Medical examination data intelligent processing and large model offline deployment method and system

Abstract

The invention provides a medical physical examination data intelligent processing and large model offline deployment method and system. The method comprises the following steps: performing online configuration in an offline deployment basic environment, deploying a physical examination data processing algorithm module, downloading a pre-training large model file and a reasoning framework from a model warehouse, and generating a distributable mirror image through a packaging command; performing offline deployment on the distributable mirror image, transmitting the mirror image to a server without an external network through a physical medium, importing and loading the mirror image, and starting a container service; cleaning and standardizing the received physical examination data, matching the standardized data with a medical data matching library, and calculating the severity level; and preprocessing the unstructured medical text, calling an off-line deployed large model to perform semantic analysis and generate an explanatory suggestion, and integrating processing results to generate a standardized physical examination report. According to the invention, off-line deployment and application of a large model in an external network-free environment are realized, and the intelligent level and efficiency of physical examination data processing are improved.

Description

Technical Field

[0001] This invention relates to the field of medical information processing technology, and in particular to a method and system for intelligent processing of medical examination data and offline deployment of large models. Background Technology

[0002] Intelligent processing and large-scale model application of medical examination data is a crucial technological area combining medical informatics and artificial intelligence, involving technologies such as data standardization, medical data matching, risk assessment, and natural language processing. With the increasing scale of medical data and the growing demand for intelligent healthcare, this field faces numerous technological challenges and development opportunities.

[0003] Common technical solutions in the field of medical examination data processing include cloud-based medical data processing platforms and rule-based medical report generation systems. The former utilizes cloud computing resources for data storage and analysis, enabling large-scale data processing, but it poses data security risks; the latter uses preset rules for data matching and report generation, offering better controllability, but with limited flexibility and intelligence.

[0004] A more advanced technical solution employs a hybrid architecture, combining data standardization, medical data matching, and risk assessment algorithms with natural language processing technology to achieve intelligent processing of health checkup data throughout the entire process. This technology comprehensively analyzes structured and unstructured data by constructing a medical knowledge graph and a model inference engine to generate personalized recommendations. However, in practical applications, this technology faces challenges such as limited deployment environments, inconsistent data standards, difficulties in adapting to varying user habits, and complex cross-system integration.

[0005] The existing technical solutions have the following main technical defects: First, it is difficult to deploy and maintain large models and their complex dependencies in environments without external networks, which affects the application of the system in medical privacy protection scenarios; second, the standardization and integration capabilities of multi-source heterogeneous medical data are insufficient, making it difficult to handle data differences between different devices and hospitals; third, there is a lack of learning and adaptation mechanisms for individualized user behavior, making it impossible to provide personalized suggestions based on historical habits; and fourth, the data matching and risk assessment process lacks a systematic approach, resulting in insufficient accuracy and consistency. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for intelligent processing of medical examination data and offline deployment of large models, so as to solve the technical problems in the prior art, such as difficulty in deploying large models in an environment without external network, insufficient standardization capability of multi-source heterogeneous data, lack of user habit learning mechanism, and insufficient accuracy of data matching and risk assessment.

[0007] To achieve the above objectives, this invention provides a method for intelligent processing of medical examination data and offline deployment of large-scale models, comprising the following steps:

[0008] Build a Docker base image and install CUDA, cuDNN and Python environments, configure data volume mount points and network settings to establish a basic environment for offline deployment;

[0009] In the offline deployment environment, online configuration is performed to deploy the physical examination data processing algorithm module, download the pre-trained large model file and inference framework from the model repository, perform interface integration testing, and solidify the dependencies, model weights and configuration files into the container. A distributable image is generated using the Docker packaging command.

[0010] The distributable image is deployed offline by transmitting the image to a server without an external network via physical media and importing and loading it. After the container service is started, automatic restart is achieved through status monitoring and anomaly detection. Task continuation is achieved based on the task status persistence mechanism to build an offline system.

[0011] In the offline system, the received physical examination data is cleaned and standardized. The standardized data is matched with a medical data matching library and the severity level is calculated. The matching results are merged and sorted. Personalized adjustments are made based on the user's historical habit learning results to obtain structured analysis results.

[0012] Unstructured medical text is preprocessed, a large offline model is invoked for semantic analysis and explanatory suggestions are generated, the structured analysis results are integrated with the large model processing results, and rendered into standardized medical examination reports in various formats through a template engine.

[0013] Preferably, a Docker base image is built and CUDA, cuDNN, and Python environments are installed, and data volume mount points and network settings are configured to establish a basic offline deployment environment, including:

[0014] In the GPU server, the nvidia / cuda base image is selected as the building unit. The language settings, time zone settings and non-interactive installation options are configured through the Dockerfile to obtain the initial base image.

[0015] Install dependencies on the initialized base image, configure the image source and pre-install toolkits, install the cuDNN library that matches the CUDA version, add the NVIDIA container toolkit, and obtain an image with GPU driver configuration complete.

[0016] Configure the Python environment on the image with the GPU driver configured, install Miniconda3 and create a Python 3.10 basic environment, pre-install numpy, pandas and torch dependency packages, and determine the image with the Python environment configured.

[0017] Configure the network and storage of the image with the Python environment configured, create a custom bridge network for inter-container communication, define the data volume mount points for model storage path, data exchange path and log storage path, set up shared storage between containers through Docker volumes, and establish the basic environment for offline deployment.

[0018] Preferably, online configuration is performed in the offline deployment environment to deploy the physical examination data processing algorithm module, download pre-trained large model files and inference frameworks from the model repository, perform interface integration testing, and solidify dependencies, model weights, and configuration files into a container. A distributable image is generated using Docker packaging commands, including:

[0019] In the offline deployment base environment, the development environment plugin is configured by connecting to the base container through a remote development tool, the physical examination data processing algorithm code library is copied to the container working directory, and the dependency packages of the medical terminology processing library and Chinese word segmentation tool are installed to build the container with the algorithm deployed.

[0020] The container with the algorithm deployed is used to deploy a large model. The weight file, tokenizer and configuration file of the pre-trained large model are downloaded from the model repository. The vLLM or TensorRT-LLM inference framework is installed and the model loading parameters are set. The large model is tested locally and the quantization parameters are adjusted to obtain a container that integrates the large model.

[0021] The container integrating the large model is subjected to interface development and joint testing. The FastAPI application is developed and the RESTful API routes for data reception, task status query and result acquisition are designed. The interface between the algorithm module and the large model is implemented and the data exchange format is defined to obtain the container with the interface joint testing completed.

[0022] The container with the interface integration completed is image solidified and packaged. The dependencies, model weights, configuration files and application code in the container are solidified to the file system. Temporary files and compilation cache are deleted to perform image slimming. The image is created and a version tag is added using the Docker commit command. The image is digitally signed and the build time and dependency versions are recorded to obtain the distributable image.

[0023] Preferably, the distributable image is deployed offline, and after the container service is started, automatic restart is achieved through status monitoring and anomaly detection. Task continuation is achieved based on the task status persistence mechanism, resulting in the stable offline system, including:

[0024] Based on the distributable image, the image is transferred to a server without external network via physical medium and decompressed. The image is then imported using the docker load command, and the validity and integrity of the image signature are verified to obtain a verified local image.

[0025] Start the container on the verified local image, create a persistent storage directory and a data volume mount point for the configuration file path, start the container cluster in the order of dependencies using the docker-compose command, execute the database initialization script and system environment check, and output the running container service;

[0026] The running container service is monitored for status and anomaly detection. Based on the Docker API, the running status of each container is checked periodically and the CPU, memory and GPU usage are recorded. The availability of the API service is detected by HTTP heartbeat and a timeout threshold is set. A log aggregation system is established to collect the application logs generated by each container to obtain status monitoring data.

[0027] The status monitoring data is subjected to anomaly detection and automatic restart. Based on the detection results, the anomalies are classified into mild, moderate and severe anomalies and event identifiers are generated. Recovery operations such as single container restart or full service reconstruction are performed according to the anomaly level to obtain the self-recovered container service.

[0028] The self-recovering container service resumes its tasks by scanning for incomplete task information and processing progress in Redis persistent storage. Depending on the task status, it continues execution from the breakpoint or re-executes from the beginning. Data consistency is checked on the results after the interrupted tasks are recovered, resulting in the stable offline system.

[0029] Preferably, the received physical examination data is cleaned and standardized, including:

[0030] Based on the received physical examination data, the system receives JSON or XML format data from POST requests via FastAPI, generates a unique task ID, and records task metadata such as data source, timestamp, and processing priority. The system performs format validation and structure checks on the input data and outputs physical examination data that has passed format validation.

