124 results about "Metabolic rate" patented technology

Systems and methods for cooling an organ to an individualized temperature based on organ parameters. A transport container can store the organ at a target temperature determined based on characteristics of the organ and the donor. A processor can determine the target temperature for preservation of the organ. The determination may be based on preservation time, the type of the organ, the age of the donor, the circulatory condition of the donor, the brain condition of the donor, and the metabolic rate of the donor.

The invention discloses a vehicle seat self-adaptive adjusting method and system based on environment perception and a medium. The method comprises the steps that physiological state data and human body image data of a target passenger and cabin environment data of a target cabin are obtained; determining body surface temperature and humidity, heart rate parameters and human body metabolic rate of the target passenger according to the physiological state data, and identifying clothing thermal resistance parameters of the target passenger according to the human body image data; determining a human body thermal comfort index of the target cabin according to the human body metabolic rate, the clothing thermal resistance parameter and the cabin environment data; inputting the body surface temperature and humidity, the heart rate parameter and the human body thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain a target thermal comfort demand of the target passenger; and performing heating adjustment and / or ventilation adjustment on the seat of the target cabin according to the target thermal comfort demand. The riding comfort and riding experience of the user are improved, and the method can be widely applied to the technical field of vehicle control.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention discloses a multivariate heterogeneous data fusion method and system for an equipment operation and maintenance security pre-control system. The method comprises the following steps: receiving a multi-source heterogeneous modal data set; constructing a multivariate heterogeneous data fusion matrix, and calculating to obtain matrix variable elements; according to the matrix variable elements, constructing a multi-source heterogeneous modal data feature fusion calculation formula, and fusing multi-source heterogeneous modal data features; a dynamic metabolism model between the equipment health state and the external environment is constructed, and a digital metabolism rate is obtained; constructing a health prediction model, and analyzing the digital metabolic rate to obtain health degree prediction data; according to the equipment prediction health degree, the equipment prediction health degree is analyzed and judged, an early warning level is obtained, and a self-healing strategy is generated. The method is used for reflecting the operation and maintenance safety of the equipment according to the pre-equipment health degree by fusing the modal features, so that the pre-control of the operation and maintenance safety of the equipment is fed back more intelligently.

The invention relates to the technical field of hemodialysis monitoring, and provides a hemodialysis on-line monitoring system and method, and the system comprises a data acquisition module which is used for collecting blood physiological parameters in a dialysis pipeline in real time; the blood component analysis module is connected with a blood loop of the dialysis machine and is used for detecting serum iron, ferritin and ferritin saturation indexes on line; and the data processing module is used for calculating the risk index of anemia, evaluating the iron utilization rate, constructing a model for measuring the iron deficiency erythropoiesis trend according to the collected and analyzed data, and training and optimizing the model. According to the method, individual parameters of patients are fused, personalized calculation is performed for iron metabolism rates and dialysis parameters of different patients, and data such as iron content, blood flow and dialysis fluid volume are acquired online in real time, so that iron concentration and infusion rate are accurately matched with real-time iron metabolism requirements of the patients, and anemia of the patients in the dialysis process is reduced.

A medical monitoring system comprises a wearable garment incorporating multiple physiological sensors for real-time patient monitoring. The garment integrates an embedded processing unit that receives sensor data and transmits it wirelessly to a base station or remote server. A multi-modal physiological signal processing unit analyses the data to generate composite physiological indices, including metabolic rate, work of breathing, and fall risk assessments. The garment includes optional monitoring features such as transcutaneous carbon dioxide measurement, electrocardiography, pulse oximetry, blood pressure monitoring, and accelerometer - gyroscope-based motion tracking. The system provides early-warning alerts for clinical deterioration, enabling proactive therapeutic interventions. A wireless communication module ensures real-time data transmission, supporting remote monitoring and integration with hospital networks. The garment's modular design allows for non-invasive physiological assessment while maintaining patient mobility, making it suitable for critical care, ambulatory monitoring, and telemedicine applications.

