29 results about "Body Temperature Changes" patented technology

The invention relates to the technical field of abnormal body temperature prediction, in particular to a newborn abnormal body temperature prediction method and device and a storage medium. The method comprises the following steps: collecting the body temperature and physiological signals of a newborn in real time, and carrying out preprocessing; based on fast Fourier transform, time sequence features reflecting body temperature changes in the body temperature and physiological signals are extracted, feature vectors are generated, the body temperature and physiological signals are processed through phase coupling analysis and a frequency domain mask inhibition mechanism, and a low-frequency noise interference interval caused by neonatal breathing irregularity and skin conduction disturbance is dynamically recognized; on the basis of the feature vectors, the body temperature abnormal risk probability is predicted in real time through the long-short-term memory network, the gating weight of the long-short-term memory network is dynamically regulated and controlled through two factors of the body temperature change rate and the physiological state label, and a feature fusion mechanism is optimized. According to the invention, through multi-modal physiological signal fusion and a dynamic feature weighting mechanism, the accuracy and real-time performance of newborn body temperature anomaly prediction are significantly improved.

The application relates to the technical field of intelligent sensors and big data processing, in particular to a smart watch multi-source data fusion method and system based on environment sensing. The method comprises the following steps: obtaining an original data set through a smart watch; generating a data frame sequence according to a preprocessing result of the original data set, and generating a fusion feature vector based on the data frame sequence; inputting each fusion feature vector into a preset health mode classification model, and outputting an initial recognition result; generating a health risk level and an environmental influence factor according to the initial recognition result, and outputting user feedback information; extracting features such as a heart rate variation interval, a body temperature change rate and an air quality fluctuation cycle, constructing a fusion feature vector, combining a pre-trained health mode classification system, analyzing a linkage relationship between environmental changes and physiological reactions, and accurately identifying a stress mode caused by environmental pollution; and finally outputting personalized health prompt information based on a risk score mechanism and a historical mode library.

The utility model relates to a pet accompanying robot. The pet accompanying robot comprises a robot body and an elastic playing piece. The robot body comprises a moving main body, a main controller capable of being in communication connection with a user terminal and a temperature detector used for detecting the body temperature of a pet, the main controller and the temperature detector are both arranged on the moving main body, and the temperature detector is in communication connection with the main controller; one end of the elastic playing piece is connected with the moving main body, and the other end of the elastic playing piece protrudes out of the moving main body. The temperature detector, the main controller and the elastic playing piece are arranged on the mobile main body, and the body temperature change of the pet can be known in time through the cooperation of the temperature detector and the main controller, so that the health state of the pet can be monitored conveniently; the moving main body moves to drive the elastic playing piece to move, so that the elastic playing piece can interact with the pet and play with the pet, and the use requirements of pet farmers can be met.

The invention provides a thermal analysis method and system for cross-scale body temperature mode fusion. The method comprises the steps that S1, continuous body temperature collection is conducted at a preset time interval to obtain body temperature data at least from a first time scale, a second time scale and a third time scale; s2, performing cross-scale feature extraction on the body temperature data to obtain a first feature, a second feature and a third feature; wherein the first feature corresponds to the first time scale, the second feature corresponds to the second time scale, and the third feature corresponds to the third time scale; s3, respectively establishing a first modeling unit, a second modeling unit and a third modeling unit according to the first feature, the second feature and the third feature; s4, modeling is carried out on the modeling unit, and a modeling result is fused to obtain a body temperature mode fusion result; and S5, classifying the body temperature change modes according to the body temperature mode fusion result.