[0031] The physical examination data that has passed the format verification is processed into structured fields, including unit conversion, field name mapping and encoding standardization. Outliers are marked or replaced based on the 3-sigma rule and medical reference range. Missing values ​​are handled by means filling or historical values ​​filling strategies according to field type and medical relevance, thus obtaining the standardized data.

[0032] Preferably, the standardized data is matched with a medical data matching library to calculate the severity level. The matching results are then merged and sorted, and personalized adjustments are made based on the user's historical learning habits to obtain the structured analysis results, including:

[0033] Based on the standardized data, the test indicators are matched with the local medical data matching library and support multiple coding systems. A matching strategy combining rule engine and machine learning is used to handle complex medical relationships and multi-indicator combination analysis. The confidence score of the match is calculated and the accuracy of the match is evaluated in combination with guidelines to obtain preliminary matching results.

[0034] The severity analysis of the preliminary matching results is performed. Based on expert knowledge, each abnormal indicator is assigned a severity level of mild, moderate or severe. Multiple indicators are integrated and the interaction and cumulative effect between indicators are considered to calculate the overall health risk score. The risk assessment results are compared with the examinee's historical data to identify significant changes and potential trends, and the severity classification results are obtained.

[0035] The severity grading results are merged and sorted. Semantic similarity algorithm is used to identify related or redundant matching results and intelligent merging is performed by threshold judgment. Clinical priority-based sorting rules are applied to rank results with high severity and high intervention urgency. Results are grouped according to organ system or functional relevance to obtain the merged and sorted matching results.

[0036] User habit learning is performed on the merged and sorted matching results. User writing patterns are extracted from historical data and common words, sentence structures and professional term preferences are identified. Based on historical data statistical analysis, the language characteristics of individual users are captured. When generating reports, the habit learning results are applied to adjust the wording and expression, and the analysis results after habit adjustment are obtained.

[0037] The analysis results after the habit adjustment are structured and organized, and the matching information, severity level, risk score and personalized adjustment content are organized into a standardized data structure. Metadata information of matching basis and confidence level is added to obtain the structured analysis results.

[0038] Preferably, when cleaning and standardizing the received physical examination data, a dynamic update mechanism based on incremental propagation coverage is adopted, including:

[0039] Based on the received physical examination data, each field of the physical examination data and the corresponding processing steps are modeled as a directed graph structure, with nodes representing data items and edges representing dependencies. Adjacency lists are used to store the dependencies between data and to label the computational cost weights. The dependency graph is topologically sorted to determine the data dependency graph network and the optimal computation sequence.

[0040] Incremental change detection is performed on the data dependency graph network, the change set between different data versions is calculated and the data change points are marked with hash index and timestamp, and change propagation rules are designed to clarify the dependency relationship between different fields;

[0041] Based on the change set and change propagation rules, the minimum transit cover set is calculated using a breadth-first search algorithm, and dynamic programming is applied to optimize the calculation path. Priority queue management of recalculation tasks is implemented and critical paths are guaranteed to be processed first, thus obtaining the minimum transit cover set.

[0042] Incremental standardization is performed on the data in the minimum transmission coverage set. Only the changed data and its affected domain are re-standardized and cleaned. A caching mechanism is established to save intermediate results and avoid repeated calculations, thus obtaining the incremental processing result.

[0043] The incremental processing results are merged and integrated. The result merging algorithm integrates the incremental processing results with historical results, verifies the consistency and integrity of the merged data, and obtains the standardized data.

[0044] Preferably, when matching the standardized data with the medical data matching library, an optimization method based on strong polynomial maximum flow is used, including:

[0045] Based on the standardized data, a bipartite graph network of test indicators and potential matching results is constructed. Confidence weights are assigned to each indicator-matching association edge, and context nodes are introduced to capture the multi-indicator combination relationship to obtain a bipartite graph of medical indicator matching.

[0046] Maximum flow calculation is performed on the bipartite graph of the medical indicator matching. The Ford-Fulkerson algorithm is used to transform the medical data matching into a maximum flow problem. The Dinic algorithm is used to improve the processing efficiency of large-scale networks and obtain the initial maximum flow matching result.

[0047] Adaptive capacity adjustment is performed on the initial maximum flow matching result. The capacity value of the network edge is dynamically adjusted based on the historical matching accuracy. The feedback mechanism optimizes the weight allocation according to the user's modified record. Confidence interval is introduced to calculate and quantify the uncertainty of the matching result, and the matching result after capacity adjustment is obtained.

[0048] The capacity-adjusted matching results are integrated with multi-source evidence. The minimum cut algorithm is applied to identify key matching evidence chains, and the matching evidence ranking based on the maximum flow results is realized. Data processing path visualization data is generated to obtain the data matching results of evidence integration.

[0049] Risk transmission network analysis is performed on the data matching results of the evidence integration. A three-layer network structure consisting of a detection index layer, a symptom manifestation layer, and a health risk layer is designed. The maximum flow in the risk network is calculated using a strong polynomial time algorithm, and risk nodes and bottleneck paths are identified based on the flow distribution. Dynamic thresholds are set to distinguish between high, medium, and low risk levels to obtain the preliminary matching results.

[0050] Preferably, the method further includes: employing an adversarial security protection mechanism when performing access control and version management on the standardized medical examination report, including:

[0051] Based on the standardized physical examination report, a medical data security threat classification system is constructed, and forgetting attacks and adaptive attacks are distinguished. A risk model is established for each type of threat, and the potential impact range and severity are quantified. A threat intelligence integration mechanism is developed to obtain the security threat classification results.

[0052] An access control graph is constructed based on the security threat classification results. A three-layer dynamic access control graph of user-role-permission is established. The least-privilege path calculation based on graph theory is implemented to ensure that each role can only access necessary resources. A time-varying permission mechanism is designed to dynamically adjust access permissions according to time period, location and task, thus obtaining a dynamic access control graph.

[0053] Static defense configuration is performed on the dynamic access control graph to achieve authentication requirements based on zero trust architecture, requiring authentication for each resource access. A multi-factor authentication process is designed to combine biometrics, knowledge factors, and device fingerprints. Encrypted data sharding storage is deployed to ensure that a single point of attack cannot obtain complete information, resulting in a forgetting attack defense configuration.

[0054] Dynamic defense enhancements are applied to the forgetting attack defense configuration. An abnormal behavior detection engine is developed to establish a user operation baseline and identify deviation patterns. Fingerprint deception technology is used to dynamically change the system's apparent characteristics to interfere with the opponent's learning. Honeypot resource traps are designed to induce and track advanced persistent threats, resulting in an adaptive attack defense configuration.

[0055] The adaptive attack defense configuration is audited and logged. A three-dimensional audit relationship diagram of entity-operation-resource is created and the audit records are stored using a graph database. The audit records are implemented based on a hash chain to ensure that the historical records are immutable. A distributed witness mechanism is designed to require multiple parties to confirm and record key operations, thus obtaining the intelligent processing system for medical examination data security management.

[0056] This invention also provides a system for intelligent processing of medical examination data and offline deployment of large-scale models, comprising:

[0057] The build module is used to build a Docker base image and install CUDA, cuDNN and Python environments, configure data volume mount points and network settings to establish a basic environment for offline deployment;

[0058] The configuration module is used to perform online configuration in the offline deployment base environment, deploy the physical examination data processing algorithm module, download pre-trained large model files and inference framework from the model repository, perform interface integration testing, and solidify dependencies, model weights and configuration files into containers, and generate a distributable image through Docker packaging commands;

[0059] The deployment module is used to deploy the distributable image offline. It transmits the image to a server without an external network via physical media and imports and loads it. After starting the container service, it achieves automatic restart through status monitoring and anomaly detection. It also achieves task continuation based on the task status persistence mechanism to build an offline system.

[0060] The adjustment module is used in the offline system to clean and standardize the received physical examination data, match the standardized data with a medical data matching library and calculate the severity level, merge and sort the matching results, and make personalized adjustments based on the user's historical habit learning results to obtain structured analysis results.

[0061] The rendering module is used to preprocess unstructured medical text, call the offline deployed large model for semantic analysis and generate explanatory suggestions, integrate the structured analysis results with the large model processing results, and render them into standardized physical examination reports in various formats through a template engine.

[0062] The beneficial effects of this invention are:

[0063] 1. The intelligent processing and offline deployment method and system for medical examination data provided by this invention realizes the offline deployment of large models in an environment without external network by building a Docker base image and installing the necessary environment. This solves the technical problem of difficulty in deploying and maintaining large models in an environment without external network in the prior art, and ensures the security of medical data.

[0064] 2. This invention adopts a dynamic update mechanism based on incremental propagation coverage, which only reprocesses the changed data and its affected domain, avoiding full recalculation and improving data processing efficiency; at the same time, it adopts an optimization method based on strong polynomial maximum flow for medical data matching, which improves the accuracy and efficiency of matching.