The invention discloses a hospital obese patient intelligent weight loss dynamic matching intervention method and decision making system based on multi-modal data fusion, and relates to the field of medical health, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-modal data; s2, dynamic metabolism modeling and plateau period breakthrough; s3, generating a hierarchical intervention strategy; s4, performing psychological-physiological linkage intervention; and S5, performing real-time safety control and feedback optimization. By collecting multi-dimensional data, constructing the model, accurately predicting the metabolic rate, the metabolic plateau period and the sudden eating tendency, dynamically adjusting the exercise intensity and the diet scheme according to the conditions of different patients, triggering a low-intensity exercise mode for the obese patients with cardiac insufficiency and calibrating diet, ensuring the exercise safety, and improving the exercise efficiency. Operation suggestions are generated on the basis of the standard, precise preoperative evaluation is conducted, postoperative rehabilitation is optimized in combination with nerve stimulation data, dangerous postures are monitored, the sport injury risk is reduced, and personalized, safe and efficient intelligent weight reduction intervention for obese patients is achieved.

The invention provides a heating ventilation air conditioner control method, system and device based on dynamic heat load prediction and a storage medium. The method comprises the following steps: acquiring indoor individual physiological data, environmental parameters and regional personnel distribution data in real time; determining a real-time individual metabolic rate equivalent based on the individual physiological data and the environmental parameters; aggregating the individual metabolic rate equivalents according to the regional personnel distribution data, determining the total heat load and the total heat load change rate of each indoor region, and decomposing the total heat load into a sensible heat load and a latent heat load; and on the basis of the sensible heat load, the latent heat load and the total heat load change rate, air supply temperature, air supply amount and humidity control instructions of the heating ventilation air conditioning system for feed-forward adjustment are generated. According to the technical scheme, dynamic thermal load prediction based on individual physiological data and environmental parameters can be achieved, the indoor total thermal load is quantified in real time, and the feedforward adjustment control instruction of the heating ventilation air conditioning system is generated.

The invention discloses a thermal comfort monitoring robot system and method based on multi-modal body language feature recognition and fusion, and aims to realize individual thermal comfort monitoring, regulation and control in a dynamic indoor environment. The intelligent mobile platform carries multiple types of environment sensors and visual acquisition equipment, and performs autonomous navigation and acquires environment parameters and human body image data; the multi-modal feature recognition module performs human body detection, posture recognition and identity maintenance on a video stream frame by frame based on a deep learning network, and extracts multi-modal human body features; the data processing and fusion module realizes cross-frame identity consistency through an algorithm and fuses multi-modal human body features and environmental parameters; the thermal comfort calculation module dynamically adjusts a metabolic rate parameter based on a predicted average voting model, incorporates the metabolic rate parameter into a psychological correction factor and calculates an individual thermal comfort index; the three-dimensional visualization and regulation and control module generates a comfort distribution map, is in butt joint with a building environment automatic control system to achieve dynamic regulation and control, and provides support for intelligent building environment management.

The invention relates to the technical field of health management, and discloses an intelligent food recommendation and diet optimization system based on body temperature change, and the system comprises a body temperature dynamic fluctuation curve generation module which collects data in real time through a multi-source temperature sensor of wearable equipment, and is used for generating a body temperature dynamic fluctuation curve; the metabolism state label acquisition module is used for matching the time-frequency domain characteristics with a preset metabolism mode library so as to acquire corresponding metabolism state labels; the calculation module is used for calling nutrient element intake records in the historical diet database of the user and a preset health target and is used for calculating a current metabolic rate deviation value and a nutrition gap; the diet scheme generation module is used for generating a suggested diet scheme containing a recommendation priority score and a real-time eating suggestion based on the metabolic rate deviation value, the nutrition gap and a preset health target; and the monitoring module is used for monitoring body temperature change data after the user executes the recommended diet scheme. The accuracy of health management can be improved.

