219 results about "Continuous analysis" patented technology

The invention relates to the technical field of communication data analysis, and discloses a communication network service health degree analysis method, system and device and a medium, and the method comprises the steps: obtaining real-time link state data, carrying out the quantitative analysis, and judging whether the link jitter exceeds a preset threshold value or not; when abnormality is detected, abnormal points can be positioned and the influence range of the abnormal points can be analyzed, so that the influence degree of nodes on the abnormal diffusion path can be identified; calculating a service quality index change value, and determining a starting point and a diffusion direction of chain degradation; by analyzing historical traffic data of the key nodes, a service traffic change trend and traffic dense area distribution characteristics are obtained. According to the method, the bandwidth allocation strategy is dynamically optimized, the bandwidth resources at the junction are reconfigured, and the regional service health degree is evaluated. And if the degradation is not effectively controlled, monitoring and detection mechanism parameters can be adaptively adjusted, and continuous analysis and control of link abnormity are realized, so that the network performance and the service quality are effectively improved.

The invention relates to the technical field of agricultural intelligent monitoring, in particular to a crop growth detection system and method based on machine vision, and the system comprises an image collection module, a morphological feature capture module, a growth trend judgment module, an abnormal region marking module and a state information output module. According to the method, crop images are collected through a multi-angle camera, leaf contours, stem bending and plant spacing are extracted, multi-dimensional modeling of morphology is realized, structural change identification is enhanced, time sequence comparison of key morphological characteristics is realized, identification precision and time efficiency are improved, leaf and stem change trends are continuously analyzed, offset is quantified, and growth abnormity is early warned in advance; health degradation identification is combined with fluctuation area marking, dynamic monitoring is achieved, high-risk positioning is carried out on a time overlapping area, time-space locking is enhanced, precise management is assisted, the process is from image analysis to abnormal focusing, an information reasoning chain is established, and the monitoring precision and response efficiency are remarkably improved.

Described herein are variations of an analyte monitoring system, including an analyte monitoring device. For example, an analyte monitoring device may include an implantable microneedle array for use in measuring one or more analytes (e.g., glucose), such as in a continuous manner. The microneedle array may include, for example, at least one microneedle including a tapered distal portion having an insulated distal apex, and an electrode on a surface of the tapered distal portion located proximal to the insulated distal apex. At least some of the microneedles may be electrically isolated such that one or more electrodes is individually addressable.

The invention relates to the technical field of soil organic carbon monitoring, and particularly discloses a monitoring method and system for soil organic carbon, and the method comprises the steps: dividing a monitoring region, fusing an unmanned aerial vehicle and a remote sensing image, analyzing an attribute parameter set, and dynamically judging and adjusting an initial image collection process to ensure the data quality; sub-region division is carried out based on the target region fusion image, and whether a region division strategy is optimized or not is dynamically decided by evaluating image feature parameters of each sub-region; and finally, after region division is completed, collecting and marking the soil organic carbon content of each sub-region, and judging and optimizing a subsequent monitoring scheme through comparative analysis with historical data. According to the method, a feedback workflow including three core links of data acquisition, space dynamic division, monitoring result calibration and scheme optimization is established. Wherein the key parameters of each link are continuously analyzed, and the subsequent steps are dynamically judged and adjusted, so that a self-perfect dynamic optimization mechanism is formed.

AI-powered data breach detection and prevention system that includes: a threat monitoring module for continuous analysis of network traffic, system logs and user activities; an AI-powered anomaly detection module that uses machine learning to detect threats, including zero-day attacks and insider threats; a behavioral analysis module that tracks user authentication, access patterns, and privilege escalations; an automated incident response module that isolates threats, blocks unauthorized access, and alerts security teams; a threat intelligence module that integrates global cybersecurity databases for proactive risk mitigation; a multi-layered security system with endpoint protection, encryption and role-based access control; a privacy-preserving mechanism that uses homomorphic encryption and federated learning to protect sensitive data; a compliance and audit module that ensures compliance with cybersecurity regulations.