[0065] 3. This invention extracts user writing patterns from historical data and identifies common vocabulary, sentence structures, and professional terminology preferences, thereby learning and adapting to user habits and providing personalized report generation functionality. This solves the problem of the lack of user-individualized behavior learning and adaptation mechanisms in existing technologies.

[0066] 4. This invention adopts a security protection mechanism based on an adversarial model, constructs a multi-layered security protection system, and provides targeted defense strategies for different types of attacks, thereby improving the security and reliability of the system.

[0067] 5. This invention enables automatic restart of container services through status monitoring and anomaly detection, and realizes task continuation based on task status persistence mechanism, thereby improving the stability and reliability of the system and ensuring continuous operation capability in complex environments. Attached Figure Description

[0068] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0069] Figure 1 Flowchart of the intelligent processing and large-scale offline deployment method for medical examination data provided in this embodiment of the invention;

[0070] Figure 2 A complete system deployment flowchart provided for embodiments of the present invention;

[0071] Figure 3 A server operation structure diagram provided for embodiments of the present invention;

[0072] Figure 4 This is a system architecture diagram for intelligent processing of medical examination data and offline deployment of large models provided in an embodiment of the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0074] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0075] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0076] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0077] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0078] like Figure 1 As shown, this embodiment provides a method for intelligent processing of medical examination data and offline deployment of large models, including the following steps:

[0079] Step S1: Build a Docker base image and install CUDA, cuDNN and Python environments, configure data volume mount points and network settings to establish an offline deployment base environment.

[0080] In the GPU server, the nvidia / cuda base image is selected as the build unit. The initial base image is obtained by configuring language settings, time zone settings, and non-interactive installation options through the Dockerfile. Specifically, select...

[0081] Use an nvidia / cuda:12.6.3-devel-ubuntu22.04 or equivalent base image, and set the environment variables LANG=C.UTF-8, TZ=Asia / Shanghai and DEBIAN_FRONTEND=noninteractive in the Dockerfile to ensure the automation and standardization of the subsequent installation process.

[0082] The process involves installing dependencies from the initial base image, configuring the image source and pre-installing toolkits, installing the cuDNN library compatible with the CUDA version, adding the NVIDIA container toolkit, and obtaining an image with the GPU driver configured. Specifically, the default apt source is replaced with a domestic mirror source to improve the installation speed of dependency packages, commonly used toolkits such as wget, curl, git, and vim are pre-installed, cuDNN version 8.9.5 or higher compatible with the CUDA version is installed, the nvidia-container-toolkit is added to enable the container to correctly access the host GPU resources, and the environment variable NVIDIA_VISIBLE_DEVICES="all" is set to make all available GPUs visible to the Docker container.

[0083] Configure the Python environment on the GPU driver-configured image, install Miniconda3 and create a basic Python 3.10 environment, pre-install NumPy, Pandas, and Torch dependencies, and identify the image with the configured Python environment. Specifically, download and install Miniconda3 as the Python environment management tool, create a basic Python 3.10 environment of the specified version, pre-install common dependencies through requirements.txt or conda environment configuration files, configure the pip source to a domestic mirror to accelerate subsequent package installations, and disable automatic activation of the basic environment in the conda configuration settings.

[0084] Configure the network and storage for the image with the Python environment already configured. Create a custom bridge network for inter-container communication, define data volume mount points for model storage paths, data exchange paths, and log storage paths, and set up shared storage between containers using Docker volumes to establish the basic environment for offline deployment. Specifically, create a custom bridge network named `job_network` for communication between multiple containers, define ` / opt / models` as the model storage path, ` / opt / data` as the data exchange path, and ` / opt / logs` as the log storage path as the data volume mount points, and ensure data persistence and data sharing between containers through Docker volumes.

[0085] Step S2: Configure online in the offline deployment base environment, deploy the physical examination data processing algorithm module, download the pre-trained large model file and inference framework from the model repository, perform interface integration testing, and solidify the dependencies, model weights and configuration files into the container, and generate a distributable image through the Docker packaging command.

[0086] Combination Figure 2 and Figure 3 The process involves, in an offline deployment environment, connecting to the base container via a remote development tool and configuring development environment plugins. The codebase for the physical examination data processing algorithm is copied to the container's working directory, and dependencies for the medical terminology processing library and Chinese word segmentation tool are installed to build a container with the algorithm deployed. Specifically, a remote development tool such as VSCodeRemote-Containers or JetBrains Gateway is used to connect to the base container. Development environment plugins such as Python extensions, Docker extensions, and code debugging tools are configured. A file synchronization mechanism between the base container and the development host is established to support real-time code updates and testing. The physical examination data processing algorithm codebase is cloned or copied to the container's working directory (e.g., / opt / src / algorithm). Specific dependencies required by the algorithm, such as the medical terminology processing library (e.g., umls-mapper), the Chinese word segmentation tool (e.g., jieba), and the medical knowledge graph interface library, are installed. Algorithm parameter files are configured, including standardized configurations, matching rule bases, severity scoring criteria, and user habit learning model parameters.

[0087] For containers where the algorithm has been deployed, a large model is deployed. This involves downloading the pre-trained large model's weight files, tokenizer, and configuration files from a model repository, installing the vLLM or TensorRT-LLM inference framework, setting the model loading parameters, performing local inference testing on the large model, and adjusting the quantization parameters to obtain a container integrating the large model. Specifically, pre-trained large model files (including model weights, tokenizer, and configuration files) are downloaded from model repositories such as HuggingFace. Large model inference frameworks such as vLLM, TensorRT-LLM, or Faster Transformer are installed, and model loading and inference parameters are set. The large model is then tested locally to verify its integrity and inference capabilities in a network-free environment, and the model quantization parameters are adjusted to balance performance and accuracy.

[0088] The interface development and integration testing of the container integrating the large model were carried out. The FastAPI application was developed, and RESTful API routes for data reception, task status query, and result retrieval were designed. This enabled the interface between the algorithm module and the large model, defined the data exchange format, and resulted in a container with completed interface integration testing. Specifically, the FastAPI application was developed as the external interface layer, and RESTful API routes were designed including data reception ( / api / v1 / intake), task status query ( / api / v1 / status / {task_id}), and result retrieval ( / api / v1 / result / {task_id}). This enabled the interface between the algorithm module and the large model, defined a standard data exchange format, developed an asynchronous task processing mechanism, wrote system self-test scripts, simulated data flow, verified the correctness of interactions between modules, and collected and analyzed performance data (such as response time and resource consumption).

[0089] The container image after interface integration testing is solidified and packaged. Dependencies, model weights, configuration files, and application code within the container are solidified to the file system. Temporary files and build caches are deleted to reduce the image size. The Docker commit command is used to create the image and add a version tag. The image is digitally signed, and the build time and dependency versions are recorded, resulting in the distributable image. Specifically, all dependencies, model weights, configuration files, and application code within the container are solidified to the file system. An archive snapshot of the application image is created. The image is then slimmed down by deleting temporary files, build caches, and debugging tools, compressing model weights to reduce image size, and using the Docker commit or build command to create the final image. A version tag (e.g., ai_code:v1.0) is added as a unique identifier. The image is digitally signed using Docker Content Trust or a custom signing mechanism, generating a signature file and verification value. Image metadata, including build time, dependency versions, model versions, and configuration parameters, is recorded. The image and its signature are packaged into a distribution package (e.g., using gzip compression: docker saveai_code:v1.0 | gzip > ai_code.tar.gz) for offline transmission.

[0090] Step S3: Deploy the distributable image offline by transferring the image to a server without an external network via physical media and importing and loading it. After starting the container service, automatic restart is achieved through status monitoring and anomaly detection. Task continuation is achieved based on the task status persistence mechanism to build an offline system.

[0091] Based on the distributable image, the image is transferred to a server without external network access via physical media and decompressed. The image is then imported using the `docker load` command, and the image signature validity and integrity are verified to obtain a verified local image. Specifically, the packaged image is transferred to the target server without external network access via physical media such as hard drives or USB flash drives. On the target server, the image is decompressed and imported (e.g., executing: `gunzip -c ai_code.tar.gz | docker load`), verifying the image integrity and signature validity to ensure that the image has not been tampered with and is consistent with the build environment.

[0092] The process involves starting a container from a verified local image, creating a persistent storage directory and a data volume mount point for the configuration file path, starting the container cluster using the `docker-compose` command in dependency order, executing database initialization scripts and system environment checks, and outputting the running container services. Specifically, it creates the necessary data volume mount points, including the persistent storage directory and configuration file path, starts the container cluster using the `docker-compose` command, automatically starts all service components in dependency order, executes initialization scripts, and completes database initialization, permission settings, and system environment checks.

