Big data hub management system for digital hospital

Abstract

The application provides a digital hospital big data hub management system, relates to the fields of medical informatization and big data management, and comprises a data twin hub system, a trust chain collaboration system, a breathing storage system, a narrative presentation system, a microclimate regulation system, a memory remodeling system and a central nervous control console; the data twin hub system comprises a data flow acquisition module, a life body modeling module and a visual interaction module, the data flow acquisition module acquires data from a hospital information system, an imaging device and an Internet of Things sensor and generates a structured record, the life body modeling module generates a patient data flow model and calculates a health index, and the visual interaction module displays a data flow timeline and a relationship diagram and sends an alarm; the system realizes efficient multi-source data integration and improves data quality, and solves the problems of non-uniform data formats of a hospital information system, an imaging system and an Internet of Things sensor, and slow integration speed in the prior art.

Description

Technical Field

[0001] This invention relates to the field of medical informatics and big data management, specifically to a big data central management system for digital hospitals. Background Technology

[0002] With the introduction of computer technology, hospital information systems have become increasingly widespread, and electronic medical records have replaced paper records, enabling electronic storage and basic retrieval of data. Medical image archiving systems and communication systems have further digitized image data, supporting radiologists in remotely accessing CT and MRI images. The development of Internet of Things (IoT) technology has led to the widespread application of real-time monitoring devices such as heart rate and blood pressure sensors in wards, generating continuous streams of vital signs data. These technological advancements have significantly improved data recording efficiency and initially achieved data integration within hospitals. Despite the achievements of existing technologies in medical data management, the following problems still exist in practical applications:

[0003] Problem 1: Existing technologies have limitations in data acquisition and integration. Currently, hospital information systems, medical image archiving systems, and IoT sensors operate independently, generating data in inconsistent formats. For example, medical records use text format, images use the DICOM standard, and monitoring data is time-series, lacking a unified standardized processing mechanism. Data integration typically relies on manual or semi-automatic scripts, resulting in slow processing speeds, handling only a few hundred records per second, and is prone to data loss or inconsistencies. For instance, patient medical records and image data may be misaligned due to timestamp discrepancies, affecting the accuracy of diagnostic and treatment decisions. Furthermore, existing systems lack real-time assessment of data integrity, timeliness, and consistency, making it difficult to dynamically identify data breakpoints or quality issues, thus preventing doctors from obtaining reliable patient information in a timely manner.

[0004] The second issue is the inadequacy of existing technologies in terms of data storage and access efficiency. Traditional storage systems often use a single storage medium, such as hard drives or cloud storage, without dynamically allocating storage locations based on data access frequency. The mixed storage of frequently accessed emergency data and infrequently accessed historical data leads to high access latency, averaging over 100 milliseconds. Data compression technology is limited in its application; archived data consumes a large amount of storage space, and compression rates are typically below 50%, increasing storage costs. Furthermore, existing systems lack mechanisms for predicting future access demands, and the migration of data from low-speed storage media to high-speed storage is time-consuming, impacting response speeds in time-sensitive scenarios such as emergency care.

[0005] Thirdly, existing technologies have shortcomings in cross-hospital data sharing and privacy protection. Data sharing between hospitals relies on manual authorization or simple encrypted transmission, lacking efficient authentication and access control mechanisms. Data access records are typically stored in local databases, making them susceptible to tampering and failing to meet regulatory traceability requirements. Patients have limited control over data sharing, and access control updates are time-consuming, averaging several hours, making it impossible to respond to patient needs in real time. Furthermore, data transmission encryption strength is insufficient, commonly using 128-bit encryption, which is vulnerable to security threats, resulting in high transmission latency and low efficiency in cross-hospital collaboration.

[0006] Question 4: Existing technologies have limited capabilities in data analysis and presentation. Current systems primarily generate raw data reports, lacking the ability to extract semantics from complex data and present it in a narrative manner. Doctors need to manually analyze medical records, images, and monitoring data, making it difficult to quickly obtain a comprehensive picture of a patient's diagnosis and treatment. Data inconsistency detection relies on manual verification, which is inefficient, finding only a few dozen abnormalities per hour on average. This can easily lead to missed issues such as drug dosage or diagnostic inconsistencies, increasing the risk of misdiagnosis and treatment.

[0007] Question 5: Existing technologies lack dynamic control capabilities in resource management and system monitoring. Hospital information systems operate on fixed hardware resources. When facing high-concurrency scenarios such as emergency data processing, uneven processor and memory utilization leads to excessive load on some modules, resulting in increased latency, averaging 200 milliseconds. System status monitoring is mostly offline analysis with long update cycles, making it difficult to reflect data flow rates or resource allocation status in real time, thus affecting system stability.

[0008] Therefore, a big data central management system for digital hospitals is needed to solve the above problems. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a big data central management system for digital hospitals, which solves the problems mentioned in the background section.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solution: a digital hospital big data central management system, including a data twin central system, a trust chain collaboration system, a respiratory storage system, a narrative presentation system, a microclimate control system, a memory remodeling system, and a central nervous system control console;

[0011] The data twin central system includes a data stream acquisition module, a life form modeling module, and a visualization interaction module. The data stream acquisition module collects data from the hospital information system, imaging equipment, and IoT sensors and generates structured records. The life form modeling module generates a patient data stream model and calculates health indices. The visualization interaction module displays the data stream timeline and relationship diagram and sends alarms.

[0012] The trust chain collaboration system includes a trust negotiation module, a data footprint module, and a patient authorization module. The trust negotiation module generates inter-hospital data sharing contracts, the data footprint module records access history, and the patient authorization module sets data sharing permissions.

[0013] The respiratory storage system includes an activity assessment module, a storage scheduling module, and a data preheating module. The activity assessment module analyzes the data access frequency, the storage scheduling module adjusts the data storage location and compression strategy, and the data preheating module predicts data demand and migrates data.

[0014] The narrative presentation system includes a data integration module, a narrative generation module, and an anomaly detection module. The data integration module extracts patient data entities, the narrative generation module generates diagnostic and treatment text, and the anomaly detection module identifies data contradictions.

[0015] The microclimate control system includes a load monitoring module, a resource scheduling module, and an ecological visualization module. The load monitoring module collects performance data, the resource scheduling module allocates computing and storage resources, and the ecological visualization module displays the system status.

[0016] The memory reconstruction system includes an association analysis module, a fragment generation module, and a context retrieval module. The association analysis module extracts historical data relationships, the fragment generation module generates thematic data packages, and the context retrieval module performs semantic queries.

[0017] The central nervous system console manages data flow and resource allocation through service registration, message queues, and a visual interface system.

[0018] Preferably, the specific steps of the data twin central system include:

[0019] Step A1, Data Stream Acquisition: The data stream acquisition module extracts electronic medical records from the hospital information system, standard format image data from the medical image archiving system, and real-time monitoring data from IoT sensors through a high-speed message queue. It processes 1,000 records per second and generates structured data format records, which include patient identification, data type, and generation time.

[0020] Step A2, Data Life Modeling: The life modeling module uses a graph database engine to transform structured data format records into a patient data flow model represented by nodes and edges. Nodes include patients, examinations, and medical orders, while edges represent time sequence and logical relationships. Based on the time series database, the health index is calculated. The health index is weighted and summed using a weighted average of 0.4 for completeness, 0.3 for timeliness, and 0.3 for consistency, with 1000 records updated every minute.

[0021] Step A3, Visualization and Alarms: The visualization and interaction module generates a data flow timeline and relationship diagram through dynamic chart tools. The timeline displays the generation time of each record, and the relationship diagram shows the logical connections between nodes. When the health index is below 80%, an alarm is sent to medical staff through server push event technology. The alarm includes the data breakpoint location and missing type, and 500 alarms are generated per hour.

[0022] Preferably, the specific steps of the trust chain collaboration system include:

[0023] Step B1, Trust Contract Generation: The trust negotiation module verifies the hospital's identity through a two-way transport layer security authentication protocol and uses the key management system to generate a 256-bit encrypted signature trust contract. The contract specifies shared data types, including examination results and medical records, and is valid for 24 hours, verifying 100 identity requests per hour.

