657 results about "Baseline data" patented technology

The invention relates to the technical field of electricity meter metering, and discloses an electricity meter adaptive error compensation method and system, and the method comprises the steps: calibrating an electricity meter, collecting baseline data under different environment conditions, and recording a measurement error; environment data and ammeter data are collected in real time through an ammeter, and key features are extracted; constructing an error model, and predicting the error of the current ammeter data; and performing real-time correction and parameter adjustment according to the prediction error, and outputting the corrected measurement data. By introducing a real-time error prediction and dynamic compensation mechanism based on environmental data and combining a particle swarm optimization algorithm to optimize regularization parameters in a ridge regression model, the measurement precision of the electric meter under a complex environmental condition is effectively improved. The method can adaptively adjust the measurement data of the electric meter, reduces errors caused by environmental factors, improves the accuracy and stability of electric power measurement, and provides more reliable data support for an electric power management and monitoring system.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention provides a lower limb and gravity center dynamic balance posture early warning method based on a body measurement all-in-one machine, and the method comprises the steps: obtaining a pelvis forward inclination angle, an arch height and a gravity center position under a standard standing posture, taking the pelvis forward inclination angle, the arch height and the gravity center position as individualized baseline data, and carrying out the statistical analysis of the baseline data, and generating a dynamic balance reference value reflecting the normal balance capability of an individual; on the basis of the dynamic balance reference value, in combination with a gravity center forward trend and a lower limb muscle compensation mode, a balance offset index in standing posture change is calculated through a time sequence analysis algorithm, and a dynamic balance risk assessment result is obtained; and if a dynamic balance risk assessment result exceeds a danger threshold, clustering analysis is carried out on associated parameters of the pelvic forward inclination angle and the arch height, grouping processing is carried out on the associated parameters, and potential reasons causing balance capability degradation are identified.

The invention relates to the field of pilot psychological competency evaluation, and discloses a civil aviation pilot psychological competency evaluation method and system fused with biofeedback, and the method comprises the following steps: S1, collecting the physiological data and behavior data of a pilot in a baseline test situation, and building individualized psychological load baseline data; s2, in the virtual situation, collecting multi-modal data, including physiological data and behavior data, of the pilot in real time; S3, analyzing the data collected in real time through a multi-modal data fusion model, and generating a psychological state score of the pilot; s4, providing real-time feedback and situation difficulty adjustment based on the psychological state score; and S5, generating a personalized psychological competency assessment report. The psychological state analysis of multi-modal data fusion is matched with the real-time physiological data acquisition, so that the dynamic monitoring and accurate evaluation effects on the psychological load of the pilot are realized, and the defect that the psychological state of the pilot is difficult to dynamically capture in real time in the prior art is overcome.

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The invention relates to a nurse handover management system and method, and relates to the technical field of data processing, and the system comprises a real-time monitoring module which is used for constructing an individualized baseline database of a patient; the data integration module is used for outputting a multi-dimensional structured data set; the intelligent analysis engine is used for identifying clinical state change information of the patient and generating a corresponding risk early warning score; the decision support module is used for generating targeted nursing intervention suggestions; the interactive shift exchange platform is used for generating a structured shift exchange abstract based on the risk early warning score, the clinical change information and the nursing intervention suggestion, and dynamically displaying the structured shift exchange abstract through a mixed reality technology; and the closed-loop optimization module is used for receiving shift-change feedback information and subsequent execution data of the shift-change party, evaluating the completeness of shift-change information, updating a nursing plan, and optimizing and adjusting analysis and recommendation parameters of the system. The system can improve the accuracy and efficiency of nurse handover management.

The invention discloses an aero-engine group health evaluation method based on multi-working-condition dynamic clustering, and belongs to the field of aero-engine health state evaluation. The method comprises the following steps: firstly, carrying out clustering analysis on set parameters in engine operation data, and carrying out merging processing on small-scale abnormal clusters to obtain a working condition category division result; secondly, constructing a health baseline data set, carrying out standardized preprocessing on sample data in a working condition category division result, and carrying out nonlinear dimensionality reduction to obtain a low-dimensional feature data set; thirdly, performing clustering analysis on the low-dimensional feature data set by adopting a Gaussian mixture model, and calculating an average mahalanobis distance between a sample of each clustering category and a health reference center to obtain multi-level health levels corresponding to different clustering categories; and finally, through fusing the membership soft probability and the sample individual mahalanobis distance, constructing a continuous health score and obtaining a health grade determination interval. According to the method, health state characteristics under different working conditions can be effectively identified, individual difference modeling and group transverse comparison evaluation are supported, and the accuracy is improved.