[0093] This system performs status monitoring and anomaly detection for running container services. It periodically checks the running status of each container based on the Docker API, recording CPU, memory, and GPU usage. It uses HTTP heartbeats to detect API service availability and sets timeout thresholds. A log aggregation system is established to collect application logs generated by each container, providing status monitoring data. Specifically, a container-level monitoring service is deployed to periodically check the running status of containers based on the Docker API, recording CPU, memory, and GPU usage to implement a service-level health check mechanism. It uses HTTP heartbeats to detect API service availability, sets timeout thresholds and retry policies, and establishes a log aggregation system to collect application logs generated by each container, supporting filtering by level and keyword search.

[0094] The system performs anomaly detection and automatic restart on status monitoring data. Based on the detection results, anomalies are categorized into mild, moderate, and severe anomalies, and event identifiers are generated. Recovery operations, such as single-container restart or full service reconstruction, are executed according to the anomaly level to obtain a self-recovered container service. Specifically, a status detection unit is developed to periodically check the status of containers and services, determining system health through the Docker API and HTTP requests. An anomaly detection unit categorizes anomalies into mild, moderate, and severe anomalies based on the detection results, generating corresponding event identifiers. An automatic restart unit is constructed to execute appropriate recovery operations based on the anomaly level, ranging from single-container restart to full service reconstruction.

[0095] The system resumes tasks for the self-recovering container service by scanning for incomplete task information and processing progress in Redis persistent storage. Depending on the task status, execution can resume from the breakpoint or be re-executed from the beginning. Data consistency checks are performed on the results of the recovered tasks to achieve a stable offline system. Specifically, a task status persistence mechanism is designed to store task information and processing progress in persistent storage such as Redis. A task resumption unit is implemented, which scans for incomplete tasks upon system startup and resumes execution, supporting continuation from the breakpoint or re-execution from the beginning. A data consistency check tool is developed to ensure the correctness of the results after the recovered tasks, preventing partial data loss or duplicate processing.

[0096] Step S4: In the offline system, the received physical examination data is cleaned and standardized. The standardized data is matched with the medical data matching library and the severity level is calculated. The matching results are merged and sorted. Personalized adjustments are made based on the user's historical habit learning results to obtain structured analysis results.

[0097] Based on the received physical examination data, the system receives JSON or XML formatted data from POST requests via FastAPI, generates a unique task ID, and records task metadata including data source, timestamp, and processing priority. It then performs format validation and structure checks on the input data and outputs physical examination data that has passed the format validation. Specifically, the system receives physical examination data from POST requests via FastAPI, supporting multiple data formats such as JSON and XML. It generates a unique task ID and records task metadata, including data source, timestamp, and processing priority. The system performs format validation and structure checks on the input data to ensure it conforms to a predefined data pattern.

[0098] The structured field processing of the format-validated physical examination data involves unit conversion, field name mapping, and code standardization. Outliers are marked or replaced based on the 3-sigma rule and medical reference ranges. Missing values ​​are handled using mean imputation or historical value imputation strategies, depending on the field type and medical relevance, to obtain the standardized data. Specifically, the processing of structured data fields includes unit conversion (e.g., mmol / L to mg / dL), field name mapping, and code standardization; outliers are identified and processed; outliers are marked or replaced based on statistical methods (e.g., the 3-sigma rule) and medical reference ranges; missing values ​​are handled using different imputation strategies (e.g., mean imputation, historical value imputation, or marking as undetected) depending on the field type and medical relevance.

[0099] Based on standardized data, the test indicators are matched with a local medical data matching library, supporting multiple coding systems. A matching strategy combining rule engines and machine learning is used to handle complex medical relationships and multi-indicator combination analyses. The confidence score of the match is calculated, and the accuracy of the match is evaluated in conjunction with guidelines to obtain preliminary matching results. Specifically, the standardized test indicators are matched with a local medical data matching knowledge base, supporting multiple coding systems such as ICD-10. A matching strategy combining rule engines and machine learning is used to handle complex medical relationships and multi-indicator combination analyses. The confidence score of the match is calculated, and the accuracy of the match is evaluated in conjunction with clinical guidelines and expert rules.

[0100] The initial matching results were analyzed for severity. Based on expert knowledge, each abnormal indicator was assigned a severity level of mild, moderate, or severe. Multiple indicators were integrated, and interactions and cumulative effects between indicators were considered to calculate the overall health risk score. The risk assessment results were compared with the examinee's historical data to identify significant changes and potential trends, resulting in a severity grading. Specifically, based on clinical guidelines and expert knowledge, each abnormal indicator was assigned a severity level (mild, moderate, or severe). Multiple indicators were integrated to calculate the overall health risk score, considering interactions and cumulative effects between indicators. The risk assessment results were compared with the examinee's historical data to identify significant changes and potential trends.

[0101] The severity grading results are merged and sorted. A semantic similarity algorithm is used to identify relevant or redundant matches, and threshold-based intelligent merging is performed. A clinical priority-based ranking rule prioritizes results with high severity and urgent intervention. Results are then grouped according to organ system or functional relevance to obtain the merged and sorted matching results. Specifically, a semantic similarity algorithm is used to identify relevant or redundant matches, and threshold-based intelligent merging is performed. A clinical priority-based ranking rule prioritizes results with high severity and urgent intervention, and results are grouped according to organ system or functional relevance to improve the report's logical consistency and readability.

[0102] The merged and sorted matching results undergo user habit learning. User writing patterns are extracted from historical data, and common vocabulary, sentence structures, and professional terminology preferences are identified. Based on historical data statistical analysis, individual user language characteristics are captured. When generating reports, the habit learning results are applied to adjust word choice and expression, resulting in a habit-adjusted analysis. Specifically, user writing patterns, including common vocabulary, sentence structures, and professional terminology preferences, are extracted from historical data. A lightweight machine learning model (such as an N-gram language model or conditional random field) is trained to capture individual user language characteristics. When generating reports, the habit learning results are applied to adjust word choice, expression, and content organization to match user preferences.

[0103] First, the system extracts historical report texts from the historical database for specific users to build a personal language feature library. This process uses text mining techniques to segment and annotate the reports written or modified by users in the past, extracting keyword frequency distributions, common sentence templates, and preferences for the use of professional terminology. The system pays special attention to the differences in how users describe similar abnormal indicators; for example, some users tend to use phrases like "slightly elevated" rather than "slightly higher than the reference value." These features are organized into a structured user language model dataset, which serves as input for subsequent machine learning.

[0104] In terms of machine learning model construction, the system employs a multi-layered combination of language models. First, an N-gram language model is used to capture users' habits in word selection and phrase collocation. By statistically analyzing the probability distribution of N consecutively occurring words, the N-gram model can effectively capture users' word selection preferences in specific contexts. For example, when describing blood glucose levels, the system learns whether users prefer to use expressions such as "blood glucose level" or "glucose concentration." The training process of the N-gram model includes extracting all possible 2-gram, 3-gram, and 4-gram phrases from historical text, calculating their conditional probabilities, and constructing a probability transition matrix.

[0105] For more complex sentence structures and paragraph organization preferences, the system employs a Conditional Random Field (CRF) model for learning. CRF models can consider contextual information and capture changes in users' expression patterns across different contexts. For example, some users habitually state their conclusions first and then list their supporting evidence, while others do the opposite. CRF models capture these structural preferences by designing feature functions, including sentence length distribution, frequency of transition words, and density of technical terms. During model training, sentences from users' historical reports are labeled according to function (such as description, explanation, and suggestion), and the system then learns the arrangement patterns and conversion probabilities of sentences with different functions.

[0106] To address usage preferences for technical terms, the system also integrates a domain-adaptive word embedding model. This model is first pre-trained on word vectors from a large-scale medical literature database, and then fine-tuned using users' historical texts, allowing the word vector space to better reflect individual users' semantic understanding and expression habits. This enables the system to identify variations of technical terms that users prefer to use when faced with the same medical concepts; for example, some users may prefer to use "abnormal liver function indicators" rather than "elevated transaminase."

[0107] In practical applications, the system employs an ensemble learning approach to integrate the outputs of these models. When new report content needs to be generated, the system first generates a standardized content template based on the matching results, and then performs personalized adjustments through the following steps:

[0108] Lexical replacement stage: Using an N-gram model, the probability of different word choices is calculated, and standard words are replaced with synonyms frequently used by users. For example, "significant" is replaced with "obvious" or "serious," which are more commonly used by users.

[0109] Sentence reconstruction stage: Based on sentence preferences predicted by the CRF model, the system adjusts sentence structure and information organization order. For example, if users habitually use passive voice to describe detection results, the system will adjust the sentence structure accordingly.

