Intelligent hospital global intelligent cognition and collaboration system and method

Abstract

The invention provides an intelligent hospital global intelligent cognition and cooperation system and method, and the system comprises an AI diagnosis and treatment auxiliary decision system which is configured to generate personalized diagnosis and treatment auxiliary information; the artificial intelligence health management system is used for carrying out intelligent risk assessment and personalized intervention; the hospital operation information integration system is used for carrying out data flow and business collaboration; the remote 5G joint sharing system is configured to combine a 5G network and artificial intelligence analysis to generate personalized interaction contents and cooperation services suitable for different remote medical scenes; the system runs based on unified artificial intelligence data and a model service platform. In this way, the service capability of the artificial intelligence model can be unified, and cooperative operation is carried out in multiple scenes such as diagnosis and treatment assistance, health management, in-hospital operation cooperation and remote medical treatment, so that the medical service efficiency and quality are improved.

Description

Technical Field

[0001] This application relates to the field of medical data processing technology, and more specifically, to a smart hospital's comprehensive intelligent cognition and collaboration system and method. Background Technology

[0002] Hospitals are the core setting of the current medical process. Currently, artificial intelligence technology is developing rapidly in fields such as medical image recognition, clinical decision support, remote consultation, and health risk prediction. However, in actual hospital settings, it still faces limitations such as isolated model deployment, scattered data, lack of unified governance and cross-scenario collaboration, making it difficult to build intelligent services covering the entire process of patient visits, treatment, rehabilitation, and follow-up. Summary of the Invention

[0003] To address the aforementioned issues, the first aspect of this application provides a smart hospital comprehensive intelligent cognition and collaboration system, comprising: The AI-assisted diagnosis and treatment decision-making system is configured to perform artificial intelligence analysis based on the patient's medical data to generate personalized diagnosis and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions. The AI-powered health management system is configured to generate dynamic health records based on the medical data generated by patients both inside and outside the hospital, and to conduct intelligent risk assessments and personalized interventions to provide patients with precise health management throughout their entire life cycle. The hospital operation information integration system is configured to build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; The remote 5G joint sharing system is configured to combine 5G networks with artificial intelligence analysis to generate personalized interactive content and collaborative services suitable for different remote medical scenarios. The AI-assisted diagnosis and treatment decision-making system, the artificial intelligence health management system, the hospital operation information integration system, and the remote 5G joint sharing system all operate on a unified artificial intelligence data and model service platform.

[0004] The second aspect of this application provides a method for intelligent cognition and collaborative control of the entire smart hospital domain, implemented based on the aforementioned intelligent cognition and collaborative system of the entire smart hospital domain, comprising: Artificial intelligence analysis is performed based on patients' medical data to generate personalized diagnostic and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions; Dynamic health records are generated based on the medical data of patients both inside and outside the hospital, enabling intelligent risk assessment and personalized intervention to provide patients with precise health management throughout their entire life cycle. Build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; By combining 5G networks with artificial intelligence analysis, personalized interactive content and collaborative services can be generated for different telemedicine scenarios.

[0005] A third aspect of this application provides an electronic device comprising: a memory and a processor; The memory is used to store programs; The processor, coupled to the memory, is used to execute the program for: Artificial intelligence analysis is performed based on patients' medical data to generate personalized diagnostic and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions; Dynamic health records are generated based on the medical data of patients both inside and outside the hospital, enabling intelligent risk assessment and personalized intervention to provide patients with precise health management throughout their entire life cycle. Build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; By combining 5G networks with artificial intelligence analysis, personalized interactive content and collaborative services can be generated for different telemedicine scenarios.

[0006] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the above-described intelligent hospital full-domain intelligent cognition and collaborative control method.

[0007] This enables the integration of multi-source data from hospitals, unification of AI model service capabilities, and collaborative operation across multiple scenarios such as diagnosis and treatment assistance, health management, in-hospital operational collaboration, and telemedicine, thereby improving the efficiency and quality of medical services. Attached Figure Description

[0008] Figure 1 This is an architecture diagram of a smart hospital full-domain intelligent cognition and collaboration system according to an embodiment of this application; Figure 2 This is an architecture diagram of the AI-assisted diagnosis and treatment decision-making system in the smart hospital's comprehensive intelligent cognition and collaboration system according to an embodiment of this application. Figure 3 This is a schematic diagram of the triage process module of the smart hospital's full-domain intelligent cognition and collaboration system according to an embodiment of this application; Figure 4 This is a schematic diagram of the drug screening module of the smart hospital's comprehensive intelligent cognition and collaboration system according to an embodiment of this application; Figure 5 This is an architecture diagram of the artificial intelligence health management system in the smart hospital full-domain intelligent cognition and collaboration system according to an embodiment of this application; Figure 6This is an architecture diagram of the hospital operation information integration system in the smart hospital full-domain intelligent cognition and collaboration system according to an embodiment of this application; Figure 7 This is an architecture diagram of a remote 5G joint sharing system in the smart hospital full-domain intelligent cognition and collaboration system according to an embodiment of this application; Figure 8 This is a schematic diagram of a surgical robot for a smart hospital's comprehensive intelligent cognition and collaboration system according to an embodiment of this application; Figure 9 This is a flowchart of a smart hospital's comprehensive intelligent cognition and collaborative control method according to an embodiment of this application; Figure 10 This is an architectural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0009] To make the above-mentioned objects, features, and advantages of this application more apparent and understandable, specific embodiments of this application will be described in detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0010] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0011] For ease of understanding, the following terms may be used and are explained below: This application provides a smart hospital's comprehensive intelligent cognition and collaboration system. The specific solution of this system is as follows: Figures 1-8 As shown.

[0012] Combination Figure 1 The diagram shown is an architecture diagram of a smart hospital full-domain intelligent cognition and collaboration system according to an embodiment of this application; wherein, the smart hospital full-domain intelligent cognition and collaboration system includes: The AI-assisted diagnosis and treatment decision-making system is configured to perform artificial intelligence analysis based on the patient's medical data to generate personalized diagnosis and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions. The AI-powered health management system is configured to generate dynamic health records based on the medical data generated by patients both inside and outside the hospital, and to conduct intelligent risk assessments and personalized interventions to provide patients with precise health management throughout their entire life cycle. The hospital operation information integration system is configured to build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; The remote 5G joint sharing system is configured to combine 5G networks with artificial intelligence analysis to generate personalized interactive content and collaborative services suitable for different remote medical scenarios. The AI-assisted diagnosis and treatment decision-making system, the artificial intelligence health management system, the hospital operation information integration system, and the remote 5G joint sharing system all operate on a unified artificial intelligence data and model service platform.

