7737 results about "Breathing" patented technology

The invention relates to the technical field of mine ventilation, in particular to a mine ventilation dynamic regulation and control method and system based on a space-time diagram convolutional network, and the regulation and control method comprises the following steps: 1, carrying out the modeling and data collection of a ventilation system; step 2, modeling and anomaly prediction of the graph convolutional network; step 3, generation and execution of a ventilation equipment coordinated regulation and control strategy; step 4, edge node calibration and model optimization; and 5, performing full-process control and emergency response. A ventilation network topological structure and multi-modal time series data are fused through a space-time diagram convolutional network, a joint model containing spatial feature extraction and time series modeling is constructed, 30-minute early warning of ventilation abnormity and propagation path prediction are achieved, the lag response limitation that a traditional system only depends on a single parameter threshold value is broken through, and the risk pre-judgment capability is improved.

The invention relates to the technical field of granary ventilation and energy consumption optimization, in particular to an intelligent granary ventilation and energy consumption optimization decision-making method based on reinforcement learning, and the method comprises the steps: obtaining the internal and external multi-dimensional state data of a granary, respectively constructing a basic state vector, a space correlation feature vector and a grain condition change trend vector based on the multi-dimensional state data inside and outside the granary; combining the basic state vector, the space correlation feature vector and the grain condition change trend vector into a space-time feature tensor, inputting the space-time feature tensor into a space-time topology perception decision model based on a graph attention network, and outputting an optimal composite action vector for controlling ventilation equipment; and analyzing and converting the optimal composite action vector to drive ventilation equipment of the granary to execute control. According to the method, through fusion of space-time topology perception and deep reinforcement learning, accurate prediction and active regulation and control of the granary state are realized, and energy consumption of a ventilation system can be reduced to the greatest extent while grain storage safety is ensured.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention relates to the field of grain depot ventilation management, and discloses an automatic management system for ventilation in intelligent grain depot production, which comprises an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision module, a ventilation equipment linkage control module and a dynamic feedback optimization module, the environmental parameter monitoring module is used for collecting temperature, humidity and gas concentration data in the bin in real time through the Internet of Things communication unit; the data processing and analyzing module is connected with the environment parameter monitoring module; the intelligent ventilation decision-making module receives the multi-dimensional data atlas, analyzes the temperature gradient, the humidity sensitivity index and the mildew risk value in the atlas through a dynamic classification algorithm, and fuses external meteorological data; the ventilation equipment linkage control module is used for receiving the instruction sequence of the intelligent ventilation decision module; the dynamic feedback optimization module is used for monitoring the efficiency coefficient and the grain pile environment change rate in the ventilation process; the system has the advantages that the ventilation precision and efficiency are improved, and granary environment management is optimized.

In some implementations, a low dead space airway adapter for sampling fluid flowing through a ventilation assembly includes an outer wall configured to couple to an endotracheal (ET) tube adapter and an internal projection positioned at least partially within an internal cavity defined by the outer wall and configured to extend within an internal cavity of the ET tube adapter when the airway adapter is in use. The internal projection can include a fluid passageway configured for fluid communication with at least a portion of the internal cavity of the ET tube adapter and a free end comprising an opening in fluid communication with the fluid passageway. At least a portion of the free end can be chamfered around the opening and configured to contact an inner surface of the ET tube adapter when the outer wall is coupled to the ET tube adapter.