[0024] Step B2, Data Access Records: The data footprint module stores access records through a distributed database. The records include access time, hospital identifier, and data type. An immutable log is generated using a tree structure. 500 audit logs are generated every hour, and the log storage capacity is 10 million records.

[0025] Step B3, Patient Permission Settings: The patient authorization module receives patient input through the front-end interface, sets the scope of shared data to include examination results and medical records, stores permission data in a high-speed cache, updates 1000 permission records every 5 minutes, and uses a 192-bit encryption standard for transmission.

[0026] Preferably, the specific steps of the respiratory storage system include:

[0027] Step C1, Activity Analysis: The activity assessment module collects data access logs through a distributed tracking tool. The logs include access time, data type, and user role. Based on the state transition chain algorithm, it predicts the access probability in the next 12 hours and generates an activity score of 5,000 records every 12 hours, with the score ranging from 0 to 1.

[0028] Step C2, Dynamic Storage Adjustment: The storage scheduling module uses the object storage system to allocate data to high-speed solid-state drives, medium-speed hard drives, or tape storage based on activity scores. Data with scores higher than 0.8 is stored on solid-state drives, while data with scores lower than 0.2 is compressed to tape storage using a high compression ratio algorithm. It processes 1,000 storage requests per minute with a compression rate of 90%.

[0029] Step C3, Data Preheating and Migration: The data preheating module uses a workflow scheduling tool to analyze patient medical history and seasonal disease trends in the database. It predicts the access demand for 1,000 records every 24 hours and migrates the predicted data from tape storage to solid-state drive at a migration speed of 100 records per second.

[0030] Preferably, the specific steps of the narrative presentation system include:

[0031] Step D1, Entity Extraction: The data integration module obtains structured data format records from the data twin central system through a distributed computing framework, extracts disease, symptom, and drug entities, and generates a semantic relationship graph stored in a multi-modal database. The graph contains 1,000 nodes and 2,000 edges, and processes 2,000 records per hour.

[0032] Step D2, Treatment Story Generation: The narrative generation module uses a pre-trained language model to transform the semantic relationship graph into a text narrative. The narrative includes event time, treatment event, and treatment result. 100 treatment stories are generated every 30 minutes, with a text length of 200 to 500 words.

[0033] Step D3, Anomaly Detection: The anomaly detection module analyzes the semantic relationship graph using graph query language, detects and diagnoses contradictions with drug dosage, and generates reports containing the contradiction type and location. 500 reports are generated per hour and stored in a multi-model database.

[0034] Preferably, the specific steps of the microclimate control system include:

[0035] Step E1, Performance Data Collection: The load monitoring module collects performance data through container cluster status monitoring tools. The data includes request latency, processor utilization, and memory consumption, recording 1,000 requests per second and storing them in a time-series database.

[0036] Step E2, Dynamic Resource Scheduling: The resource scheduling module uses reinforcement learning algorithms to allocate computing and storage resources based on performance data. High-priority tasks, such as emergency data processing, are allocated 80% of the resources. 500 scheduling requests are adjusted every minute, and the scheduling latency is less than 20 milliseconds.

[0037] Step E3, System Status Display: The ecological visualization module generates data flow and resource status diagrams through 3D rendering tools, displaying a data flow rate of 2000 data entries per second and resource allocation ratios, and updating 1000 view nodes every 5 minutes.

[0038] Preferably, the specific steps of the memory reshaping system include:

[0039] Step F1, Semantic Relationship Extraction: The association analysis module obtains historical data from the respiratory storage system through a streaming query tool, extracts disease and treatment topics based on the word embedding algorithm, processes 5,000 records every 12 hours, and generates a semantic index containing 1,000 topics;

[0040] Step F2, Thematic Data Package Generation: The fragment generation module uses a high-performance document database to transform semantic indexes into thematic data packages. The data packages contain disease, examination, and treatment records. 100 data packages are generated every 24 hours, and each data package has a storage capacity of 100,000 records.

[0041] Step F3, Semantic Query Execution: The contextual retrieval module supports natural language queries through an open-source search framework. The query input includes the disease name or treatment type. It processes 50 queries per second and returns relevant topic data packages. The query response time is less than 100 milliseconds.

[0042] Preferably, the specific structure and steps of the central nervous system control console include:

[0043] Step G1, Module Registration Management: The service discovery component manages system modules through a distributed registry center, checking the health status of modules 1,000 times per minute, recording the status as running or faulty, and storing it in a distributed database;

[0044] Step G2, Data Flow Coordination: The event bus coordinates data interaction between systems through a high-performance message queue, processing 5,000 events per second. Events include data streams, permissions, and scheduling instructions, and are stored in a high-speed cache.

[0045] Step G3, Resource Allocation Optimization: The resource scheduling component allocates computing and storage resources through a hybrid workload management tool, adjusting 1,000 allocation strategies every minute with an allocation accuracy of 95%.

[0046] Step G4, Visual Monitoring: The visual management component generates a management interface through a front-end framework and dynamic styles, displaying a 3D view with a screen resolution of 2560×1440, a brightness of 500 nits, a contrast ratio of 1000:1, and updating 1000 system status records every 5 minutes.

[0047] Preferably, the data interaction and transmission mechanism between the systems includes:

[0048] Step H1, Real-time Data Transmission: The data twin central system transmits structured data format records to the narrative presentation system and memory reconstruction system through a high-speed message queue at a rate of 2,000 records per second, using 256-bit encryption and an efficient remote call protocol to ensure a latency of less than 10 milliseconds;

[0049] Step H2, Permission Data Synchronization: The Trust Chain Collaboration System shares data access permissions with the Respiratory Storage System through a distributed database, synchronizing 1,000 permission records per hour, using 192-bit encryption, with a synchronization time of less than 1 second;

[0050] Step H3, Performance Monitoring and Scheduling: The microclimate control system monitors the performance of other systems through network optimization tools, adjusting 500 resource allocation strategies every 5 minutes to ensure that data stream latency is less than 50 milliseconds;

[0051] Step H4, Cross-system collaboration: The central nervous system console receives system status and logs through a high-performance message queue, processes 1,000 records per minute, and generates a system operation report containing performance, fault, and resource allocation information.

[0052] Preferably, the system includes a data security and privacy protection system, the composition and steps of which include:

[0053] Step I1, Data Encryption: The data encryption component encrypts data transmission through a two-way transport layer security authentication protocol, using a 256-bit encryption standard, processing 2000 data streams per second, with an encryption time of less than 5 milliseconds;

[0054] Step I2, Access Control: The access control component implements fine-grained permission management through a policy proxy tool, updating 1000 access policies every minute to restrict unauthorized access. The policies are stored in a distributed database.

[0055] Step I3, Audit Trail: The audit trail component records data access operations through a distributed database. The records include time, user, and operation type, generating 500 audit reports per hour, with a report storage capacity of 10 million records. Beneficial effects

[0056] This invention provides a big data central management system for digital hospitals. It has the following beneficial effects:

[0057] This system achieves efficient integration of multi-source data and improves data quality, solving the problems of inconsistent data formats and slow integration speeds in existing hospital information systems, imaging systems, and IoT sensor data. The data twin central system acquires electronic medical records, imaging data, and monitoring data in real time through a data flow acquisition module, processing 1000 records per second to generate structured records in a unified format, including patient identification, data type, and generation time, overcoming the limitations of inconsistent formats. The vital entity modeling module uses a graph database engine to construct a patient data flow model, with nodes including patients, examinations, and medical orders, and edges representing temporal order and logical relationships, ensuring data association accuracy of up to 98%.

[0058] This system achieves dynamic storage optimization and improves access efficiency, solving the problems of single storage medium and high access latency in existing technologies. The respiratory storage system analyzes data access logs through an activity assessment module, predicts the access probability for the next 12 hours based on a state transition chain algorithm, and generates 5000 activity scores every 12 hours, ranging from 0 to 1. The storage scheduling module allocates data to high-speed solid-state drives (SSDs), medium-speed hard drives, or tape storage based on the scores. Data with scores higher than 0.8 is stored on SSDs, while data with scores lower than 0.2 is compressed to tape storage, achieving a compression rate of 90%, and processing 1000 storage requests per minute. The data preheating module predicts high-demand data based on patient history and disease trends, migrating 1000 records to SSDs every 24 hours at a migration speed of 100 records per second. This breaks through the limitations of single storage, reducing access latency from 100 milliseconds to 20 milliseconds, increasing storage space utilization from 50% to 90%, and reducing storage costs by 50%, meeting the high-timeliness requirements of emergency scenarios.

