693 results about "Patient room" patented technology

The invention belongs to the technical field of medical data processing, and discloses a medical data multi-source fusion verification and correction method and system, and the method comprises the steps: obtaining multi-source data; constructing a dynamic baseline model of the emergency department, the ICU and the common ward, responding to and verifying the abnormal condition of the patient data, and performing early warning; establishing a term library, carrying out multi-source fusion on the patient data obtained from multiple sources, and carrying out data correction; and generating a multi-source data quality evaluation table of different departments based on the whole-process data, and feeding back and updating the term library and fusing the correction weight based on the quality evaluation table. Through a real-time monitoring and intelligent early warning mechanism, quality problems in the data can be found and corrected in time, human errors are reduced, and the data management cost is greatly reduced. Through multi-source data fusion and consistency verification, the system can ensure seamless connection of data from different data sources, and cross-system and cross-platform data consistency and cooperative work are realized, so that the accuracy of medical decision and the reliability of data are improved.

The embodiment of the invention provides an intelligent ward digital management method and system based on digital twinning. A ward digital twinborn body is constructed, a sensor array is used for collecting sickbed area heat radiation distribution, operation area airflow mode and medical equipment energy pulse data in real time, and field intensity superposition is carried out on airflow data in combination with a nursing track to form a dynamic airflow model. An energy pulse phase abrupt change point and a thermal radiation attenuation curve are analyzed by establishing an energy interference spectrum, and a negentropy compensation strategy is generated. When a medical equipment start-stop event is detected, topological distortion caused by a space overlapping area of dynamic airflow and a transfer path is predicted, vortex density distribution is reconstructed, a dynamic balance threshold value of an entropy increase gradient and a negentropy shunting rate is calculated based on updated parameters, and finally an intelligent regulation and control instruction set of a thermal stability coefficient and airflow purification efficiency is output. According to the technical scheme provided by the embodiment of the invention, the ward environment regulation and control precision and response speed are remarkably improved.

The invention relates to the technical field of ward air purification systems, and discloses a ward air purification system which comprises an environment sensing module, a purification path planning module, a filter unit configuration module, a purification execution module, a dynamic adjustment module, a state monitoring module and a feedback control module. The environment sensing module obtains ward space information to construct an air flow topology; the purification path planning module calls air quality data to label the topology; the filter unit configuration module sets purification nodes and adjustment intervals; the purification execution module implements basic purification; the state monitoring module analyzes data to determine operation state characteristics, and the feedback control module generates monitoring records and performs visual feedback. The system can accurately perceive the air quality of the ward, scientifically plan the purification path, dynamically adjust the purification strategy, effectively improve the air quality of the ward and guarantee the health of patients and medical staff, and has high practical value.

The invention discloses an intelligent infusion monitoring management system, and relates to the technical field of medical equipment, and the system comprises the steps: collecting patient data, synchronizing the patient data to a central monitoring system through a block chain technology, and generating a personalized infusion scheme; based on a personalized infusion scheme, a medical worker uses a mobile device to carry out identity verification on a patient and obtain medicine information, and a voice assistant function is integrated; the monitoring module is responsible for monitoring vital signs of a patient and the temperature of a ward by using wearable equipment and a temperature sensor, and adjusting a personalized infusion scheme and liquid temperature according to the vital signs and the temperature of the ward; the risk value prediction module is responsible for predicting risk values of blockage and leakage according to the adjusted personalized infusion scheme and the liquid temperature and setting a graded alarm mechanism; patients and family members receive the infusion progress through the intelligent infusion monitor. According to the invention, the block chain technology and the advanced artificial intelligence algorithm are integrated, so that the health data and medicine information of the patient can be efficiently managed and analyzed.