The invention discloses an intelligent regulation and control method and system for dust removal and air control of an underground high-gas heading face, and relates to the field of intelligent control. Firstly, an environment parameter set and an equipment parameter set are obtained in real time; constructing a digital twinborn model of the tunneling face and the ventilation system based on the environmental parameter set and the equipment parameter set, and simulating gas concentration and dust concentration under different working conditions; the prediction model predicts the gas emission amount trend and the dust concentration trend; and inputting the environment parameter set, the equipment parameter set and the risk level into an optimization model, and generating a target decision instruction by the optimization model. The operation parameters in the ventilation system can be timely regulated and controlled in real time based on the monitored environment parameter set of the tunneling face and the equipment parameter set in the ventilation system, so that the ventilation and dust removal efficiency of the ventilation system is improved. Moreover, the decision instruction is generated by referring to the trend of the gas emission amount and the dust concentration in the future, so that the accuracy of the decision instruction of the ventilation system is further improved.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention relates to the technical field of mine ventilation control, in particular to a mine fan ventilation control method and system based on digital twinning. The method comprises the following steps: carrying out data preprocessing on acquired mine monitoring data; performing feature extraction on the preprocessed mine monitoring data; utilizing a random forest algorithm to predict the relationship between the data characteristics and the fan air volume; a digital twinborn model is used for resolving the relation between the data characteristics and the fan air volume, and a fan ventilation control strategy is obtained; and generating a PLC control instruction based on the fan ventilation control strategy. And through multi-source data fusion and deep analysis, the real-time ventilation requirement of the mine can be accurately grasped. Based on random forest algorithm prediction and digital twinborn model calculation, dynamic and accurate adjustment of the air volume of the draught fan is achieved, it is ensured that the concentration of harmful gas in all underground areas is maintained within a safety threshold value, the oxygen content is sufficient, an ideal environment is created for personnel operation and equipment operation, and the safety and reliability of a ventilation system are remarkably improved.

The invention provides a mobile henhouse ventilation regulation and control method and system based on an environment model, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining original temperature data, original humidity data, a three-dimensional space wind speed component, an opening geometric distribution data set, and original latitude and longitude coordinate data and time data; calculating a dynamic environment weight coefficient matrix according to the original latitude and longitude coordinate sequence and the time data; generating a self-adaptive heat exchange flow field according to the dynamic environment weight coefficient matrix, the original temperature data and the original humidity data; according to the self-adaptive heat exchange flow field and the trepanning geometric distribution data set, a ventilation compensation amount demand curve is obtained through calculation; generating a ventilation intensity adjusting instruction set according to the ventilation compensation amount demand curve; and according to the ventilation intensity adjusting instruction set, outputting an optimal ventilation control parameter. According to the method, ventilation response self-adaptive accurate matching, airflow dead zone self-suppression and energy consumption-effect dynamic balance under the moving sudden change working condition are achieved.

The invention relates to a dynamic regulation and control method and system for mine ventilation. The method comprises the following steps: acquiring multi-source data in a mine, and preprocessing the multi-source data to obtain a standardized data set; calculating the risk of each region in the mine based on the standardized data set to obtain a risk prediction matrix; based on the standardized data set and the risk prediction matrix, the air volume demand of each area is calculated, and an air volume demand table is obtained; based on the air volume demand table, an optimization proposition corresponding to the air volume demand is constructed and solved, and a solution set of the optimization proposition is obtained; mapping the solution set based on a preset rule to obtain a feasible allocation scheme; and based on the feasible allocation scheme, generating an equipment cooperation instruction, and obtaining a security instruction set. According to the method, dynamic factors can be considered, the air volume is dynamically adjusted, multi-fan cooperation is achieved, and the effects of real-time sensing and autonomous dynamic decision making of mine ventilation are achieved.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to the technical field of mine intelligent ventilation and poison filtration, and discloses a mine intelligent ventilation and poison filtration dynamic regulation and control method and system.The mine intelligent ventilation and poison filtration dynamic regulation and control method comprises the steps that a neural physical hybrid model is constructed, physical constraint conditions are combined with a deep neural network, and mine airflow field distribution is predicted; decomposing the neural physical hybrid model into three hierarchical sub-models, and deploying the three hierarchical sub-models to corresponding computing nodes to realize a hierarchical computing architecture; establishing a multi-level cooperation mechanism based on federal learning, and realizing distributed model training and optimization; constructing a task allocation decision model, and dynamically determining an optimal execution hierarchy of an airflow field calculation task according to task characteristics, resource states and real-time requirements; dynamically adjusting the precision and performance balance of airflow field calculation; according to the method, the key technical problems in the mine ventilation and poison filtration system are solved through the neural physical hybrid model, the multi-level distributed computing architecture and the federal learning collaboration mechanism.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

The invention discloses a real-time image registration and path planning system in bronchoscope surgical navigation, relates to the technical field of image registration, and is used for solving the problems that in a thoracic and abdominal minimally invasive interventional operation, organ movement and deformation caused by breathing of a patient can cause obvious influence on the registration precision of a navigation system, and the navigation precision is poor. Space registration of preoperative images and an anatomical structure of a patient in an operation often has errors due to mismatching of breathing phase changes; multi-attitude data are fused, the respiration interference degree is comprehensively evaluated through double indexes, the problem that a traditional static deformation field cannot be matched with dynamic respiration is solved through respiratory cycle driving deformation prediction, displacement errors are corrected through tidal volume observation values, and B spline control point weights are reversely optimized through registration residual errors. A deformation field can adapt to breathing amplitude and frequency differences of different patients, online distillation learning is carried out by taking a registration error output by a filter as a teacher signal, and local residual deformation and chronic changes which are difficult to describe by a traditional global model are accurately captured.