The invention relates to the technical field of biomechanical measurement and analysis, in particular to a home textile recommendation method and system based on child health data monitoring and analysis. The method comprises the following steps: collecting an original point cloud coordinate set of a sleeping posture of a child; extracting a cervical curvature parameter group according to the extracted cervical curvature parameter group of the skeleton key point spatial distribution diagram; performing cervical vertebra and body position analysis according to the cervical vertebra curvature parameter group to obtain cervical vertebra curvature-body position holographic data; collecting a static pressure baseline data set according to the cervical curvature-body position holographic data; performing body position-pressure correlation analysis according to the static pressure baseline data set to obtain a body position-pressure mapping relation table; and generating a pressure distribution index map according to the body position-pressure mapping relation table. According to the method, the physical sign data, the pressure distribution and the physiological curvature change of the children in the natural sleep state are comprehensively collected and analyzed, so that high-precision, whole-process and personalized healthy home textile recommendation is realized.

The invention relates to the technical field of electrical fault detection, in particular to a mobile power supply fault detection method and system based on intelligent diagnosis, and the method comprises the following steps: collecting voltage and current data of a mobile power supply in a normal operation state, and generating an electrical performance baseline data set through recording the timestamp of the data and a corresponding electrical performance index; and based on the electrical performance baseline data set, extracting time sequence features of voltage and current to obtain electrical performance feature data. According to the invention, based on voltage and current data acquisition in a normal state of the mobile power supply, a baseline data set covering timestamps and electrical performance indexes is established, and detail information of electrical performance changes is captured by extracting time sequence features such as peak values, mean values and standard deviations; meanwhile, frequency spectrum features are accurately extracted by means of fast Fourier transform, the intensity and phase information of frequency components is mastered, then signals are decomposed through wavelet transform, and richer electrical performance anomaly information under multiple scales is obtained.

The embodiment of the invention discloses an equipment health assessment method and device based on multi-dimensional data fusion and dynamic weight, and relates to an intelligent monitoring and health assessment technology of an equipment complex system in the field of equipment health management. The method comprises the following steps: describing the change condition of health parameters along with the increase of equipment storage life from the angles of the regression trend of the health parameters along with time, the incidence relation among different health parameters and the probability distribution of health parameter data, and accurately reflecting the difference between health parameter monitoring data and baseline data. A dynamic weight updating method in the whole storage process is provided, and the weights of different health parameters in the process of calculating the health indexes are scientifically calculated. Therefore, the problem that health parameter monitoring data are scarce due to the fact that the equipment cannot be frequently powered on for testing in the actual storage and use process is effectively solved, and the assessment accuracy of the health state of the equipment is improved under the condition that the monitoring data are limited.

The present invention relates to the technical field of waste gas component monitoring, specifically a waste gas component monitoring method for organic waste treatment, comprising the following steps: by continuously sampling waste gas, capturing waste gas samples within a target time period, analyzing the compound components in the samples, obtaining preliminary analysis results of waste gas compounds, setting a baseline of compound concentration and identifying a floating interval according to the preliminary analysis results of waste gas compounds, and obtaining waste gas compound baseline data. In the present invention, dynamic monitoring of waste gas samples is achieved through continuous waste gas sampling and analysis, and a compound concentration baseline is set to identify abnormal compounds. This monitoring method can deeply analyze the cause of the abnormality, quickly locate the problem, and adjust parameters such as the amount of input materials and the processing temperature to achieve rebalancing of waste gas components, continuously monitor changes in compound concentration to verify the adjustment effect, greatly improve waste gas treatment efficiency and reliability, effectively meet environmental protection standards, and reduce pollution risks.

Systems, methods, and apparatus are provided for automated identification of baseline data and changes in state in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the baseline data and changes in state based upon the measured and analyzed data in near real time, which is stored on each apparatus or device and / or on a remote server computer that aggregates data from each apparatus or device.