The present application relates to the technical field of heating tube control, and specifically discloses a kind of integrated multi-channel medical heating blanket infusion heating tube control method and system, comprising the following steps: step S1: through historical data, the collaborative relationship of heating blanket and infusion heating tube is established, and the influence of both on patient temperature is quantified;Step S2: generate normal body temperature change curve and calculate standard change rate, provide reference for real-time adjustment;Step S3: according to the collaborative relationship and the standard change rate, the temperature of heating blanket and infusion heating tube is adjusted in real time, to ensure that patient temperature reaches target value smoothly and safely;The present application can meet the needs of multiple places at the same time for multi-channel heating components, greatly reduce the cost of equipment, and through data driving and dynamic adjustment, precise and efficient body temperature regulation is realized.

The invention provides a vehicle-mounted air conditioner control method and device and a vehicle. The method comprises the steps that the body temperature change gradient and the palm sweat secretion rate of a current vehicle driver are obtained; determining a current physiological state classification result of the driver according to the palm sweat secretion rate and the body temperature change gradient; searching a pre-distribution weight corresponding to the body temperature change gradient and the palm sweat secretion rate according to the current physiological state classification result; performing weighted summation on the body temperature change gradient and the palm sweat secretion rate through pre-distribution weight to obtain a comprehensive regulation factor; the target output power and the target output temperature of the vehicle-mounted air conditioner are determined through the comprehensive regulation and control factors; and when the current physiological state classification result meets the preset condition, the vehicle-mounted air conditioner is controlled to operate according to the target output power and the target output temperature, and therefore the air conditioner starting time is accurately matched with the actual requirement.

The utility model belongs to the technical field of medical supplies, and particularly discloses a constraint robe for delirium patients. The robe comprises a robe body, a lining is arranged in the robe body, fixing assemblies are arranged on the two sides of the robe body and the two sides of the lining, the lining is located in the robe body, the two sides of the robe body are connected with gloves, and restraining assemblies are arranged in the two gloves. The situation that a patient takes off a pulse oxygen clamp in gloves and digs out the net face end is avoided, the nursing problem after a delirium patient is covered with a quilt is solved, the observation blind area of a nurse is reduced, the patient is prevented from rubbing off a pipeline in the quilt, stimulation caused by the fact that the patient lifts the quilt due to body temperature changes after the operation is avoided, and therefore the adverse effect on postoperative recovery is reduced. Meanwhile, nurses can conveniently observe the condition of the patient, unnecessary stimulation of the patient is reduced, more comprehensive and effective constraint and protection are provided for the delirium patient, and the nursing quality and the rehabilitation effect of the patient are improved.

The present application relates to the technical field of body temperature abnormality prediction, in particular to a neonatal body temperature abnormality prediction method, device and storage medium. It comprises the following steps: real-time acquisition of the body temperature and physiological signals of the neonate and pretreatment; based on fast Fourier transform, the time sequence characteristics reflecting the body temperature change in the body temperature and physiological signals are extracted to generate a characteristic vector, and the body temperature and physiological signals are processed through phase coupling analysis and frequency domain mask suppression mechanism to dynamically identify the low-frequency noise interference interval caused by irregular breathing and skin conductive disturbance of the neonate; based on the characteristic vector, the long short-term memory network is used to real-time predict the body temperature abnormality risk probability, and the gating weight of the long short-term memory network is dynamically regulated through the body temperature change rate and physiological state label double factors to optimize the feature fusion mechanism. Through the multi-modal physiological signal fusion and dynamic feature weighting mechanism, the accuracy and real-time performance of the neonatal body temperature abnormality prediction are significantly improved.

A system is provided.SOLUTION: A system comprising means for receiving data collected from sensors installed in a farm, means for pre-processing the received sensor data into a format suitable for analysis, means for artificial intelligence to analyze abnormal behavior and temperature changes using the pre-processed data and score risk, means for generating and notifying alerts if the risk score exceeds a certain threshold, means for suggesting prevention and countermeasures based on the risk score, and means for collecting feedback data from users and updating the artificial intelligence's models.SELECTED DRAWING: Figure 1