[0059] This system achieves dynamic resource regulation and improves system stability, solving the problems of fixed resource allocation and lagging monitoring in existing technologies. The microclimate control system collects performance data 1000 times per second through the load monitoring module, including request latency, processor utilization, and memory consumption. The resource scheduling module uses a reinforcement learning algorithm to allocate resources, with high-priority tasks receiving 80% of the resources, adjusting 500 scheduling requests per minute, and achieving latency of less than 20 milliseconds. The ecological visualization module generates a 3D data stream and resource status map, updating 1000 view nodes every 5 minutes, breaking through the limitations of offline monitoring, reducing latency from 200 milliseconds to 20 milliseconds, improving processor utilization balance from 50% to 90%, and increasing system stability by 30%, ensuring efficient operation in high-concurrency scenarios. Attached Figure Description

[0060] Figure 1 This is a system framework diagram of the present invention;

[0061] Figure 2 This is the core flowchart of the system of the present invention;

[0062] Figure 3 This is a data interaction diagram of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0065] like Figures 1 to 3 As shown, the digital hospital big data central management system operates collaboratively through six systems and a central nervous system console to achieve real-time integration, cross-hospital sharing, dynamic storage, narrative presentation, resource allocation, historical data retrieval, and security protection of medical data. The six systems are: a data twin central system, a trust chain collaboration system, a respiratory storage system, a narrative presentation system, a microclimate control system, and a memory reshaping system. The system is deployed in a distributed container cluster environment, employing high-speed message queues to coordinate data flow, distributed databases to store data, and network optimization tools to ensure low-latency transmission. The central nervous system console manages the system uniformly, monitors its status in real time, and dynamically allocates resources to ensure data flow and system stability. The following details the operation of each system and module, covering the entire process of data acquisition, processing, interaction, and transmission.

[0066] The data twin central system is responsible for real-time acquisition of multi-source data from the hospital, generating a patient data flow model, calculating health indices, displaying the system's data flow status, and sending anomaly alerts to optimize the diagnosis and treatment process and manage data integrity. It extracts electronic medical records from the hospital information system, including patient identification, diagnosis, and recording time; standard-format image data from the medical imaging system, including image number and capture time; and real-time monitoring data from IoT sensors, including temperature records, blood pressure values, and heart rate data. The system processes 1000 records per minute using high-speed data acquisition technology, generating structured data format records. Each record includes patient information identification, record data type, and generation time. The data flow is protected by 256-bit cryptographic encryption technology, transmitted to a high-speed message queue, and stored in a temporary data buffer to ensure data transmission security. Subsequently, the system transmits the structured data flow to a high-performance graph database engine, transforming it into a patient data flow model represented by nodes and edges. Nodes include individual patients, medical examinations, and medical orders; edges represent temporal relationships, such as the generation of medical orders after examinations, and logical associations, such as the correspondence between medical orders and diagnoses. The system calculates a health index based on a time-series database. The index comprehensively considers data completeness, timeliness, and consistency, assigning weights of 0.4, 0.3, and 0.3 respectively. It updates 1000 records per minute. Data completeness is determined by detecting the proportion of missing fields, timeliness is calculated based on data generation latency, and consistency is evaluated through cross-system data matching. The model and index are stored in a graph database with a storage capacity supporting 10 million nodes. Subsequently, the system uses a dynamic chart generation tool to create a data flow timeline and relationship graph. The timeline displays the generation time of each record, and the relationship graph shows the logical connections between nodes, such as the association between examinations and medical orders. When the health index falls below 80%, the system sends an alarm to medical staff via server-pushed event technology. The alarm content includes the data breakpoint location, such as missing examination results, and the type of missing data, such as data not uploaded. 500 alarm messages are generated per hour. The alarm interface supports a resolution of 2560×1440, a screen brightness of 500 nits, and a contrast ratio of 1000:1. Data interaction is achieved through a high-speed message queue, transmitting 2000 records per second, protected by 256-bit encryption. An efficient remote call protocol ensures transmission latency of less than 10 milliseconds. Alarm information and model data are transmitted to the central nervous system console and stored in a high-speed cache area.

[0067] The Trust Chain Collaboration System enables data sharing between hospitals, ensuring transparent and traceable data access, supporting patient control over data permissions, and promoting cross-hospital collaboration and privacy protection. Through a two-way transport layer security authentication protocol, it obtains identity information from the hospital's identity management system, including the hospital number and digital certificate. It verifies 100 identity requests per hour to ensure legitimate access. The system uses a key management system to generate a 256-bit encrypted signature trust contract. The contract specifies the types of data to be shared, including medical examination results and electronic medical records. The contract is valid for 24 hours and stored in a distributed in-memory database with a storage capacity supporting 10 million records. Simultaneously, the system receives patient input through a front-end user interface. The interface supports mobile devices and has a display resolution of 2560×1440. Patients can set the scope of data sharing, including examination results and electronic medical records. Permission data is stored in a high-speed cache area, with 1000 permission records updated every 5 minutes and transmitted to the trust negotiation module using 192-bit encryption. The system transmits access request records to a distributed database. These records include access time, hospital identifier, and data type. An immutable audit log is generated using a tree structure, producing 500 log entries per hour. The storage capacity supports 10 million records. The logs are queryable by regulatory agencies, with a query response time of less than one second. Data interaction is completed via a high-speed message queue, transmitting 100 records per second, protected by 192-bit encryption. Permission data is transmitted to the trust negotiation module, and access logs are pushed to the central control console and stored in a high-speed cache area.

[0068] The breathing-style storage system dynamically adjusts data storage location and compression strategies to predict data access needs and optimize storage efficiency and data access speed. It collects data access logs using a distributed tracing tool, including access time, data type, and user role information. A state transition chain algorithm is used to analyze the logs and predict the probability of data access within the next 12 hours. Every 12 hours, it generates an activity score for 5000 records, ranging from 0 to 1. This score data is stored in a time-series database with a storage capacity supporting 10 million records. Based on the activity score, the system allocates data to different storage media using an object storage system. Data with scores higher than 0.8 is stored on high-speed solid-state drives (SSDs), data with scores between 0.2 and 0.8 is stored on medium-speed hard drives, and data with scores lower than 0.2 is compressed using a high-compression algorithm and stored on tape storage, achieving a compression rate of 90%. The system processes 1000 storage requests per minute, with a storage location adjustment time of less than 1 second. Simultaneously, the system uses a workflow scheduling tool to retrieve patient medical history data and seasonal disease trend information from the analysis database. It predicts the access demand for 1000 records every 24 hours, based on historical access patterns and disease epidemic cycles. The predicted data is then migrated from tape storage to high-speed solid-state drives at a migration speed of 100 records per second. Data interaction is completed through an object storage system interface, transmitting 500 records per second, protected by 256-bit encryption. Activity scores are transmitted to the storage scheduling module, migration instructions are pushed to the data preheating module, and storage status information is transmitted to the central control console and stored in the cache area.

[0069] The narrative presentation system transforms patient data into clinical text narratives, detects data inconsistencies, and improves doctors' efficiency in understanding complex data. It acquires structured data records from a data twin central system, processing 2000 records per hour. Through a distributed computing framework, it extracts disease entities, symptom entities, and drug entities from the data, generating a semantic relationship graph stored in a multi-modal database. This graph contains 1000 nodes and 2000 edges, with a storage capacity supporting 10 million nodes. Subsequently, the system uses a pre-trained language model to transform the semantic relationship graph into a text narrative. The narrative includes event occurrence times (e.g., examination execution times), clinical events (e.g., diagnosis results), and treatment results (e.g., drug dosage information). It generates 100 clinical stories every 30 minutes, each with a text length of 200 to 500 words, stored in the multi-modal database, accessible to doctors through a query interface. Simultaneously, the system analyzes the semantic relationship graph using graph query language to detect inconsistencies between diagnosis and drug dosage. For example, if the drug dosage exceeds the recommended range, it generates 500 anomaly reports per hour, including the type and specific location of the inconsistency, also stored in the multi-modal database. Data interaction is accomplished through a distributed computing framework interface, transmitting 200 records per second, protected by 256-bit encryption. The semantic relationship graph is transmitted to the narrative generation module and the anomaly detection module, anomaly reports are pushed to the central nervous system console, and diagnostic texts are transmitted to the memory remodeling system and stored in a multi-modal database.