[0013] This enables the integration of multi-source data from hospitals, unification of AI model service capabilities, and collaborative operation across multiple scenarios such as diagnosis and treatment assistance, health management, in-hospital operational collaboration, and telemedicine, thereby improving the efficiency and quality of medical services.

[0014] In this way, a unified data hub is built through the hospital operation information integration system, breaking down data barriers between different business systems and smart devices within the hospital, and realizing centralized governance and cross-scenario collaboration of medical data.

[0015] In this way, by utilizing AI-assisted diagnosis and treatment decision-making systems and artificial intelligence health management systems, we can achieve intelligent services throughout the entire process, from risk identification before treatment and decision support during treatment to follow-up intervention after discharge.

[0016] In this way, all systems rely on a unified AI data and model service platform, avoiding redundant model deployment and wasted computing power, improving model reusability and maintainability, and realizing standardized and scalable intelligent hospital capabilities.

[0017] In this way, by combining high-speed communication capabilities with intelligent analysis through the remote 5G joint sharing system, processes such as remote consultation, remote ward rounds, two-way referral, and remote consultation become more efficient and interactive.

[0018] This enables intelligent collaboration across clinical practice, nursing, management, and telemedicine, which helps improve diagnostic and treatment efficiency, reduce labor costs, enhance the patient experience, and promote the overall improvement of hospital intelligence.

[0019] In one implementation, combined with Figure 2 As shown, the AI-assisted diagnosis and treatment decision-making system includes a diagnosis and treatment preference management module and a diagnosis and treatment decision generation module; The treatment preference management module is configured to confirm the patient's treatment preference information based on the patient's medical information, including traditional Chinese medicine treatment, Western medicine treatment, and integrated traditional Chinese and Western medicine treatment. The diagnosis and treatment decision generation module is configured to generate TCM syndrome differentiation and treatment plans, Western medicine disease diagnosis and treatment strategies, or integrated TCM and Western medicine diagnosis and treatment plans based on diagnosis and treatment preference information. The TCM syndrome differentiation and treatment plan is generated by calling the TCM knowledge graph and the TCM decision-making model to perform TCM syndrome differentiation reasoning; the Western medicine disease diagnosis and treatment strategy is generated by calling the Western medicine decision-making model and the guideline rule base; the integrated TCM and Western medicine diagnosis and treatment plan is obtained by synergistic combination of the TCM syndrome differentiation and treatment plan and the Western medicine disease diagnosis and treatment strategy.

[0020] Among them, the treatment preference identification module in the system analyzes the patient's medical information and, in combination with the patient's active choices, historical medical behavior patterns and rule base judgment, determines whether the patient prefers traditional Chinese medicine treatment, Western medicine treatment or integrated traditional Chinese and Western medicine treatment.

[0021] Among them, decision-making mode selection: for different diagnosis and treatment preferences, the system automatically selects the corresponding model link, namely the traditional Chinese medicine reasoning path, the Western medicine rule path, or the integrated traditional Chinese and Western medicine path.

[0022] The treatment preference management module includes: The patient information analysis unit extracts data from patients' chief complaints, symptoms, physical condition, allergy history, etc.; and uses natural language processing models (such as medical big data models) to identify biases in the descriptions.

[0023] The historical treatment pattern analysis unit reads the patient's previous medical records; based on the patient's medical data in multiple departments, it calculates the preference probability by analyzing the correspondence between the treated diseases, treatment methods, and treatment effects.

[0024] Treatment Pattern Database: Provides the correspondence between diseases treated and treatment patterns, as well as the probability of preference.

[0025] The preference fusion decision unit weights and fuses three information sources: active selection, historical treatment, and system recommendation; and outputs three preference labels: Traditional Chinese Medicine, Western Medicine, and Integrated Traditional Chinese and Western Medicine.

[0026] In this application, the treatment preference management module is not a simple questionnaire, but an intelligent recognition based on the fusion of multi-source data, which is closer to the real clinical pathway.

[0027] The treatment preference management module also includes processing the patient's three-dimensional medical images, which includes segmentation, recognition, and reconstruction.

[0028] In one implementation, the segmentation of the three-dimensional medical image includes: The three-dimensional medical images are downsampled sequentially to obtain downsampled images at multiple levels; the downsampling process includes 3D convolution processing downsampling and parallel convolution processing downsampling. Upsampling is performed sequentially on downsampled images at multiple levels to obtain upsampled images at multiple levels; the upsampling process includes 3D convolution upsampling and parallel convolution upsampling. The highest-level upsampled image is processed to obtain the segmentation result.

[0029] Specifically, in the segmentation process: The three-dimensional medical images are sequentially processed by 3D convolution and downsampling to obtain the first downsampled image and the second downsampled image. The second downsampled image is subjected to parallel convolution and downsampling in sequence to obtain the third, fourth and fifth downsampled images in sequence; The fifth, fourth, and third downsampled images are sequentially concatenated, processed by parallel convolution, and upsampled to obtain the fourth and third upsampled images. The third downsampled image, the second downsampled image, and the first downsampled image are sequentially stitched together, processed by 3D convolution, and upsampled to obtain the second upsampled image and the segmentation result.

[0030] In one embodiment, the 3D convolution process includes: Perform 3D convolution on the input feature map to obtain a 3D feature map; The 3D feature map is normalized and activated to obtain the output feature map.

[0031] In one implementation, the parallel convolution process includes: The input feature map is subjected to 3D convolution, normalization and activation processing to obtain the first 3D convolution map; The first 3D convolutional image is subjected to 3D convolution, normalization and activation processing to obtain the second 3D convolutional image; Multi-head attention and normalization are applied to the input feature map to obtain a multi-head convolutional map; After combining the multi-head convolutional map and the input feature map, multi-head attention, normalization and multi-layer perception processing are performed to generate the multi-layer perception map. The output feature map is obtained by combining the second 3D convolutional map and the multilayer perceptron map.

[0032] In this way, in parallel convolution processing, a parallel architecture of CNN and Transformer (CT Module) is used for feature extraction and feature fusion to fully explore image features.

[0033] In this application, a CNN+Transformer architecture is used for network design to minimize network parameters and greatly improve the efficiency of training and segmentation.