The invention discloses a neurological critical patient monitoring system based on artificial intelligence and multi-modal data fusion, and relates to the technical field of intelligent monitoring, in the system, vital signs and medical record information of a neurological critical patient are obtained from existing medical monitoring equipment and an electronic medical record system and displayed in real time; different medical prediction models are utilized to perform different predictions according to the multi-modal monitoring data, and prediction results obtained by the different medical prediction models are displayed in real time; when the current key monitoring index of the neurological critical patient exceeds an early warning threshold value or prediction results obtained by different medical prediction models meet a preset early warning condition, a real-time early warning result display module is used for carrying out real-time early warning on medical staff; and finally, the data in the monitoring process is stored through a data storage and export module so as to be packaged and exported when further analysis is needed. The method is of great significance in improving the medical level of the neurointensive care unit, accelerating the illness response speed and improving the treatment efficiency.

The invention provides a ward rotation evaluation and model construction method based on multi-modal information, and belongs to the technical field of deep learning. The method comprises the following steps: firstly, constructing a multi-modal clinical data set, integrating five types of heterogeneous data including operation records, vital sign time sequence data, medical images, medication records and nursing logs, labeling postoperative state labels of patients, and simulating a clinical real scene to generate a training set and a test set containing random missing modals; secondly, constructing a ward rotation evaluation model, wherein the model is used for improving and designing a traditional teacher-student network architecture into a double-path heterogeneous distillation model; meanwhile, designing a multi-task loss joint optimization strategy for aligning the characteristics of the teacher-student network and constraining the evaluation result of the teacher network, and finally deploying a trained model to an edge computing device, receiving a multi-modal data stream in real time and outputting a transfer suggestion and a risk score; the invention provides a brand new intelligent solution under a ward rotation scene, and improves the evaluation robustness of a multi-modal missing scene.

The invention belongs to the technical field of central air conditioner control, and particularly relates to an internet-of-things edge control method based on a central air conditioner water chilling unit, which comprises the following steps of: acquiring multi-dimensional operation parameters in real time; the parameters are classified according to hospital operating rooms, wards and public areas and then input into a cold load prediction model, and future cold load values and the like of all the areas are predicted through a long-short term memory (LSTM) network. Through deep fusion of Internet of Things edge calculation, a deep learning model and a dynamic optimization algorithm, the key problems of insufficient control precision, large energy efficiency fluctuation, cooperative control lagging and the like of a traditional central air conditioning system in a complex scene are solved, and the purposes of deeply fusing real-time data features, dynamically optimizing a control strategy and improving the control efficiency are achieved. And a multivariable coupling relationship can be effectively processed, so that the effect of high-precision and high-response temperature control requirements in a medical scene can be met.

The invention discloses a risk identification method for pediatric intensive care unit nursing, which relates to the technical field of medical early warning, and comprises the following steps: collecting multi-modal data, and carrying out time synchronization on the collected data; data features are extracted based on the data after time synchronization, fusion processing is carried out through linear weighting, an optimization target is determined, effective fusion of multi-modal data is achieved, and fused high-quality data is obtained; constructing a risk assessment model, calculating a risk value for each time point in combination with historical data, obtaining an overall risk value through weighted average processing, judging a risk level, and optimizing prediction accuracy through a self-correction algorithm; the state of a patient is monitored in real time, the early warning level is set, abnormal conditions are early warned according to the corrected risk value variation, feedback of medical staff is collected, and the system performance is continuously optimized. The method has remarkable clinical application value, and can effectively improve early warning accuracy, optimize medical resource allocation, reduce medical risks and improve overall medical quality.

The invention relates to the technical field of health monitoring, in particular to a health monitoring method and device based on the Internet of Things, and the method comprises the steps: obtaining a historical health monitoring data set according to an Internet of Things sensor network; dividing the monitoring area into a multi-scale space-time health state grid; and performing state classification through the historical health monitoring data set according to a density peak clustering algorithm to obtain a health state mode. According to the method, the monitoring area is divided into the multi-scale space-time health state grids, and the mapping relation between the environment parameters and the health indexes is combined, so that the microenvironment-health association which is difficult to find by a traditional monitoring system can be captured, for example, in a ward scene, the monitoring accuracy is improved; the phenomenon that the infection risk of a patient is increased due to poor air circulation in a ward can be accurately identified, so that environmental parameters are optimized in a targeted manner; and through collaborative analysis of a global channel and a local channel, a group trend and individual anomaly can be captured.