The invention relates to the technical field of health information processing, in particular to an energy metabolism analysis and patient information integration method and system.The energy metabolism analysis and patient information integration method comprises the following steps that on the basis of metabolic rate data and blood detection indexes, classification matching is conducted on local deviation of the metabolic rate and distribution characteristics of biochemical indexes in blood detection; and extracting statistical characteristics in the local abnormal region, and analyzing the correlation change between the metabolic migration and the biochemical indexes. According to the method, potential health risks are revealed through dynamic correlation analysis of metabolic migration characteristic values and blood detection indexes, accurate capture of dynamic physiological changes is achieved in combination with correlation evaluation of vital sign fluctuation data and exercise intensity in the time period, and the dynamic physiological changes are accurately captured through stratified analysis of causal paths and high-frequency health risk parameter adjustment. The accuracy and layering of health risk identification are improved, the nutritional ingredient proportion is optimized to match metabolic requirements, the food intake range is adjusted, personalized diet recommendation is generated through data comparison, and the individual health management requirements are met.

The invention discloses a donor heart preservation method based on a crystal cardioplegic solution (CC solution) and a UW solution and application of the donor heart preservation method, and belongs to the field of biomedical engineering. The method comprises the following steps: (a) preparing CC liquid; (b) perfusing CC liquid through the aorta at the pressure of 150mmHg for 3-5 minutes to cause cardiac arrest; (c) taking out the heart, and performing secondary perfusion with 2000ml of 4 DEG C UW solution for 8-12 minutes; and (d) immersing the heart into a UW solution for cold preservation, wherein the cold ischemia time is less than or equal to 8 hours. In combination with a CC liquid perfusion step, the protection effect of the cold preservation liquid is remarkably improved by reducing the metabolic rate of cardiac cells; through double perfusion of CC liquid and UW liquid, optimization of metabolic balance and effective inhibition of inflammatory response are realized in the cold storage process of the heart; the invention has the functions of prolonging cold ischemia time and keeping heart cells, and provides a new technical means for long-time cold preservation.

The embodiment of the invention provides a multi-dimensional data processing method and system for intelligent scoring of emergency patients. The method comprises the following steps: acquiring dynamic temperature data and humidity fluctuation data of a burn wound surface through a flexible wearable monitoring device; inputting the time sequence change of the dynamic temperature data into a temperature-drug release model to generate drug concentration distribution; constructing an infection diffusion map taking the anatomical structure as a topological reference, wherein the infection diffusion map comprises an edge attribute representing the flow direction of tissue fluid and a node attribute representing a necrotic region; combining the metabolic rate parameter in the node attribute and the fluid resistance parameter in the edge attribute, iteratively updating the infection propagation intensity, and generating a weight distribution diagram; matching burn depth grading standards according to geometric deformation characteristics and gradient attenuation trends of the weight distribution map, and generating infection risk dynamic scores integrated with drug concentration distribution feedback. According to the technical scheme provided by the embodiment of the invention, the multi-dimensional data processing efficiency and precision can be improved.

A method and device is taught to improve the appearance of cellulite by combining mild exfoliation of the skin, various wavelengths of low level light, ultrasound, and a vacuum or suction. The device is intended to increase the metabolic rate of the fat cells and reduce their size, while increasing the extensibility or length of the septae. Such increased activity will promote blood flow to the affected area thereby increasing cell nutrients and removing cellular exudates while stimulating the growth of new collagen thereby alleviating the cellulite.

The invention discloses an air conditioner control method, device and equipment based on a neural network particle swarm and a medium. The method comprises the following steps: predicting an indoor environment parameter at a next moment through a pre-trained first neural network model according to a current indoor environment parameter; calculating a thermal comfort value at the next moment by combining the human body metabolic rate, the clothing thermal resistance and the parameter; determining a particle swarm constraint condition containing a target thermal comfort value and an air conditioner control parameter range according to the target thermal comfort value and a preset thermal comfort interval; under the constraint, a pre-trained second neural network model is reversely solved through a particle swarm optimization algorithm, and a target air conditioner control parameter combination meeting the target thermal comfort value and with the minimum operation power is obtained; and air conditioner operation is controlled according to the target air conditioner control parameter combination, so that energy consumption is effectively reduced while human body comfort is guaranteed, and the effects of adapting to environment changes in real time and balancing comfort and energy consumption are achieved.

