83results about How to "Improve predictive ability" patented technology

The invention provides an unmanned aerial vehicle multi-target tracking method and system based on visual and millimeter-wave radar information fusion, and the method comprises the steps: obtaining an association result through employing a space-time consistency model, constructing a self-adaptive joint probability data association frame according to the association result, so as to obtain an initial target state estimation value, and carrying out the recognition of the initial target state estimation value. A prospective path planning algorithm is adopted to obtain an optimal observation angle flight path, a Bayesian network is utilized to obtain initial observation data, based on the initial observation data, an optimal target state estimation value is generated, a distributed cooperative sensing mechanism is used to generate an initial tracking task allocation scheme, and an intelligent resource allocation strategy is implemented. Generating an initial resource allocation scheme, starting a quick response protocol to obtain an optimal tracking task allocation scheme, and generating an optimal resource allocation scheme based on the optimal tracking task allocation scheme; according to the technical scheme provided by the invention, the accuracy of target identification is improved, the environmental adaptability is enhanced, the tracking efficiency is improved, and the working time is prolonged.

The invention provides an intelligent power distribution cabinet safety early warning method, system and device and a storage medium, and relates to the field of electric power. The method comprises the steps of obtaining multi-source heterogeneous operation parameters of the intelligent power distribution cabinet, and performing feature extraction on the multi-source heterogeneous operation parameters to obtain operation feature parameters; performing parameter fusion on the operation characteristic parameters to obtain fused characteristic parameters, and inputting the fused characteristic parameters into a preset fault early warning model to obtain fault early warning data; and inputting the fault early warning data into a preset rule matching engine for rule matching to obtain a matching result, and generating an early warning signal according to the matching result. According to the method, the accuracy and timeliness of safety early warning of the intelligent power distribution cabinet are improved.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention relates to the technical field of APT killing chain reconstruction and prediction methods, and provides an APT killing chain reconstruction and prediction method and system based on causal reasoning. The method aims at solving the problems that in an existing APT detection and defense technology, multi-source heterogeneous data integration is difficult, space-time dynamic feature modeling is insufficient, killer chain reconstruction is inaccurate, the predictive capacity is limited, interpretability is poor, and real-time performance and accuracy are difficult to consider at the same time. The method comprises the steps of obtaining multi-source heterogeneous data, constructing a causal graph, performing multi-modal feature extraction based on the causal graph, and obtaining node initial feature representation; inputting the node initial feature representation into a time sequence causal graph convolution learning algorithm to obtain an optimized node representation; executing killer chain reconstruction based on the optimized node representation to obtain a possible killer chain sequence; predicting a future attack path according to the possible killing chain sequence to obtain a potential attack path; and generating an interpretability analysis report based on the potential attack path.

The invention discloses a ship dynamic path adjusting method combining visual navigation and radar data. The ship dynamic path adjusting method comprises the following steps: S1, collecting data; s2, carrying out pretreatment; s3, performing data fusion to generate an environment perception model; s4, combining the generative adversarial network and the space-time diagram neural network to construct an environment prediction model; s5, performing initial path planning by using a heuristic search algorithm to generate an initial path; s6, refining and optimizing the path by using a fast extension random tree algorithm to generate an optimal path; s7, generating future possible environment change according to the environment prediction model, and calculating a coexistence reserve path for each prediction scene in combination with the environment perception model; s8, when the current path is predicted to have risks in the future, switching to a preset standby path in advance; and S9, automatically generating a navigation control instruction, and transmitting the instruction to a ship automatic driving system. According to the invention, intelligent optimization of the ship path is realized through a multi-sensor fusion and environment prediction method.

The invention discloses a new energy automobile lithium battery thermal runaway monitoring system and method. The system comprises a vehicle-mounted lithium battery, a multi-source data acquisition module, a multi-source data fusion module, a hidden Markov model monitoring module, a state identification module, a data analysis processing module, an early warning module, an emergency processing module, a wireless communication module, a cloud platform and a battery management system. The multi-source data acquisition module is responsible for acquiring multi-dimensional data such as voltage, temperature and internal resistance of a lithium battery, and the data fusion module processes the data and then transmits the data to the hidden Markov model monitoring module for state monitoring and prediction. According to the system, a high-order hidden Markov model and a hidden semi-Markov model are adopted, state retention time modeling and a self-adaptive online learning mechanism are combined, and conversion of a battery from a normal state to an abnormal state and a thermal runaway state can be accurately recognized. The system remarkably improves the safety and stability of the lithium battery of the new energy automobile, and is suitable for power battery management of the new energy automobile.