The invention discloses a micro-reagent ammonia nitrogen on-line continuous analyzer and an analysis method, and belongs to the technical field of water quality ammonia nitrogen monitoring, the micro-reagent ammonia nitrogen on-line continuous analyzer comprises a first injection pump, a second injection pump, a multi-channel valve, a liquid storage ring and a reaction tank; liquid outlets of the first injection pump and the second injection pump are respectively connected with one end of the liquid storage ring and a circulating port of the reaction tank through different channels of the multi-channel valve; a circulating port of the reaction tank is connected with a first peristaltic pump; the other end of the liquid storage ring is connected with a second peristaltic pump; liquid outlets of the first injection pump and the second injection pump are connected with a third peristaltic pump; a first reagent and a second reagent for ammonia nitrogen analysis are respectively stored in the first injection pump and the second injection pump; different channels of the multi-channel valve are respectively connected with a standard sample and a water sample which are used for ammonia nitrogen analysis, and cleaning water. The micro-reagent ammonia nitrogen on-line continuous analyzer provided by the invention can reduce cross contamination of reagents, reduce reagent consumption and realize short-interval continuous analysis.

The invention provides a cardiac autonomic neurodynamics quantitative evaluation method based on a phase ordering signal averaging technology. The cardiac autonomic neurodynamics quantitative evaluation method comprises an ECG signal preprocessing step, a QRS wave detection and processing step, a phase ordering signal averaging step and an autonomic neurodynamics quantitative analysis step. According to the cardiac autonomic neurodynamics quantitative evaluation method based on the phase ordering signal averaging technology, on the basis of inhibiting aperiodic interference and improving index stability in a complex physiological environment, continuous analysis of cardiac sympathetic nerve activity and cardiac parasympathetic nerve activity is carried out; time-varying CSI and CPI indexes are extracted to realize quantification of cardiac autonomic neurodynamics, and a lightweight algorithm framework is designed to adapt to real-time monitoring requirements of portable equipment.

The invention discloses an oil abrasive particle enrichment chip based on sound control and an LIBS (laser-induced breakdown spectroscopy) detection system and method.The enrichment chip is internally provided with an enrichment flow channel, and enrichment of oil abrasive particles is achieved through sound field driving; the detection system comprises a driving device, an enrichment chip, a detection chip, a pulse pump and an LIBS spectral analysis module, the driving device is used for sending out an electric signal with a preset frequency to excite piezoelectric ceramics to form a preset sound field in an enrichment flow channel, and oil abrasive particles are directionally gathered under the action of the sound field; the pulse pump is used for pumping the oil liquid enriched by the enrichment chip into the detection chip; the detection chip is used for carrying out secondary dispersion on the enriched oil liquid; the LIBS spectrum analysis module is used for carrying out breakdown detection on the oil liquid dispersed by the detection chip so as to obtain oil liquid spectrum data. Through a closed-loop system of sound field dynamic enrichment, pulse pump time sequence conveying and LIBS high-frequency detection, real-time continuous analysis of oil abrasive particle components is achieved, and the limitation of a traditional static sampling mode is broken through.

The invention discloses an automatic attendance checking, abnormal behavior detection and classroom condition analysis device based on computer vision, and relates to the technical field of intelligent education, the device comprises a front-end user interface, a rear-end server and a database; the system integrates attendance checking, abnormal behavior monitoring and classroom condition analysis functions, the classroom attendance checking module is provided with a dynamic window, a comparison algorithm is optimized, caching and early warning are combined, accurate non-inductive attendance checking and abnormity reminding are achieved, the abnormal behavior detection module fuses multiple algorithms and a dynamic threshold value, behaviors are recognized, instantaneous actions are filtered, continuous analysis is achieved, and the classroom attendance checking system is suitable for being popularized and applied. The method fills the blank of abnormal behavior analysis, realizes visual display of attendance and abnormal behavior detection results by superposing state labels through a front-end real-time monitoring panel and supporting historical data calling, automatically generates a classroom condition analysis report containing analysis data according to the detection results, provides real-time behavior feedback, assists teachers in mastering classroom dynamic conditions in real time, and improves the teaching efficiency. And support is provided for teaching decision making.