The invention relates to the technical field of medical care informatics, and discloses a laryngeal mask ventilation control method suitable for outpatient anesthesia, which comprises the following steps: before core analysis, injecting and monitoring a high-frequency signal to confirm the integrity of an original breathing waveform data stream; constructing a real-time ventilation waveform trajectory in a differential phase space only on the premise that a data stream is complete, and performing morphological comparison with an individualized baseline template; meanwhile, when a parameter adjustment event of the respirator is monitored, the baseline template is automatically re-calibrated, a collaborative analysis framework with front information source quality inspection and dynamic reference self-adaption capabilities is constructed, ventilation monitoring is converted from passive response to an isolated peak point into active insight to a system dynamics evolution trajectory, and the system dynamics evolution trajectory is optimized. Therefore, the risk of gradual deterioration of the ventilation state caused by tiny air leakage of the laryngeal mask or secretion accumulation can be clinically recognized in an early stage, and intervention time is won for anesthetists.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention discloses a respiratory rehabilitation training management system based on an applet, and relates to the technical field of medical health, and the system comprises a data collection module which collects the physiological data of a user in real time through a sensor at an applet end, and comprises a respiratory rate, thoracic and abdominal movement displacement, airflow pressure and a physiological state calculation module; according to physiological data collected in real time, the actual breathing flow of each patient is dynamically calculated through a thoracico-abdominal movement-airflow dynamics combined equation. According to the respiratory rehabilitation training management system based on the applet, by adopting a thoracico-abdominal movement-airflow dynamics combined equation and a lightweight LSTM network, delay between physiological feedback and training instructions is reduced, the real-time performance and individuation of rehabilitation training are ensured, through local real-time processing and feedback, the problem of traditional cloud delay is avoided, and the system is suitable for popularization and application. And the training compliance and the rehabilitation effect of the patient are effectively improved.

The invention relates to the field of ventilation equipment early warning, in particular to a ventilation equipment early warning method and system for a smart laboratory, and the method comprises the steps: building a window with a target moment as a center, and obtaining a multi-dimensional state parameter sequence of ventilation equipment in the window; calculating the maximum cross correlation coefficient of the target moment, obtaining the optimal lag, weighting the optimal lag to obtain a final lag coefficient, and obtaining the cross correlation difference significance according to the maximum cross correlation coefficient of each sampling moment in the window; and aligning the state parameter sequences of different dimensions in the window based on the final lag coefficient, obtaining the correlation of the state parameter sequences of any two dimensions, calculating a risk value of the target moment, and correcting the risk value to obtain a risk correction value. According to the technical scheme, the early warning precision of the ventilation equipment can be improved, and the influence of hysteresis on early warning is reduced.

The invention discloses an intelligent real-time monitoring regulation and control system for coal mine ventilation, and relates to the technical field of coal mine ventilation, through a ventilation duct layout analysis module, the system can construct pressure loss of each branch in a coal mine ventilation duct, and further analyze the relevance between the pressure loss and ventilation data information of a corresponding area; the analysis provides detailed air flow distribution and ventilation duct pressure loss data, accurate decisions can be made on layout optimization, pressure regulation and air flow distribution of the mine ventilation duct, and stability of air flow and balance of pressure in the mine are ensured. And dynamic prediction and evaluation of a mine stability index: the system fits and outputs the mine stability index through a mine performance analysis module in combination with the trained ventilation stability prediction model, and compares the mine stability index with a set stability threshold value to evaluate the stability of the mine in real time. When the mine ventilation system faces an unstable risk, the system can give out early warning in time.