The invention provides a micro robot motion control method and system, and the method comprises the steps: carrying out the synchronization and calibration processing of environment state information, and generating a comprehensive perception image; analyzing the comprehensive perception image to obtain a classification result, and predicting the environment state change influencing the motion track of the micro robot in a period of time in the future based on the classification result; generating a candidate path by using a fast random tree, and optimizing the candidate path in combination with an AI search algorithm to obtain a target path; constructing a three-dimensional dynamic probability graph, continuously updating the three-dimensional dynamic probability graph by using a reinforcement learning algorithm, and generating an instant decision map; and estimating the position of the micro-robot by using a Kalman filter to obtain the position state of the micro-robot, adjusting the speed and steering angle of a driving motor of the micro-robot, and generating trajectory tracking of the micro-robot. According to the technical scheme provided by the invention, the flexibility and intelligence of the micro robot are improved.

The invention is suitable for the technical field of escalator monitoring, and provides an escalator health assessment method and device based on deep learning and terminal equipment. The method comprises the steps that escalator operation data are collected through a sensor, and the escalator operation data comprise escalator vibration signals, escalator temperature, escalator operation noise and escalator operation current; local features used for representing the health state of the escalator are extracted from the escalator operation data through a convolutional neural network, the local features of various escalator operation data are fused through an attention mechanism, and global health state representation of the escalator is obtained; and an escalator domain knowledge graph is constructed, when the global health state characterization shows that the escalator state is abnormal, the reason of the escalator state abnormality is reasoned based on the relationship between entities in the escalator domain knowledge graph, and the escalator health condition is evaluated according to the reason of the escalator state abnormality. The escalator operation data can be comprehensively analyzed, the health state of the escalator is effectively evaluated, and the safety, reliability and operation efficiency of the escalator are improved.

The invention discloses a data resource management system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. According to the method, multi-source data is collected, and data cleaning and preprocessing are carried out; selecting meteorological indexes and soil indexes, and calculating an influence factor of each index in the meteorological indexes and the soil indexes; determining the weight of each influence factor, and calculating to obtain an agricultural product external environment influence factor; historical data are collected, meteorological data and soil data are matched with agricultural product planting areas and planting periods, and a multi-dimensional data set is constructed; performing feature engineering on the multi-dimensional data set to obtain a feature set; training a long short-term memory network model, and performing model evaluation and optimization; after training is completed, meteorological data and soil data are input into the model, and agricultural product yield prediction data are output; and performing data alignment, comparing the predicted agricultural product yield value with the average value of the market demand, and analyzing a confidence interval to obtain a decision suggestion.

The invention discloses a medical instrument full-trace supply chain management method and system, and relates to the technical field of medical instrument supply chain management. The medical device all-tracking supply chain management method comprises the steps of obtaining current order demand data, order delivery destination information, medical device inventory position information and historical order data of each medical device; according to the method, the current order demand and the inventory state of the medical apparatus and instruments are tracked in real time, the historical order data are combined, the potential inventory insufficiency problem is recognized in advance, calculation of the safety inventory value is based on detailed historical analysis, and the safety inventory value of the medical apparatus and instruments is obtained. Comprising order demand and processing cycle, so that inventory management is more accurate and high in predictability, application of real-time data and intelligent calculation of safe inventory significantly improve the response speed and reliability of a supply chain, and it is ensured that medical services are not interrupted due to supply problems.

The invention provides an optical power prediction method based on WRF-Solar assimilation multi-source data and a related device, and belongs to the technical field of optical power prediction. According to the method, a WRF-Solar model and a WRF-DA 4D-Var assimilation framework are constructed, preprocessed historical meteorological data and historical radiation observation data are input into the WRF-Solar model and the WRF-DA 4D-Var assimilation framework, and a prediction result using the historical meteorological data and the historical radiation observation data is obtained; performing error analysis on prediction results of the historical meteorological data and the historical radiation observation data to obtain an optimal WRF-Solar model parameter and an optimal WRF-DA 4D-Var assimilation parameter; the optimal WRF-Solar model parameters and the optimal WRF-DA 4D-Var assimilation parameters are utilized to construct a final prediction model; predicting the solar radiation and meteorological elements to be predicted by using the final prediction model to obtain prediction results of the solar radiation and meteorological elements; and obtaining an optical power prediction result based on the prediction results of the solar radiation and the meteorological elements. According to the invention, the problem of low accuracy of optical power prediction is solved.