The application belongs to the field of medical equipment, and provides a rewarming rate adjusting method of a mild hypothermia therapeutic instrument, which comprises the following steps: acquiring a current body temperature change rate, a target body temperature change rate and a current body temperature, and calculating a limiting water temperature according to the current body temperature change rate, the target body temperature change rate and the current body temperature; acquiring a target body temperature, and calculating a target water temperature according to the target body temperature and the current body temperature; acquiring a current water temperature, and calculating a water temperature adjusting duty cycle according to the current water temperature and the target water temperature or the limiting water temperature; and adjusting the temperature of the mild hypothermia therapeutic instrument according to the water temperature adjusting duty cycle. The application further provides a rewarming rate adjusting device, a rewarming rate adjusting system and a storage medium. In the rewarming rate adjusting method, the water temperature adjusting duty cycle is adjusted by the limiting water temperature or the target water temperature, and then the temperature of the mild hypothermia therapeutic instrument is adjusted, so that the body temperature rising rate of the mild hypothermia therapeutic instrument can be controlled.

The invention discloses a body temperature adjusting system based on digital twinning and an adjusting method thereof, and relates to the technical field of body temperature management. According to the system, multi-dimensional physiological data of a patient, operation related data, operating room environment data and heat preservation equipment operation state data are obtained in real time through a data acquisition module, a patient body temperature digital twin model is constructed, and the model is dynamically updated and calibrated in the operation process; the body temperature change trend of the patient is predicted based on the digital twinborn model, an individualized body temperature adjusting strategy is generated before the body temperature deviates from a preset safety threshold value, a plurality of devices such as body surface heating, infusion heating, flushing fluid heating and environment adjusting are coordinated through the cooperative control module for cooperative execution, and accurate regulation and control of the body temperature are achieved. Real-time mapping, predictive adjustment and closed-loop optimization of the body temperature of the patient can be achieved, the risk of low body temperature and excessive heat preservation in the perioperative period is effectively reduced, the safety and the intelligent level of body temperature management are improved, and good clinical application value is achieved.

The invention discloses a multi-part sensing and machine vision fused vital sign monitoring method for a large-area burn patient, relates to the technical field of medical monitoring, and is used for solving the problems of monitoring blind areas and shock early warning lag in a large-area burn scene. The method comprises the following steps: firstly, combining waveform statistical characteristics and body temperature change, and accurately dividing a hypoperfusion channel and a fall-off channel; then, eliminating eschar dressing interference based on a three-dimensional mask and chromaticity features, and extracting high-signal-to-noise-ratio non-contact signs; then, a self-adaptive weighting matrix is constructed according to the channel state, and the visual weight is dynamically improved to compensate a contact blind area; finally, by analyzing the cross covariance of the heart rate and the perfusion index, haemodynamic discrete features are captured, shock compensation period advanced early warning is achieved, and continuity and accuracy of monitoring data are guaranteed.

The present invention provides an information processing system comprising: means for receiving data collected from a sensor provided in a farm; the device is used for preprocessing the received sensor data and converting the sensor data into a data format suitable for analysis; the device is used for analyzing abnormal behaviors and body temperature changes by adopting an artificial intelligence model based on the preprocessed data and carrying out risk scoring; means for generating and notifying an alarm when the risk score exceeds a predetermined threshold; means for proposing prevention and countermeasure suggestions based on the risk score; and means for collecting feedback data from the user and updating the artificial intelligence model.

The application relates to the technical field of data processing, and particularly discloses a control method and system based on medical temperature management, which comprises the following steps: generating a coordinate point based on the body temperature of a patient in a preset monitoring period, and drawing a reference curve based on the coordinate point; drawing a comparison curve based on the body temperature change of the patient in the preset monitoring period; judging whether an abnormality exists in a body temperature management module based on the reference curve and the comparison curve; if yes, performing a control step in the next monitoring period to correct a duty ratio D1 set based on a PID algorithm; and if not, continuously judging in the next monitoring period. The application avoids the influence of aging while automatically controlling the temperature in a closed loop.