The invention discloses a land utilization dynamic change monitoring method and system based on multi-modal remote sensing data, and relates to the technical field of multi-modal remote sensing data analysis. The system comprises a data acquisition and preprocessing module, a consistency baseline construction module, a real-time data analysis module and an abnormality diagnosis and processing module. The data acquisition and preprocessing module realizes space-time alignment of multi-source data; the consistency baseline construction module divides land utilization types based on geographic features and establishes a baseline database; the real-time data analysis module screens suspected abnormal areas through difference indexes; the abnormity diagnosis and processing module distinguishes data quality abnormity and emergency abnormity based on the characteristic association relation, the data quality abnormity triggers data restoration and verification, and the emergency abnormity marks a high-priority area and outputs position information. According to the system, through multi-modal data fusion and correlation analysis, high-precision monitoring and abnormal intelligent classification of land utilization changes are realized.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

The invention provides a multi-source network equipment fault mode identification and positioning method and system, and relates to the technical field of computer fault diagnosis, and the method comprises the steps: collecting equipment hardware data and software data, generating a fault feature vector, collecting adjacent equipment information, and constructing a communication relation graph and a link abnormity probability distribution graph; determining fault source equipment and probability through correlation analysis; according to the method, the processing instruction is generated, and the baseline data is updated, so that the computer network fault can be accurately identified and quickly positioned, the fault processing efficiency is improved, and the network maintenance cost is reduced.

Systems and methods are described for batch materialization of an incremental change data capture (CDC) changeset with full row changes. The primary keys are extracted from the incremental CDC changeset and an indication of the extracted primary keys are broadcast to a plurality of executors. The primary keys may be added to Bloom filter or a plurality of Bloom filters that are broadcast to the executors. Each executor filters a baseline data table based on the extracted primary keys to generate a baseline match dataframe with all primary keys matching the extracted primary keys, and a baseline unmatched dataframe with all primary keys not matching the extracted primary keys. Each executor receives full row changes from a partitioned incremental CDC changeset and combines the changes with the baseline unmatched dataframe to produce a final changed baseline data table.

The invention discloses an underground pipeline three-dimensional deformation quantitative inspection method, which comprises the following steps: processing a multi-source sensing sequence to generate a baseline data packet; the method comprises the following steps of: performing first-stage on-line correction, acquiring a medium state agent quantity by applying micro-excitation sampling combined by a micro-amplitude angle and double power to laser, and performing joint inversion to obtain section geometric data subjected to medium correction by utilizing a forward model with endogenous refraction and scattering correction items; second-stage scale robustness processing is carried out, in a unified optimization framework, motor current-based dynamic consistency constraint, geometric conservation constraint and wellhead external reference strong constraint are fused, and a globally consistent pose track is solved; and performing three-dimensional reconstruction and deformation quantification according to the corrected section data and the pose track. According to the invention, the accuracy of geometric measurement of the pipeline and the global consistency of the pose track in a complex environment such as a humid environment can be improved.

The invention relates to a wind power plant current collection line fault early warning method, system and device and a medium, and belongs to the technical field of line fault detection of intelligent power grids, and the method comprises the steps: dividing a 35KV current collection line of a wind power plant into a plurality of sections; respectively judging whether each section has a fault or not; if yes, determining the section as a fault section; if not, performing dynamic deviation calculation on the current environment data of the section and the environment baseline data to obtain a dynamic environment feature vector; calculating a dust retention prediction growth rate and a dust retention evaluation index; determining the section with the dust retention evaluation index exceeding a preset threshold value as a dust retention risk section; and fusing the multi-dimensional feature vectors of the dust accumulation risk section, generating a comprehensive fault feature vector, performing hidden arc fault prediction, obtaining a hidden arc fault risk probability, determining a risk early warning level, and further generating risk early warning information and a risk maintenance strategy. According to the invention, rapid positioning of dominant faults and accurate prediction of hidden faults of the current collection line can be realized.

The invention discloses a pneumatic joint sealing performance intelligent test method and system based on dynamic pressure spectrum and multi-parameter fusion, and belongs to the technical field of pneumatic element sealing detection. According to the method, a dynamic pressure spectrum simulating an actual working condition is applied to a tested joint, pressure, temperature and ultrasonic signals are synchronously acquired, environmental thermodynamic interference is eliminated by adopting a temperature real-time compensation algorithm, and pressure change purely caused by leakage is separated by deducting system baseline data; and finally, fusing double characteristic parameters of the pressure leakage rate and the ultrasonic energy, and comprehensively evaluating the sealing performance through an intelligent judgment model. The system comprises a gas circuit tool module, a multi-sensing module and a control processing module, can effectively solve the problems that a traditional static pressure maintaining method is greatly influenced by temperature, transient leakage cannot be detected and the like, has the advantages of being high in detection precision, high in anti-interference capability, capable of simulating real working conditions and the like, and remarkably improves the test reliability and efficiency.