The invention discloses an AI-based printing success rate judgment method and system. The method comprises the following steps: firstly, collecting real-time data of a printer; then preprocessing the real-time data of the printer to generate feature data used for judging the printing success rate; constructing an initial multi-dimensional feature prediction model for judging a printing success rate, and training the initial multi-dimensional feature prediction model by using historical printing job data to obtain a trained target multi-dimensional feature prediction model; and finally, inputting the feature data into a trained target multi-dimensional feature prediction model, and outputting a success rate prediction result of the printing job. By providing an AI-based target multi-dimensional feature prediction model, the pre-judgment capability of printing faults is improved, printing parameters can be adjusted in real time, the printing process can be optimized, and the printing failure rate can be reduced.

The invention relates to a method for constructing a pork water-retaining property prediction model based on a low-field nuclear magnetic resonance technology. The method comprises the following steps: (1) preparing a sample; (2) sample low-field nuclear magnetic resonance signal acquisition and inversion; (3) measuring water-retaining property parameters of the sample; and (4) model construction and evaluation. According to the method, an LF-NMR instrument is used for analyzing and optimizing acquisition parameters of a pork sample, proper characteristic parameters are selected as input parameters, proper pork water-retaining property parameters are selected as output parameters, and a pork water-retaining property prediction model based on a neural network, support vector regression and a random forest is constructed. The prediction method provided by the invention provides a scientific basis for the water-retaining property change of the pork in the processing, storage and transportation processes, and also provides more efficient and accurate detection service for food quality and safety monitoring.

The invention discloses a lithium ion battery health state prediction system and method based on multi-scale circulating voltage data. The system comprises a data acquisition module, a multi-scale feature extraction module, a graph structure sample construction module, a multi-scale prediction modeling module and a prediction result output module. The method comprises the following steps: collecting circulating voltage data of a battery in a charging and discharging process, extracting multi-scale health indexes closely related to the health state of the battery, and constructing dynamically connected graph structure data to reflect degradation conditions of the battery in different circulating periods. The system carries out training and prediction by using a multi-scale image convolution network, and fuses the characteristics of different time scales through an adaptive weighting mechanism, thereby enhancing the capability of capturing the change of the health state of the battery, and remarkably improving the prediction precision. The system can accurately predict the decline trend of the battery capacity and the future health state, and has excellent cross-material system universality. According to the method, the prediction error on a public battery data set is smaller than 0.7%, excellent prediction precision and wide application applicability are shown, the method can be used for a battery management system, and real-time monitoring and early warning of the health state of the battery are achieved.

The invention relates to the technical field of traditional Chinese medicine detection, in particular to a UPLC-Q / TOF-MS identification method for pinellia ternate and rhizoma typhonii flagelliformis. According to the method, on the basis of pinellia ternate, ginger processed pinellia, rhizoma typhonii flagelliformis and ginger processed rhizoma typhonii flagelliformis component information collected by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, characteristic substances are screened in combination with a multivariate statistics method, OPLS-DA analysis is carried out on pinellia ternate and rhizoma typhonii flagelliformis, differential compounds of the pinellia ternate and the rhizoma typhonii flagelliformis are found, and The method comprises the following steps: determining five key compounds for identifying pinellia ternate, rhizoma typhonii flagelliformis and processed products thereof by combining a plurality of analysis methods such as box plot analysis, thermogram analysis and correlation analysis, then determining the relative content of the five key compounds, and establishing a pinellia ternate and rhizoma typhonii flagelliformis identification model by utilizing OPLS-DA analysis. The detection of rhizoma typhonii flagelliformis doped in the rhizoma pinelliae and processed products thereof can be realized.

The invention discloses a drug-target affinity prediction method based on a multi-scale convolutional neural network, and the method comprises the steps: obtaining a sequence of a target protein and an SMILES character string of a drug small molecule, obtaining an SMILES word vector matrix and a protein word vector matrix based on word vector coding, constructing the multi-scale convolutional neural network, carrying out the learning of the SMILES word vector matrix, and carrying out the prediction of the drug-target affinity. The method comprises the following steps: constructing a protein word vector matrix, acquiring chemical and structural characteristics of drug small molecules, constructing a one-dimensional convolutional neural network, learning the protein word vector matrix, acquiring structural characteristics of target protein, and fusing the chemical and structural characteristics of the drug small molecules and the structural characteristics of the target protein through splicing operation. And expressing the fused features as drug-target features, constructing an affinity prediction model, and learning the drug-target features to obtain an affinity prediction value between the drug and the protein. Compared with a graph neural network and a pre-training model, the drug-target affinity can be better predicted, and the prediction capability is better than that of a deep learning method.