The invention relates to the technical field of live pig breeding, in particular to a live pig breeding health monitoring method, device and equipment and a medium. By integrating three different types of sensing data, namely thermal imaging data, millimeter wave radar data and audio data, multi-dimensional and three-dimensional monitoring of the health state of the live pig is realized. The thermal imaging data can accurately capture the body temperature change of the live pig, the millimeter wave radar data can obtain vital sign parameters such as respiration and heartbeat of the live pig in real time, and the audio data can sense the potential abnormal state of the live pig through sound analysis. According to the multi-source data fusion mode, the health condition of the live pigs can be reflected more comprehensively and accurately, and the accuracy of health monitoring of the live pigs is improved.

The invention discloses a sheep epidemic disease control method and system based on thermal imaging, particularly relates to the technical field of body temperature monitoring in biomedical engineering, and aims to solve the problems that in the prior art, target loss and identity recognition interruption are likely to occur in sheep body temperature monitoring, and body temperature changes cannot be continuously tracked. The method comprises the following steps: collecting continuous thermal imaging sequence frames to extract body surface temperature distribution data, carrying out identity matching and tracking based on the body surface temperature distribution data in combination with behavior characteristics and spatial characteristics, constructing a time sequence body surface temperature change curve, identifying standard resting posture fragments, establishing a reference resting body temperature, and carrying out standardization processing. And comparing the standardized curve with an epidemic disease characteristic database, analyzing and identifying a temperature anomaly trend, introducing a fuzzy logic decision mechanism for grading evaluation, and judging epidemic disease risks in combination with historical data.

1. The name of the design product: pharmacological pyrogen instrument. 2. The use of the design product: the design product detects the pyrogen in the injection drug by observing the body temperature change of the house rabbit. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective drawing.

The invention discloses a traditional Chinese medicine cold-heat property evaluation method based on small animal metabolism analysis and body temperature monitoring, and belongs to the technical field of traditional Chinese medicine modernization evaluation. A C57BL / 6 mouse is used as a model, a small animal energy metabolism monitoring system is used for continuously collecting metabolic indexes of the mouse, and the metabolic indexes are compared with a blank control group according to the metabolic indexes, so that the cold-heat property of the traditional Chinese medicine is objectively judged. The influence of the processing technology on the medicine property is further evaluated by comparing the metabolic indexes of the same crude traditional Chinese medicine product and the processed product. In order to improve the discrimination reliability, multi-modal data fusion discrimination can be carried out in combination with a body temperature change index measured on a cold / heat syndrome model animal. The invention provides a rapid, quantitative and repeatable traditional Chinese medicine property evaluation scheme.

The application discloses a body temperature measurement method and device, electronic equipment and a readable storage medium, and belongs to the technical field of vital sign measurement. The method comprises the following steps: acquiring a target parameter at a first time point, wherein the target parameter comprises a component temperature value of a component in the electronic equipment, a heart rate difference value of a measurement object, an ambient temperature value of an environment in which the electronic equipment is located, a body surface temperature value of the measurement object, and a core body temperature value of the measurement object; determining a core body temperature calculation coefficient in a target model based on the target model and the target parameter, and calculating a target core body temperature change value based on the core body temperature calculation coefficient and the target parameter, wherein the target core body temperature change value is a core body temperature change value of the measurement object from the first time point to a second time point, and the second time point is a time point after the first time point; and calculating the core body temperature value of the measurement object at the second time point based on the core body temperature value of the measurement object at the first time point and the target core body temperature change value.