The invention discloses a diet data analysis method and device based on a large language model, and belongs to the technical field of diet data analys.The method comprises the steps that user basic information and taste preferences described by natural languages are collected through a user interface; unstructured data of food is captured regularly, and a healthy diet database is constructed after large language model cleaning and tagging processing; basic metabolic rate and daily total energy consumption are calculated in combination with the basic information of the user, and a nutrient intake target is determined; the big language model application layer generates a daily diet plan containing cooking suggestions; and receiving the diet recorded by the user photographing or natural language, analyzing the diet, comparing the analyzed diet with the nutrition target, and generating personalized feedback containing problems, reasons and adjustment suggestions. The device correspondingly comprises five functional modules. According to the invention, automatic maintenance and personalized analysis of diet data are realized, and intelligence and practicability of diet management are improved.

The invention discloses a laying hen breeding simulation system based on digital twinning, and relates to the technical field of intelligent breeding, and the system comprises a data collection unit which is used for obtaining multi-dimensional state parameters and outputting the multi-dimensional state parameters to a biological state quantification unit; the biological state quantification unit is used for resolving an individual metabolic rate index and a group aggregation entropy; the time delay characteristic analysis unit is used for quantifying and generating a biological response time delay complexity index; the regulation and control instruction generation unit is used for calculating a temperature demand reference value and further generating a final temperature set target value; and the model self-optimization unit is used for predicting a biological state at the next moment, comparing the biological state with an actual observation state after regulation and control execution, generating a prediction error, and carrying out iterative updating on preset weights and coefficients in the system. According to the method, by accurately quantifying the biological state and response characteristics of the laying hens and combining model self-optimization, accurate, flexible and continuous self-perfect intelligent environment regulation and control are achieved, and the breeding benefits and animal welfare are remarkably improved.

The present invention discloses a method for environmental control and temperature control of subway stations, including: the present invention introduces a dynamic thermal comfort model, which takes the relative thermal index RWI as the core, comprehensively considers multiple influencing factors such as human metabolic rate, ambient temperature, and clothing thermal resistance, and can accurately evaluate the thermal comfort of passengers under different environmental conditions. By real-time monitoring of outdoor temperature changes and combining the thermal comfort needs of passengers during the ride, the system can intelligently adjust the temperature of the station hall and platform to match the physiological and psychological feelings of the passengers. This dynamic adjustment mechanism can not only significantly improve the passenger's riding experience and reduce passenger dissatisfaction caused by temperature discomfort, but also effectively reduce energy consumption and reduce operating costs. In short, the present invention realizes the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment.

The invention provides an air conditioner, a control method and device of the air conditioner, a storage medium and a computer program product. The method comprises the steps that human body physiological parameters of a user in the environment where the air conditioner is located are monitored; calculating a comprehensive metabolic rate index of the user according to the monitored human body physiological parameters of the user in the environment; according to the calculated comprehensive metabolic rate index of the user, the air supply temperature and / or air supply humidity of the air conditioner are / is adjusted; and / or, according to the calculated comprehensive metabolic rate index of the user, the upper and lower air outlet of the air conditioner is controlled. According to the scheme provided by the invention, the temperature and humidity can be dynamically adjusted in combination with the human physiological parameters, and the up-down air outlet of the up-down air outlet air conditioner is dynamically controlled.