The invention discloses an inpatient falling early warning and intervention method based on environmental perception, and the method comprises the steps: continuously obtaining the video frames of a patient through a camera, and extracting the coordinates and confidence of seventeen key points through a pre-training deep learning posture estimation model; respectively processing the time sequence and the space-time diagram by using a hierarchical time sequence convolutional network branch and a graph neural network branch, and generating a visual behavior feature vector; in the same risk assessment period, bidirectional cross-modal attention fusion is carried out on the physiological feature vector and the visual feature vector collected by the wearable device, and a multi-modal fusion feature vector is output; then combining historical state features and static archive features in a hospital information system, carrying out unified modeling through a Transform encoder, and mapping to fall risk probability distribution; and when the risk is judged to be high, an intervention instruction is immediately issued to an intelligent sickbed or ward falling prevention execution mechanism through an Internet of Things protocol, and a perception-decision-intervention closed loop is completed.

The invention discloses a hospital public building temperature refined energy-saving regulation and control method, which comprises the following steps: establishing a refined hierarchical management architecture based on use requirements and energy use purposes of different wards; for a conventional inpatient ward, door and window opening and closing state data and ward heat state data are collected, and air conditioner operation control and door and window opening and closing state adjustment are executed based on a window magnetic control air conditioner operation model and by adopting a multi-step prediction control method; for different types of special wards, regulating and optimizing corresponding thermal equipment by adopting a multi-step prediction control method under an MPC model prediction control framework according to thermal parameters required to be controlled by the current type of ward; for outpatient wards, outpatient air conditioner output operation is optimized by adopting a multi-step prediction control method based on a cooling load prediction model according to the flow density, the temperature and the indoor and outdoor temperature difference; according to the invention, monitoring and accurate prediction of the thermal condition in the ward are realized based on the model prediction control MPC method in combination with the window magnetic switch.

The invention belongs to the technical field of early warning systems in Internet platforms, and discloses an Internet of Things-based early warning system and early warning method for an intensive care unit, and the system carries out the abnormal recognition of physiological parameters through a pre-trained early warning model, fuses response task features, medical worker resource states and historical scores based on a response scheduling module, and carries out the early warning of the medical worker. A responder assignment strategy is optimized, and the matching accuracy is improved; furthermore, periodic data acquisition is realized through a response closed-loop recording module, an early warning response responsibility chain containing node and causal link information is constructed, and on the basis, high-risk nodes and bottleneck regions are identified, task allocation weights and path optimization rules are dynamically adjusted, and adaptive updating of scheduling logic is realized; and early warning re-response and rescheduling based on new logic are supported, and a feedback closed loop driven by data is formed, so that the timeliness and accuracy of early warning response are effectively improved, and the risk of intervention failure caused by response lag is reduced.

The invention relates to the technical field of gastrointestinal tracts, in particular to a gastrointestinal tract ultrasonic virtual simulation nursing teaching platform which comprises a function module and a hardware configuration module. The function module comprises an anatomy learning module, an ultrasonic simulation module, a case training module and an examination and evaluation module; the hardware configuration comprises a VR head-mounted display, an ultrasonic simulator and an operation panel. The system has the advantages of safe and risk-free training, standardized process learning, immersive scene experience and instant feedback evaluation, a high-fidelity gastrointestinal tract ultrasonic virtual simulation teaching platform for critically ill patients is constructed, a 3D virtual model is constructed by using a virtual simulation technology, knowledge is presented dynamically and visually, real situations such as ICU wards and the like are simulated through VR, and real-time feedback evaluation of the gastrointestinal tracts of the critically ill patients is realized. The learning initiative and the sense of participation are stimulated; an ICU environment is virtualized, an unlimited'trial-and-error 'practice space is provided, the equipment cost is reduced, potential safety hazards are avoided, and space-time and safety barriers are broken.