The invention belongs to the technical field of intelligent wearable equipment, and particularly relates to a missing early-warning intelligent glasses system, which comprises a main control chip, which is used as the core of the system and is responsible for coordinating the work of each module, processing audio and video data, body temperature data and position information and controlling the transmission of the data; the audio and video acquisition assembly and the separation induction device are used for monitoring the environment and the state of a wearer in real time and ensuring that important information is recorded at a critical moment; the heat-sensitive sensor is used for monitoring the body temperature change of the wearer and providing additional safety guarantee; the positioning module and the data transmission module are used for acquiring position information in real time and transmitting the position information and audio and video data to a specified terminal together, so that the timeliness and the integrity of the information are ensured; a Bluetooth module; a data storage module; a power supply module; and the beacon module sends a beacon with extremely low power consumption after a return link is interrupted, so that subsequent rescue is guaranteed. According to the invention, the ability to guarantee the safety of students is significantly improved.

The invention relates to the technical field of artificial airway nursing, in particular to a critical patient artificial airway humidification intelligent regulation and control system which comprises a body temperature and airway collaborative monitoring module used for synchronously collecting the real-time body temperature of a patient, body temperature change data in a continuous time period and real-time humidity data in an airway. According to the system, synchronous acquisition of body temperature and airway humidity data is achieved through the body temperature and airway collaborative monitoring module, the body temperature change state is accurately pre-judged in combination with the body temperature trend pre-judgment sub-module, and the airway humidity loss calculation sub-module dynamically calculates the moisture loss rate and the needed humidification amount according to the body temperature trend and moisture evaporation association rule; a mucous membrane state indirect feedback sub-module monitors the actual state of an airway to calibrate the regulation and control direction, and an intelligent regulation and control center module is linked with a dynamic humidification execution module and a linkage heating module to output a cooperation instruction and is matched with closed-loop control to continuously finely adjust parameters; the problem that humidification and temperature are not matched due to the fact that the body temperature trend is not related to airway humidity loss and regulation lag is solved.

The invention provides an infant body temperature real-time monitoring and early warning method and system, and relates to the technical field of infant guardianship, and the method comprises the steps: continuously collecting body surface temperature data and environment temperature data of an infant in real time, and preprocessing the body surface temperature data to obtain a preprocessed body surface temperature sequence; determining a dynamic body temperature baseline of the individual based on the body surface temperature sequence, and dynamically compensating the early warning threshold by combining the real-time environment temperature data to obtain an environment self-adaptive dynamic early warning threshold; based on the body surface temperature sequence and the dynamic early warning threshold, calculating a comprehensive early warning index reflecting the multi-dimensional characteristics of the body temperature change; and triggering corresponding graded early warning according to the value of the comprehensive early warning index, and dynamically adjusting monitoring parameters based on an early warning result. According to the invention, the problems of inaccurate early warning, untimely early warning and poor adaptability in the prior art can be integrally solved.

The invention discloses a nursing risk early warning method and system based on artificial intelligence, and relates to the technical field of nursing behavior feature recognition early warning. The upper limb wearing device and the lower limb wearing device are respectively used for calibrating pulse frequency and body temperature change synchronism at the two wrists and the two ankles; and risk early warning: the monitoring terminal analyzes the signs, behavior characteristics, abnormal limb vibration and abnormal static state of the current person to be nursed. According to the nursing risk early warning method and system based on artificial intelligence, the upper limbs and the lower limbs of the to-be-nursed person are worn by the monitoring terminal, upper limb collision and lower limb stride frequency abnormity are synchronously recognized, risk early warning is carried out in cooperation with abnormal behavior characteristics in an analyzable period, unjudged nursing is abandoned, early warning type behavior characteristic recognition is adopted, and the risk early warning efficiency is improved. And the comprehensiveness and timeliness of artificial intelligence nursing are improved.

The application provides a simulation dummy for simulating heatstroke treatment, which comprises a skeleton system, a blood pumping system, an internal organ system, a skin system, a fluid temperature control pulsating pumping system and a temperature sensing detection system; the skeleton system, the blood pumping system, the internal organ system, the skin system and the fluid temperature control pulsating pumping system are arranged to simulate human body tissue structure and body temperature change; when in use, the simulation dummy can be used to simulate heatstroke symptoms and human body temperature regulation mechanism; the temperature sensing detection system is arranged to monitor the temperature of each region of the human body in real time, so that the user can more accurately and comprehensively master the temperature change of each part of the human body in the heatstroke treatment process when using the simulation dummy, and further more accurately master the heatstroke treatment research and the emergency treatment knowledge of heatstroke.