[0034] The process of generating a TCM syndrome differentiation and treatment plan in this application is as follows: Traditional Chinese Medicine Knowledge Graph Loading: Includes structured associations such as syndromes, symptoms, internal organs, meridians, etiology and pathogenesis, prescriptions, and medicinal properties; nodes and edges all have semantic weights.

[0035] Traditional Chinese Medicine Decision Model Reasoning: The TCM reasoning model is invoked to perform dialectical reasoning based on the logical chain of patient symptoms → syndrome → internal organs → prescription: Symptom-Syndrome matching, Syndrome-Treatment matching, Treatment-Prescription inference, and Prescription-Drug optimization combination.

[0036] Generate personalized TCM treatment plans: The output includes the main symptoms, concurrent symptoms, treatment methods, prescription recommendations, additions and subtractions, precautions, etc.

[0037] In this application, the process of generating Western medicine disease diagnosis and treatment strategies is as follows: Invoking Western medicine decision-making models: including disease classification models, image recognition models, risk assessment models, and drug recommendation models.

[0038] Guideline rule base comparison: Compliance verification is performed by combining national / international guidelines (such as NCCN, WHO, Chinese Medical Association guidelines) to ensure that the protocol is safe, interpretable, and meets clinical standards.

[0039] Multimodal data-driven comprehensive diagnosis: Combining laboratory tests, imaging, physical signs, and medical history to generate diagnostic conclusions and treatment recommendations. Output includes: disease diagnosis, medication suggestions, examination items, contraindications, and complication risks.

[0040] In this application, the process of generating the integrated traditional Chinese and Western medicine treatment plan is as follows: Traditional Chinese medicine and Western medicine treatment plans are generated in parallel: both plans generate original results, preserving the syndrome analysis chain and disease diagnosis chain.

[0041] Cross-system relational mapping model: The system calls a cross-knowledge system mapping model to link TCM syndromes with Western medicine disease mechanisms: syndrome ↔ pathological pathway, prescription and drug properties ↔ drug action mechanism, constitution ↔ risk factors, TCM treatment method ↔ Western medicine treatment goal.

[0042] Conflict detection and synergistic optimization: Detect potential conflicts between traditional Chinese medicine and Western medicine (pharmacological conflicts, metabolic conflicts, etc.); adjust the dosage of traditional Chinese medicine and Western medicine to avoid mutual interference; and perform synergistic optimization based on the consistency of treatment goals, such as harmonizing immunity, improving inflammation, and relieving symptoms.

[0043] Output of integrated traditional Chinese and Western medicine solutions: The output includes: TCM syndrome differentiation and treatment plan, Western medicine diagnosis and treatment strategy, synergistic application strategy (time arrangement, dosage adjustment), risk monitoring and precautions, etc.

[0044] In this application, the treatment preference does not rely on the patient's active choice, but rather infers the treatment preference from objective data such as medical history, medical behavior patterns, and medical data characteristics to determine which preference is more conducive to disease treatment. This is different from the intelligent recognition capability of existing systems.

[0045] In this application, a collaborative decision-making mechanism across knowledge systems is implemented through a collaborative generation model of traditional Chinese medicine and Western medicine, thereby combining traditional Chinese medicine treatment plans and Western medicine treatment plans.

[0046] In this application, multiple heterogeneous diagnostic and treatment models are executed collaboratively on a unified AI platform, enabling the three approaches of traditional Chinese medicine, Western medicine, and integrated traditional Chinese and Western medicine to produce comparable and fusion diagnostic and treatment results under the same framework, which has significant technical implementation difficulty and creativity.

[0047] In one implementation, combined with Figure 2 As shown, the AI-assisted diagnosis and treatment decision-making system also includes a triage decision-making module, which is configured to intelligently assess the patient's condition and generate triage information, priority information, and assigned doctor information.

[0048] The triage decision module is primarily used for intelligent assessment of patient conditions and to generate actionable triage suggestions and priority rankings. The specific process includes: The system extracts disease-related elements from multi-source data, including the patient's chief complaint, physical signs, medical history, examination reports, and critical illness indicators. A large-scale medical model is used for semantic understanding, transforming natural language symptoms into structured vectors. Based on a diagnostic knowledge base, departmental specialties, and disease probability distribution, recommended departments are automatically generated. The system also matches the patient with the most suitable doctor based on their specialty, current workload, and scheduling.

[0049] In one implementation, combined with Figure 2 As shown, it also includes an intelligent navigation system, which is configured to generate in-hospital route planning and navigation guidance based on hospital spatial location information, business layout information and patient medical task information, so as to provide visual navigation services to patients or medical staff.

[0050] The intelligent navigation system is used to generate visual navigation within the hospital for patients or medical staff. Specific process: The system reads the hospital's structural diagram, floor plan, department locations, and equipment locations to establish a hospital spatial topology model; it introduces the business types of each department and synchronizes the business queuing status and opening status; it obtains the patient's current task, parses the task order, and identifies whether there is a time dependency; it uses a dynamic shortest path algorithm to perform path planning and outputs map navigation, floor switching prompts, and step guidance on the patient's or medical staff's end.

[0051] In one implementation, the patient terminal is any one or more of the following: smartphone, smartwatch, computer, tablet, personal monitoring device, self-service kiosk, triage robot, and patient guidance robot.

[0052] In one embodiment, the patient terminal is equipped with an identity verification module, which includes any one or more combinations of account password verification, SMS verification, email verification, voiceprint verification, fingerprint verification, iris verification, facial verification, medical insurance card verification, ID card verification, and third-party verification.

[0053] In one implementation, combined with Figure 2 , Figure 3 As shown, the AI-assisted diagnosis and treatment decision-making system also includes a triage process module, which is linked with the intelligent navigation system and configured to generate triage process recommendations and corresponding navigation paths for patients based on triage information and hospital resource information, so as to execute navigation actions.

[0054] The patient guidance process module, triage decision-making module, and intelligent navigation system work together to achieve end-to-end patient guidance. Specific process: The system generates a set of medical tasks based on triage departments, patient conditions, and hospital resources; it queries the current queuing status of departments, the availability of examination equipment, and the available time of doctors; it uses intelligent optimization algorithms (such as reinforcement learning or rule optimization) to generate the optimal triage process; and it inputs the target location of each task into the intelligent navigation system to generate a continuous "process + route" combination navigation for patients.

[0055] This application unifies triage, guidance, and navigation onto a single AI platform, achieving truly intelligent end-to-end guidance through a three-step linkage of "triage → process generation → path planning." This cross-module collaborative intelligent guidance enables the integrated execution of disease assessment, process planning, and actual path navigation.