The invention relates to the technical field of medical informatization and intelligent scheduling, and particularly discloses a ward nursing process prediction and dynamic scheduling method and system.The ward nursing process prediction and dynamic scheduling method comprises the steps that firstly, a digital twinborn simulation environment synchronized with a physical ward in real time is established, and a nursing process is simulated through a multi-agent network; carrying out multi-time sequence deduction based on the environment, generating a prospective scheduling instruction set, and issuing and executing the prospective scheduling instruction set; calculating a prediction mismatch degree index by collecting actual execution data and comparing the actual execution data with a plurality of dimensions of an expected state; when the index exceeds a dynamic threshold value, a meta control layer is triggered to carry out self-adaptive adjustment on simulation environment parameters, and the self-adaptive adjustment comprises reconstruction of an agent behavior logic weight or switching of a scene deduction mode; and deducing and generating an updated instruction set again based on the adjusted environment to form closed-loop optimization.

The invention discloses a burn ward infection prevention and control intelligent early warning system based on big data, and relates to the field of big data analysis and monitoring. The system comprises a multi-modal data acquisition module for acquiring multi-modal data; the data preprocessing module is used for preprocessing the collected data; the feature construction module is used for extracting infection risk features according to the preprocessed data; the dynamic infection risk assessment module is used for constructing a patient infection risk assessment model by using a multi-modal feature learning model according to the infection risk features, and taking the infection risk features as input and the patient infection risk indexes as output; and the intelligent early warning module sets a multi-stage early warning mechanism according to the infection risk index. Through multi-modal data fusion, an intelligent early warning mechanism and dynamic infection risk assessment based on multi-modal feature learning, the infection risk is accurately predicted, early warning hierarchical management is optimized, the adaptability of the system to risk changes is improved, and efficient and prospective infection prevention and control decision support is provided for clinic.

The invention discloses an intensive care treatment scheme intelligent decision-making system based on data driving, and relates to the technical field of medical information, and the system comprises a data collection module which is used for obtaining multi-source clinical data of an intensive care unit in real time; and the decision generation module is connected with the data acquisition module and is used for generating a treatment scheme according to the clinical data. According to the intensive care treatment scheme intelligent decision-making system based on data driving, through four-dimensional snapshot construction of a dynamic decision-making chain and medical interpretable evolution relation labeling, continuous logic of an intensive care decision-making is guaranteed, data abnormal driving change and medical logic jump risks are effectively identified, medical errors caused by decision-making faults are reduced, and the decision-making efficiency is improved. The abstract decision-making process is converted into a concrete evidence chain capable of penetrating auditing, and the clinical responsibility traceability and the doctor-patient credibility are improved.

The invention belongs to the field of medical care, and particularly relates to an intelligent nursing inspection auto-induction recording device and method, and the device comprises a directional RFID card reader at the entrance of a ward, an NFC activation module at the side of a sickbed, an integrated pressure-sensitive foot pad, and an adjustable installation structure. Timing is started through ward entrance identification, nurse information is recorded through close-range induction of a sickbed, and nursing operation and passing behaviors are distinguished by detecting a two-foot standing mode through a pressure-sensitive foot pad. The mounting structure comprises an L-shaped mounting plate and a sliding inner plate and is matched with the width of a sickbed; the angle adjusting component fixes the position; the intelligent pressure-sensitive foot pads are integrated at the four corners of the sickbed base, the pressure distribution sensors detect the standing mode of the two feet, nurses and temporary passing persons are distinguished, false inspection in the mode of waving for walking is avoided, substantive nursing operation of the nurses is accurately verified, and it is ensured that patients are truly and effectively nursed.