The invention discloses a container anomaly detection method, electronic equipment and a storage medium. Acquiring a system call log, network flow data and a resource utilization rate in the container, and comparing the system call log, the network flow data and the resource utilization rate in the container with respective baseline data to obtain abnormal behavior alarm data; performing time-space alignment on the abnormal behavior alarm data and the process tree topological structure, and calculating an edge weight between process nodes by analyzing a mapping relationship between the abnormal behavior alarm data after time alignment and the process nodes in the process tree topological structure based on a triggering condition probability; a weighted causal graph is constructed through the mapping relation between the abnormal behavior alarm data and process nodes in the process tree topological structure and the edge weight between the process nodes; a root cause node with in-degree of 0 is extracted from a weighted causal graph, a root cause propagation path is backtracked along a causal edge, multi-modal data can be fused, the false alarm rate is reduced, and evolution of attack behaviors is reflected in real time.

The invention discloses a new energy charging pile remote control method considering a battery health state. The method comprises the following steps of obtaining battery full life cycle data, charging pile real-time working condition data and charging area power grid dynamic data, and performing preprocessing to generate battery health baseline data, pile body operation reference data and power grid dispatching reference data; extracting current health state parameters of the battery based on the health baseline data of the battery, calculating a charging adaptation coefficient in combination with the operation reference data of the pile body, analyzing the redundant capacity of the power grid, and constructing a charging control decision model; outputting an initial charging parameter combination based on the charging control decision model, performing health protection correction, and generating a basic remote control instruction; and issuing the basic remote control instruction to the target charging pile to calculate an instruction execution deviation degree, and performing dynamic iterative optimization to generate a new energy charging pile remote control scheme. According to the invention, collaborative optimization of safety and high efficiency of new energy vehicle charging and battery charging protection can be realized.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention discloses a sand and dust pollution health risk early warning terminal and early warning method, and the method comprises the steps: obtaining multi-source input data, such as meteorological parameters, sand and dust pollutant concentration and geographical environment, in real time, and carrying out the preprocessing through a data fusion algorithm, thereby obtaining a standardized data set; based on the data set, utilizing a trained time sequence prediction model to output a sand and dust pollution level prediction value of a future preset time length; meanwhile, an exposed crowd sensitivity index and regional health baseline data containing the disease incidence rate and the meteorological sensitive crowd proportion are constructed; calculating a comprehensive health risk index through a dynamic health risk assessment model in combination with the predicted value, the sensitivity index and the baseline data; and when the index exceeds a preset threshold dynamically adjusted based on the incidence peak range in the baseline data, generating a graded early warning signal and issuing the graded early warning signal in real time, thereby realizing accurate early warning of the dust pollution health risk.

The invention discloses a concrete structure tall formwork supporting system stress deformation actual measurement method, and relates to the technical field of constructional engineering structure safety, the method comprises the following steps: determining initial stress distribution reference data through a pre-established supporting system geometric model database, and determining final construction adjustment guidance data; according to the final digital construction scheme, a construction safety assessment report is automatically generated, final conclusion data of safety assessment is determined, a result is fed back to the design optimization module through the data transmission module, and the unsolved uneven stress or deformation problem is extracted from the feedback data; obtaining input parameters of a new round of design optimization, and obtaining a continuously improved design iteration result; according to the actual measurement method for stress deformation of the high and large formwork supporting system of the concrete structure, the design rationality and construction safety of the high and large formwork supporting system are improved, and intelligent and digital whole-process management and control are achieved.

The invention relates to the technical field of pipe network detection, in particular to a target detection method, system and device based on image recognition and a medium, and the method comprises the following steps: obtaining a data packet in a mechanical mining process; preprocessing the data packet to obtain a target point cloud; performing target positioning of the underground pipe network according to the preprocessed data to obtain a target area; a baseline database of the underground pipe network in the target area is obtained, target detection is conducted on the change trend of the baseline database along with the time sequence, and health data are obtained; according to the acquired health data and the data packet, acquiring detection data of the target area; through time synchronization packaging and semantic fusion of heterogeneous sensor data, a target point cloud containing space-time correlation characteristics is constructed, and progressive monitoring of the cable health state is realized in combination with time sequence analysis of a dynamic baseline database. The problems of leak detection and false detection of cable detection in a complex environment are effectively solved.