The invention discloses a landslide geological disaster deduction method and system based on an MPM method, and the method comprises the steps: collecting the multi-source data of a disaster scene, and synchronously constructing a digital twinborn model; a qualitative and quantitative two-stage inversion calibration model is adopted; a qualitative layer combines a monitored displacement direction and a deformation mode, and preliminarily inverts key parameters such as slip band cohesive force and an internal friction angle; the quantitative layer comparison model calculates the actual deformation range, collapse position and displacement, and parameters are optimized to match the actual landslide state; in landslide long-term evolution deduction, a rock-soil body aging characteristic model is embedded, an exponential function is used for describing annual scale parameter deterioration, prediction is divided into continuous analysis steps, MPM calculation is carried out based on a material point method, full-cycle simulation from the current year to the future years is completed, and deformation range, displacement and outburst risk results are output. And reliable decision support is provided for disaster prevention and reduction.

The invention provides a decision-making method and system for the influence of water and sand linkage above a slope on the erosion amount below the slope. The system comprises an experiment control unit, an REE element layout unit, a dynamic monitoring network unit and a data acquisition and analysis unit. The experiment control unit comprises a parameterization experiment design module and a process sample dynamic sampling module, and the REE element laying unit comprises a layering marking module and a tracer agent selection and stability control module; the method not only objectively describes the temporal and spatial change rule of relative erosion amounts of different parts of the slope in the erosion occurrence process, but also can continuously analyze the influence of water and sand coming from the upper part of a certain section of the slope on the erosion process below, thereby dynamically judging and measuring the slope sediment source and analyzing the influence of water and sand linkage above on the erosion process below. The purpose of monitoring the sediment source and the soil erosion amount of different terrain parts is achieved.

The invention relates to the technical field of resistance adjustment, in particular to a multifunctional spinning and a multifunctional spinning system, and the system comprises a pedaling frequency recognition module, a state judgment module, a stage control module, a load adjustment module and an output response module. According to the invention, through continuous analysis of the pedaling frequency and time information, accurate recognition of the rhythm fluctuation state, and combination of the linkage change of the flywheel rotation speed and the pedal pressure, the dynamic judgment of the motion stability and the matching mechanism of the training time and stage setting are realized; a resistance adjusting strategy is deduced according to the relation between flywheel acceleration and pedal pressure, the sensitivity and precision of regulation and control response are enhanced, the load weight is set by fusing the heart rate change trend, the resistance output is more matched with the individual physiological state, the load rhythm matching degree is optimized, and the training efficiency is improved. And the rhythm following capability, the resistance adjustment rationality and the training individuation level are integrally improved.

The invention discloses an automatic ship collision avoidance decision-making system, and relates to the technical field of ships, and the technical scheme is characterized in that the method comprises the steps: collecting environment data of a target ship in a navigation region and target ship dynamic data of the target ship in a navigation process; dividing the navigation area into navigation area blocks according to the risk level and the environmental data, and performing feature extraction on the dynamic data of the target ship to obtain dynamic feature parameters; analyzing and determining the normal variation range of the influence coefficients of the navigation region blocks with different risk levels to obtain a reference interval value; according to the reference interval value, performing statistics on influence coefficients between the current target ship and the surrounding environment and between the current target ship and other ships in the sailing process to obtain a statistical result, and comparing the statistical result with the reference interval value to obtain a collision avoidance risk early warning or continuous analysis instruction; the method has the effects of reducing the occurrence rate of collision accidents and improving the safety and orderliness of marine navigation.