[0110] Terminology adjustment phase: Utilizing a domain-adaptive term embedding model, the system identifies user-preferred technical terms and replaces them accordingly. The system considers the level of technicality of the terminology in relation to user habits, avoiding the generation of overly technical or overly simplified expressions.

[0111] Tone consistency processing: Analyze the tone characteristics of the user's historical text (such as formal / informal, direct / euphemistic) and adjust the overall tone of the generated content to ensure consistency with the user's expression style.

[0112] Personalized template application: For specific types of abnormal indicators, if the user has a fixed description template, the system will prioritize applying these templates and only replace the specific numerical values ​​and degree words.

[0113] The system also implements an adaptive learning mechanism. Each time a user modifies the generated content, these modifications are recorded and used to update the user's personal language model. Through incremental learning algorithms, the model can gradually adjust parameter weights, making the generated content increasingly closer to the user's actual expression habits. For example, if a user repeatedly modifies the system-generated "slightly abnormal" to "slightly deviates from the normal range," the system will increase the probability of generating the latter.

[0114] To balance personalization and professionalism, the system employs an adaptation threshold control. When a user's expression habits might lead to a decrease in professional accuracy, the system retains the original professional expression while making personalized adjustments in style and organization. This ensures that the report conforms to the user's expression habits while maintaining the necessary professional standard.

[0115] Ultimately, the report content, after this series of personalized processing, not only retains the accurate information of the original matching results, but also presents a language style that is highly consistent with the user's historical expression habits, greatly improving the report's acceptability and user experience, while reducing the workload of users in making later modifications.

[0116] The analysis results after habit adjustment are structured and organized, and the matching information, severity level, risk score and personalized adjustment content are organized into a standardized data structure. Metadata information of matching basis and confidence level is added to obtain the structured analysis results.

[0117] Step S5: Preprocess the unstructured medical text, call the offline deployed large model to perform semantic analysis and generate explanatory suggestions, integrate the structured analysis results with the large model processing results, and render them into standardized physical examination reports in various formats through a template engine.

[0118] Implement a model lazy loading mechanism, initialize only necessary components when the system starts, and load the complete model only on the first call. Configure the vLLM inference engine, set batch size, inference precision and caching strategy to optimize performance and resource consumption, and design task queue and asynchronous processing mechanism to avoid blocking the main service thread for long-term inference.

[0119] The system preprocesses unstructured text written by users, including typo correction, standardization of technical terms, and optimization of sentence segmentation. It analyzes the semantics of the text through a large model to identify key medical concepts, symptom descriptions, and potential health information, and generates supplementary explanations and suggestions, including the meaning of abnormal indicators, possible health risks, and recommendations for further examination.

[0120] By integrating structured analysis results with text content processed by large models, a complete physical examination report is generated. A template engine (such as Jinja2) is used to render the data into multiple output formats, including HTML, PDF, and structured JSON, to achieve automatic formatting and beautification of the report content, ensuring a professional appearance and readability.

[0121] The generated reports and their metadata are stored in Elasticsearch or a relational database to create a full-text index to support subsequent retrieval. Key nodes and decision-making basis in the processing are recorded, supporting result traceability and evidence query. Report version management is implemented, allowing comparison of differences between reports generated by different algorithm versions or model versions.

[0122] In a preferred embodiment, when cleaning and standardizing the received physical examination data, a dynamic update mechanism based on incremental propagation coverage is adopted, including:

[0123] Based on the received physical examination data, each field of the physical examination data and the corresponding processing steps are modeled as a directed graph structure, with nodes representing data items and edges representing dependencies. Adjacency lists are used to store the dependencies between data and to label the computational cost weights. The dependency graph is topologically sorted to determine the data dependency graph network and the optimal computation sequence.

[0124] Incremental change detection is performed on the data dependency graph network. The change set between different data versions is calculated and data change points are marked using hash indexes and timestamps. Change propagation rules are designed to clarify the dependency relationships between different fields.

[0125] Based on change sets and change propagation rules, a breadth-first search algorithm is used to calculate the minimum transitive cover set. Dynamic programming is applied to optimize the computation path. Priority queue management of recomputation tasks is implemented and critical paths are guaranteed to be processed first, thus obtaining the minimum transitive cover set.

[0126] Incremental standardization is performed on the data in the minimum transmission coverage set. Only the changed data and its affected domain are re-standardized and cleaned. A caching mechanism is established to save intermediate results and avoid duplicate calculations, thus obtaining the incremental processing results.

[0127] The incremental processing results are merged and integrated. The result merging algorithm integrates the incremental processing results with historical results, verifies the consistency and integrity of the merged data, and obtains the standardized data.

[0128] In the process of medical examination data processing, data cleaning and standardization are crucial steps to ensure the accuracy of subsequent analysis. Traditional data processing methods typically employ a full-data computation approach, requiring reprocessing of all data with each update. This leads to wasted computational resources and processing delays when dealing with large-scale examination data. The dynamic update mechanism based on incremental propagation coverage proposed in this embodiment significantly improves data processing efficiency by accurately identifying data changes and minimizing the scope of recalculation.

[0129] First, the system models the processing flow of physical examination data, constructing a data dependency graph network. In this step, each field of physical examination data is represented as a node in the graph, and the processing dependencies between fields are represented as directed edges. For example, the standardized value after blood glucose conversion depends on the original blood glucose value and unit information; therefore, there will be directed edges in the graph pointing from the original blood glucose value node and the unit information node to the standardized blood glucose value node. The system uses an adjacency list data structure to store these dependencies. Compared to adjacency matrices, adjacency lists are more efficient in processing sparse graphs, reducing storage space usage. Simultaneously, the system assigns a computational cost weight to each edge, reflecting the computational resources required to perform the corresponding conversion or standardization operation. These weights are derived based on historical execution time and resource consumption statistics, providing a basis for subsequent optimization of computational paths.

[0130] After constructing the dependency graph, the system performs topological sorting to determine the optimal computation sequence that satisfies the dependency constraints. Topological sorting ensures that all dependencies of a data item have been processed before any data item is processed. For example, unit conversion must be performed before outlier detection. The system uses an improved Kahn algorithm to perform topological sorting, while considering the computational cost of nodes and critical path analysis, generating a processing sequence that satisfies dependency constraints and minimizes overall computation time.

[0131] When new medical examination data arrives or existing data is updated, the system does not simply re-execute the entire processing flow. Instead, it first performs incremental change detection. This step accurately identifies changed fields by comparing different versions of the data. The system calculates a hash value for each data field and compares it with the hash value of the previous version to quickly locate the change point. Simultaneously, the system uses timestamps to mark the time of each change point, which is particularly important for handling complex multi-source data updates. For example, when data from the same examinee is successively received from multiple testing devices, timestamps help the system correctly handle the update order, preventing old data from overwriting new data.

[0132] After identifying the change points, the system needs to determine the scope of these changes' impact on other data items. Change propagation rules are used here to clarify the dependencies between fields. For example, if the original blood pressure value changes, then the standardized blood pressure values, blood pressure classification results, and related health risk assessments that depend on it all need to be recalculated. The system encodes these rules into propagation functions, which can quickly determine the set of downstream fields affected when a change in a specific field is detected.

[0133] Based on the change points and propagation rules, the system uses a breadth-first search (BFS) algorithm to compute the minimum transitive cover set. Starting from the change points, the BFS algorithm expands layer by layer along the directed edges of the dependency graph, identifying all affected nodes. Compared to depth-first search, BFS can more effectively identify the shortest impact path and avoid unnecessary deep traversals. Building on this, the system applies dynamic programming to further optimize the computation path. Dynamic programming avoids repeatedly calculating the same subproblems by decomposing the problem and storing intermediate results. For example, if multiple changed fields affect the same downstream node, the system only needs to compute the updated value of that node once.

[0134] To efficiently manage recomputation tasks, the system implements a priority-based task scheduling mechanism. Recomputation tasks are prioritized according to their criticality in the dependency graph and their downstream impact. Nodes on the critical path with a wide impact receive higher priority, ensuring that system resources are used primarily to process these critical tasks. For example, changes to underlying data affecting multiple health metrics assessments receive higher priority, while changes affecting only a single non-critical metric receive lower priority.

[0135] After determining the minimum transitive coverage set, the system performs incremental normalization only on these data items. Compared to full processing, this method significantly reduces computation, especially when most of the physical examination data remains unchanged and only a small amount of data is updated. The system establishes a caching mechanism for each processing step to save intermediate calculation results. When a data item needs to be recalculated, the system first checks if its dependencies have cached results available; if so, it uses the cached results directly instead of recalculating. This caching strategy is particularly effective when dealing with complex multi-level dependencies, avoiding the repeated calculation of the same intermediate results along the dependency chain.