The invention belongs to the field of geological engineering construction, discloses a TBM (Tunnel Boring Machine) real-time slag monitoring system and a TBM real-time slag monitoring method for advanced geological forecast, solves the problems of lack of real-time performance, continuity and accuracy and low processing efficiency of surrounding rock state analysis in the traditional technology, and provides powerful support for advanced geological forecast in a TBM tunneling process. The method comprises the following steps: continuously acquiring rock slag images to obtain a rock slag acquisition video; cutting the rock slag acquisition video into an image sequence with a fixed time interval, and aligning the timestamp of each rock slag image with the recording time of the TBM tunneling parameters; then the cut rock slag image is preprocessed; then identifying the preprocessed rock slag image to obtain a classification result of the rock slag; and finally, the classification result of the rock slag and TBM tunneling parameters are fused to serve as input, and the surrounding rock grade is calculated through a surrounding rock grade grading model so that the tunneling strategy can be adjusted in real time. The method is suitable for underground engineering construction scenes.

The invention discloses a head and neck cancer local recurrence prediction method based on clinical data text coding and multi-mechanism domain adversarial learning. The method specifically comprises the steps of sample set division and image preprocessing; expanding multi-view data; performing text coding on the clinical data; constructing a multi-mechanism domain adversarial learning network based on fusion text coding; training the constructed multi-mechanism domain adversarial learning network to obtain a coding network of the CT image; according to the obtained coding network, coding the CT image data and carrying out pooling coding; a classifier network based on multi-modal decoupling bilinear pooling fusion is constructed; the classifier is trained; a multi-modal domain adversarial learning mode is adopted to train an encoder, auxiliary training in a domain discriminator is used for capturing domain invariant features needing to be used in a prediction task, visual features are enhanced by including cross-modal interaction in a classifier, class invariant features used in the prediction task are better captured, and the prediction efficiency is improved. And meanwhile, the trained feature extraction network can be used in a plurality of downstream tasks, so that the prediction capability is better, and the generalization capability is stronger.

The invention provides an earthquake nucleation seismic phase identification and model training method and system, a terminal and a medium, and particularly relates to the technical field of data monitoring. Performing feature extraction on the to-be-measured seismic data to obtain a plurality of types of data features; a trained machine learning model is utilized to process the data features, a plurality of seismic phase moments are determined, and the machine learning model is obtained through training based on seismic simulation data of different roughness; and based on the seismic phase moment, obtaining an earthquake nucleation seismic phase identification result. According to the scheme, the seismic simulation data with different roughness are used as training samples to train the model, so that the diversity and data volume of the training samples are increased, and the generalization ability of the model is improved; and meanwhile, the machine learning model has the capabilities of processing complex time sequence data and multi-target prediction, so that the trained model has good prediction capability, can process the complex time sequence data and is beneficial to improving the nucleation seismic phase recognition precision.

The invention discloses a ship lift accident safety mechanism thread pair gap detection device and method. The device mainly comprises a laser gap detection assembly and a via hole electric slip ring assembly. The laser gap detection assembly comprises a hoop, the hoop is mounted on the spherical trunnion, a spiral support supporting surface is mounted at the upper end of the hoop, a plurality of laser sensors are mounted on the spiral support supporting surface, and the plurality of laser sensors are spirally distributed and have the same lead angle as the safety screw; the plurality of laser sensors emit laser in the radial direction of the spherical trunnion, and the laser is vertically emitted to the nut column 1; the via hole electric slip ring assembly comprises a stator and a rotor. The lower end of the stator is mounted on the upper guide frame, the rotor is mounted on the spherical trunnion, a conducting ring is embedded in the rotor, an electric brush is embedded in the stator, and the conducting ring is in contact with the brush; the via hole electric slip ring assembly is used for supplying power to the laser sensor; meanwhile, the via hole electric slip ring assembly transmits data detected by the laser sensor to the PLC. According to the ship lift accident safety mechanism thread pair gap detection device and method, real-time detection of the thread pair gap can be achieved.