The invention provides a result judgment and use method of a portable borborygmus monitor, and relates to the technical field of medical monitoring equipment.The method comprises the steps that abdominal borborygmus is collected through a piezoelectric ceramic sensor array in multi-channel triangular distribution, and breathing and body movement interference is eliminated in combination with a dynamic noise filtering algorithm; extracting borborygmus outbreak duration, interval period and time-frequency energy indexes, and constructing an activeness and rhythm disorder two-parameter evaluation system; dynamically adapting a reference threshold value by adopting an age piecewise function, and introducing a feeding time compensation factor to optimize an individualized criterion; the intestinal obstruction risk is judged through a time sequence matching algorithm and a multi-parameter classification model dual-verification mechanism; a three-dimensional dynamic map is generated based on a sound source localization algorithm and spectral analysis, and data integrity is guaranteed in combination with a priority transmission protocol. According to the method, spatial-temporal feature analysis and an intelligent decision model are fused, sensor anti-interference, individual adaptability and pathological early warning precision are considered, and the core pain points of high misjudgment rate and poor clinical compatibility of traditional equipment are solved.

The invention provides a dynamic compensation augmented reality surgical navigation system based on respiratory movement prediction, and the system comprises a medical image processing unit which is used for generating a lesion three-dimensional model and planning a puncture path; the respiratory motion prediction module is used for analyzing a three-dimensional displacement sequence of the registration marker through a time sequence feature extraction network and a long-distance dependence modeling network so as to predict a respiratory trajectory; the dynamic compensation AR module is used for constructing a three-dimensional deformation field based on respiration prediction data and updating an AR virtual path in real time; the mechanical arm cooperative control module is used for adjusting the laser pointing pose in real time according to the breathing prediction data and the dynamic AR path; a closed-loop data interaction architecture realizes real-time cooperation of respiration prediction, dynamic compensation and mechanical arm control.

The invention discloses an outdoor workstation air quality monitoring and intelligent ventilation system and method, and belongs to the technical field of environment monitoring and intelligent ventilation control. The system is composed of an environment sensing module, an intelligent control unit and a ventilation execution device. And the environment sensing module realizes pollutant concentration field reconstruction through a multi-dimensional data fusion algorithm. The intelligent control unit constructs a dynamic ventilation model based on an improved fuzzy PID algorithm, and generates an optimal ventilation strategy in combination with meteorological prediction data and workstation structure parameters. And the ventilation execution device adopts a modular design to realize directional air supply and pollutant blocking. The method comprises four steps of real-time environment data acquisition, dynamic risk assessment, ventilation parameter optimization and execution mechanism linkage control, and remote intelligent regulation and control are realized through cooperation of edge computing and a cloud platform. According to the scheme, a passive response mode of a traditional ventilation system is broken through, and the technical advantages of accurate pollutant tracing, dynamic optimization of ventilation efficiency and intelligent adjustment of equipment energy consumption are achieved.

The invention relates to the field of control systems, and discloses a vent valve control system with a pressure regulation function for a generator set, which is used for breaking through the limitation of static state, single target and passive response of a traditional vent valve control method. Comprising the following steps: acquiring pressure, flow velocity and valve opening data of a ventilation system in real time, generating a dimensionless parameter set for dynamic calibration, integrating parameter uncertainty disturbance terms to form a hybrid prediction model, solving an optimal valve opening instruction sequence by adopting a multi-target robust optimization algorithm, and designing a layered safety control mechanism. The system safety is guaranteed through dual-threshold monitoring and emergency intervention, redundant sensor data fusion and parameter reverse optimization are introduced, and fault tolerance and model self-adaption are achieved. According to the method, through collaborative innovation of dynamic modeling, robust optimization and intelligent fault tolerance, the control precision, the anti-interference capability, the safety and the reliability of the ventilation system of the generator set are remarkably improved, and the method is suitable for complex and changeable industrial operation scenes.

The invention relates to the technical field of intelligent control, in particular to an inspection system and method for a heating and ventilation system based on a knowledge graph, and the system comprises a feature extraction module, a graph construction module, an anomaly tracing module, a causal analysis module and a priority module. According to the method, the inlet and outlet water temperature and flow data of the water chilling unit are collected, deviation characteristics are extracted in combination with principal component analysis, the operation state is quantified, an embedded map participates in state judgment, the anomaly detection capacity is improved, initial anomaly and upstream nodes are focused, the analysis range is narrowed, and causal tracing is carried out based on the state influence between devices; by means of a Bayesian network, a state evolution chain is constructed, a time sequence and an inter-structure fault are bridged, task priority is evaluated according to a link starting point and length, equipment dynamic sorting and state updating are carried out, inspection scheduling accuracy and efficiency are improved, and a closed-loop process which is high in recognition accuracy, small in analysis boundary, clear in path and excellent in resource allocation is formed.