The invention relates to the technical field of intelligent medical prediction, in particular to an intelligent early warning method and system for adverse reactions of chemotherapy medication of tumor patients, and the method comprises the steps: inputting gene sequencing data and time sequence physiological index data of a current patient into a network model, and outputting an injury stage by the network model; the training data set of the network model comprises gene sequencing data, time sequence physiological index data and injury stages; the acquisition process of the injury stage comprises the following steps: acquiring the relative metabolic rate of the gene sequencing data to chemotherapeutics, and further determining the relative speed of in-vivo toxicity accumulation of the gene sequencing data; after time sequence physiological index data of meaningless physiological fluctuation of a historical patient is smoothed, a nerve injury change curve is obtained according to the time sequence physiological index data, whether a potential subclinical signal segment exists in the nerve injury change curve or not is recognized, and if yes, the injury stage is judged according to the potential subclinical signal segment. According to the method, individualized and accurate risk prediction can be carried out before irreversible nerve injury caused by chemotherapy occurs.

The invention belongs to the technical field of human body attention evaluation in a high-temperature environment, and provides a human body attention level evaluation method and system in a high-temperature environment, and the method comprises the steps: dividing an environment temperature range into a plurality of temperature ranges according to a value sequence of temperatures from low to high; performing feature extraction on the near-infrared fNIRS data in the plurality of temperature ranges to obtain feature data in each temperature range; according to the method, range division is carried out on the environment temperature, feature extraction and screening are carried out on the corresponding near-infrared fNIRS data in different environment temperature ranges on the basis, the expression of the near-infrared fNIRS data features in different high-temperature ranges can be reflected, and the near-infrared fNIRS data features in different temperature sections in the high-temperature environment can be reflected. According to the method, the attention performance value is calculated through different attention performance value calculation models, so that the influence caused by obvious change of human brain metabolic rate difference in a high-temperature range is avoided, the coupling relationship between the blood oxygen signal of the fNIRS and the neural activity can be better expressed, and the evaluation accuracy is improved.

This invention discloses a non-invasive method, system, device, and storage medium for monitoring student attention based on gas sensing, belonging to the field of non-invasive sensing technology. The method includes: calculating a net physiological gas signal based on environmental baseline data and initial gas concentration data of the target student; calculating physiological characteristic indicators of the target student based on the net physiological gas signal, and determining the target student's metabolic rate level based on the physiological characteristic indicators; obtaining a state time-series signal based on the net physiological gas signal according to a preset discrimination criterion; inputting the net physiological gas signal and the state time-series signal into a pre-trained attention monitoring model to obtain the probability distribution of the target student's attention state; and determining the target student's attention state based on the target student's metabolic rate level and the probability distribution of the attention state. This invention assesses the cognitive state by capturing the gas released by the student, achieving non-invasive monitoring.

This invention relates to a method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model. First, a refined, segmented, multi-layered human physiological thermal model is constructed. Based on this model, a set of coupled biological heat transfer equations is established, embedding an active thermoregulation mechanism mediated by the human central nervous system. This set of equations is solved by deeply and dynamically coupling skin temperature, core temperature, and physiological parameters such as blood perfusion rate, metabolic rate, and sweat evaporation. Subsequently, by setting the initial and boundary conditions of a fire scene operation, the coupled equations are solved in one go using numerical methods. During the same solution process, the system synchronously and in parallel performs segmented skin burn risk assessment and overall human heat stress risk assessment. This invention overcomes the limitations of traditional models in segmented prediction and coupled active thermoregulation, achieving effective synchronous early warning of skin burn risk and overall heat stress risk in various parts of the human body under extreme thermal environments.

The application discloses fluorescent molecules, calcium phosphate nanoparticles thereof, and a preparation method and application thereof. Specifically, the application discloses a compound as shown in formula I, or a salt thereof. The calcium phosphate nanoparticles formed by the fluorescent molecules provided by the application have both improved fluorescence quantum yield and metabolic rate.