The present invention relates to a self-propelled forage harvester (1) comprising a height-adjustable header (4) for receiving crop (2), working units (20) for processing the received crop (2), an unloading device (15) for discharging the processed crop (2), a camera system (16) for capturing images (41) of a crop flow (21) passing through the forage harvester (1), an image evaluation device (27) for evaluating the images (41), and a driver assistance system (17) for controlling the header (4), the working units (20), and the unloading device (15), wherein the driver assistance system (17) has a memory (40) for storing data and a computing device (39) for processing the data stored in the memory (40), wherein the image evaluation device (27) together with the driver assistance system (17),The front attachment (4) and the working units (20) form an automatic setting system, in that the image evaluation device (27) is configured to continuously analyze the images (41) of the crop flow (21) for the proportion of, in particular inorganic, impurities contained in the crop flow (21) by means of a machine learning algorithm and to transmit a derived degree of contamination (DC) to the driver assistance system (17), which autonomously and continuously adapts a setting of the front attachment (4) and / or at least one of the working units (20) depending on the degree of contamination (DC).

According to the building intelligent monitoring method and system provided by the invention, the same target person is locked through target detection / target tracking, and continuous analysis is carried out in a detection period, so that the misjudgment risk caused by other persons, shielding and instantaneous noise can be remarkably reduced, and the evaluation stability is improved. By fusing the deviation (abnormal sight value during walking) of the sight, the head orientation and the walking direction with abnormal gravity center and upper limb action (abnormal walking value), an early risk signal can be captured before the abnormal behavior is upgraded, and the sensitivity and the recognition timeliness of the abnormal state are improved. And an abnormal total value is formed by using weighted average and is compared with a threshold value, so that the balance of false alarm and missing alarm can be realized in different building scenes, and the alarm result has higher availability and credibility. Finally, by marking the target personnel and pushing the warning command, security personnel can obtain clear risk objects and early warning prompts in the first time, the response time is shortened, and the field disposal efficiency and the overall security level are improved.

The invention relates to a cerebrospinal fluid dynamic monitoring management method. Comprising the following steps: performing mode identification by using dynamic characteristics of cerebrospinal fluid pressure and flow velocity; by monitoring the flow velocity and pressure fluctuation of cerebrospinal fluid in real time, a comparison library of a normal mode and an abnormal mode is established; deep learning is carried out by using a fluid mechanics model and a neural network, and abnormal points of non-linear change are identified; through combination of a pressure sensor array and a flow velocity sensor, dynamic flow changes are captured in different areas of a cerebrospinal fluid system including a ventricle and a subarachnoid space, and abnormal fluctuations are identified through continuous analysis of time sequence data; by analyzing the interaction between the intracranial flow mode of cerebrospinal fluid and the compression expansion effect of brain tissues, an intelligent adjustment mechanism based on the flow pressure coupling effect is introduced; under the influence of cerebrospinal fluid pressure changes, deformation of brain tissue including ventricular expansion influences blood flow and tissue pressure in other intracranial areas.

A continuous analyte monitoring device includes an outer housing, a cover, a puncture unit, and a monitoring unit. The outer housing has first and second ends opposite to each other in a first direction. The second end has an implantation opening. The cover is detachably connected to the outer housing. A driving unit is movable along the first direction to perform an implantation operation. The puncture unit is movable along a second direction to perform a needle withdrawal operation. The monitoring unit includes a first monitoring component including a sensor and a second monitoring component including a signal processing module. Prior to use, remove the cover and perform a single trigger to enable the driving unit to move along the first direction, thereby partially inserting the sensor into a host and establishing an electrical connectivity between the first and the second monitoring components.

The invention relates to the technical field of network security, and discloses an enterprise data security management system based on a cloud platform, which comprises a data acquisition module, a data analysis module, a security evaluation module and an operation management module, the encryption state and the backup frequency of enterprise data stored in each resource pool of the cloud platform by a target enterprise are periodically acquired, behavior logs in the storage and reading process of the enterprise data in each resource pool are acquired in real time, and the behavior logs comprise an access behavior log, a transmission behavior log and a processing behavior log. Encryption states, backup frequencies and behavior logs in the resource pools are comprehensively obtained through regular collection and real-time collection of enterprise data in the resource pools, and static risk scores, dynamic risk trend scores and comprehensive risk scores in the resource pools are calculated by dividing a monitoring period into a plurality of continuous analysis windows. And identifying high-risk data in the resource pool.