The invention discloses a machine vision emotion recognition and interaction system and method based on multi-modal fusion, and the method comprises the steps: obtaining the multi-modal emotion expression data of a user and the basic information data of the user, constructing a user emotion feature initial recognition model, carrying out the calculation through the user emotion feature initial recognition model, and obtaining a user emotion feature recognition result; obtaining an original emotion recognition result; based on the original emotion recognition result, historical recognition deviation data in the long-term interaction process of the user is obtained and preprocessed, and a processed individual emotion baseline data set is obtained; optimizing the user emotion feature initial recognition model by using the individual emotion baseline data set, and constructing a user feature adaptive emotion recognition model; and performing multi-modal emotion recognition prediction based on the user feature adaptive emotion recognition model. According to the method, the emotion discrimination accuracy in a multi-user recognition scene is remarkably improved, and the problem of misrecognition caused by expression differences among the users is solved.

The invention discloses an inter-satellite baseline measurement method and device based on a rotary table, and belongs to the technical field of space measurement. The method is realized by an inter-satellite baseline measurement device in the measurement system, and the measurement system further comprises a three-axis turntable, a laser vision fusion measurement device and a GNSS (Global Navigation Satellite System), the laser vision fusion measurement device comprises a laser ranging module and a vision measurement module. The laser vision fusion measurement device is fixedly connected with the three-axis turntable, the laser vision fusion measurement device rotates along with the rotation of the three-axis turntable, and an inter-satellite baseline preliminary measurement result is determined by using GNSS navigation data; the inter-satellite preliminary measurement result comprises a preliminary baseline direction; according to the initial base line direction, controlling the three-axis turntable to adjust towards the initial base line direction, so that the laser vision fusion measurement device is aligned with the auxiliary star target; and performing fusion measurement on the auxiliary star target by using the laser vision fusion measurement device to obtain actual baseline data between the auxiliary star target and the laser vision fusion measurement device. According to the invention, high-precision inter-satellite baseline measurement can be rapidly realized.

The invention relates to the technical field of spectrum baseline correction, and provides a spectrograph baseline correction method and system based on improved empirical mode decomposition, and the method comprises the following steps: employing a sliding window smoothing method, and carrying out the preprocessing of obtained measurement spectrum data; an improved empirical mode decomposition algorithm is adopted, fitting is carried out on the preprocessed measurement spectrum data to obtain baseline data, and the improved empirical mode decomposition algorithm adopts least square fitting to construct upper and lower boundary envelopes of the spectrum data; subtracting the baseline data obtained by fitting from the obtained measurement spectrum data to obtain the measurement spectrum data after the baseline is removed; and sequentially carrying out abnormal point detection and removal, spectral power calibration and wavelength calibration on the measured spectral data after baseline removal to obtain spectral data after baseline correction. The spectral baseline is identified by improving empirical mode decomposition, the stability and boundary adaptability of mode decomposition are enhanced, and a more accurate baseline fitting result is obtained.

The invention discloses a PLC behavior measurement method, device and equipment based on trusted 3.0 and national cryptographic algorithms, and relates to the field of safety control in an industrial control system.The method comprises the steps that a hardware trust root module installed on a PLC in advance is used for conducting trusted verification on the PLC during starting, and the module is constructed based on the trusted computing 3.0 standard; after verification succeeds, a behavior feature vector of the PLC operation state is obtained based on trusted computing 3.0, wherein the vector comprises at least one of an integrity verification parameter, a resource consumption parameter, an operation parameter and an associated parameter; generating behavior measurement data based on trusted computing 3.0, and measuring a vector through an Euclidean distance algorithm in combination with behavior baseline data; comparing the measurement result with a preset Euclidean distance threshold value, encrypting the measurement result by adopting a domestic key algorithm, and transmitting a comparison result to a security management platform; according to the method, trusted computing 3.0 (TC3.0) and the national cryptographic algorithm are combined, accurate behavior measurement is carried out on the operation state, and the equipment safety and the attack resistance can be enhanced.