[0070] The microclimate control system dynamically balances data flow, computing resources, and storage resources to optimize system performance. Performance data is collected through a controller cluster status monitoring tool, including request processing latency, processor utilization, and memory consumption. 1000 requests are recorded every 5 seconds and stored in a time-series database with a storage capacity supporting 10 million records. Performance data analysis takes less than 1 second. Based on the collected performance data, the system dynamically allocates computing and storage resources using reinforcement learning algorithms. High-priority tasks, such as emergency data processing, receive 80% of the resources. 500 scheduling requests are adjusted every minute, with scheduling latency less than 20 milliseconds. The allocation strategy is stored in a high-speed cache. Subsequently, the system generates a data flow status diagram using a 3D rendering tool, displaying the data flow transmission rate (2000 records per second) and a resource allocation ratio diagram showing the resource usage of each system. 1000 graphical nodes are updated every 5 minutes. The display interface supports a resolution of 2560×1440 and a screen brightness of 500 nits. Data interaction is completed through a network optimization tool, transmitting 300 records per second, protected by 256-bit encryption. Performance data is transmitted to the resource scheduling module, allocation strategies are pushed to the ecosystem visualization module, and status graphical data is transmitted to the central nervous system console and stored in the time series database.

[0071] The memory remodeling system optimizes historical data organization, generating structured thematic data packages that support rapid semantic search and improve the efficiency of historical data utilization. It retrieves historical data records from the respiratory storage system, processing 5,000 records every 12 hours. Using streaming query processing tools and a semantic analysis word embedding model, it extracts disease and treatment themes from the data, generating a semantic index containing 1,000 thematic contents, stored in a high-performance document database with a storage capacity supporting 50 million records. Subsequently, the system uses a high-performance document processing module to transform the semantic index into thematic data packages. These packages include disease information, examination records, and treatment records, generating 100 data packages every 24 hours, each with a storage capacity of 100,000 records, stored in the high-performance document database, supporting rapid retrieval. Simultaneously, the system receives natural language queries from users through an open-source search framework, with query content including disease names or treatment types. It processes 50 query requests per second, extracting relevant thematic data packages from the high-performance document database. The query response time is less than 1 second, and the query results are displayed through a front-end interface supporting a 2560×1440 resolution for clear screen display. Data interaction is completed through a streaming query interface, transmitting 100 records per second, protected by 256-bit encryption. Semantic indexes are transmitted to the fragment generation module, thematic data packages are pushed to the context retrieval module, and query logs are transmitted to the central nervous system console and stored in the cache area.

[0072] The central nervous system control console centrally manages six systems, coordinating data flow and resource allocation, and monitoring system operation status in real time to ensure efficient system collaboration. This console manages all system modules through a distributed registry center, checking module health status 1000 times per minute, recording module operational or fault status, and storing the data in a distributed database with a storage capacity supporting 10 million records. The system coordinates data interaction between systems through a high-performance message queue, processing 5000 interaction events per second. Event content includes data flow transmission, permission verification, and scheduling instructions; event data is stored in a high-speed cache area. Simultaneously, the system dynamically allocates computing and storage resources through a hybrid workload management tool, adjusting 1000 allocation strategies per minute with an allocation accuracy of 95%, ensuring maximum resource utilization. The console generates a management interface through a front-end framework and dynamic style design, displaying a 3D view. The view includes data flow status, resource allocation, and module health status, updating 1000 system status records every 5 minutes. The interface supports a resolution of 2560×1440, a screen brightness of 500 nits, and a contrast ratio of 1000:1. Data interaction is accomplished through a high-performance message queue, transmitting 5,000 records per second, protected by 256-bit encryption. System status and logs are transmitted to the console, and the system generates operation reports, including performance metrics, fault records, and resource allocation details, which are stored in a distributed database.

[0073] The data security and privacy protection system ensures the security of system data transmission and access, provides operational traceability, and meets the requirements of medical data privacy regulations.

[0074] This system encrypts all data transmissions using a bidirectional transport layer security authentication protocol, employing a 256-bit encryption standard. It processes 2000 data streams per second with an encryption processing time of less than 5 milliseconds. The system utilizes a policy proxy tool for fine-grained access control, updating 1000 access policies per minute to restrict unauthorized access. These access policies are stored in a distributed database with a capacity supporting 10 million records. Simultaneously, the system records all data access operations in the distributed database, including access time, user identity, and operation type. It generates 500 audit reports per hour, with a report storage capacity supporting 10 million records, supporting queries by regulatory agencies with a query response time of less than 1 second. Data interaction is accomplished through a high-performance message queue, transmitting 2000 records per second with 256-bit encryption protection. Audit reports are transmitted to the central control console and stored in the distributed database, ensuring operational traceability.

[0075] The Digital Hospital Big Data Central Management System, through the collaborative operation of six systems and a central nervous system console, achieves real-time acquisition, dynamic modeling, cross-hospital sharing, storage optimization, narrative presentation, dynamic resource control, rapid historical data retrieval, and comprehensive security protection of medical data. Each system module has clearly defined functions, and data flows through high-speed message queues and distributed databases for efficient interaction. All transmissions are protected with 256-bit or 192-bit encryption, and data processing latency is controlled within 10 to 50 milliseconds, ensuring efficient and secure system operation. This system supports hospital treatment process optimization, cross-hospital collaboration, and patient privacy protection, and is suitable for large-scale medical data management scenarios, providing a high-performance, highly reliable data management solution. Specific Implementation Example 2:

[0077] like Figures 1 to 3 As shown below, the application logic, algorithm logic, and specific application steps of each system module are described in detail below, focusing on the input-output relationships between modules, data transmission paths, and algorithm implementation details to ensure feasibility.

[0078] The data twin central system processes medical data collaboratively through three modules, generating a patient data flow model and providing visual alerts. The following describes the algorithmic logic and application steps of each module, clarifying data interaction. The data flow acquisition module standardizes multi-source data input, providing a unified data format for subsequent modeling. After acquiring data from the hospital information system, medical imaging diagnostic system, and IoT sensors, the module performs field extraction, mapping patient identifiers to unique numbers, marking data types as medical records, images, or monitoring, and generating time records with second-level timestamps. The module verifies data integrity, removing records with missing key fields, and generating structured data format records. Each record includes patient number, type, time, and content fields, such as a diagnosis of "acute appendicitis" or a body temperature of 37.5 degrees Celsius. Output records are transmitted to the vital organism modeling module via a high-performance message queue. Queue priority is based on data type, with emergency data having the highest priority. The transmission rate is 2000 records per second, with a latency of less than 10 milliseconds, and 256-bit encryption protection is used.

[0079] The life modeling module transforms structured data into a graph model and assesses data quality. The module receives structured records from the data stream acquisition module and uses a graph database engine to build a patient data stream model. The algorithm logic is as follows: First, the records are parsed, with the patient ID as the master node and examination and medical orders as child nodes. Node attributes include time and content. Second, edges are generated based on time-series relationships, such as connecting examination time 2025-07-08 14:00 to medical order time 2025-07-08 14:30. Then, a health index is calculated by analyzing completeness (proportion of missing fields, such as 5% missing examination results), timeliness (generation delay, such as a 2-second data upload delay), and consistency (cross-system data matching, such as 90% consistency between medical records and imaging diagnoses). A weighted sum is then used to generate the index, with an example index of 85%. The model and index are updated 1000 times per minute, stored in the graph database, and output to the visualization interaction module. The data transmission path is a high-performance message queue with encryption protection.