The invention discloses a blood gas result judgment method and system, and the method comprises the steps: collecting a blood sample of a patient, and obtaining a multi-dimensional biological identification parameter set for blood gas analysis from the blood sample; carrying out comprehensive analysis on the multi-dimensional biological identification parameter set, and calculating a blood comprehensive dynamic index; analyzing a molecular interaction condition in the blood sample, and calculating a blood molecular interaction index; synthesizing the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biological spectrum performance score; and comparing the blood biological spectrum performance score with a preset standard performance score range to obtain a blood gas analysis and judgment result. According to the embodiment of the invention, more biological identification parameters can be integrated, a more individualized and dynamic diagnosis result is provided, and the method and the device have stronger predictive ability and wider clinical adaptability.

The invention discloses an NFC testing method based on machine learning, and relates to the technical field of wireless communication testing, and the testing method comprises the specific steps: S100, data collection and preprocessing: carrying out the basic testing of an NFC signal under different environment conditions, collecting the multi-dimensional data of signal intensity, transmission speed and error rate, and carrying out the data collection and preprocessing; by introducing deep fusion of the electromagnetic field theory and the machine learning technology, dimensions and methods of NFC testing are greatly enriched, the electromagnetic field theory serves as an important branch of physics, an accurate mathematical model and an experimental verification method of the electromagnetic field theory provide a solid theoretical basis for propagation characteristics of NFC signals in a complex environment, and the electromagnetic field theory and the machine learning technology are combined in advance. In combination with the strong data processing and prediction capability of a machine learning algorithm, the attenuation, reflection and diffraction phenomena of NFC signals in different media can be predicted theoretically, the model can be continuously corrected and perfected through actual test data, and the accuracy and reliability of a prediction result are ensured.

The invention relates to an automobile supply chain order user data management method, system and device and a medium, and the method comprises the steps: obtaining multi-channel order data, and carrying out the initial user analysis, and obtaining the initial user data; supply chain management information is acquired, and synchronization processing is performed on the initial user data, so that corresponding order user data can be obtained; acquiring historical order data, and performing order quantity trend prediction on the multi-channel order data and the supply chain management information to obtain corresponding order period prediction information; performing management analysis on the order user data to obtain an initial order management scheme; and performing real-time scheme optimization on the initial order management scheme according to the order period prediction information to obtain a supply chain order management strategy. According to the invention, the overall operation efficiency of the supply chain can be improved, and stock overstock can be effectively reduced.

The embodiment of the invention provides a vehicle part fault prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of vehicle fault prediction. The method comprises the following steps: acquiring historical part operation data; performing fault identification on the historical part operation data to obtain historical fault part information; performing associated information mining on the historical fault part information to obtain historical fault part associated information; performing fault information clustering on the historical fault part information to obtain historical fault part clustering information; performing parameter optimization on a preset part fault prediction model according to the historical fault part information, the historical fault part association information and the historical fault part clustering information to obtain a target part fault prediction model; and obtaining current fault part information, and performing fault prediction on the current fault part information through the target part fault prediction model. According to the embodiment of the invention, the prediction accuracy of the vehicle part fault can be improved.

This invention discloses a liquid-cooled collaborative intelligent temperature control method and system based on layered PCM, relating to the field of liquid-cooled temperature control technology. The method includes: S1, real-time acquisition of liquid-cooled temperature control data and preprocessing of the data; S2, calculation of the sensible and latent heat absorption of the outer and inner PCM layers, assessment of the risk of thermal imbalance on the conductor surface, determination of the current thermal state, and output of a thermal risk warning signal; S3, assessment of the degree to which cable bending weakens thermal coupling performance, triggering a layered PCM collaborative temperature control strategy, and execution of graded control commands for the power pump, circulation pump, and three-way valve; S4, assessment of the deviation between the current heat exchange capacity and the heat load demand, correction of the cooling response coefficient, and optimization of the PCM collaborative temperature control strategy. This solves the problems of unstable thermal coupling between the coolant pipe and the heating conductor in existing liquid-cooled cables, and the tendency for pipe deformation and contact thermal resistance and localized hot spots to occur with frequent bending.

