP Swedish mutations over universally highly active variants.

[0098] Example 3: Closed-loop neuromodulation of refractory epilepsy

[0099] 1. Scene Description

[0100] The patient has drug-resistant temporal lobe epilepsy and has had a NeuroPace RNS System (Reactive Neurostimulation System) and subcutaneous EEG electrodes implanted. Traditional RNS systems only use fixed-parameter stimulation based on the detection of abnormal discharges in local field potentials, lacking dynamic modeling of the patient's whole-brain network state and prospective intervention.

[0101] 2. Multimodal data access

[0102] The central control system is connected via IoT interface to: - RNS System (Brain-Computer Interface / Neuromodulation Device), which collects local field potentials in the hippocampus at a sampling frequency of 500Hz; - Wearable multi-lead EEG (Central Nervous System Sensor), which collects EEG data from the prefrontal-temporal cortex; - Hospital PET-MRI (Central Nervous System Acquisition Instrument), which collects images of glucose metabolism and structural fusion.

[0103] Triple verification ensures secure data transmission in implantable devices, preventing wireless channels from being eavesdropped on or injected with malicious stimuli.

[0104] 3. Edge-cloud collaborative computing and multi-agent identification

[0105] The MOE routing network identifies the current data modality as a combination of "high-frequency electrophysiology + metabolic imaging," activating time series experts and metabolic imaging experts. In the multi-agent system: - Agent-D (Epileptic Focal Point Localization Agent) locates the epileptogenic network based on historical SEEG data; - Agent-E (Seizure Prediction Agent) processes LFP features using LSTM to predict the pre-seizure state; - Agent-F (Stimulation Strategy Agent) optimizes stimulation parameters based on historical stimulus response data.

[0106] The "Pre-seizure Intervention for Temporal Lobe Epilepsy" skill block (executed 8 times, success rate 87%) has been solidified in the skill and process memory bank. The DAG structure is: LFP preprocessing → feature extraction → seizure prediction → stimulus parameter calculation → stimulus execution → response evaluation. The Harness cluster schedules the above agents to execute in parallel.

[0107] 4. Closed loop between clinical trials and research and development

[0108] Clinical diagnosis closed loop: The hospital extracts the patient's 5-year seizure diary and antiepileptic drug blood concentration monitoring data through the HL7 FHIR R4 interface. After desensitization by ε-differential privacy (ε=1.0), the seizure prediction sub-model is trained locally and the gradient is uploaded to the federated server.

[0109] Closed-loop drug development: Preclinical data of potassium channel openers (Retigabine analogues) from collaborating pharmaceutical companies are uploaded after secure multi-party computation processing. AI models are jointly trained in a federated environment, predicting a 0.75 probability of response to the novel potassium channel modulator in this patient.

[0110] Cross-linked closed-loop model: The federated server aggregates gradients from the hospital and pharmaceutical companies to generate a global model. The hospital optimizes the onset prediction threshold based on the global model; the pharmaceutical company corrects the affinity parameters of compounds for the KCNQ2 / 3 mutation channels based on the global model.

[0111] 5. Large-scale model intervention strategies

[0112] An improved JEPA module in the large-scale life science model of the central nervous system processes patients' LFP data from the past 30 days. Non-stationarity index calculation: The standard deviation of the power ratio of the β band (13-30Hz) to the γ band (30-80Hz) is calculated every 2-second window as a measure of the non-stationarity of epileptic activity. Masking ratio dynamic adjustment: When the non-stationarity index > 0.5 (a preictal characteristic), the masking ratio is reduced to 10% to retain more preictal warning signals. The predictor LSTM layer (2 layers, 256 dimensions) learns the state evolution from interictal to preictal, while the attention layer focuses on spike rhythms.

[0113] The cross-modal dynamic world model fusion module integrates JEPA temporal embedding (pre-seizure state sequence), V-JEPA visual embedding (fMRI hippocampal-cortical network dynamics), and GNN topological embedding (epileptic network connectivity graph), and inputs them into the Transformer after fusion through a graph-sequence attention mechanism. The model predicts that if a 50Hz stimulus with a 2mA current and a 160μs pulse width is applied within 300ms after the LFP detects a high-frequency oscillation (HFO, 80-500Hz), the seizure blocking probability can be increased to 78%.