[0080] The visualization and interaction module generates dynamic views and triggers alarms. The module receives the graph model and health index from the organism modeling module, uses dynamic charting tools to generate a timeline displaying the record generation time, such as 14:00 to 15:00 on July 8, 2025. A relationship graph shows node connections, such as patient number P001 connecting to examination C001 and medical order M001. The algorithm logic is as follows: compare the health index with a threshold of 80%; if it falls below the threshold, generate an alarm. The alarm content includes the breakpoint location, such as "Examination C001 missing result," and is sent to medical staff terminals via server push technology, generating 500 alarms per hour. Output data is transmitted to the central nervous system console, stored in a high-speed cache, and routed via a high-performance message queue at a rate of 2000 messages per second.

[0081] The Trust Chain Collaboration System achieves data sharing and access control through three modules. The algorithm logic and application steps are described below. The Trust Negotiation Module verifies identity and generates a shared contract. This module receives identity information from the hospital's identity management system, including hospital number H001 and a digital certificate, as well as access data from the patient authorization module, including the scope of sharing as examination results. The algorithm logic is as follows: It uses a two-way transport layer security authentication protocol to verify the certificate's validity, generating a 256-bit encrypted signature trust contract. The contract content includes the shared data type as examination results, a validity period of 24 hours, access to hospital number H002, and 100 verification requests per hour. The contract is stored in a distributed in-memory database and output to the data footprint module via a high-performance message queue, processing 100 messages per second with 192-bit encryption.

[0082] The data footprint module records access history to ensure traceability. This module receives access requests from the trust negotiation module, including the access time (2025-07-08 14:00), hospital number H002, and data type check result. The algorithm logic is as follows: Request information is written to a distributed database, organized in a tree structure, with the root node being the patient number and child nodes representing the access time and data type. An immutable log is generated, producing 500 entries per hour, with a storage capacity of 10 million entries. The logs are output to the central control console via a high-performance message queue, protected by encryption.

[0083] The patient authorization module manages patient permissions. The module receives patient input through a front-end interface, displaying the patient ID (P001) and the shareable data types. The patient selects to share examination results. The algorithm logic is as follows: parse the input, generate permission records containing the patient ID, sharing scope, and update time, updating 1000 records every 5 minutes and storing them in a high-speed cache. Permission records are transmitted to the trust negotiation module via a high-performance message queue, at a rate of 100 messages per second, encrypted with 192 bits.

[0084] The respiratory storage system optimizes data storage and access efficiency through three modules. The activity assessment module analyzes data access frequency and predicts usage demand. This module receives access logs from a distributed tracing tool, including access time (2025-07-08 14:00), data type (medical record), and user role (doctor). The algorithm logic is as follows: using a state transition chain algorithm, it analyzes the data access time intervals and frequencies in the logs, predicts the access probability for the next 12 hours (e.g., medical records P001-C001 have a probability of 0.9), generates an activity score ranging from 0 to 1 (example score 0.85), and generates 5000 scores every 12 hours, stored in a time-series database. The scores are output to the storage scheduling module via an object storage system interface, at a rate of 500 records per second, encrypted with 256 bits.

[0085] The storage scheduling module dynamically adjusts storage locations. It receives scores from the activity assessment module, and the algorithm allocates storage media based on these scores: data with scores above 0.8, such as medical records P001-C001, is stored on a high-speed solid-state drive; data with scores between 0.2 and 0.8 is stored on a medium-speed hard drive; and data with scores below 0.2 is compressed to tape storage using a high compression ratio algorithm, achieving a 90% compression rate. In the example, the compressed size was reduced from 10MB to 1MB, and 1000 requests are processed per minute. Storage instructions are output to the data preheating module via the object storage system interface, with encrypted protection.

[0086] The data preheating module predicts and migrates high-demand data. This module receives instructions from the storage scheduling module and retrieves patient medical history and disease trend data from the analysis database, such as July, the peak season for acute appendicitis. The algorithm logic is as follows: analyze historical access patterns, such as medical records P001-C001 being accessed 10 times in the past 7 days, combine this with disease trends to predict access demand in the next 24 hours, generate 1000 predicted records, and migrate the data from tape storage to solid-state drive at a migration speed of 100 records per second. The migration status is transmitted to the central nervous system control console via a high-performance message queue, protected by encryption.

[0087] The narrative presentation system generates diagnostic texts and detects inconsistencies through three modules. The data integration module extracts data entities and generates a semantic relationship graph. This module receives structured records from the data twin central system, including patient ID P001, diagnosis acute appendicitis, and drug penicillin. The algorithm logic is as follows: Records are parsed using a distributed computing framework to extract the disease entity acute appendicitis, the symptom entity abdominal pain, and the drug entity penicillin, generating a semantic relationship graph. Nodes represent entities, and edges represent relationships, such as disease causing symptoms. 2000 records are processed per hour, generating a graph with 1000 nodes and 2000 edges, stored in a multi-modal database. The relationship graph is output to the narrative generation module and the anomaly detection module via the distributed computing framework interface, processing 200 records per second with 256-bit encryption.

[0088] The narrative generation module generates medical text. The module receives a semantic relationship graph. The algorithm logic is as follows: it analyzes the nodes and edges in the graph using a pre-trained language model to generate a text narrative. An example is "Patient P001 was diagnosed with acute appendicitis on 2025-07-08 at 14:00, with symptoms of abdominal pain, and was treated with penicillin." 100 narratives are generated every 30 minutes, with a length of 200 to 500 characters, and stored in a multi-model database. The text is output to the memory reshaping system via a distributed computing framework interface, and is encrypted.

[0089] The anomaly detection module identifies data contradictions. The module receives a semantic relationship graph, and the algorithm logic is as follows: traverse nodes and edges using a graph query language to detect contradictions, such as a penicillin dosage of 500mg exceeding the recommended dosage of 300mg for acute appendicitis. 500 reports are generated per hour, containing the contradiction type (dosage error) and the location of the drug entity, and are stored in a multi-modal database. The reports are transmitted to the central nervous system control console via a high-performance message queue, protected by encryption.

[0090] The microclimate control system optimizes resource allocation through three modules. The load monitoring module collects performance data. This module receives data from container cluster status monitoring tools, including request latency (10 milliseconds), processor utilization (70%), and memory consumption (80%). The algorithm parses the data, categorizing it into latency, processor, and memory metrics, recording 1000 times per second and storing it in a time-series database. Data is output to the resource scheduling module via a network optimization tool, at a rate of 300 records per second, encrypted with 256 bits.

[0091] The resource scheduling module dynamically allocates resources. It receives data from the load monitoring module, and its algorithm logic is as follows: It analyzes metrics using reinforcement learning to calculate resource requirements. Emergency data processing tasks are allocated 80% of processor and memory, while regular tasks are allocated 20%. 500 scheduling requests are adjusted every minute, with latency below 20 milliseconds. The strategy is stored in a cache. The strategy is output to the ecosystem visualization module via the network optimization tool, and is protected by encryption.

[0092] The ecological visualization module displays the system status. This module receives strategies from the resource scheduling module, with the following algorithm: a data flow graph is generated using a 3D rendering tool, displaying 2000 data points per second; the resource allocation graph shows the processor allocation ratio at 80%; 1000 view nodes are updated every 5 minutes and stored in a time-series database. The status graph is transmitted to the central control console via a high-performance message queue, protected by encryption.

[0093] The memory remodeling system optimizes historical data retrieval through three modules. The association analysis module extracts historical data topics. This module receives historical data from the respiratory storage system, including medical records P001-C001, examination C001, and treatment (penicillin). The algorithm logic is as follows: it analyzes the data using a word embedding algorithm, extracting the disease topic "acute appendicitis" and the treatment topic "antibiotic treatment," generating a semantic index containing 1000 topics. It processes 5000 records every 12 hours and stores them in a high-performance document database. The index is output to the fragment generation module via a streaming query interface, processing 100 records per second with 256-bit encryption.

[0094] The fragment generation module generates thematic data packets. This module receives the semantic index, and its algorithm logic is as follows: topics are organized into data packets, including diseases such as acute appendicitis, examinations such as C001, and treatments such as penicillin. 100 data packets are generated every 24 hours, with a storage capacity of 100,000 records, stored in a high-performance document database. The data packets are output to the context retrieval module via a streaming query interface, and are encrypted.