The invention relates to the technical field of image recognition, in particular to a charging data anomaly detection method and system, and the method comprises the following steps: collecting multi-dimensional operation data in a charging process, and storing the multi-dimensional operation data according to a charging session; performing cleaning and time reference processing on the operation data, and generating a relative time sequence taking the charging start time as a zero point; carrying out abnormal value detection based on the relative time sequence, and removing data records judged to be abnormal; when it is detected that the recording time interval of adjacent data exceeds a preset threshold value, missing data are complemented through an interpolation method, and continuous analysis data are obtained. According to the method, comprehensive calculation and judgment are carried out by utilizing multiple characteristic parameters such as SOC consistency, voltage consistency, temperature consistency, capacity consistency and temperature rise rate, and the operation state of the battery in the charging process is reflected from multiple angles; the multi-index fusion analysis mode can effectively identify potential anomalies which are difficult to discover by a single index.

The invention belongs to the technical field of alarm monitoring, and discloses a fire-fighting equipment power supply monitoring alarm method and system, and the method comprises the following steps: obtaining an amplitude characteristic to generate a voltage reference, recognizing a difference amplitude direction to judge voltage abnormity, analyzing a phase change to prompt current abnormity, comparing temperature and pressure data to generate a combined alarm, and evaluating a link state to generate an uploading instruction. Multi-source data collaborative monitoring and abnormal response are completed; through continuous analysis of amplitude fluctuation, difference amplitude direction and phase change, a multi-parameter linkage trend identification mechanism is constructed, cross validation of measurement data is realized by combining bidirectional deviation comparison of pressure and temperature, a link packet loss rate and a delay monitoring result are fused to establish a communication state evaluation channel, an upload path switching instruction is dynamically generated, and a communication state is evaluated. The data transmission stability and response efficiency in an abnormal state are improved, early warning, type identification and linkage processing of electrical abnormality in a complex working condition are realized, and the sensing capability and the adaptive processing level of the system are enhanced.

Continuous analyte monitoring transmitter

Systems, devices, and methods provide up-to-date defibrillation shock recommendations. In an example method, multiple segments of an electrocardiogram (ECG) of an individual are detected from an individual receiving chest compressions. The multiple segments are evaluated to determine whether the individual is exhibiting a shockable heart rhythm. A medical device outputs a recommendation indicating whether a defibrillation shock is advised based on the most recent determination of the individual's heart rhythm. For example, the medical device outputs an up-to-date recommendation on-demand in response to an input signal from a user. In some examples, the medical device updates the recommendation based on ongoing analysis of the ECG.

The present invention provides a block stability analysis method within a continuum numerical analysis framework, comprising the following steps: S1, establishing a numerical analysis model; S2, separating the continuous grid; S3, reconstraining the separated grid; S4, applying initial constraints; S5, applying initial stress; S6, obtaining the initial stable state of the block; S7, analyzing the block safety factor; and S8, determining the block safety factor after support. This analysis method overcomes the limitations of traditional block stability analysis, which relies on theoretical and discontinuous analysis methods, and enriches the technical means of block stability analysis.

The application relates to a sampling device, in particular to a sampling device for real-time continuous analysis of end-exhaled air. The sampling device comprises first to fourth three-way valves, a first sampling ring, a second sampling ring, an air suction pump, a purge gas pipeline, an exhaled air pipeline, a carbon dioxide sensor and an analysis detector; the air inlets of the first sampling ring and the second sampling ring are connected with the purge gas pipeline through the first three-way valve and connected with the exhaled air pipeline provided with the carbon dioxide sensor through the second three-way valve; the air outlets of the first sampling ring and the second sampling ring are connected with the analysis detector through the third three-way valve and connected with the air suction pump through the fourth three-way valve; through linkage switching of the first to fourth three-way valves, the first sampling ring and the second sampling ring alternately carry out sampling and sampling, real-time accurate sampling of end-exhaled air in each breathing cycle and continuous sampling analysis are realized. The application has high time resolution, accurate end-exhaled air sampling and constant volume, and good consistency of detection results.