A stand-alone, fully integrated self-contained wearable metabolic analyzer for metabolic rate and respiratory quotient measurement. It includes a device body, disposable mask, and headgear. The device body comprises multiple different miniaturized modules, including a colorimetric sensing module, flow module, control circuit, power module, environmental sensors, wireless module, communication module, memory module, signal processing module, and display module. A disposable sensor chip, coated with chemical sensing probes, is utilized in the colorimetric sensing module for breath O2 and CO2 detection. A Venturi tube and pressure sensor-based flow module measures breath flow rate. The self-contained wearable metabolic analyzer derives physiological parameters including resting energy expenditure (REE), respiratory quotient (RQ), oxygen consumption (VO2), carbon dioxide production (VCO2), minute ventilation (VE), breath frequency (BF), and tidal volume (TV) from the measurement.

The application belongs to the field of biological analysis, and provides a method for precise metabolic flow analysis of TCA cycle based on isotope tracing technology combined with high-resolution mass spectrometry. By optimizing mass spectrometry parameters and liquid chromatography method, one injection within 6 min is realized, and high-throughput, high-sensitivity and high-resolution detection is realized on 11 biomarkers related to TCA cycle metabolic pathways. At the same time, the application innovatively uses LC-MS / MS to decipher 13 C the stepwise position-specific transfer from glucose to subsequent metabolites by glycolysis and TCA cycle, thereby calculating 13 C the integrated steady-state analysis of key metabolic rates of position-specific transfer from sequential precursors to their products (pyruvate dehydrogenase, beta-oxidation, pyruvate carboxylase, isocitrate dehydrogenase and pyruvate cycle). This technology has wide applicability and can potentially characterize mitochondrial metabolism of any tissue or cell.

The present application provides techniques for predicting the amount of at least one biological material produced or consumed by a biological system in a bioreactor. Process conditions and metabolite concentrations of the biological system are measured as a function of time. The metabolic rates of the biological system, including the specific consumption rate of metabolites and the specific production rate of metabolites, are determined. The process conditions and metabolic rates are provided to a hybrid system model configured to predict the production of the biological material. The hybrid system model includes a kinetic growth model configured to predict cell growth in the form of a function of time and a metabolic condition model based on the specific consumption rate or secretion rate of metabolites and selected process conditions. Wherein the metabolic condition model is configured to classify the biological system into a metabolic state. The amount of the biological material is predicted based on the hybrid system model.

The present application relates to the technical field of artificial intelligence, and particularly relates to an individualized medication prediction method based on artificial intelligence, comprising: acquiring a plurality of standard historical medical data sets; acquiring metabolic capacity scores and drug metabolism rates corresponding to all data groups; training to obtain relationship models corresponding to all standard historical medical data sets; acquiring liver metabolism parameters, gene expression parameters and each drug name in a treatment scheme of a patient to obtain a drug metabolism rate; and determining a corresponding prediction drug amount determination method according to whether the drug types in the treatment scheme have a competitive relationship. The present application integrates drug metabolism enzyme-related gene expression and liver metabolism function, simultaneously incorporates drug absorption competition and metabolism enzyme competition when drugs are used in combination, corrects the final output prediction drug amount when drugs are used in combination, so that the model is more suitable for the actual drug use scene, and the accuracy of the prediction result is increased.

The application belongs to the technical field of biological medicine, and particularly relates to a radix morindae officinalis-inulin composition and application thereof in a kidney fibrosis product. The radix morindae officinalis-inulin composition provided by the application is composed of radix morindae officinalis polysaccharide, inulin and water, can exert the synergistic effect of the two polysaccharides, improve the physical and chemical properties and biological activity of the radix morindae officinalis-inulin composition, can significantly enhance the metabolic rate of urea nitrogen creatinine of the kidney, and can significantly reduce the fibrosis positive area, and has a significant effect on the treatment of kidney fibrosis diseases. The raw materials of the composition are based on natural polysaccharides, have good safety and degradability, and meet the high safety requirements of biological materials in the medical field. Compared with the existing single component, the composite design can realize multi-target effect, significantly improve the treatment effect, and has a great market competitive advantage.

Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.