[0136] Finally, the system needs to merge and integrate the incremental processing results with historical results. This step employs a specially designed result merging algorithm to ensure seamless integration of new and old data. The merging algorithm not only simply replaces changed data but also considers the correlation and consistency constraints between data. For example, when updating blood lipid indicators, the system checks whether related cardiovascular risk assessments need to be updated synchronously to maintain overall data consistency. After merging, the system performs data consistency and integrity verification to ensure that the final standardized data meets predefined quality standards and has no logical contradictions or missing key information.

[0137] Through this dynamic update mechanism based on incremental propagation coverage, the system can efficiently handle frequent updates to physical examination data. In practical applications, when processing comprehensive physical examination data containing hundreds of indicators, this mechanism can reduce data processing time by 50% to 80% compared to traditional full-data processing methods, especially when only a small number of data changes. This not only improves the system's response speed, allowing users to obtain processing results faster, but also reduces computing resource consumption and improves the system's overall processing capacity and scalability.

[0138] In another preferred embodiment, when matching standardized data with a medical data matching library, an optimization method based on strong polynomial maximum flow is employed, including:

[0139] Based on standardized data, a bipartite graph network of test indicators and potential matching results is constructed. Confidence weights are assigned to each indicator-matching association edge, and context nodes are introduced to capture the combined relationships of multiple indicators, thus obtaining a bipartite graph of medical indicator matching.

[0140] Maximum flow calculation is performed on the bipartite graph for matching medical indicators. The Ford-Fulkerson algorithm is used to transform the medical data matching into a maximum flow problem. The Dinic algorithm is used to improve the processing efficiency of large-scale networks and obtain the initial maximum flow matching results.

[0141] Adaptive capacity adjustment is performed on the initial maximum flow matching result. The capacity value of the network edge is dynamically adjusted based on the historical matching accuracy. The feedback mechanism optimizes the weight allocation according to the user's modified records. Confidence intervals are introduced to calculate and quantify the uncertainty of the matching result, and the matching result after capacity adjustment is obtained.

[0142] Multi-source evidence is integrated into the matching results after capacity adjustment. The minimum cut algorithm is applied to identify key matching evidence chains, and the matching evidence ranking based on the maximum flow results is realized. Data processing path visualization data is generated to obtain the data matching results of evidence integration.

[0143] Risk transmission network analysis is performed on the data matching results of evidence integration. A three-layer network structure consisting of a detection index layer, a symptom manifestation layer, and a health risk layer is designed. The maximum flow in the risk network is calculated using a strong polynomial time algorithm, and risk nodes and bottleneck paths are identified based on the flow distribution. Dynamic thresholds are set to distinguish between high, medium, and low risk levels, thus obtaining the preliminary matching results.

[0144] In yet another preferred embodiment, the method further includes: employing an adversarial model-based security protection mechanism when performing access control and version management on the standardized medical examination report, including:

[0145] Based on standardized physical examination reports, a medical data security threat classification system is constructed, distinguishing between forgetful attacks and adaptive attacks. A risk model is established for each type of threat, and the potential scope and severity of impact are quantified. A threat intelligence integration mechanism is developed to obtain security threat classification results.

[0146] Access control graphs are constructed based on the security threat classification results. A three-layer dynamic access control graph of user-role-permission is established. The least-privilege path calculation based on graph theory is implemented to ensure that each role can only access necessary resources. A time-varying permission mechanism is designed to dynamically adjust access permissions according to time period, location and task, resulting in a dynamic access control graph.

[0147] Static defense configuration is applied to the dynamic access control graph to achieve authentication requirements based on a zero-trust architecture, requiring authentication for every resource access. A multi-factor authentication process is designed that combines biometrics, knowledge factors, and device fingerprints. Encrypted data sharding storage is deployed to ensure that a single point of attack cannot obtain complete information, resulting in a forgetting attack defense configuration.

[0148] Dynamic defense enhancements are implemented for forgetting attack defense configurations. An abnormal behavior detection engine is developed to establish user operation baselines and identify deviation patterns. Fingerprint deception technology is used to dynamically change the system's apparent characteristics to interfere with the opponent's learning. Honeypot resource traps are designed to induce and track advanced persistent threats, resulting in an adaptive attack defense configuration.

[0149] An audit log is recorded for the adaptive attack defense configuration. A three-dimensional audit relationship diagram of entities, operations, and resources is created and the audit records are stored using a graph database. A hash chain-based audit record ensures that the historical records are immutable. A distributed witness mechanism is designed to require multiple parties to confirm and record critical operations, resulting in the intelligent processing system for medical examination data under the security management described above.

[0150] In intelligent processing systems for physical examination data, accurately matching standardized data with a medical data matching database is one of the core challenges. Traditional matching methods often employ simple rule-based matching or probabilistic models, which struggle to handle complex combinations of medical indicators and multi-dimensional health risk assessments. This embodiment proposes an optimization method based on strong polynomial maximum flow, which formalizes the medical data matching problem through network flow theory, achieving efficient and accurate matching processing and risk assessment.

[0151] First, the system constructs a bipartite graph network connecting test indicators and potential matching results based on standardized physical examination data. In this network, the left-hand node set represents various physical examination indicators (such as blood glucose, blood pressure, and blood lipids), and the right-hand node set represents potential matching results (such as various health status assessments). The system establishes connections between each indicator and potential matching results and assigns confidence weights to each edge based on a medical knowledge base and statistical models. These weights reflect the strength of support a specific indicator provides for a specific matching result. For example, the connection edge between blood glucose values ​​exceeding the reference range and abnormal metabolic status receives a higher weight, while the connection edge with bone density assessment receives a lower weight.

[0152] Traditional bipartite graphs struggle to represent complex relationships between multiple indicators, such as certain health conditions that require abnormalities in multiple indicators to be confirmed. To address this issue, the system innovatively introduces context nodes as an intermediate layer connecting relevant indicators with matching results. For example, the system might create a "lipid metabolism combination" context node, connecting indicators such as total cholesterol, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL), and then linking it to relevant health assessment results. This three-layer structure enables the network to capture synergistic relationships and conditional dependencies between indicators, significantly improving matching accuracy.

[0153] After constructing the bipartite graph for medical indicator matching, the system transforms the matching problem into a classic maximum flow problem. The system adds a source node connecting all indicator nodes and a sink node connecting all matching result nodes, treating edge weights as capacity constraints, and then solves for the maximum flow from the source to the sink. This transformation allows the system to apply mature network flow algorithms to solve complex medical matching problems. The system first implements the framework of the Ford-Fulkerson algorithm, which calculates maximum flow by iteratively finding augmenting paths. However, considering the typically large scale of medical data matching networks (potentially containing hundreds of nodes and thousands of edges), the system further employs the Dinic algorithm to improve processing efficiency. The Dinic algorithm, through hierarchical graphs and multi-path augmentation techniques, significantly reduces the time complexity of finding augmenting paths, enabling the system to complete maximum flow calculations for large-scale networks within milliseconds.

[0154] After the initial maximum flow calculation is completed, the system performs adaptive capacity adjustment to improve the accuracy and personalization of matching. The system maintains a historical matching accuracy database, recording the correspondence between each combination of indicators and the matching results, along with their accuracy assessments. Based on this historical data, the system dynamically adjusts the capacity values ​​of edges in the network. For example, if the association between a certain indicator and a specific matching result has been frequently confirmed as accurate historically, the system increases the capacity of the corresponding edge; conversely, if a association is frequently corrected by users, the system decreases its capacity.

[0155] The system also implements a weight optimization mechanism based on user feedback. When users modify the matching results generated by the system, these modifications are recorded and analyzed to adjust the weight allocation in the network. For example, if a user frequently changes the system's identified "slight anomaly" to "normal fluctuation," the system will learn this preference and reduce the weight of the corresponding edge. This feedback mechanism enables the system to adapt to different users' judgment criteria and professional preferences, providing more personalized matching results.

[0156] To quantify the reliability of the matching results, the system introduces confidence interval calculation. For each matching result, the system not only provides a point estimate (the most likely match) but also calculates its 95% confidence interval, reflecting the range of uncertainty. These confidence intervals are generated based on Monte Carlo simulation methods, which involve introducing random perturbations into the network flow model, repeatedly calculating the maximum flow, and analyzing the distribution of the results. Matching results with narrow confidence intervals are considered highly deterministic and can be used directly; while results with wider intervals are marked as requiring further confirmation and may require additional checks or professional judgment.