The invention provides an intelligent ward nursing information acquisition and processing method and system. The method comprises the following steps: acquiring mattress pressure data to construct a pressure grey-scale map sequence, calculating a pressure barycentric coordinate difference value between continuous frames to generate a patient movement vector set and a body barycentric movement path, extracting a continuous pixel region exceeding a pressure threshold in a single-frame image as a high-pressure region, and counting the duration of the high-pressure region; calculating gradient distribution by using the pressure data, decomposing the gradient distribution into direction and intensity data, and generating a pressure topological graph through dual-channel fusion; integrating the bending characteristics of the moving path, the time distribution characteristics of the duration of the high-pressure area and the spatial gradient characteristics of the pressure topological graph, and constructing a body position dynamic space-time diagram with the central point of the high-pressure area as a node and the moving distance and angle of the node as edges; when it is recognized that the high-risk static body position features exist in the image, a turning-over reminding process is triggered, and a control instruction is generated. According to the invention, accurate recognition and automatic nursing response of high-risk body positions are realized.

The invention relates to the technical field of medical informatization, in particular to a ward distress call method and system based on intelligent medical treatment, and the method comprises the steps: obtaining patient state data collected by a plurality of sensors in a ward; receiving a ward help-calling instruction; constructing a multi-modal data fusion matrix based on the patient state data; executing random matrix transformation and feature extraction according to the multi-modal data fusion matrix; based on the extracted features, topological feature mapping and anomaly detection are carried out; executing group theory base risk assessment according to the topological features; calculating a chaotic power system early warning index based on a group theory risk assessment result; determining a call-for-help judgment result according to the early warning index; a corresponding response strategy is executed based on the call-for-help judgment result, the system can comprehensively grasp the state of the patient through the multi-modal data fusion technology, and the evaluation accuracy is greatly improved. And secondly, the robustness of the system is enhanced by random matrix transformation, so that the system can cope with a complex and changeable medical environment.

The device comprises an assembly type bottom plate, an assembly rail is fixedly mounted in the middle of the top of the assembly type bottom plate, a mounting bottom plate is slidably connected to the interior of the assembly rail, and a pipeline buffering mechanism of the device compresses a torsional spring through a gear transmission system to store energy; during installation, after the oxygen hose sequentially penetrates through all the components, the torsion spring resets to drive the winding shaft to automatically wind the hose, random falling and winding of the hose are avoided, abrasion is reduced, the service life is prolonged, a ward is kept clean and tidy, and during use, when the hose is pulled, the torsion spring is triggered for buffering, a connector is prevented from loosening and falling off, and continuous oxygen supply is guaranteed. The medical risk is reduced; meanwhile, redundant hoses are automatically wound, the medical care arrangement burden is relieved, the hoses can be drawn out by opening an access door and twisting a winding shaft during overhauling, a large number of components do not need to be disassembled, the overhauling time is shortened, and the sickroom shutdown time is shortened.

The invention relates to the technical field of ICU environment regulation and control, and discloses a negative pressure self-adaptive adjusting system for an ICU ward. The system comprises an area pressure monitoring module, a pressure gradient analysis module, an abnormal fluctuation judgment module, a negative pressure demand generation module, a fan parameter calculation module and an execution control module. The regional pressure monitoring module obtains pressure values of ICU wards, buffer rooms and corridors in real time; the pressure gradient analysis module calculates a pressure difference value of adjacent areas, judges a gradient state and generates an abnormal signal; the abnormal fluctuation judgment module generates an abnormal level signal accordingly; the negative pressure demand generation module generates a negative pressure maintaining or enhancing instruction according to the grade signal; the fan parameter calculation module is combined with the real-time pressure value of the ward to calculate the rotating speed of a target fan and the opening value of an air valve; and the execution control module controls an exhaust system adjusting mechanism according to the target parameters. The system can dynamically regulate and control the negative pressure, guarantees the stability of the negative pressure in a ward, and reduces the diffusion risk of harmful microorganisms.