The utility model provides a remote fire extinguishing control system for an unattended machine room. The remote fire extinguishing control system comprises a programmable logic controller (PLC), a network camera, a mobile router, a remote control terminal, a fire eye visible fire alarm terminal and a plurality of groups of fire extinguishing mechanisms, each fire extinguishing mechanism comprises a starting gas cylinder, a fire extinguishing agent gas cylinder, an electromagnetic cylinder head valve, a pneumatic container valve and a pneumatic valve. According to the remote fire extinguishing control system for the unattended machine room, fire behavior occurring in the machine room can be pre-judged in the first time, fire extinguishing operation can be remotely started in the first time, the pre-judgment performance and the fire extinguishing effect of the fire behavior occurring in the unattended machine room are effectively improved, and meanwhile the remote fire extinguishing control system for the unattended machine room can be adjusted according to the increasing, decreasing and updating requirements of equipment in the machine room. And additional fire extinguishing mechanisms are added at any time.

The invention provides a method for rapidly characterizing physical parameters of traditional Chinese medicine powder. The method comprises the following steps: (1) determining a near infrared spectrum of a traditional Chinese medicine powder sample; (2) measuring physical parameters of the traditional Chinese medicine powder sample, and constructing a physical fingerprint spectrum; and (3) preprocessing the near infrared spectrum, screening spectrum variables related to the physical parameters, establishing a spectrum variable model, and representing the physical parameters. According to the method for rapidly representing the physical parameters of the traditional Chinese medicine powder, rapid conversion of the physical fingerprint spectrum of the powder is achieved; after model performance investigation, the GRNN model is found to have the best prediction capability on the physical parameters of the medicinal material powder, and the GRNN simplified model still shows satisfactory prediction performance; the result proves that the NIR spectrum technology has great application potential in the rapid characterization of the physical properties of the pellet core and the traditional Chinese medicine powder.

The application provides a trajectory planning method and system of a mechanical arm continuous synchronization mode, relates to the technical field of mechanical arms, and collects operation related data of a mechanical arm system; a digital twin model is used to simulate the movement process of the mechanical arm along a preset movement trajectory, so as to generate a risk prediction result of the mechanical arm in the continuous synchronization mode; the preset movement trajectory is divided into multiple trajectory segments, and each trajectory segment is optimized and integrated based on the risk prediction result and the movement characteristic parameters of the mechanical arm, so as to form a preliminary operation trajectory; operation state data is analyzed, so as to generate data anomaly information; each mechanical arm in the mechanical arm system is controlled to move according to the preliminary operation trajectory, and real-time operation data of each mechanical arm is collected; the preliminary operation trajectory is adjusted in combination with the data anomaly information and the risk prediction result, so as to obtain a target operation trajectory of the mechanical arm, and safe, efficient and self-adaptive target trajectory operation of the mechanical arm in the continuous synchronization mode is realized.

The invention discloses a biomarker for predicting severe treatment of a COVID-19 patient and application of the biomarker, and relates to the technical field of biomedicine. According to the invention, a COVID-19 patient severity prediction or auxiliary prediction model which is obtained by constructing biomarkers including Gemelia haemolsos, Streptococcus sp.A12, Fusobacterium pseudoperiodontium, Gemelia mollissima, Villonella atypical, Villonella rogosa and Prevotella melaninogena is found, the model is used for predicting and evaluating the severity of the COVID-19 patient, the sensitivity is high, and the specificity is good. The invention further discloses a method for predicting and evaluating the severity of the COVID-19 patient severity, and the method has the advantages that the method can be used for predicting and evaluating the severity of the COVID-19 patient severity;

This invention relates to the field of fall detection technology, specifically disclosing a millimeter-wave radar fall detection method that integrates HRV nonlinear features and 3D point clouds. The method includes the following steps: acquiring the inter-heart rate interval (IBI) sequence and 3D point cloud of the target in parallel based on millimeter-wave radar signals; extracting HRV nonlinear physiological features based on the IBI sequence and biomechanical features based on the 3D point cloud; aligning the HRV nonlinear physiological features and biomechanical features to a unified time axis and performing feature coupling calculations to obtain four types of coupling indices; the four types of coupling indices are: cardiac-gait phase coupling, exercise-induced heart rate response, cardiac-triggered motion variability, and joint entropy; and performing fall detection on the target based on a three-level progressive detection mechanism. This invention effectively captures abnormal physiological signals before a fall by establishing an intrinsic correlation between physiological and motion signals, effectively solving the problem of warning lag caused by traditional methods relying solely on motion features.