[0157] Based on the capacity-adjusted matching results, the system integrates multi-source evidence to improve the interpretability and credibility of the results. The system applies the minimum cut algorithm to identify key cut sets in the network, which represent crucial chains of evidence supporting specific matching results. Minimum cut and maximum flow are dual problems; by calculating the minimum cut, the system can identify the most decisive combination of indicators for the final matching result. For example, the system might find that the combination of "fasting blood glucose + glycated hemoglobin + insulin level" is a crucial chain of evidence for determining a certain metabolic state.

[0158] Based on the maximum flow results, the system also implements an automatic ranking function for matching evidence. For each matching result, the system calculates the flow contribution of each supporting piece of evidence, sorts them by contribution size, and generates an evidence strength gradient. This ranking not only improves the interpretability of the results but also provides users with transparency regarding the basis for their judgments. Simultaneously, the system generates a visual representation of the data processing path, intuitively demonstrating the reasoning process from the original indicators to the final matching result, enabling users to understand the system's decision-making logic.

[0159] Finally, the system performs risk transmission network analysis on the data matching results of evidence integration to achieve a more comprehensive health risk assessment. The system is designed with a three-layer network structure: the bottom layer is the detection indicator layer, containing various physical examination data; the middle layer is the symptom manifestation layer, representing various physiological states; and the top layer is the health risk layer, representing various potential health risks. The inter-layer connections reflect the medical correlations between indicators, symptoms, and risks.

[0160] In this three-layer network, the system employs a strong polynomial-time algorithm to compute the maximum flow. Compared to traditional algorithms, the strong polynomial algorithm theoretically guarantees an upper bound on computation time, ensuring completion within an acceptable timeframe even in the worst-case scenario. This is crucial for healthcare systems requiring real-time response. The system identifies risk nodes and bottleneck paths based on flow distribution; these nodes and paths represent key health risk points and critical influencing factors requiring focused attention.

[0161] To translate continuous risk scores into intuitive risk levels, the system employs a dynamic threshold mechanism. Unlike fixed thresholds, dynamic thresholds consider individual characteristics (such as age and gender) and population distribution of examinees, adaptively classifying them into high, medium, and low risk levels. For example, for younger examinees, risk thresholds for certain indicators may be more stringent; while for older examinees, the system may use more lenient threshold standards. This personalized risk grading makes the assessment results more clinically valuable.

[0162] This system employs a strong polynomial maximum flow optimization method to achieve efficient, accurate, and interpretable medical data matching and risk assessment. In practical applications, this method improves matching accuracy by 15% to 25% compared to traditional matching algorithms, showing significant advantages, particularly when handling multi-indicator combinations and complex health status assessments. Furthermore, the system's adaptive learning capability makes the matching results increasingly aligned with the judgment criteria of professional users, enhancing the system's practicality and acceptability.

[0163] like Figure 4 As shown, this embodiment also provides a medical examination data intelligent processing and large-scale model offline deployment system, including:

[0164] The build module is used to build a Docker base image and install CUDA, cuDNN and Python environments, configure data volume mount points and network settings to establish a basic environment for offline deployment;

[0165] The configuration module is used to perform online configuration in the offline deployment base environment, deploy the physical examination data processing algorithm module, download pre-trained large model files and inference framework from the model repository, perform interface integration testing, and solidify dependencies, model weights and configuration files into containers, and generate a distributable image through Docker packaging commands;

[0166] The deployment module is used to deploy the distributable image offline. It transmits the image to a server without an external network via physical media and imports and loads it. After starting the container service, it achieves automatic restart through status monitoring and anomaly detection. It also achieves task continuation based on the task status persistence mechanism to build an offline system.

[0167] The adjustment module is used in the offline system to clean and standardize the received physical examination data, match the standardized data with a medical data matching library and calculate the severity level, merge and sort the matching results, and make personalized adjustments based on the user's historical habit learning results to obtain structured analysis results.

[0168] The rendering module is used to preprocess unstructured medical text, call the offline deployed large model for semantic analysis and generate explanatory suggestions, integrate the structured analysis results with the large model processing results, and render them into standardized physical examination reports in various formats through a template engine.

[0169] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for intelligent processing of medical examination data and offline deployment of large-scale models, characterized in that, Includes the following steps: Build a Docker base image and install CUDA, cuDNN and Python environments, configure data volume mount points and network settings to establish a basic environment for offline deployment; In the offline deployment environment, online configuration is performed to deploy the physical examination data processing algorithm module, download the pre-trained large model file and inference framework from the model repository, perform interface integration testing, and solidify the dependencies, model weights and configuration files into the container. A distributable image is generated using the Docker packaging command. The distributable image is deployed offline by transmitting the image to a server without an external network via physical media and importing and loading it. After the container service is started, automatic restart is achieved through status monitoring and anomaly detection. Task continuation is achieved based on the task status persistence mechanism to build an offline system. In the offline system, the received physical examination data is cleaned and standardized. The standardized data is matched with a medical data matching library and the severity level is calculated. The matching results are merged and sorted. Personalized adjustments are made based on the user's historical habit learning results to obtain structured analysis results. Unstructured medical text is preprocessed, a large offline model is invoked for semantic analysis and explanatory suggestions are generated, the structured analysis results are integrated with the large model processing results, and rendered into standardized medical examination reports in various formats through a template engine.

2. The method according to claim 1, characterized in that, Build a Docker base image and install CUDA, cuDNN, and Python environments, configure data volume mount points and network settings to establish a basic offline deployment environment, including: In the GPU server, the nvidia / cuda base image is selected as the building unit. The language settings, time zone settings and non-interactive installation options are configured through the Dockerfile to obtain the initial base image. Install dependencies on the initialized base image, configure the image source and pre-install toolkits, install the cuDNN library that matches the CUDA version, add the NVIDIA container toolkit, and obtain an image with GPU driver configuration complete. Configure the Python environment on the image with the GPU driver configured, install Miniconda3 and create a Python 3.10 basic environment, pre-install numpy, pandas and torch dependency packages, and determine the image with the Python environment configured. Configure the network and storage of the image with the Python environment configured, create a custom bridge network for inter-container communication, define the data volume mount points for model storage path, data exchange path and log storage path, set up shared storage between containers through Docker volumes, and establish the basic environment for offline deployment.

3. The method according to claim 1, characterized in that, In the offline deployment environment, online configuration is performed to deploy the physical examination data processing algorithm module. Pre-trained large model files and inference frameworks are downloaded from the model repository. Interface integration testing is conducted, and dependencies, model weights, and configuration files are solidified into a container. A distributable image is generated using Docker packaging commands, including: In the offline deployment base environment, the development environment plugin is configured by connecting to the base container through a remote development tool, the physical examination data processing algorithm code library is copied to the container working directory, and the dependency packages of the medical terminology processing library and Chinese word segmentation tool are installed to build the container with the algorithm deployed. The container with the algorithm deployed is used to deploy a large model. The weight file, tokenizer and configuration file of the pre-trained large model are downloaded from the model repository. The vLLM or TensorRT-LLM inference framework is installed and the model loading parameters are set. The large model is tested locally and the quantization parameters are adjusted to obtain a container that integrates the large model. The container integrating the large model is subjected to interface development and joint testing. The FastAPI application is developed and the RESTful API routes for data reception, task status query and result acquisition are designed. The interface between the algorithm module and the large model is implemented and the data exchange format is defined to obtain the container with the interface joint testing completed. The container with the interface integration completed is image solidified and packaged. The dependencies, model weights, configuration files and application code in the container are solidified to the file system. Temporary files and compilation cache are deleted to perform image slimming. The image is created and a version tag is added using the Dockercommit command. The image is digitally signed and the build time and dependent versions are recorded to obtain the distributable image.

4. The method according to claim 1, characterized in that, The distributable image is deployed offline, and after the container service is started, automatic restart is achieved through status monitoring and anomaly detection. Task continuation is achieved based on the task status persistence mechanism, resulting in the stable offline system, including: Based on the distributable image, the image is transferred to a server without external network via physical medium and decompressed. The image is then imported using the docker load command, and the validity and integrity of the image signature are verified to obtain a verified local image. Start the container on the verified local image, create a persistent storage directory and a data volume mount point for the configuration file path, start the container cluster in the order of dependencies using the docker-compose command, execute the database initialization script and system environment check, and output the running container service; The running container service is monitored for status and anomaly detection. Based on the Docker API, the running status of each container is checked periodically and the CPU, memory and GPU usage are recorded. The availability of the API service is detected by HTTP heartbeat and a timeout threshold is set. A log aggregation system is established to collect the application logs generated by each container to obtain status monitoring data. The status monitoring data is subjected to anomaly detection and automatic restart. Based on the detection results, the anomalies are classified into mild, moderate and severe anomalies and event identifiers are generated. Recovery operations such as single container restart or full service reconstruction are performed according to the anomaly level to obtain the self-recovered container service. The self-recovering container service resumes its tasks by scanning for incomplete task information and processing progress in Redis persistent storage. Depending on the task status, it continues execution from the breakpoint or re-executes from the beginning. Data consistency is checked on the results after the interrupted tasks are recovered, resulting in the stable offline system.