The invention provides a traumatic lethal triple sign multi-modal prediction method and system based on machine learning, and the method comprises the steps: generating multi-modal data based on collected blood coagulation function data, arterial blood pH value, core body temperature data and bleeding amount, and processing the multi-modal data to obtain target fusion information; generating supplementary dose suggestions of fibrinogen by adopting a hierarchical storage strategy; generating target early warning information according to the causal association strength and the supplementary dose suggestion; generating an intervention parameter set according to the real-time bleeding amount, the acidosis degree and the body temperature variation in the target fusion information in combination with the target early warning information; based on the target early warning information and the intervention parameter set, obtaining a multi-modal prediction result of the traumatic and lethal triad sign; according to the application, accurate prediction, real-time risk early warning and personalized intervention recommendation of the traumatic lethal triple symptoms are realized, and the treatment efficiency and the patient survival rate are effectively improved.

The invention discloses a novel thermochromic medical fever cooling patch and a preparation method thereof, and belongs to the technical field of medical physical cooling instruments. The fever cooling patch comprises a base material non-woven fabric, a temperature response structural color developing layer, a cooling gel layer and an anti-sticking layer, the temperature response structural color developing layer is of an inverse opal three-dimensional photonic crystal structure formed by taking temperature-sensitive hydrogel as a base material, and reversible and high-contrast color change along with body temperature change can be achieved; therefore, a visual body temperature monitoring function is provided. The invention further discloses a corresponding preparation method, a photonic crystal template is constructed through a vertical deposition method, and the color development layer is prepared in combination with an ultraviolet polymerization technology. The physical cooling function and the optical temperature indicating function are integrated, the defects that a traditional thermochromic material is poor in stability and prone to aging are overcome, and the material has the advantages of being sensitive in color change, stable, reliable, visual in temperature indicating and controllable in preparation.

Described herein is a method of determining a change in myocardial strain of a subject. The method can include receiving a first signal representing a change in myocardial strain sensed by an FBG sensor and a change in body temperature, receiving a temperature signal representing a change in body temperature sensed by a temperature sensor, and determining the change in myocardial strain based on the first signal and the temperature signal.

The invention relates to the technical field of medical monitoring equipment, and discloses an infant real-time body temperature detection and early warning system and method.The infant real-time body temperature detection and early warning method comprises the steps that a medical NTC temperature sensor is adopted for collecting the body surface temperature; preprocessing the original body temperature signal data; transmitting the processed body temperature data to a mobile terminal by adopting a Bluetooth low-power-consumption communication protocol and uploading the body temperature data to a cloud server; a hierarchical anomaly detection algorithm is adopted to establish a three-level anomaly detection system for body temperature state analysis; starting a corresponding early warning response mechanism, and executing different early warning strategies; comprehensively evaluating the individual body temperature change mode by adopting a statistical analysis method; continuously optimizing the detection threshold value, the early warning strategy and the algorithm model by adopting a parameter self-adaptive adjustment algorithm; according to the invention, the problems of insufficient body temperature monitoring continuity, imperfect early warning mechanism and low intelligent level are solved.

The present invention introduces a method for providing substantially optimal training instructions to a user (102) based on temperature measurements performed by a wearable device (104). The algorithm determines non-REM sleep phases of the user (102), measures the maximum temperature of the NREM sleep phase or an averaged value among the maximum values of several NREM sleep phases in a night, and it determines most appropriate training instructions for the user (102) based on that temperature information. Gender and age may also have an effect in the training instruction determinations. Training information and alerts can be given via a smartphone (106) application to the user (102).