[0095] The contextual retrieval module supports semantic queries. The module receives user-input queries, such as "acute appendicitis treatment." The algorithm logic is as follows: it matches the index using an open-source search framework and returns relevant data packets, such as data packet P001 containing penicillin treatment. It processes 50 queries per second with a response time of less than 100 milliseconds. Query results are transmitted to the central nervous system console via a high-performance message queue, protected by encryption.

[0096] The central nervous system control console manages the system through four functional components. The service discovery component monitors module status, receiving system module registration information, including module name and running status, checking 1000 times per minute, generating status records of running or faulty states, and storing them in a distributed database. The event bus component coordinates data interaction, receiving system events, including data streams, permissions, and scheduling instructions, processing 5000 events per second, and storing them in a cache. The resource scheduling component optimizes resource allocation, receiving performance data, adjusting 1000 strategies with 95% allocation accuracy, and storing the results in a cache. The visualization management component displays the system status, receiving status and event data, generating a 3D view with a data stream rate of 2000 messages per second, updated with 1000 records every 5 minutes. Data interaction is completed through a high-performance message queue, processing 5000 messages per second with 256-bit encryption.

[0097] The data security and privacy protection system ensures data security through three functional components. The data encryption component protects transmission, receiving all data streams, employing 256-bit encryption, processing 2000 data entries per second, and with an encryption time of less than 5 milliseconds. The access control component restricts access, receiving permission records, updating 1000 policies every minute, and storing them in a distributed database. The audit trail component records operations, receiving access requests, generating 500 audit reports, including the time (2025-07-08 14:00), user information, and operation type, and storing them in a distributed database. Data interaction is completed through a high-performance message queue, protected by encryption. Specific Implementation Example 3:

[0099] like Figures 1 to 3 As shown, the following are specific use cases of the content described in the above embodiments:

[0100] Emergency data management in large general hospitals:

[0101] A large general hospital sees an average of 500 emergency patients daily, involving multiple data sources including electronic medical records, imaging data, and vital sign monitoring data. The emergency department needs to integrate this data in real time for rapid diagnosis and optimized resource allocation. The system is deployed in the hospital's distributed container cluster environment, connecting the hospital's information system, medical imaging diagnostic system, IoT sensors, and regional medical collaboration network.

[0102] After system startup, the data twin central system extracts electronic medical records from the hospital information system. For example, the emergency room record for patient P001 includes admission time 2025-07-08 14:00 and preliminary diagnosis of acute myocardial infarction. It also retrieves a chest CT image (C001) from the medical imaging diagnostic system, taken at 14:05; and heart rate data (120 beats / minute, taken at 14:06) from an IoT sensor. The data stream acquisition module processes 1000 records per second, generating structured data format records, which are then transmitted to the life modeling module. The life modeling module constructs a patient data stream model, with patient P001 as the main node, connecting examination C001 and monitoring data. The calculated health index is 75%, with a 3-second delay due to image data upload. The model is then transmitted to the visualization interaction module, generating a timeline displaying data generation time and a relationship diagram showing the correlation between examinations and monitoring. When the health index falls below 80%, an alarm is triggered with the message "Image C001 delayed upload," and pushed to the emergency room doctor's terminal. Fifty alarms are generated per hour.

[0103] The respiratory storage system analyzes data access frequency, with emergency room data scoring 0.9. This data is stored on a high-speed solid-state drive (SSD), and the storage scheduling module adjusts 1000 storage requests per minute. The data preheating module, based on the high incidence trend of acute myocardial infarction (AMI), predicts similar data needs for the next 24 hours and migrates 100 historical AMI records to the SSD at a migration speed of 100 records per second. The narrative presentation system extracts the disease entity "acute myocardial infarction," the symptom entity "chest pain," and the drug entity "aspirin," generating a semantic relationship graph. It processes 2000 records per hour. The narrative generation module generates the medical text "Patient P001 was admitted to the hospital on 2025-07-08 at 14:00 due to chest pain, diagnosed with acute myocardial infarction, and treated with aspirin," generating 20 text entries every 30 minutes. The anomaly detection module detects an aspirin dose of 500mg, exceeding the recommended 300mg, and generates a report that is pushed to the doctor.

[0104] The microclimate control system monitors the emergency department's data processing load, with a request latency of 10 milliseconds and processor utilization at 80%. The resource scheduling module allocates 90% of resources to emergency tasks, adjusting 500 scheduling requests per minute. The ecological visualization module generates 3D data flow graphs, displaying 2000 data points per second. The trust chain collaboration system supports data sharing with regional cardiovascular specialty hospitals. The trust negotiation module generates 256-bit encrypted contracts, and the data footprint module records access logs, generating 50 logs per hour. The patient authorization module sets up shared examination results, with permissions updated every 5 minutes. The memory reconstruction system extracts the topic of myocardial infarction from historical data, generating 10 thematic data packages every 24 hours. The scenario retrieval module supports queries for "acute myocardial infarction treatment," processing 10 queries per second. The central nervous system control console coordinates the data flow, processing 5000 events per second and generating operation reports to display the efficiency of emergency department data processing.

[0105] The system integrates emergency room data, reducing diagnosis time from 15 minutes to 8 minutes, and data breakpoint alarms reduce data loss rate by 50%. Storage optimization reduces access latency to 50 milliseconds, and improves emergency room resource allocation efficiency by 30%. Medical records and abnormal reports improve diagnostic accuracy by 20%, and cross-hospital sharing supports joint consultations with a success rate of 95%. Historical data retrieval accelerates chronic disease follow-up, reducing query time from 5 seconds to 0.1 seconds.

[0106] Specialized hospital imaging data analysis:

[0107] A specialized orthopedic hospital processes an average of 300 orthopedic imaging data sets daily, requiring rapid image analysis, optimized storage, generation of diagnostic reports, and support for research data retrieval. The system is deployed on the hospital's private cloud, connecting the imaging diagnostic system and research databases.

[0108] The data twin central system retrieves X-ray images of patient P003 (identified as X001) from the medical imaging diagnostic system, diagnosing a fracture at 11:00 AM on July 8, 2025; it also retrieves medical records from the hospital information system, recording the fracture surgical plan at 11:30 AM. The data stream acquisition module generates structured records, which are transmitted to the life modeling module to construct a model of patient P003, connecting image X001 and the surgical plan, with a health index of 90%. The visualization interaction module generates a timeline and relationship diagram, without triggering any alarms.

[0109] The respiratory storage system score image data (0.8) is stored on a solid-state drive. Based on the high demand for fracture images, the data preheating module predicts a need for 50 data entries in the next 24 hours and migrates them to the solid-state drive. The narrative presentation system extracts the disease entity (fracture), the symptom entity (pain), and the medication entity (analgesics), generating a semantic relationship graph. It processes 500 records per hour, generating the text "Patient P003 was diagnosed with a femoral fracture on 2025-07-08 at 11:00, surgery is planned, and analgesics are used for relief," generating 30 records every 30 minutes. The anomaly detection module detects analgesic dosages exceeding recommended values, generates a report, and pushes it to the doctor.

[0110] The microclimate control system monitors the image processing load with an 8-millisecond latency, while the resource scheduling module allocates 85% of resources, adjusting 300 requests per minute. The ecological visualization module generates data flow graphs, displaying 1500 data points per second. The memory reconstruction system generates fracture-themed data packages, producing 15 every 24 hours, and the scenario retrieval module supports queries for "femoral fracture surgery," processing 15 queries per second for research use. The trust chain collaboration system records research data access logs, generating 20 logs per hour. The central nervous system control console coordinates the data flow, processing 3000 events per second and generating reports to display image analysis efficiency.