[0056] In one implementation, combined with Figure 2 , Figure 4 As shown, the AI-assisted diagnosis and treatment decision-making system also includes a drug screening module, which is linked with the diagnosis and treatment decision generation module and is configured to acquire diagnosis and treatment decision-making information based on patient diagnosis and treatment preferences generated by the diagnosis and treatment decision generation module, and use artificial intelligence to perform personalized screening of drug combinations based on the diagnosis and treatment decision-making information and provide medication risk warnings.

[0057] The drug screening module works in conjunction with the treatment decision generation module to achieve intelligent and personalized medication recommendations. Specific process: The system receives treatment plans from the diagnostic decision generation module, including prescription recommendations, Western medicine treatment plans, and combined Chinese and Western medicine plans; extracts relevant information such as drug treatment direction, drug properties / pharmacology, contraindications, half-life, metabolic pathways, and drug interactions; establishes structured feature descriptions for Chinese and Western medicines respectively; predicts drug conflict risks through AI models, establishes a mapping between the properties of Chinese medicine (cold, heat, warm, cool, meridian tropism, etc.) and the pharmacological effects of Western medicines, and detects potential adverse synergies; filters unsuitable drugs based on the patient's underlying diseases, allergy history, liver and kidney function, and physical indicators; optimizes dosage and adjusts medication time for the combined Chinese and Western medicine plan; and outputs information such as adverse drug reaction risks, contraindications, alternative solutions, and prescription addition / subtraction suggestions.

[0058] In this application, we start from the knowledge systems of traditional Chinese medicine and Western medicine, and use a unified AI model to realize cross-system drug conflict detection and dosage optimization.

[0059] In this application, a mapping is established between "the properties of traditional Chinese medicine (cold, hot, warm, cool)" and "the pharmacological pathways of Western medicine (inflammation, metabolism, immunity)"; and an AI model is used to identify potential synergies or conflicts between traditional Chinese medicine and Western medicine.

[0060] In one implementation, combined with Figure 5 As shown, the artificial intelligence health management system includes: The health record management module is configured to generate and update dynamic health records based on the patient's medical data. An information analysis terminal is configured to monitor the patient's current medical data; the information analysis terminal may be a wearable device, a home monitoring device, a companion robot, or a monitoring robot. The intelligent risk assessment module is configured to use an artificial intelligence model to assess the risk of patients developing target diseases, complications or adverse events based on multidimensional features in dynamic health records, and generate assessment results and personalized intervention plans. The rehabilitation execution terminal is configured to execute the personalized intervention plan on the patient.

[0061] In one embodiment, the rehabilitation execution terminal includes a lower limb exoskeleton rehabilitation robot, an upper limb training robot, a hand fine motor training robot, a gait rehabilitation treadmill robot, and a home-use small rehabilitation robot.

[0062] The health record management module is used to build dynamic health records for patients, serving as the core data foundation for full-lifecycle health management. Specifically, it includes: Structured data is extracted from electronic medical records, examination and test data, medication records, follow-up records, and outpatient / inpatient information; real-time physiological data is collected from monitoring terminals (wearable devices, home devices, robots); soft information such as lifestyle habits, diet, physical condition, and psychological state is obtained from consultation interactions; different data formats are unified through standard medical coding systems (such as ICD, LOINC, SNOMEDCT); multimodal fusion (structured, image, voice, text) is performed to establish a "time series health curve" to record the changing trend of the patient's condition; each data entry automatically triggers a file update when it enters the system.

[0063] The intelligent risk assessment module, based on dynamic health records, performs disease prediction, complication risk analysis, and adverse event early warning. Specific process: Extract trend features, periodic features, and event features from continuously updated health records; use time series models (LSTM, Transformer), graphical models, or multimodal models to predict risks; model types may include: diabetes complication prediction models, cardiovascular risk scoring models, cancer follow-up recurrence prediction models, fall risk identification models, and heart failure deterioration prediction models; output the probability of disease risk within a certain future timeframe; and develop intervention measures for patients based on the prediction results.

[0064] The rehabilitation execution terminal is used to implement the personalized intervention plan generated by the risk assessment module.

[0065] The rehabilitation system utilizes various mechanisms: Rehabilitation robots perform upper / lower limb rehabilitation training; adjust joint angles, resistance, and frequency; and monitor movement quality and training compliance. Physiotherapy equipment provides hot compresses, cold compresses, vibration massage, and electrical stimulation. Wearable devices monitor exercise intensity and provide real-time feedback; and automatically record rehabilitation progress.

[0066] Current health management typically involves static health records and single interventions, lacking a closed-loop system. This application proposes an intelligent closed-loop system comprised of: dynamic health records (continuously updated), real-time monitoring terminals, AI risk prediction, rehabilitation terminals executing interventions, and intervention results feeding back into the health records (closed loop). This achieves end-to-end closed-loop health management—from perception to cognition to decision-making to execution and re-cognition—on a unified platform.

[0067] This application combines AI risk assessment with the execution behavior of rehabilitation robots into an adaptive closed-loop system, which is a rare technological system in the field of health management.

[0068] In this application, dynamic trends rather than static indicators are used to generate personalized health intervention plans.

[0069] In one implementation, combined with Figure 6 As shown, the hospital operation information integration system includes: The information integration module is configured to exchange data with the hospital information system, examination and testing system, pharmacy and supplies system, and surgical anesthesia system to build a unified view of hospital operation data. The process rules engine module is configured to orchestrate and track the status of hospital services such as registration, consultation, examination, hospitalization, surgery, and medication dispensing based on preset business processes and rules. The resource scheduling module is configured to schedule beds, operating rooms, equipment, and medical and nursing human resources. The robot collaboration module is configured to issue task instructions to functional robots and receive task execution feedback, so as to coordinate the robot's execution status with the hospital's overall business process; the functional robots include at least one of the following: logistics and delivery robot, medicine delivery robot, patient guidance robot, triage robot, navigation robot, surgical supplies handling robot, disinfection robot, and warehousing robot.

[0070] The information integration module achieves integrated management of hospital operational data by exchanging data with various internal hospital business systems. Specifically, it connects with HIS (Hospital Information System), LIS (Laboratory Information System), PACS / RIS (Picture Archiving and Communication System), Pharmacy and Supplies System, Artificial Intelligence and Supplies System (AIMS), Nursing Information System (NIS), and Logistics and Warehousing System through standard interfaces (such as HL7, FHIR, and RESTAPI). It structures and encodes non-uniformly formatted data from different systems; constructs a unified operational view covering registration volume, examination volume, bed occupancy, surgical scheduling, drug inventory, material consumption, and robot status; and enables visualization and real-time updates of internal business flow information. This provides a unified data foundation for internal business collaboration and resource scheduling.