[0114] The central control system sends stimulation parameter update instructions to the RNS System through the IoT security interface, realizing a paradigm shift from "responding after detection of an attack" to "predicting and blocking before an attack".

[0115] Example 4: Precision drug delivery and gene therapy for gliomas

[0116] 1. Scene Description

[0117] The patient has IDH1-mutant glioblastoma (GBM) and has undergone surgical resection and standard radiotherapy and chemotherapy. Recurrence is being monitored. It is necessary to integrate data from intraoperative neurophysiological monitoring, postoperative imaging follow-up, and gene therapy drug development to achieve recurrence early warning and precise intervention.

[0118] 2. Multimodal data access

[0119] Central control system integration: - Intraoperative cortical electrode array (brain-computer interface device) to acquire tumor boundary functional area localization data; - Implantable intracranial pressure and pH sensor (central nervous system sensor) to monitor the tumor microenvironment; - Hospital 7.0T MRI and liquid biopsy platform (central nervous system acquisition instrument) to acquire high-resolution images and ctDNA data.

[0120] 3. Multi-agent collaboration and dual closed loop

[0121] The multi-agent system performs the task of "glioma recurrence monitoring and intervention": - Agent-G (radiomics agent) analyzes tumor perfusion and diffusion characteristics on 7.0T MRI; - Agent-H (molecular pathology agent) analyzes the IDH1 mutation abundance and methylation status of ctDNA; - Agent-I (drug delivery agent) optimizes the time window for opening the blood-brain barrier using focused ultrasound (FUS).

[0122] Clinical-R&D Dual Closed Loop: - Clinical Diagnosis Closed Loop: At the hospital level, RANO standard imaging assessment, neurological function scores (KPS), and AI diagnostic data based on NCCN guidelines are integrated through the HL7 FHIR R4 interface to determine the current stage of differential diagnosis between pseudoprogression and true relapse. Data is locally trained to train the image-molecular joint diagnostic model after differential privacy anonymization. - Drug R&D Closed Loop: At the pharmaceutical company level, data on IDH1 mutation-targeted CAR-T cell therapy drugs under development undergoes secure multi-party computation processing. CAR structures optimized based on multi-scale mutation-aware graph networks (scFv affinity-directed evolution data) show a 2.3-fold improvement in recognition specificity for IDH1 R132H mutant peptides. - Cross-Mixed Closed Loop: The federated learning server aggregates clinical imaging gradients and R&D CAR-T structure optimization gradients. The clinical end obtains optimized CAR-T intracranial delivery time window predictions; the R&D end obtains real-time tumor microenvironment monitoring data (pH, lactate concentration) to guide CAR-T cell metabolic adaptive modification.

[0123] 4. Large-scale model analysis and intervention

[0124] The cross-modal dynamic world model fusion module integrates intraoperative electrophysiological embedding (functional area localization), postoperative imaging embedding (tumor volume dynamics), and metabolic sensing embedding (pH / lactate timing) to construct a dynamic world model of the tumor-immune-neural microenvironment. The GNN module models the spatial competition between tumor-infiltrating lymphocytes and neurons. The Transformer module generates an intervention strategy: within a time window of intracranial pressure <15 mmHg and pH >7.2, CAR-T cells are delivered after opening the blood-brain barrier via FUS, which is expected to reduce tumor burden by 40%.

[0125] Example 5: Comparison and Verification of Technical Effects

[0126] Under the same hardware environment (NVIDIA H100 cluster, 8 interconnected cards, NVIDIA IGX Orin deployed at the hospital edge), and using Parkinson's disease management tasks as a benchmark, the system of this invention is quantitatively compared with existing technical solutions:

[0127] Test metrics Traditional BCI + Hospital HIS General LLM + Multi-Agent This invention system Device access security Single-factor authentication (password) Single-factor authentication (API Key) Triple verification + BSD open-source code recognition Multimodal data fusion latency >500ms (centralized processing) 300ms (cloud processing) <80ms (MOE edge dynamic routing) Similar task duplication rate Not applicable (No Agent) 100% (each LLM decomposition) 0% (Skill blocks are directly invoked after being solidified) Interoperability of clinical and R&D data No (physical isolation) None (No data interface) Two-way closed loop (federated learning + differential privacy) Neural state prediction p (Spearman) 0.62 (Static SVM) 0.71 (General LLM Inference) 0.89 (Improved JEPA+ Dynamic World Model) Success rate of drug development candidate variant recommendations not applicable not applicable 85% (Top 20 dual-target type outperforms wild type) Task completion time (complex query) >120s (manual retrieval) 45s (General Agent Collaboration) 22s (Skill Block + Memory Fusion) Wet experiment validation count (3 iterations) 10,000 (traditional screening) not applicable 45 (AI-directed evolution + calibration closed loop) Data privacy compliance None (plaintext transmission) None (centralized storage) This meets the requirements of (differential privacy ε=1.0+ federated learning).