[0111] Application Results:

[0112] The system accelerates image analysis, reducing processing time from 10 minutes to 3 minutes; storage optimization lowers costs by 30%; and access latency is reduced from 150 milliseconds to 40 milliseconds. Diagnostic texts and anomaly reports improve surgical planning accuracy by 25%. Historical data retrieval supports research, reducing query time from 10 seconds to 0.2 seconds and increasing research data utilization by 60%. Specific Implementation Example 4:

[0114] like Figures 1 to 3 As shown below, the detailed hardware components and hardware specifications are as follows:

[0115] Compute Servers: The system utilizes a high-performance rack-mount server cluster, comprising 50 main compute servers, each equipped with a 32-core processor, a 3.2GHz clock speed, 256GB of RAM, and 2TB of local solid-state drive storage. The servers run a distributed container cluster management platform, supporting 2000 container instances to ensure concurrent operation of system modules.

[0116] The servers allocate tasks through a distributed container cluster management platform. Container instances dynamically deploy modules, sharing 256GB of memory and a 2TB SSD. Servers communicate with each other via high-speed message queues, with a transmission rate of 2000 messages per second and a latency of less than 10 milliseconds. The computing servers support high-concurrency data processing, such as processing 500 emergency room records per second in an emergency room scenario to generate patient data stream models, calculate health indices, and have a response time of less than 1 second.

[0117] The storage system includes a 100TB high-speed solid-state drive (SSD) array, a 500TB mid-speed hard disk array, and a 10PB tape storage library. The SSD array uses the NVMe protocol with read / write speeds of 10GB / s; the hard disk array uses a SAS interface with read / write speeds of 1GB / s; and the tape storage library uses LTO-9 technology with a single disk capacity of 18TB and a compression ratio of 90%. The storage devices interact with the compute server through an object storage system interface. The SSDs and hard disk arrays support real-time read / write operations, while the tape storage library is accessed through a dedicated controller. A storage scheduling module dynamically adjusts data location, and instructions are transmitted to the storage devices via a high-speed message queue with a latency of less than 1 second. In a chronic disease management scenario, the SSDs store blood glucose data for diabetic patients with an access latency of 50 milliseconds; the tape storage library stores 5 years of historical medical records, saving 90% of space after compression, and pre-warm-up migration supports rapid access to follow-up data.

[0118] The network system comprises 10 high-performance 40Gbps switches and 5 network security gateways. Each switch supports 1000 ports and a throughput of 40 million packets per second; each gateway supports 256-bit encryption, processing 2000 data streams per second, and the firewall has a throughput of 10Gbps. The switches connect servers and storage devices via fiber optic links, while the gateways are deployed at the hospital network boundary to handle inter-hospital communication. High-speed message queues operate on the switches, coordinating system data interaction, and the gateways verify data transmission security. Gateways encrypt transmissions with latency below 50 milliseconds, ensuring the security of joint consultation data.

[0119] The display terminal system is equipped with 50 high-resolution display terminals, each with a 32-inch screen, a resolution of 2560×1440, a brightness of 500 nits, a contrast ratio of 1000:1, and a graphics processor with 4GB of video memory, supporting 3D rendering. The display terminals are connected to the computing server via switches, receiving graph models and status data. Data is transmitted via a high-speed message queue at a rate of 1000 messages per second. The graphics processor handles 3D rendering with a response time of less than 100 milliseconds.

[0120] The hardware system operates in a coordinated manner through a distributed container cluster management platform. The main compute server cluster handles tasks from various system modules, allocating 2000 container instances to ensure concurrent processing. Storage devices support dynamic allocation; solid-state drives and hard disk arrays respond to real-time requests, while tape repositories handle archived data. Network switches and gateways ensure data flow and security, display terminals provide an intuitive interface, and security devices ensure privacy protection. All hardware interacts via a high-performance message queue at a rate of 5000 messages per second, with 256-bit encryption and latency below 10 milliseconds. The central control console runs on five main servers, monitoring hardware status, generating operational reports, and optimizing resource allocation accuracy to 95%. In emergency, chronic disease management, and image analysis scenarios, the hardware supports the system processing 2000 data streams per second, storing 10 million records, and displaying 1000 view nodes, ensuring efficient and reliable operation. Specific Implementation Example 5:

[0122] The following are application experiment test data based on the technical solution of Specific Embodiment 1:

[0123] ;

[0124] Emergency Department of a Large General Hospital: Simulating an average of 400 emergency patients per day, generating 4,000 electronic medical records, 2,500 image data points, and 8,000 IoT sensor data points (such as heart rate and blood pressure), testing real-time data integration, anomaly alarms, storage optimization, and resource scheduling. The data includes 5% missing fields and 3% delayed uploads to simulate real-world complexity.

[0125] Regional medical alliance chronic disease management: Simulating 15,000 diabetic patients, generating 15,000 medical records, 8,000 insulin injection records, and 40,000 step count data points, testing cross-hospital data sharing, historical data retrieval, and storage efficiency. Data includes 10% cross-hospital transmission latency, simulating network instability.

[0126] Specialized hospital image analysis: Simulating an average of 250 orthopedic images per day, generating 2500 image data entries and 1500 medical records, testing image processing, diagnostic text generation, and research data querying. The data contains 2% format inconsistencies and simulation equipment compatibility issues.

[0127] The data in the table reflects the system's performance and application effectiveness in different medical scenarios:

[0128] The emergency room scenario boasts the highest processing speed, at 1800 records per second, meeting the demands of high-concurrency emergency care. Processing 4000 medical records and 8000 sensor data points takes approximately 6 seconds. The image analysis scenario achieves a speed of 1200 records per second, suitable for high-throughput image data, processing 2500 images in approximately 2 seconds. The chronic disease management scenario achieves a speed of 800 records per second, adapting to low-frequency cross-hospital interactions, processing 15000 medical records in approximately 19 seconds. The data demonstrates that the system can flexibly adjust its processing capacity according to the needs of different scenarios.

[0129] Request latency: The image analysis scenario has the lowest latency at 10 milliseconds, thanks to the high degree of structured image data and solid-state drive storage. The emergency scenario has a latency of 12 milliseconds, and the chronic disease management scenario has a latency of 18 milliseconds, affected by inter-hospital transmission latency. All scenarios have latencies below the target of 50 milliseconds, demonstrating the system's high efficiency in real-time processing.

[0130] Storage utilization: 90% utilization in emergency scenarios, due to high-activity data being stored on SSDs, reducing redundancy. 85% utilization in image analysis scenarios, balancing SSD and hard drive usage. 80% utilization in chronic disease management scenarios, due to a large amount of low-activity data being compressed to tape storage. Data reflects that storage optimization significantly improves space utilization and reduces storage costs by approximately 15%.

[0131] Semantic query accuracy: The highest accuracy rate was 97% in image analysis scenarios, due to the clear keywords in orthopedic imaging data. Accuracy rates were 95% in emergency scenarios and 93% in chronic disease management scenarios, influenced by data complexity and cross-hospital consistency. All accuracy rates exceeded 90%, indicating that the system supports accurate historical data retrieval and is suitable for research and follow-up.

[0132] Data anomaly detection rate: 92% in emergency room scenarios, identifying medication dosage discrepancies, such as aspirin overdose. 90% in image analysis scenarios, detecting analgesic dosage anomalies. 88% in chronic disease management scenarios, as discrepancies between blood glucose and insulin levels are less frequent. The data indicates that the system effectively reduces diagnostic errors and improves diagnostic accuracy by approximately 10%.

[0133] Resource allocation efficiency: 85% in emergency scenarios, prioritizing emergency tasks and allocating 90% of processor resources. 80% in image analysis scenarios, supporting image processing. 75% in chronic disease management scenarios, due to a large number of low-priority tasks. The data reflects that resource scheduling optimization improves system stability, with performance increasing by approximately 20% in high-load scenarios.

[0134] Data access audit coverage: 100% for image analysis scenarios, 99% for emergency scenarios, and 98% for chronic disease management scenarios. A small number of low-frequency accesses were not recorded. The data demonstrates that the system comprehensively ensures data security and traceability, meeting medical privacy regulations.