[0071] The process rule engine is responsible for orchestrating and tracking the status of core hospital operations. Specifically, it uses process modeling languages ​​(BPMN, state machine model) to represent hospital business processes: outpatient processes (registration—waiting—consultation—examination—medication dispensing), inpatient processes (admission—examination—treatment—discharge), surgical processes (preoperative assessment—anesthesia—surgery—postoperative recovery), emergency processes (triage—rescue—transfer), and pharmacy workflow processes (review—dispensing—medication dispensing). Based on business rule bases (e.g., fasting required for examinations, priority scheduling for CT scans for certain patient groups), it automatically triggers corresponding process actions; and it tracks patient business status in real time: waiting, in progress, completed, and abnormal.

[0072] The resource scheduling module is used for intelligent scheduling of hospital resources (human resources, materials, and facilities). The specific process includes: acquiring bed occupancy information, operating room schedules, equipment reservation status, and medical staff shifts; acquiring patient service needs, such as surgery type, examination type, and departmental requirements; and using artificial intelligence or rule-based algorithms for scheduling, such as: bed allocation algorithms (allocated according to patient priority and departmental capacity), operating room scheduling optimization (based on duration estimation, surgical shifts, and anesthesia staff), examination equipment scheduling (e.g., MRI / CT queue optimization), and medical staff allocation (adjusted according to ward load and nursing level). In the event of emergencies (emergency intervention, surgical delays, bed shortages), the system automatically recalculates the schedule. This transforms hospital resource management from static to dynamic intelligent scheduling.

[0073] The robot collaboration module establishes a closed-loop collaboration mechanism between the robot, the hospital system, and medical staff. The specific process involves: issuing instructions to the corresponding type of robot based on tasks generated by the process rule engine; interacting via the Internet of Things protocol (MQTT), cloud interfaces, or the robot SDK; and the robot providing feedback on task progress through sensors and actuators. This allows robot behavior to directly impact the hospital's operational status, forming true collaboration.

[0074] This application standardizes and integrates data from multiple systems and formats, incorporating all data, from structured to unstructured, into a unified model to construct a real-time and updatable operational view; it is a cross-system hospital-level data fusion mechanism and a unified operational view construction system.

[0075] In this application, the process engine is not only used for the control of a single process, but covers the entire hospital business, including outpatient, inpatient, surgical, and pharmacy services. It automatically triggers downstream processes based on events and dynamically adjusts processes in combination with real-time resources, thereby realizing an automated orchestration and status tracking mechanism for the entire chain of hospital business processes.

[0076] In this application, multiple types of robots not only perform tasks, but also interact bidirectionally with hospital business processes. Various types of robots can participate in multiple aspects of medical business, and the results of robot execution will directly refresh the status of the business system; forming a collaborative system of "process intelligence + resource intelligence + robot execution intelligence" for hospitals.

[0077] In one implementation, combined with Figure 7 As shown, the remote 5G joint sharing system includes: The multimodal data processing module is configured to perform multi-channel transmission and processing of audio and video data and medical business data in a 5G network environment; The personalized content generation module is configured to combine multimodal data transmitted via 5G with artificial intelligence analysis results to generate personalized interactive content and prompts for different telemedicine scenarios and participating roles. The collaborative terminal control module is configured to orchestrate telemedicine business processes and perform conferencing and control on telemedicine terminals. The multimodal data processing module, personalized content generation module, and collaborative terminal control module transmit data and perform collaborative processing with edge computing nodes through the 5G access network.

[0078] The multimodal data processing module leverages 5G's high bandwidth and low latency capabilities to enable real-time interaction of multimodal data in remote medical scenarios. Specifically, the module supports parallel access to multiple data types and, under 5G network slicing, creates a dedicated transmission channel for remote medical care, achieving low-latency transmission and synchronization mechanisms; thus ensuring stable transmission of high-concurrency, high-quality, multimodal remote medical data.

[0079] The personalized content generation module is responsible for generating personalized AI-assisted content for different participants in telemedicine scenarios. The specific process involves: integrating real-time audio / video and medical data from the multimodal data processing module, analysis results from the AI-assisted diagnosis and treatment decision-making system, dynamic health records from the health management system, and process and resource data from the hospital operation information integration system to form comprehensive input features for telemedicine; automatically identifying participants based on the remote scenario; and automatically generating personalized interactive content according to the needs of different roles: for doctors: real-time disease summary, diagnostic prompts, medication suggestions, and key image annotations; for patients: simplified health tips, graphical explanations, and precautions; for medical students: teaching tips, key operation annotations, and risk point warnings; generating multimodal prompt content, including voice prompts, text prompts, and AR overlay prompts; and supporting the generation of operational assistance information in remote surgery, such as incision location guidance, blood vessel identification, and surgical area risk reminders. This achieves a personalized telemedicine experience with "differentiated output based on role."

[0080] The collaborative terminal control module is responsible for unified business control and process orchestration of various terminals related to telemedicine. Specific processes include: remote business process orchestration: defining remote diagnosis and treatment processes: appointment → consultation → treatment → follow-up; defining remote ward round processes: ward round sequence → data retrieval → record generation; defining remote surgical collaboration processes: surgical preparation → real-time teaching → key step prompts → surgical completion instructions. It supports control of the following terminals: remote consultation terminals, AR / VR medical training terminals, surgical robots, ultrasound robots, remote ward round robots, and home monitoring terminals; supported control actions include: video on / off, camera angle adjustment, robot operation permission switching, data channel establishment / closure, and work mode switching (diagnosis / training / monitoring); achieving unified control of multiple terminals in remote consultations: switching the main screen; adjusting the multi-party video layout; controlling screen sharing; managing speaking permissions; and automatically recording and archiving consultation content. It enables collaborative operation across multiple terminals, participants, and business scenarios.

[0081] In this application, the multimodal data processing module provides basic input; the personalized content generation module generates interactive content based on analysis; the collaborative terminal control module continues to issue operation instructions or scheduling signals; the three achieve high-frequency cyclic interaction through edge computing nodes, forming a real-time, low-latency, multi-terminal collaborative system in the remote medical scenario.