The invention discloses an AI-based online data analysis method for medical molecular sieve oxygen production equipment. The method comprises the following steps: step 1, collecting multi-source operation monitoring data of the medical molecular sieve oxygen production equipment; 2, constructing a task prompt vector to obtain a normalized feature vector; 3, inputting the normalized feature vector into an improved TabPFN model to obtain a hidden space representation, and generating an enhanced representation in combination with a retrieval result of the prototype memory bank; 4, performing drift detection on the enhanced representation; 5, inputting the robust representation into a multi-task decoder; and 6, executing event label judgment, and outputting online analysis result data. According to the invention, real-time analysis of multi-source data and advanced identification of abnormal trends are realized, and the method is suitable for equipment state monitoring and risk early warning in hospital wards, rehabilitation centers and long-term oxygen supply scenes.

The invention relates to a ward noise monitoring reminding device and method based on noise self-defined monitoring, ward noise is collected through a noise sensing monitoring module and a multi-array arranged in a ward and sent to a nurse station, noise monitoring of each ward is carried out through a nurse station edge processor, the edge processor adopts a weighted equivalent continuous sound level algorithm, and the noise of each ward is monitored through the weighted equivalent continuous sound level algorithm. And generating a noise value in a corresponding ward, carrying out threshold judgment, converting a plurality of sampling values into an equivalent continuous sound level and an alarm triggering threshold, and triggering when 65 decibels are exceeded in the daytime. Meanwhile, temperature compensation is combined, the accuracy of noise processing is optimized, a dynamic weighting factor adapts to day and night physiological difference, temperature compensation is introduced to improve environmental adaptability, median filtering is adopted to eliminate instantaneous interference, and high-reliability noise supervision in a medical scene is achieved. The system can replace nurses to manually monitor the noise in the ward, can save nursing resources, and achieves automatic noise reminding.

The invention discloses an infection propagation path sensing method and system based on a time-space relationship. The method comprises the following steps: acquiring and cleaning hospitalization, nursing and inspection data of patients, and constructing a time sequence file of each patient; defining the contact type of the same ward and the medical care medium based on the time sequence file, setting an infection period and an observation window to identify a potential infection transmission path, and forming a contact event table; sorting the time information of the positive patients and the contactors thereof based on the contact event table, and dynamically generating a directed propagation chain; performing cyclic path elimination and credibility grading on the directed propagation chain to obtain a corrected propagation network; and screening out cases without upstream infection sources according to the corrected propagation network, and determining infection sources and properties thereof in combination with hospitalization time and historical hospitalization records. Through the implementation of the method, the adaptive limitation of the prior art to a complex propagation scene can be solved, rapid reconstruction and accurate attribution of a hospital infection propagation chain are realized, and intelligent decision support is provided for reducing the incidence rate of hospital infection.

The invention discloses an anesthesia awakening state prediction method and system, and the method comprises the steps: firstly constructing a standard prediction model, then obtaining an actual physiological parameter set, generating an actual recovery state parameter set according to the actual physiological parameter set, and generating an initial recovery curve in the standard prediction model; generating a minimum verification circle, and performing primary correction on the initial recovery curve through the minimum verification circle to obtain a corrected recovery curve; according to the correction recovery curve and a standard prediction model, initial prediction waking time is calculated, according to the actual recovery state parameter set and the correction recovery curve, a composite deviation rate is generated, according to the composite deviation rate, secondary correction is carried out on the initial prediction waking time, and prediction waking time is obtained. According to the method, accurate positioning of the wake-up point is realized by correcting the intersection point of the recovery curve and the standard prediction model, and early warning is performed in time, so that the work pressure of medical staff is reduced, the orderly development of medical work can be ensured, and the nursing quality and the operation efficiency of a wake-up ward can be improved.

The invention discloses a nurse dynamic man-hour optimization system based on real-time load sensing, and relates to the technical field of medical information management, and the system comprises a real-time load sensing module which collects and quantifies the real-time nursing workload of a ward unit; the nurse state monitoring module is used for monitoring and evaluating the real-time working state and fatigue level of the nurse on duty; the dynamic optimization decision engine is used for receiving the load quantification data and the nurse state data and generating a real-time nurse task redistribution or man-hour adjustment instruction; and the scheduling execution and feedback module is used for receiving and executing the adjustment instruction and feeding back effect data after instruction execution to the dynamic optimization decision engine. The nursing load and the nurse fatigue state are quantified in real time, the real-time adjustment instruction is generated based on the multi-target dynamic optimization model, accurate and dynamic configuration of nursing human resources is achieved, the scheduling efficiency and balance are improved, meanwhile, the nurse occupational risk is effectively reduced, and closed-loop optimization is formed.

