1140 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 invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

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 biomedicine, in particular to an internet-of-things-based digitized management method for graded nursing of an intensive care unit. The method comprises the following steps that heterogeneous data of a patient are collected in real time through nursing monitoring equipment, and a principle physiological data flow is obtained; performing comprehensive physiological index calculation on the principle physiological data flow to obtain a comprehensive physiological index; carrying out individualized physiological index prediction according to the comprehensive physiological index, and carrying out patient condition deterioration probability prediction to obtain condition deterioration probability data; performing nursing level demand prediction according to the individualized physiological index prediction result to obtain nursing level demand probability data; and performing illness state trend prediction vector generation on the illness state deterioration probability data and the nursing level demand probability data to obtain an illness state trend prediction vector. According to the invention, through more accurate illness state trend prediction and graded early warning, medical workers are better assisted to carry out graded nursing on patients, and the management level of an intensive care unit is 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 relates to the technical field of intelligent shared wheelchairs, in particular to an intelligent shared wheelchair control system for accompanying diagnosis, and the system comprises a reservation module which obtains a user reservation request and completes the reservation of an intelligent shared wheelchair; the demand information acquisition module acquires a target department ward of a user; the indoor navigation and state monitoring module is used for acquiring an indoor traffic map of a hospital and monitoring the treatment state of a ward of a target department in real time; the accompanying diagnosis path optimization module takes the accompanying diagnosis navigation path as an optimization target, creates a target department ward accompanying diagnosis knowledge graph based on a hospital indoor traffic map, and creates an accompanying diagnosis navigation path optimization model based on a grey wolf optimization algorithm and the target department ward accompanying diagnosis knowledge graph; optimizing the accompanying diagnosis navigation path through the accompanying diagnosis navigation path optimization model to obtain an optimal accompanying diagnosis navigation path; the intelligent navigation module controls the intelligent shared wheelchair to operate according to the optimal diagnosis accompanying navigation path. The utilization rate of the intelligent shared wheelchair can be improved, and meanwhile the user experience is improved.

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 discloses an emergency ICU ward monitoring data processing system based on big data, and relates to the technical field of big data processing, the monitoring data processing system comprises a data collection module, an abnormity marking module, a real-time data analysis module, an abnormity evaluation module and an early warning information generation module, and the modules are in electric signal connection. According to the emergency ICU ward monitoring data processing system based on big data, by analyzing vital sign data of a patient and equipment operation data, once abnormal fluctuation is found, early warning information is immediately generated and sent to medical staff, it can be ensured that the medical staff know the abnormal condition of the patient in the first time, so that processing measures are rapidly taken, and the medical staff are prevented from being damaged. Compared with a traditional manual monitoring mode, the system has the advantages that the response time is greatly shortened, the first-aid efficiency is improved, and meanwhile, the system can also provide detailed monitoring data and analysis reports to help medical staff to judge the illness state more accurately and formulate a more accurate treatment scheme.

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 discloses a postoperative illness condition monitoring device for a patient in the anorectal department of traditional Chinese medicine, and relates to the technical field of postoperative nursing, the postoperative illness condition monitoring device comprises a drug administration monitoring device and an illness condition monitoring system, and the drug administration monitoring device comprises a drug administration barrel and a retention part; the illness state monitoring system comprises a sensor integration unit, an abnormity analysis unit, an alarm prompting unit and a drug administration suggestion unit; various post-operation data information of a patient is collected through the sensor integration unit, abnormity reminding signals are generated after abnormity judgment is carried out on the different data information, the patient and family members of the patient are reminded through the prompter arranged in a ward, meanwhile, reminding is sent to the remote medical terminal in a nurse station through the remote transmission module, and the patient is reminded through the remote transmission module. And the corresponding nursing suggestions are output to the remote medical terminal in the nurse station or the mobile terminal of the patient, so that the effect of monitoring the postoperative illness state in real time is achieved, negligence of manual observation is avoided, the corresponding suggestions can be quickly given, and effective measures are provided for medical staff and the patient.

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.

A time-based wireless pairing operation between a medical device, such as a patient bed, and a wall module in a patient room is initiated in response to a power plug of the medical device being plugged into a power receptacle carried by the wall module. Times determined by timers of the medical device and the wall module are compared by the wall module or by the medical device so that wireless pairing occurs only with the medical device that was plugged into the wall module. Different types of plug detectors used in the wall module to detect connection of the power plug include optical detectors, mechanical switches, and current sensors.