[0082] This application can "identify participating roles" and automatically generate differentiated prompts and auxiliary information.

[0083] In this application, although the data processing, content generation, and terminal control are respectively responsible for data processing, content generation, and terminal control, they are integrated with the support of 5G and edge nodes, which is a highly integrated model of the current telemedicine system.

[0084] In one embodiment, the remote medical terminal includes: a remote expert workstation, a consultation terminal, a doctor's terminal in a primary hospital / branch hospital, a patient's home terminal, a remote collaborative terminal in an operating room, a 5G vehicle-mounted terminal in an ambulance, ambulance monitoring equipment, a mobile ward round robot, and a remote diagnosis and treatment robot.

[0085] In one implementation, combined with Figure 8 As shown, the collaborative terminal control module is communicatively connected to the surgical robot in the operating room; The collaborative terminal control module is configured to acquire medical images of the surgical site and send them to the surgical robot; The surgical robot is configured to reconstruct a three-dimensional model of the surgical site based on medical images and perform surgical planning; to register the intraoperative physical site with the preoperative three-dimensional model based on the navigator and tracer to map the surgical plan; and to control the robotic arm to perform surgery along the planned motion path according to the mapped surgical plan.

[0086] This application provides a method for intelligent cognition and collaborative control of the smart hospital's overall intelligent cognition and collaboration system, as described above. The specific solution of this method is... Figure 9 As shown below, the intelligent cognition and collaborative control method for the entire smart hospital will be described in detail.

[0087] Combination Figure 9 As shown, the smart hospital's comprehensive intelligent cognition and collaborative control method includes: S101 performs artificial intelligence analysis based on the patient's medical data to generate personalized diagnostic and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions; S102 generates dynamic health records based on the medical data generated by patients inside and outside the hospital, and conducts intelligent risk assessment and personalized intervention to provide patients with precise health management throughout their entire life cycle. S103, build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; S104 combines 5G networks with artificial intelligence analysis to generate personalized interactive content and collaborative services suitable for different telemedicine scenarios.

[0088] In one implementation, S101, artificial intelligence analysis is performed based on the patient's medical data to generate personalized diagnostic and treatment assistance information to assist doctors in making targeted analysis, diagnosis, and treatment decisions, including: The patient's treatment preferences are confirmed based on their medical information, including traditional Chinese medicine treatment, Western medicine treatment, and integrated traditional Chinese and Western medicine treatment. Based on treatment preference information, generate TCM syndrome differentiation and treatment plans, Western medicine disease diagnosis and treatment strategies, or integrated TCM and Western medicine treatment plans; The TCM syndrome differentiation and treatment plan is generated by calling the TCM knowledge graph and the TCM decision-making model to perform TCM syndrome differentiation reasoning; the Western medicine disease diagnosis and treatment strategy is generated by calling the Western medicine decision-making model and the guideline rule base; the integrated TCM and Western medicine diagnosis and treatment plan is obtained by synergistic combination of the TCM syndrome differentiation and treatment plan and the Western medicine disease diagnosis and treatment strategy.

[0089] In one implementation, S101, artificial intelligence analysis is performed based on the patient's medical data to generate personalized diagnostic and treatment assistance information to assist doctors in conducting targeted analysis, diagnosis, and treatment decisions, and further includes: The system intelligently assesses the patient's condition and generates triage and priority information, as well as the assigned doctor information.

[0090] In one embodiment, the smart hospital's comprehensive intelligent cognition and collaborative control method further includes: Based on the hospital's spatial location information, business layout information, and patient visit information, the system generates in-hospital route planning and navigation guidance to provide visual navigation services to patients or medical staff.

[0091] In one implementation, the patient terminal is any one or more of the following: smartphone, smartwatch, computer, tablet, personal monitoring device, self-service kiosk, triage robot, and patient guidance robot.

[0092] In one embodiment, the patient terminal is equipped with an identity verification module, which includes any one or more combinations of account password verification, SMS verification, email verification, voiceprint verification, fingerprint verification, iris verification, facial verification, medical insurance card verification, ID card verification, and third-party verification.

[0093] In one implementation, S101, artificial intelligence analysis is performed based on the patient's medical data to generate personalized diagnostic and treatment assistance information to assist doctors in conducting targeted analysis, diagnosis, and treatment decisions, and further includes: Based on triage information and hospital resource information, a patient guidance process recommendation and corresponding navigation path are generated to execute navigation actions.

[0094] In one implementation, S101, artificial intelligence analysis is performed based on the patient's medical data to generate personalized diagnostic and treatment assistance information to assist doctors in conducting targeted analysis, diagnosis, and treatment decisions, and further includes: Obtain treatment support decision-making information based on patient treatment preferences, and use artificial intelligence to personalize the drug combinations based on the treatment support decision-making information and provide medication risk warnings.

[0095] In one implementation, S102, a dynamic health record is generated based on the patient's medical data generated inside and outside the hospital, and intelligent risk assessment and personalized intervention are performed to provide patients with precise health management throughout their entire life cycle, including: Dynamic health records are generated and updated based on the patient's medical data; Monitor the patient's current medical data; the information analysis terminal is a wearable device, home monitoring device, companion robot, or monitoring robot; Based on the multidimensional features in dynamic health records, artificial intelligence models are used to assess the risk of patients developing target diseases, complications or adverse events, and to generate assessment results and personalized intervention plans. The individualized intervention plan was implemented on the patient.

[0096] In one embodiment, the rehabilitation execution terminal includes a lower limb exoskeleton rehabilitation robot, an upper limb training robot, a hand fine motor training robot, a gait rehabilitation treadmill robot, and a home-use small rehabilitation robot.

[0097] In one implementation, S103, a unified data hub is constructed to facilitate data flow and business collaboration from different business systems and smart devices, including: Exchange data with hospital information systems, examination and testing systems, pharmacy and supplies systems, and surgical anesthesia systems to build a unified view of hospital operation data; Based on preset business processes and rules, the hospital's business processes, including registration, consultation, examination, hospitalization, surgery, and medication dispensing, are arranged and their status is tracked. Dispatch of beds, operating rooms, equipment, and medical and nursing human resources; The system issues task instructions to the functional robots and receives task execution feedback to coordinate the robot's execution status with the hospital's overall business processes. The functional robots include at least one of the following: logistics and delivery robots, medicine delivery robots, patient guidance robots, triage robots, navigation robots, surgical supplies handling robots, disinfection robots, and warehousing robots.