The invention relates to the technical field of deep well ventilation intelligent control, and discloses a deep well multi-stage intelligent ventilation control method and system.The deep well multi-stage intelligent ventilation control method comprises the steps that a multi-layer mixed causal graph model is constructed; extracting a cross-layer causal relationship, identifying a causal dependency relationship among different level nodes through a mode of combining data mining and expert knowledge, and establishing an inter-level state mapping function; multi-level fault diagnosis is executed, and fault propagation path analysis and root cause positioning are achieved; generating a multi-scale collaborative intervention strategy, comprehensively considering the intervention cost and effect of each level, solving an intervention combination, and passing an intervention conflict detection and reconciliation mechanism; multi-level self-healing control is implemented, and multi-level self-healing control measures are automatically executed; according to the method, fault propagation characteristics among different levels can be accurately captured through multi-level causal reasoning, and compared with a traditional single-level fault diagnosis method, the fault root cause positioning accuracy is improved.

The invention relates to the technical field of artificial intelligence, and discloses a teenager scoliosis intelligent quantitative evaluation system fused with multi-modal imaging. The system comprises a multi-modal data management module, a cross-modal registration fusion module, a dynamic quantitative analysis module, a risk prediction module, a three-dimensional reconstruction module, a report generation module and a system management module, and by setting a multi-modal registration verification mechanism, accurate matching of a bony structure and soft tissue is ensured. Key parameter calculation errors caused by registration failure are reduced, the three-dimensional reconstruction precision is greatly improved compared with traditional single-mode evaluation, a dynamic respiration compensation module is introduced, in the spine deformation parameter quantification stage, spine physiologic changes in a respiration movement period are automatically captured, measured values are corrected based on an individualized dynamic model, and the accuracy of three-dimensional reconstruction is improved. Pathological progression and physiological fluctuation are distinguished, the misjudgment risk caused by neglecting breathing phase change in traditional static measurement is avoided, and the reliability of scoliosis progression risk assessment is improved.

The invention discloses an auxiliary renal puncture device for the nephrology department, and belongs to the technical field of renal puncture, the auxiliary renal puncture device comprises a sickbed, a plurality of supporting legs are fixedly arranged at the bottom of the sickbed, fixing plates are fixedly arranged on the two sides of the sickbed, and second sliding blocks are slidably connected to one sides of the two fixing plates; the breathing frequency and amplitude of a patient are captured in real time through an infrared breathing monitoring module, and displacement data generated by breathing of the kidney are accurately obtained in combination with motion sensing modules such as a lead screw encoder and a driving gear angle encoder. The intelligent control system drives the first stepping motor and the second stepping motor to adjust the position of the portal frame based on six-dimensional force sensor data of the force sensing module, meanwhile, the angle of the puncture needle is adjusted in real time through feedback of a mechanical arm joint encoder, and dynamic compensation of kidney respiratory movement is achieved. According to the mechanism, the puncture deviation caused by breathing can be controlled within the range of + / -0.5 mm, the puncture positioning precision is remarkably improved, and poor sample quality or operation risks caused by breathing movement are avoided.

The invention discloses a radar and WiFi channel state information fused dual-mode household respiration monitoring device which comprises a radar signal collecting and processing module used for detecting a human body respiration signal through a millimeter wave FMCW radar and processing an I / Q orthogonal echo signal obtained by the FMCW radar to obtain a respiration signal; the WiFi signal acquisition and processing module is used for acquiring a CSI signal containing respiratory activity information through a WiFi wireless network card and calculating a CSI ratio of a receiving channel so as to remove time-varying noise and further extract a respiratory signal; the data communication module is used for constructing a networking system based on a millimeter wave FMCW radar and WiFi through different network cable transmission modes; and the breathing information fusion module is used for dynamically fusing the two breathing signals. Starting from multi-sensor respiration signal extraction, accurate detection of personnel respiration signals in a home indoor environment is realized through a fusion algorithm, and the detection method is effective, feasible and reliable in performance.