[0135] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A big data central management system for a digital hospital, characterized by: It includes a data twin central system, a trust chain collaboration system, a respiratory storage system, a narrative presentation system, a microclimate regulation system, a memory reshaping system, and a central nervous system control console; The data twin central system includes a data stream acquisition module, a life form modeling module, and a visualization interaction module. The data stream acquisition module collects data from the hospital information system, imaging equipment, and IoT sensors and generates structured records. The life form modeling module generates a patient data stream model and calculates health indices. The visualization interaction module displays the data stream timeline and relationship diagram and sends alarms. The trust chain collaboration system includes a trust negotiation module, a data footprint module, and a patient authorization module. The trust negotiation module generates inter-hospital data sharing contracts, the data footprint module records access history, and the patient authorization module sets data sharing permissions. The respiratory storage system includes an activity assessment module, a storage scheduling module, and a data preheating module. The activity assessment module analyzes the data access frequency, the storage scheduling module adjusts the data storage location and compression strategy, and the data preheating module predicts data demand and migrates data. The narrative presentation system includes a data integration module, a narrative generation module, and an anomaly detection module. The data integration module extracts patient data entities, the narrative generation module generates diagnostic and treatment text, and the anomaly detection module identifies data contradictions. The microclimate control system includes a load monitoring module, a resource scheduling module, and an ecological visualization module. The load monitoring module collects performance data, the resource scheduling module allocates computing and storage resources, and the ecological visualization module displays the system status. The memory reconstruction system includes an association analysis module, a fragment generation module, and a context retrieval module. The association analysis module extracts historical data relationships, the fragment generation module generates thematic data packages, and the context retrieval module performs semantic queries. The central nervous system control console manages data flow and resource allocation through service registration, message queues, and a visual interface system; The specific steps of the microclimate regulation system include: Step E1, Performance Data Collection: The load monitoring module collects performance data through container cluster status monitoring tools. The data includes request latency, processor utilization, memory consumption, and records requests, which are stored in a time-series database. Step E2, Dynamic Resource Scheduling: The resource scheduling module uses reinforcement learning algorithms to allocate computing and storage resources based on performance data, allocating more resources to high-priority tasks; Step E3, System Status Display: The ecological visualization module generates data flow and resource status diagrams through 3D rendering tools; The specific steps of the memory reshaping system include: Step F1, Semantic Relationship Extraction: The association analysis module obtains historical data from the respiratory storage system through a streaming query tool, extracts disease and treatment topics based on word embedding algorithm, processes records, and generates a semantic index; Step F2, Thematic Data Package Generation: The fragment generation module uses a high-performance document database to transform semantic indexes into thematic data packages, which contain disease, examination, and treatment records. Step F3, Semantic Query Execution: The contextual retrieval module supports natural language queries through an open-source search framework. The query input includes the disease name or treatment type.

2. The big data central management system for a digital hospital according to claim 1, characterized in that: The specific steps of the data twin central system include: Step A1, Data Stream Acquisition: The data stream acquisition module extracts electronic medical records from the hospital information system, standard format image data from the medical image archiving system, and real-time monitoring data from IoT sensors through a high-speed message queue, generating structured data format records that include patient identification, data type, and generation time. Step A2, Data Life Modeling: The life modeling module uses a graph database engine to transform structured data format records into a patient data flow model represented by nodes and edges. Nodes include patients, examinations, and medical orders, while edges represent time sequence and logical relationships. The health index is calculated based on a time series database. The health index is weighted and summed using a weighted average of 0.4 for completeness, 0.3 for timeliness, and 0.3 for consistency. Step A3, Visualization and Alarms: The visualization and interaction module generates a data flow timeline and relationship diagram through dynamic chart tools. The timeline displays the generation time of each record, and the relationship diagram shows the logical connections between nodes. When the health index is below 80%, an alarm is sent to medical staff through server push event technology. The alarm includes the data breakpoint location and the missing type.

3. The big data central management system for a digital hospital according to claim 1, characterized in that: The specific steps of the trust chain collaboration system include: Step B1, Trust Contract Generation: The trust negotiation module verifies the hospital's identity through a two-way transport layer security authentication protocol and generates an encrypted signature trust contract using the key management system. The contract specifies shared data types, including examination results and medical records, and has an expiration date. It verifies multiple identity requests every hour. Step B2, Data Access Records: The data footprint module stores access records through a distributed database. The records include access time, hospital identifier, and data type, and generate immutable logs using a tree structure. Step B3, Patient Permission Settings: The patient authorization module receives patient input through the front-end interface, sets the scope of shared data to include examination results and medical records, stores permission data in a high-speed cache, updates multiple permission records at regular intervals, and uses encryption standards for transmission.

4. The big data central management system for a digital hospital according to claim 1, characterized in that: The specific steps of the respiratory storage system include: Step C1, Activity Analysis: The activity assessment module collects data access logs through a distributed tracking tool. The logs include access time, data type, and user role. Based on the state transition chain algorithm, it predicts the access probability in the next 12 hours and generates multiple activity scores every 12 hours, with a score range of 0 to 1. Step C2, Dynamic Storage Adjustment: The storage scheduling module uses the object storage system to allocate data to high-speed solid-state drives, medium-speed hard drives, or tape storage based on activity scores. Data with scores higher than 0.8 is stored on solid-state drives, while data with scores lower than 0.2 is compressed to tape storage using a high compression ratio algorithm. Multiple storage requests are processed per minute, with a compression rate of up to 90%. Step C3, Data Preheating and Migration: The data preheating module uses a workflow scheduling tool to analyze patient medical history and seasonal disease trends in the database, predicting the access demand for multiple records every 24 hours, and migrating the predicted data from tape storage to solid-state drives.

5. The big data central management system for a digital hospital according to claim 1, characterized in that: The specific steps of the narrative presentation system include: Step D1, Entity Extraction: The data integration module uses a distributed computing framework to obtain structured data format records from the data twin central system, extract disease, symptom, and drug entities, and generate a semantic relationship graph stored in a multi-modal database. The graph contains nodes and edges. Step D2, Treatment Story Generation: The narrative generation module uses a pre-trained language model to transform the semantic relationship graph into a text narrative, which includes event time, treatment events, and treatment results. Step D3, Anomaly Detection: The anomaly detection module analyzes the semantic relationship graph using graph query language, detects and diagnoses contradictions with drug dosage, generates a report containing the type and location of the contradiction, and stores the report in the multi-model database.

6. The big data central management system for a digital hospital according to claim 1, characterized in that: The specific structure and steps of the central nervous system control console include: Step G1, Module Registration Management: The service discovery component manages system modules through a distributed registry center, checks the health status of modules multiple times per minute, records the status as running or faulty, and stores it in a distributed database; Step G2, Data Flow Coordination: The event bus coordinates data interaction between systems through a high-performance message queue, processing multiple events per second. Events include data streams, permissions, and scheduling instructions, and are stored in a high-speed cache. Step G3, Resource Allocation Optimization: The resource scheduling component allocates computing and storage resources through a hybrid workload management tool, adjusting multiple allocation strategies every minute; Step G4, Visual Monitoring: The visual management component generates a management interface through a front-end framework and dynamic styles, displays a 3D view, and updates system status records.

7. The big data central management system for a digital hospital according to claim 1, characterized in that: The data interaction and transmission mechanisms between the systems include: Step H1, Real-time Data Transmission: The data twin central system transmits structured data format records to the narrative presentation system and the memory reconstruction system through a high-speed message queue; Step H2, Permission Data Synchronization: The Trust Chain Collaboration System shares data access permissions with the Respiratory Storage System through a distributed database, synchronizing permission records hourly; Step H3, Performance Monitoring and Scheduling: The microclimate control system monitors the performance of other systems through network optimization tools, adjusts resource allocation strategies, and ensures that data stream latency is lower than the preset time. Step H4, Cross-system collaboration: The central nervous system console receives system status and logs through a high-performance message queue, processes the records every minute, and generates a system operation report containing performance, fault, and resource allocation information.

8. The big data central management system for a digital hospital according to claim 1, characterized in that: The system includes a data security and privacy protection framework, the composition of which includes the following steps: Step I1, Data Encryption: The data encryption component encrypts data transmission through a two-way transport layer security authentication protocol; Step I2, Access Control: The access control component implements fine-grained permission management through a policy proxy tool, updates access policies every minute, restricts unauthorized access, and stores policies in a distributed database; Step I3, Audit Trail: The audit trail component records data access operations through a distributed database, including time, user, and operation type, and generates multiple audit reports every hour.

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