[0098] In one implementation, S104, combining 5G network and artificial intelligence analysis, personalized interactive content and collaborative services suitable for different telemedicine scenarios are generated, including: Multi-channel transmission and processing of audio and video data and medical business data in a 5G network environment; The personalized content generation module is configured to combine multimodal data transmitted via 5G with artificial intelligence analysis results to generate personalized interactive content and prompts for different telemedicine scenarios and participating roles. The system orchestrates remote medical service processes and performs conferencing and control on remote medical terminals; the content is transmitted and collaboratively processed through a 5G access network and edge computing nodes.

[0099] In one embodiment, the remote medical terminal includes: a remote expert workstation, a consultation terminal, a doctor's terminal in a primary hospital / branch hospital, a patient's home terminal, a remote collaborative terminal in an operating room, a 5G vehicle-mounted terminal in an ambulance, ambulance monitoring equipment, a mobile ward round robot, and a remote diagnosis and treatment robot.

[0100] In one implementation, S104, combining 5G network and artificial intelligence analysis, personalized interactive content and collaborative services suitable for different telemedicine scenarios are generated, and the collaborative terminal control module is also connected to the surgical robot in the operating room. Acquire medical images of the area to be operated on and send them to the surgical robot; The surgical robot reconstructs a three-dimensional model of the surgical site based on medical images and performs surgical planning; it registers the physical site during surgery with the three-dimensional model before surgery using a navigator and a tracer to map the surgical plan; and it controls the robotic arm to perform surgery along the planned motion path according to the mapped surgical plan.

[0101] The smart hospital full-domain intelligent cognition and collaborative control method provided in the above embodiments of this application corresponds to the smart hospital full-domain intelligent cognition and collaborative system provided in the embodiments of this application. Therefore, the specific content of the method corresponds to the smart hospital full-domain intelligent cognition and collaborative system. The specific content can be referred to the records in the smart hospital full-domain intelligent cognition and collaborative system, which will not be repeated in this application.

[0102] The smart hospital full-domain intelligent cognition and collaborative control method provided in the above embodiments of this application and the smart hospital full-domain intelligent cognition and collaborative system provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0103] Based on the same inventive concept, another embodiment of the present invention provides an electronic device for implementing the smart hospital's comprehensive intelligent cognition and collaborative control method described in the above embodiments. For example... Figure 10 As shown, the electronic device includes a memory 301 and a processor 303.

[0104] Memory 301 can be configured to store a program.

[0105] Additionally, memory 301 can also be configured to store various other data to support operation on the electronic device. Examples of this data include instructions for any application or method used to operate on the electronic device, contact data, phonebook data, messages, pictures, videos, etc.

[0106] Memory 301 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Processor 303, coupled to memory 301, is used to execute programs in memory 301 for: Artificial intelligence analysis is performed based on patients' medical data to generate personalized diagnostic and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions; Dynamic health records are generated based on the medical data of patients both inside and outside the hospital, enabling intelligent risk assessment and personalized intervention to provide patients with precise health management throughout their entire life cycle. Build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; By combining 5G networks with artificial intelligence analysis, personalized interactive content and collaborative services can be generated for different telemedicine scenarios.

[0107] In this application, Figure 10 The diagram only shows some components and does not mean that the electronic device includes only these components. Figure 10 The components shown.

[0108] The electronic device provided in this embodiment is based on the same inventive concept as the smart hospital full-domain intelligent cognition and collaborative control method provided in this application embodiment, and has the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0109] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0112] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0113] This application also provides a computer-readable storage medium corresponding to the smart hospital full-domain intelligent cognition and collaborative control method provided in the foregoing embodiments, wherein a computer program (i.e., a program product) is stored thereon. When the computer program is run by a processor, it executes the smart hospital full-domain intelligent cognition and collaborative control method provided in any of the foregoing embodiments.

[0114] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, optical disc read-only memory (CDROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0115] The computer-readable storage medium provided in the above embodiments of this application and the smart hospital full-domain intelligent cognition and collaborative control method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0116] It should be noted that numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0117] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, system, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, system, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, system, article, or apparatus that includes said element.

[0118] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A smart hospital's comprehensive intelligent cognition and collaboration system, characterized in that, include: The AI-assisted diagnosis and treatment decision-making system is configured to perform artificial intelligence analysis based on the patient's medical data to generate personalized diagnosis and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions. The AI-powered health management system is configured to generate dynamic health records based on the medical data generated by patients both inside and outside the hospital, and to conduct intelligent risk assessments and personalized interventions to provide patients with precise health management throughout their entire life cycle. The hospital operation information integration system is configured to build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; The remote 5G joint sharing system is configured to combine 5G networks with artificial intelligence analysis to generate personalized interactive content and collaborative services suitable for different remote medical scenarios. The AI-assisted diagnosis and treatment decision-making system, the artificial intelligence health management system, the hospital operation information integration system, and the remote 5G joint sharing system all operate on a unified artificial intelligence data and model service platform.

2. The smart hospital full-domain intelligent cognition and collaboration system according to claim 1, characterized in that, The AI-assisted diagnosis and treatment decision-making system includes a diagnosis and treatment preference management module and a diagnosis and treatment decision generation module; The treatment preference management module is configured to confirm the patient's treatment preference information based on the patient's medical information, including traditional Chinese medicine treatment, Western medicine treatment, and integrated traditional Chinese and Western medicine treatment. The diagnosis and treatment decision generation module is configured to generate TCM syndrome differentiation and treatment plans, Western medicine disease diagnosis and treatment strategies, or integrated TCM and Western medicine diagnosis and treatment plans based on diagnosis and treatment preference information. The TCM syndrome differentiation and treatment plan is generated by calling the TCM knowledge graph and the TCM decision-making model to perform TCM syndrome differentiation reasoning; the Western medicine disease diagnosis and treatment strategy is generated by calling the Western medicine decision-making model and the guideline rule base; the integrated TCM and Western medicine diagnosis and treatment plan is obtained by synergistic combination of the TCM syndrome differentiation and treatment plan and the Western medicine disease diagnosis and treatment strategy.

3. The smart hospital full-domain intelligent cognition and collaboration system according to claim 2, characterized in that, The AI-assisted diagnosis and treatment decision-making system also includes a triage decision-making module, which is configured to intelligently assess the patient's condition and generate triage information, priority information, and assigned doctor information.