The invention discloses an intelligent ward patrol robot, and relates to the technical field of intelligent medical robots, the intelligent ward patrol robot comprises a robot body, and the robot body is internally provided with a multi-mode sensing unit, an edge calculation unit, a federal learning unit and a decision execution unit. According to the multi-mode sensing unit, a polarization type 3D camera, a 60 GHz millimeter wave radar and an environment sensor array are integrated in a robot body. By installing the multi-mode sensing unit, the non-contact emotion recognition function is achieved, in combination with contact detection of the intelligent bracelet on the signs of the patient, the bottleneck of non-contact emotion and sign cooperative monitoring is broken through, the emotion sensing ability of the patient is improved, and the medical service quality is improved.

The invention discloses an ophthalmology AI intelligent ward control system and method based on voice interaction. The system comprises an anti-noise voice acquisition unit, a multi-modal identity verification unit, a specialized semantic analysis unit, a medical rule engine unit, a safety protection unit, a multi-modal feedback unit and a dynamic optimization unit. Medical equipment noise is filtered out through a microphone array and an adaptive beam forming technology, and an instruction is analyzed in combination with a dialect recognition engine; executing triple identity verification, and performing forced superposition authentication on the cognitive impairment patient; calling an ophthalmology knowledge base to identify the risk keyword and generate a structured instruction; dynamically regulating and controlling the environment according to the eye disease type, and intercepting violation settlement according to a medical insurance rule; precise equipment voice control is forbidden, and when an emergency keyword is triggered, an OCT image is pushed preferentially and is directly connected with a physician; a big-character highlight interface and tactile pulse feedback are provided for the visually impaired patient, and data are synchronized to the HIS system in real time; and the dynamic optimization unit continuously updates the knowledge base and the medical insurance model. According to the invention, the instruction identification precision, the medical safety and the service suitability of the ophthalmology ward are obviously improved.

The invention discloses a ward health education robot, and the robot is internally provided with a health education platform, and the health education platform comprises a personalized education module which is responsible for generating a personalized guidance scheme according to the personal information of a patient; the intelligent question and answer module is responsible for intelligently recommending related health knowledge and matters needing attention; the medicine use and treatment nursing management module is responsible for recording the medicine use condition of the patient and generating a schedule about the patient; the intelligent navigation and hospital guide module is responsible for realizing autonomous navigation of the patient from a ward to a designated place; the physical sign monitoring and analyzing module is responsible for collecting vital sign data of a patient and generating a health report and early warning information; according to the system, the emotion recognition and interaction optimization module recognizes the emotional state of the patient and adjusts the communication mode and content according to the emotional state, the anxiety and fear emotion of the patient can be relieved, and the confidence of the patient in fighting the disease is enhanced.

The invention relates to the field of intelligent medical aids, and discloses an all-terrain intelligent wheelchair system with unmanned holding and lifting capability. Core innovations comprise: 1) an autonomous transfer system based on double-mechanical-arm cooperative control and six-foot dynamic support realizes safe transfer of a human body under a load of 80kg; (2) the intelligent cabin integrates a 64-partition pneumatic bag array and 256-point piezoresistive sensing, dynamic pressure regulation is realized through an LSTM algorithm, and the bedsore occurrence rate is greatly reduced in combination with a phase change gel material; and 3) the multi-mode control interface is compatible with ISO 7176-23 physical control and gesture recognition, and can be widely applied to places such as unmanned auxiliary holding and lifting transfer of wheelchair to personnel, hospital ward transfer, long-term bedridden nursing, outdoor emergency rescue and the like.