[0128] Experimental results show that this invention significantly outperforms existing technologies in terms of data security, computational efficiency, collaborative intelligence, prediction accuracy, drug development costs, and privacy compliance. In particular, by employing a dual closed-loop clinical-development model based on federated learning and differential privacy, it achieves bidirectional data augmentation without requiring raw data to leave the local environment. This reduces the traditional 12-week drug screening cycle to 4 weeks, decreases the number of wet experimental validations by 99.55%, and meets the compliance requirements for medical and health data under China's Personal Information Protection Law and Data Security Law.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention should still fall within the scope of the present invention.

Claims

1. A full-lifecycle intelligent management method based on multimodal neural data fusion and a large-scale life science model of the central nervous system, characterized in that, Includes the following steps: S1. Multimodal neural data secure access steps: Using the IoT interface method as the central control system, the system accesses brain-computer interface devices, central nervous system sensors and central nervous system acquisition instruments through a triple verification and identification method of access encryption authentication, open source code recognition and API interface recognition to acquire multimodal neural data. S2, Edge-Cloud Collaborative Computing Steps: The multimodal neural data is transmitted to a computing architecture that combines a hybrid expert model and an inference model, and features of the multimodal neural data are extracted, time-series aligned and preliminarily parsed to generate structured neural representation data; S3. Multi-Agent Intelligent Collaborative Recognition Steps: Input the structured neural representation data into the multi-Agent collaborative system, and perform distributed task decomposition, routing and conflict resolution based on the fixed skill blocks in the skill and process memory bank, the historical task experience in the long-term contextual memory vector bank and the current session context in the short-term working memory area to generate collaborative recognition results. S4. Multi-source heterogeneous data fusion step: The collaborative recognition results are cross-fused with hospital clinical data, AI clinical diagnostic data, biopharmaceutical drug development data, wet laboratory data, and AI drug development process data to construct a dual closed-loop data flow between clinical and R&D. Specifically, the clinical diagnostic closed loop extracts clinical data from the hospital information system using the HL7 FHIRR4 standard interface, performs differential privacy anonymization, and uploads it to a secure computing environment via a federated learning framework. The drug development closed loop accesses R&D data from the biopharmaceutical R&D platform using a standardized RESTful API interface, performs secure multi-party computation processing, and then performs joint modeling with clinical data in the secure computing environment. The cross-fusion closed loop achieves bidirectional data augmentation by aggregating gradient parameters through a federated learning central server, ensuring that neither the original clinical data nor the R&D data leaves the local machine. S5. Trusted storage and backhaul step: The dual closed-loop data stream is written into a computer-readable storage medium through the IoT interface method as a trusted data source; S6. Large-scale model deep analysis steps: The large-scale life science model of the central nervous system reads the trusted data source, performs digital simulation, compression, and intelligent analysis of the central nervous system, and generates full-life-cycle neural state prediction and intervention strategies; the large-scale model includes: an LLM base for semantic understanding of clinical text and neuroscience knowledge; a JEPA module for learning the joint embedding space of the temporal evolution of neural signals. The JEPA module uses a temporal masking strategy to replace the spatial masking for the non-stationary characteristics of neural signals, and its predictor adopts an LSTM-Attention hybrid structure and introduces a frequency domain constraint term in the loss function; V The JEPA module is used for self-supervised visual representation learning of neural image video streams; the cross-modal dynamic world model fusion module is used to map the temporal embeddings of the JEPA module and the visual embeddings of the V-JEPA module to a unified representation space through a learnable cross-modal bridging layer, and then fuse them with the brain network topology embeddings of the GNN module through a graph-sequence attention mechanism to construct a dynamic world model of the central nervous system; the GNN module is used to model the multi-scale structure-function relationship graph of neurons-brain regions-whole brain; the Transformer module is used to perform global attention modeling on the fused multi-scale temporal neural data. S7. Closed-loop feedback step: Based on the intervention strategy, the central control system sends control commands to the brain-computer interface device, central nervous system sensor and central nervous system acquisition instrument to realize real-time monitoring and state control of the central nervous system.