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 belongs to the field of intelligent medical care, and discloses a method and system for managing infusion based on artificial intelligence. According to the invention, the infusion tube data, the infusion tube monitoring image data, the patient information feature data and the infusion reminding level are utilized to construct the multi-mode infusion management model; the constructed multi-mode infusion management model generates a current infusion reminding level for medical personnel in the infusion process of a patient according to real-time infusion tube data, real-time infusion tube monitoring image data and real-time patient information feature data which are received by a medical station and collected by each piece of medical equipment; the current infusion reminding level is sent to a screen of a nurse station and handheld terminal equipment, medical staff are reminded to go to a corresponding ward to withdraw a needle in time, transmission of infusion reminding level information is stopped, and accurate infusion nursing is achieved. According to the intelligent infusion monitoring system, infusion of infusion patients in all wards can be monitored in real time by medical staff, the intelligent infusion monitoring effect is achieved, the work intensity of ward patrol of the medical staff is reduced, and the doctor-patient contradiction 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 embodiment of the invention provides a nurse station ward information collection method and system based on a wireless network. A flexible electronic skin network is integrated in a patient mattress, pressure and temperature data are captured through distributed piezoresistive units, a multi-modal thermodynamic diagram is generated, and data synchronization is achieved through a wireless ad hoc network protocol. And driving an air cushion bed actuator to perform gradient compensation according to a matching result of the pressure gradient track and the physiological parameters, transmitting a compressed hierarchical data packet to a nurse station gateway by adopting a remote wide area network protocol, reconstructing a three-dimensional pressure model and generating an early warning instruction. According to the technical scheme provided by the embodiment of the invention, the occurrence probability of health problems such as bedsore caused by long-time bed lying is remarkably reduced, and the nursing quality of the patient is improved.

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 embodiment of the invention provides a method and system for providing ward round service. The method comprises the following steps: acquiring perception information, wherein the perception information is acquired by perception equipment in a ward where a patient is located during the ward round period of a doctor; during the ward round period, based on the perception information, detecting an instruction sent by a doctor for calling patient data; responding to the instruction, and controlling terminal equipment in ward equipment to display patient data by utilizing an augmented reality technology; and after the ward-round period, based on the perception information, generating a ward-round record and displaying the ward-round record to the doctor for confirmation.

The invention discloses a spasmodic torticollis quantitative analysis and evaluation method and system. The method comprises the following steps: step S1, collecting demographic information of a patient with primary cervical myodynia in an outpatient service and a ward; s2, two clinicians carry out clinical evaluation on the spasmodic torticollis of the patient; s3, recording the video according to the specification; s4, designing features through a face key point recognition algorithm, carrying out feature extraction, recognizing key anatomical points, and constructing auxiliary lines; s5, calculating angles and time through visual identification, and defining various scale indexes and scoring rules; and S6, outputting a score and a quantitative score result, and comparing the score and the score with a clinical expert score for verification. By means of the method, clinical cardiotoxin injection doctors can be assisted in accurately judging motion types, accurately positioning muscles and recommending specific injection doses, and therefore the disease curative effect and the clinical efficiency of the doctors 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.

A disposable basin for extremity procedures, irrigation and lavage in the operating room, treatment clinic or ward allows the extremity to rest comfortably, safely, and securely inside the basin, allowing the surgeon, physician, clinician, and or provider ease of access within a contained protective barrier. Contaminated fluid safely and efficiently drains into an integrated suction trough via the intrinsic design of the basin, resulting in decreased exposure to pathogens, containment of contamination, ease of execution and disposal, and cost savings.

The invention discloses a humidifying structure, an air conditioner applying the humidifying structure and an air conditioner control method, and belongs to the technical field of air conditioner humidifying, the humidifying structure comprises a wet film humidifier and a humidifying circulating mechanism arranged on the lower portion of the wet film humidifier, the humidifying circulating mechanism comprises a water storage tank, a top cover, a water pump and a water conveying pipe, and the interior of the water storage tank is connected with a blocking plate; the top of the water treatment tank body is provided with a downward concave-convex cambered-surface concave part, the center of the cambered-surface concave part is provided with a large notch part, and the water treatment tank body, the inner wall surface of the water outlet and the surface of the cambered-surface concave part are each coated with a nano-silver coating. Before the humidification structure releases water vapor for humidification into a room, water in the humidification structure can be subjected to surrounding type sterilization treatment, so that the number of bacteria released into a newborn ward of a hospital can be greatly reduced in the humidification process of the air conditioner, the sterile requirement of the newborn ward is met, and the health and safety of newborns are ensured.