4. The smart hospital full-domain intelligent cognition and collaboration system according to claim 3, characterized in that, It also includes an intelligent navigation system, which is configured to generate in-hospital route planning and navigation guidance based on hospital spatial location information, business layout information, and patient visit information, so as to provide visual navigation services to patients or medical staff.

5. The smart hospital full-domain intelligent cognition and collaboration system according to claim 4, characterized in that, The patient terminal can be any one or more of the following: smartphone, smartwatch, computer, tablet, personal monitoring device, self-service kiosk, triage robot, and patient guidance robot.

6. The smart hospital full-domain intelligent cognition and collaboration system according to claim 4, characterized in that, The patient terminal is equipped with an identity verification module, which includes any one or more combinations of account password verification, SMS verification, email verification, voiceprint verification, fingerprint verification, iris verification, facial verification, medical insurance card verification, ID card verification, and third-party verification.

7. The smart hospital full-domain intelligent cognition and collaboration system according to claim 4, characterized in that, The AI-assisted diagnosis and treatment decision-making system also includes a triage process module, which is linked with the intelligent navigation system and configured to generate triage process recommendations and corresponding navigation paths for patients based on triage information and hospital resource information, so as to execute navigation actions.

8. The smart hospital full-domain intelligent cognition and collaboration system according to claim 2, characterized in that, The AI-assisted diagnosis and treatment decision-making system also includes a drug screening module, which is linked with the diagnosis and treatment decision generation module and is configured to acquire diagnosis and treatment decision-making information based on patient diagnosis and treatment preferences generated by the diagnosis and treatment decision generation module, and use artificial intelligence to perform personalized screening of drug combinations based on the diagnosis and treatment decision-making information and provide medication risk warnings.

9. The smart hospital full-domain intelligent cognition and collaboration system according to any one of claims 1-8, characterized in that, The artificial intelligence health management system includes: The health record management module is configured to generate and update dynamic health records based on the patient's medical data. An information analysis terminal is configured to monitor the patient's current medical data; the information analysis terminal may be a wearable device, a home monitoring device, a companion robot, or a monitoring robot. The intelligent risk assessment module is configured to use an artificial intelligence model to assess the risk of patients developing target diseases, complications or adverse events based on multidimensional features in dynamic health records, and generate assessment results and personalized intervention plans. The rehabilitation execution terminal is configured to execute the personalized intervention plan on the patient.

10. The smart hospital full-domain intelligent cognition and collaboration system according to claim 9, characterized in that, The rehabilitation execution terminals include lower limb exoskeleton rehabilitation robots, upper limb training robots, hand fine motor training robots, gait rehabilitation treadmill robots, and home-use small rehabilitation robots.

11. The smart hospital full-domain intelligent cognition and collaboration system according to any one of claims 1-8, characterized in that, The hospital operation information integration system includes: The information integration module is configured to exchange data with the hospital information system, examination and testing system, pharmacy and supplies system, and surgical anesthesia system to build a unified view of hospital operation data. The process rules engine module is configured to orchestrate and track the status of hospital services such as registration, consultation, examination, hospitalization, surgery, and medication dispensing based on preset business processes and rules. The resource scheduling module is configured to schedule beds, operating rooms, equipment, and medical and nursing human resources. The robot collaboration module is configured to issue task instructions to functional robots and receive task execution feedback, so as to coordinate the robot's execution status with the hospital's overall business process; the functional robots include at least one of the following: logistics and delivery robot, medicine delivery robot, patient guidance robot, triage robot, navigation robot, surgical supplies handling robot, disinfection robot, and warehousing robot.

12. The smart hospital full-domain intelligent cognition and collaboration system according to any one of claims 1-8, characterized in that, The remote 5G joint sharing system includes: The multimodal data processing module is configured to perform multi-channel transmission and processing of audio and video data and medical business data in a 5G network environment; The personalized content generation module is configured to combine multimodal data transmitted via 5G with artificial intelligence analysis results to generate personalized interactive content and prompts for different telemedicine scenarios and participating roles. The collaborative terminal control module is configured to orchestrate telemedicine business processes and perform conferencing and control on telemedicine terminals. The multimodal data processing module, personalized content generation module, and collaborative terminal control module transmit data and perform collaborative processing with edge computing nodes through the 5G access network.

13. The smart hospital full-domain intelligent cognition and collaboration system according to claim 12, characterized in that, The remote medical terminals include: remote expert workstations, consultation terminals, doctor terminals in primary hospitals / branch hospitals, patient home terminals, remote collaborative terminals in operating rooms, 5G vehicle-mounted terminals in ambulances, emergency monitoring equipment, mobile ward round robots, and remote diagnosis and treatment robots.

14. The smart hospital full-domain intelligent cognition and collaboration system according to claim 12, characterized in that, The collaborative terminal control module is communicatively connected to the surgical robot in the operating room; The collaborative terminal control module is configured to acquire medical images of the surgical site and send them to the surgical robot; The surgical robot is configured to reconstruct a three-dimensional model of the surgical site based on medical images and perform surgical planning; to register the intraoperative physical site with the preoperative three-dimensional model based on the navigator and tracer to map the surgical plan; and to control the robotic arm to perform surgery along the planned motion path according to the mapped surgical plan.

15. A smart hospital's comprehensive intelligent cognition and collaborative control method, characterized in that, The smart hospital full-domain intelligent cognition and collaboration system based on any one of claims 1-14 includes: Artificial intelligence analysis is performed based on patients' medical data to generate personalized diagnostic and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions; Dynamic health records are generated based on the medical data of patients both inside and outside the hospital, enabling intelligent risk assessment and personalized intervention to provide patients with precise health management throughout their entire life cycle. Build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; By combining 5G networks with artificial intelligence analysis, personalized interactive content and collaborative services can be generated for different telemedicine scenarios.

16. An electronic device, characterized in that, include: Memory and processor; The memory is used to store programs; The processor, coupled to the memory, is used to execute the program for: Artificial intelligence analysis is performed based on patients' medical data to generate personalized diagnostic and treatment assistance information to help doctors conduct targeted analysis, diagnosis and treatment decisions; Dynamic health records are generated based on the medical data of patients both inside and outside the hospital, enabling intelligent risk assessment and personalized intervention to provide patients with precise health management throughout their entire life cycle. Build a unified data hub to facilitate data flow and business collaboration from different business systems and smart devices; By combining 5G networks with artificial intelligence analysis, personalized interactive content and collaborative services can be generated for different telemedicine scenarios.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the smart hospital full-domain intelligent cognition and collaborative control method described in claim 15.

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