2. A full-lifecycle intelligent management system based on multimodal neural data fusion and a large-scale life science model of the central nervous system, characterized in that, include: The multimodal neural data secure access module is configured to use an IoT interface as the central control system. It uses a triple verification method of access encryption authentication, open source code recognition, and API interface recognition to access brain-computer interface devices, central nervous system sensors, and central nervous system acquisition instruments to acquire multimodal neural data. The edge-cloud collaborative computing module includes a computing architecture that combines a hybrid expert model and an inference model. It is used to extract features, align time sequences, and perform preliminary analysis on the multimodal neural data to generate structured neural representation data. The multi-agent intelligent collaborative recognition module includes a multi-agent collaborative system. The multi-agent collaborative system performs distributed task decomposition, routing, and conflict resolution on the structured neural representation data based on the fixed skill blocks in the skill and process memory bank, the historical task experience in the long-term contextual memory vector bank, and the current session context in the short-term working memory area, and generates collaborative recognition results. The multi-source heterogeneous data fusion module is used to cross-mix and fuse the collaborative recognition results with hospital clinical data information, AI clinical diagnostic data information, biopharmaceutical drug development data information, wet laboratory data information, and AI drug development process data information to construct a dual closed-loop data flow between clinical and R&D. The multi-source heterogeneous data fusion module includes: a clinical diagnosis closed-loop unit, configured to extract clinical data from the hospital information system using the HL7 FHIR R4 standard interface, and upload it to a secure computing environment via a federated learning framework after differential privacy anonymization; a drug development closed-loop unit, configured to access development data from the biopharmaceutical development platform using a standardized RESTful API interface, and perform joint modeling with clinical data in the secure computing environment after secure multi-party computation processing; and a cross-hybrid closed-loop unit, configured to achieve bidirectional data augmentation by aggregating gradient parameters through a federated learning central server, with neither the original clinical data nor the development data leaving the local machine. A trusted storage and backhaul module is used to write the dual closed-loop data streams into a computer-readable storage medium via the IoT interface method. The Central Nervous System Life Science Large Model Module is used to read the trusted data source, perform digital simulation, compression, and intelligent analysis of the central nervous system, and generate full-life-cycle neural state prediction and intervention strategies. This large model module includes: an LLM base for semantic understanding of clinical text and neuroscience knowledge; a JEPA module for learning the joint embedding space of the temporal evolution of neural signals. The JEPA module uses a temporal masking strategy instead of a spatial mask to address the non-stationary characteristics of neural signals, and its predictor employs an LSTM-Attention hybrid structure and introduces a frequency domain constraint term into the loss function; and V-JE. The PA module is used for self-supervised visual representation learning of neural image video streams; the cross-modal dynamic world model fusion module is used to map the temporal embeddings of the JEPA module and the visual embeddings of the V-JEPA module to a unified representation space through a learnable cross-modal bridging layer, and then fuse them with the brain network topology embeddings of the GNN module through a graph-sequence attention mechanism to construct a dynamic world model of the central nervous system; the GNN module is used to model the multi-scale structure-function relationship graph of neurons-brain regions-whole brain; the Transformer module is used to perform global attention modeling on the fused multi-scale temporal neural data. The closed-loop feedback module is used to send control commands to the brain-computer interface device, central nervous system sensor and central nervous system acquisition instrument through the central control system based on the intervention strategy.

3. The method or system according to claim 1 or 2, characterized in that, The IoT interface method includes: The first dimension module is used to receive access request information and verify and identify the initial ID through public key encryption and private key decryption. The second dimension module includes a BSD open source code database, which is used to call the corresponding BSD open source code based on the initial ID and compare and verify the BSD open source code information data in the access request information to obtain the free open source code ID. The third dimension module includes an API interface database, which is used to call the corresponding API interface data based on the free and open source code ID, and to verify and compare the API interface data with the API interface data in the access request information to obtain the development authorization instruction code. The fourth-dimensional module is used to enable data transmission between the central control system and the brain-computer interface device, central nervous system sensor and central nervous system acquisition instrument based on the development authorization instruction code, according to the corresponding API interface.