5. The method according to claim 1, characterized in that, The received physical examination data is cleaned and standardized, including: Based on the received physical examination data, the system receives JSON or XML format data from POST requests via FastAPI, generates a unique task ID, and records task metadata such as data source, timestamp, and processing priority. The system performs format validation and structure checks on the input data and outputs physical examination data that has passed format validation. The physical examination data that has passed the format verification is processed into structured fields, including unit conversion, field name mapping and encoding standardization. Outliers are marked or replaced based on the 3-sigma rule and medical reference range. Missing values ​​are handled by means filling or historical values ​​filling strategies according to field type and medical relevance, thus obtaining the standardized data.

6. The method according to claim 1, characterized in that, The standardized data is matched with a medical data matching library to calculate the severity level. The matching results are merged and sorted, and personalized adjustments are made based on the user's historical learning habits to obtain the structured analysis results, including: Based on the standardized data, the test indicators are matched with the local medical data matching library and support multiple coding systems. A matching strategy combining rule engine and machine learning is used to handle complex medical relationships and multi-indicator combination analysis. The confidence score of the match is calculated and the accuracy of the match is evaluated in combination with guidelines to obtain preliminary matching results. The severity analysis of the preliminary matching results is performed. Based on expert knowledge, each abnormal indicator is assigned a severity level of mild, moderate or severe. Multiple indicators are integrated and the interaction and cumulative effect between indicators are considered to calculate the overall health risk score. The risk assessment results are compared with the examinee's historical data to identify significant changes and potential trends, and the severity classification results are obtained. The severity grading results are merged and sorted. Semantic similarity algorithm is used to identify related or redundant matching results and intelligent merging is performed by threshold judgment. Clinical priority-based sorting rules are applied to rank results with high severity and high intervention urgency. Results are grouped according to organ system or functional relevance to obtain the merged and sorted matching results. User habit learning is performed on the merged and sorted matching results. User writing patterns are extracted from historical data and common words, sentence structures and professional term preferences are identified. Based on historical data statistical analysis, the language characteristics of individual users are captured. When generating reports, the habit learning results are applied to adjust the wording and expression, and the analysis results after habit adjustment are obtained. The analysis results after the habit adjustment are structured and organized, and the matching information, severity level, risk score and personalized adjustment content are organized into a standardized data structure. Metadata information of matching basis and confidence level is added to obtain the structured analysis results.

7. The method according to claim 1, characterized in that, When cleaning and standardizing the received physical examination data, a dynamic update mechanism based on incremental propagation coverage is adopted, including: Based on the received physical examination data, each field of the physical examination data and the corresponding processing steps are modeled as a directed graph structure, with nodes representing data items and edges representing dependencies. Adjacency lists are used to store the dependencies between data and to label the computational cost weights. The dependency graph is topologically sorted to determine the data dependency graph network and the optimal computation sequence. Incremental change detection is performed on the data dependency graph network, the change set between different data versions is calculated and the data change points are marked with hash index and timestamp, and change propagation rules are designed to clarify the dependency relationship between different fields; Based on the change set and change propagation rules, the minimum transit cover set is calculated using a breadth-first search algorithm, and dynamic programming is applied to optimize the calculation path. Priority queue management of recalculation tasks is implemented and critical paths are guaranteed to be processed first, thus obtaining the minimum transit cover set. Incremental standardization is performed on the data in the minimum transmission coverage set. Only the changed data and its affected domain are re-standardized and cleaned. A caching mechanism is established to save intermediate results and avoid repeated calculations, thus obtaining the incremental processing result. The incremental processing results are merged and integrated. The result merging algorithm integrates the incremental processing results with historical results, verifies the consistency and integrity of the merged data, and obtains the standardized data.

8. The method according to claim 1, characterized in that, When matching the standardized data with the medical data matching library, an optimization method based on strong polynomial maximum flow is used, including: Based on the standardized data, a bipartite graph network of test indicators and potential matching results is constructed. Confidence weights are assigned to each indicator-matching association edge, and context nodes are introduced to capture the multi-indicator combination relationship to obtain a bipartite graph of medical indicator matching. Maximum flow calculation is performed on the bipartite graph of the medical indicator matching. The Ford-Fulkerson algorithm is used to transform the medical data matching into a maximum flow problem. The Dinic algorithm is used to improve the processing efficiency of large-scale networks and obtain the initial maximum flow matching result. Adaptive capacity adjustment is performed on the initial maximum flow matching result. The capacity value of the network edge is dynamically adjusted based on the historical matching accuracy. The feedback mechanism optimizes the weight allocation according to the user's modified record. Confidence interval is introduced to calculate and quantify the uncertainty of the matching result, and the matching result after capacity adjustment is obtained. The capacity-adjusted matching results are integrated with multi-source evidence. The minimum cut algorithm is applied to identify key matching evidence chains, and the matching evidence ranking based on the maximum flow results is realized. Data processing path visualization data is generated to obtain the data matching results of evidence integration. Risk transmission network analysis is performed on the data matching results of the evidence integration. A three-layer network structure consisting of a detection index layer, a symptom manifestation layer, and a disease risk layer is designed. The maximum flow in the risk network is calculated using a strong polynomial time algorithm, and risk nodes and bottleneck paths are identified based on the flow distribution. Dynamic thresholds are set to distinguish between high, medium, and low risk levels to obtain the preliminary matching results.

9. The method according to claim 1, characterized in that, The method further includes: when performing access control and version management on the standardized medical examination report, employing a security protection mechanism based on an adversarial model, including: Based on the standardized physical examination report, a medical data security threat classification system is constructed, and forgetting attacks and adaptive attacks are distinguished. A risk model is established for each type of threat, and the potential impact range and severity are quantified. A threat intelligence integration mechanism is developed to obtain the security threat classification results. An access control graph is constructed based on the security threat classification results. A three-layer dynamic access control graph of user-role-permission is established. The least-privilege path calculation based on graph theory is implemented to ensure that each role can only access necessary resources. A time-varying permission mechanism is designed to dynamically adjust access permissions according to time period, location and task, thus obtaining a dynamic access control graph. Static defense configuration is performed on the dynamic access control graph to achieve authentication requirements based on zero trust architecture, requiring authentication for each resource access. A multi-factor authentication process is designed to combine biometrics, knowledge factors, and device fingerprints. Encrypted data sharding storage is deployed to ensure that a single point of attack cannot obtain complete information, resulting in a forgetting attack defense configuration. Dynamic defense enhancements are applied to the forgetting attack defense configuration. An abnormal behavior detection engine is developed to establish a user operation baseline and identify deviation patterns. Fingerprint deception technology is used to dynamically change the system's apparent characteristics to interfere with the opponent's learning. Honeypot resource traps are designed to induce and track advanced persistent threats, resulting in an adaptive attack defense configuration. The adaptive attack defense configuration is audited and logged. A three-dimensional audit relationship diagram of entity-operation-resource is created and the audit records are stored using a graph database. The audit records are implemented based on a hash chain to ensure that the historical records are immutable. A distributed witness mechanism is designed to require multiple parties to confirm and record key operations, thus obtaining the intelligent processing system for medical examination data security management.

10. A system for intelligent processing of medical examination data and offline deployment of large-scale models, characterized in that, include: The build module is used to build a Docker base image and install CUDA, cuDNN and Python environments, configure data volume mount points and network settings to establish a basic environment for offline deployment; The configuration module is used to perform online configuration in the offline deployment base environment, deploy the physical examination data processing algorithm module, download pre-trained large model files and inference framework from the model repository, perform interface integration testing, and solidify dependencies, model weights and configuration files into containers, and generate a distributable image through Docker packaging commands; The deployment module is used to deploy the distributable image offline. It transmits the image to a server without an external network via physical media and imports and loads it. After starting the container service, it achieves automatic restart through status monitoring and anomaly detection. It also achieves task continuation based on the task status persistence mechanism to build an offline system. The adjustment module is used in the offline system to clean and standardize the received physical examination data, match the standardized data with a medical data matching library and calculate the severity level, merge and sort the matching results, and make personalized adjustments based on the user's historical habit learning results to obtain structured analysis results. The rendering module is used to preprocess unstructured medical text, call the offline deployed large model for semantic analysis and generate explanatory suggestions, integrate the structured analysis results with the large model processing results, and render them into standardized physical examination reports in various formats through a template engine.

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