4. The method or system according to claim 1 or 2, characterized in that, The computational architecture combining the hybrid expert model and the inference model includes: A multimodal data preprocessing subnetwork is used to encode intramodal features of neurophysiological signals, neuroimaging signals, and neurochemical signals; A hybrid expert routing network is used to dynamically select and activate the corresponding expert sub-networks based on the data modality type and task objective. The expert sub-networks include a time series expert sub-network, an image expert sub-network, and a graph structure expert sub-network. The temporal inference subnetwork is used for temporal dependency modeling and causal inference of cross-modal features; The output fusion layer is used to weight and fuse the outputs of each expert subnetwork through a gating mechanism to generate the structured neural representation data.

5. The system according to claim 2, characterized in that, The multi-agent collaborative system includes: The unified collaboration framework layer includes a task decomposition and routing engine and a capability registry, which records the unique identifier, capability tag and current task queue length of each Agent. A multi-level long-term memory system includes a short-term working memory area, a long-term contextual memory vector library, and a skill and process memory library. The skill and process memory library is configured to monitor the execution frequency and execution success rate of each sub-task process in a historical task. When the execution frequency exceeds a preset frequency threshold and the execution success rate exceeds a preset success rate threshold, the corresponding sub-task process is automatically solidified into a skill block containing a DAG structure and dependencies between sub-tasks. A distributed Harness cluster consists of multiple peer scheduler nodes that maintain the cluster state through a consensus protocol, used for decentralized task orchestration of Agents. The conflict resolution and consensus module is used to receive the results returned by multiple agents for the same subtask and their respective confidence scores, query the preset rule base, and determine the final output based on a weighted vote of confidence scores when there are no mandatory rule conflicts.

6. The method or system according to claim 1 or 2, characterized in that, The differential privacy desensitization in the clinical diagnosis closed-loop unit adopts an ε-differential privacy mechanism with a privacy budget ε set to 1.

0. Individual privacy is protected by injecting calibrated Laplace noise into the clinical data query results. The federated learning framework uses the FedAvg or FedProx algorithm. After the model is trained locally at the hospital, only the model gradient is uploaded to the central server. The central server aggregates the global model and then issues updates. The secure multi-party computation in the drug development closed-loop unit adopts a privacy-protected matrix computation protocol based on secret sharing. The cross-hybrid closed-loop unit allocates federated learning tasks through a consistent hashing algorithm to ensure a balanced computational load between the clinical and R&D ends.

7. The method or system according to claim 1 or 2, characterized in that, The improvements made by the JEPA module to address the non-stationary nature of neural signals include: Temporal masking strategy: Randomly mask the neural signal sequence according to the time window. The masking ratio is dynamically adjusted according to the nonstationarity index of the signal. The nonstationarity index is obtained by calculating the rate of change of the signal variance within the sliding window. Predictor structure: It adopts an LSTM-Attention hybrid structure. The LSTM layer captures the long-range temporal dependence of neural signals, and the attention layer dynamically focuses on the task-related neural oscillation frequency band. Frequency domain constrained loss function: A frequency domain consistency constraint term is introduced into the standard JEPA loss function. The constraint term is obtained by calculating the KL divergence between the predicted embedding and the target embedding on the power spectral density, so that the model maintains the frequency characteristics of the neural signal in the representation space.

8. The method or system according to claim 1 or 2, characterized in that, The AI ​​drug development process data includes directed evolution optimization data of gene editing tool proteins. This directed evolution optimization data is generated using a multi-scale mutation-aware graph network and adaptive temporal evolution modeling, and is used for virtual screening and activity prediction of gene therapy drugs for the central nervous system.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method as described in any one of claims 1, 3, 4, 6, 7, and 8, and the computer-readable storage medium serves as a trusted storage node of the central control system through the IoT interface method, using a triple verification and identification method of access encryption authentication, BSD open source code recognition, and API interface recognition for data access.

10. The use of the system as described in claim 2, characterized in that, Central nervous system status analysis, neurological function assessment, drug target validation simulation, or optimization of neuromodulation parameters for non-disease diagnostic purposes.

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