A system for at least partial closed-loop control of a medical condition is disclosed. The system includes at least one medical fluid pump. The medical fluid pump including a sensor for determining the volume of fluid pumped by the pump. Also, at least one continuous analyte monitor, and a controller. The controller is in communication with the medical fluid pump and the at least one continuous analyte monitor. The controller includes a processor. The processor includes instructions for delivery of medical fluid based at least on data received from the at least one continuous analyte monitor.

The present application relates to the technical field of solution detection, and in particular to a solution ion content automatic monitoring system based on the Internet of Things, comprising: a camera module for collecting plant image data of cultivated plants in a plant factory; a selection module for traversing the plant image data collected by the camera module and selecting a local area in the plant image data as a monitoring target area for this system run; a sampling module for receiving the monitoring target area selected by the selection module and sampling the solution of the cultivated plants in the monitoring target area; the present application analyzes the growth of the cultivated plants by image collection of the cultivated plants, selects two positions with the best and worst growth states as the nutrient solution sampling positions, samples the nutrient solution, further analyzes the health state of the nutrient solution based on the set nutrient solution monitoring state analysis logic, and monitors the safety of the nutrient solution used for the cultivated plants according to the continuously analyzed nutrient solution health state.

The invention discloses an intelligent log analysis and fault diagnosis system for distributed software, and relates to the technical field of software fault diagnosis, and the system comprises a data acquisition module which is used for acquiring a business log flow and an arrangement event flow, each log in the business log flow comprises a service name and a first timestamp, and the first timestamp is a first timestamp; the arrangement event flow comprises discrete events of all containers under the corresponding service names and operation nodes deployed by all the containers. According to the method, fault analysis is changed from traditional instantaneous discrete static deployment fault analysis to dynamic continuous analysis through the generated enhanced trajectory, and the real trajectory of the service request in the execution process can be accurately restored, so that a series of novel hidden faults directly caused by container dynamics can be detected; and the fault detection accuracy is effectively improved.

The invention discloses a multi-source data business travel service collaborative decision-making method and system, and relates to the technical field of business travel services, and the technical scheme is characterized in that the method comprises the steps: collecting business travel demand scene information of a current user, and calculating the resource matching complexity according to the business travel demand scene information; comparing and judging the resource matching complexity to obtain a first-level collaborative decision signal or a continuous analysis signal; if the resource matching complexity is compared and judged as a continuous analysis signal, extracting historical behavior data of the user, and constructing a resource relation model associated with the current scene according to the historical behavior data; analyzing calling time sequence characteristics of the user to the resource nodes based on the resource relation model; wherein the calling time sequence characteristic comprises an abnormal calling duration proportion and a continuous abnormal calling frequency; generating a time sequence exception index according to the exception call duration ratio and the continuous exception call frequency; the effect is that resources and service power can be flexibly allocated.

The invention discloses a protocol dynamic adaptation method for a stock power Internet of Things terminal, and relates to the technical field of power Internet of Things and information communication computers. Comprising the steps of collecting communication message data and generating a standardized message data set in combination with a protocol mode analysis method based on an interaction process of side equipment and a stock power Internet of Things terminal; performing preprocessing based on the standardized message data set, and performing feature extraction to form a feature set adapted to model training; constructing a protocol recognition model according to the feature set, and realizing online incremental training and self-adaptive modeling of a newly added unknown protocol through a dynamic updating mechanism; and deploying the protocol identification model to an edge side, performing reasoning identification on stock power Internet of Things terminal communication message data acquired in real time, and if an identification result is not in a local registry, triggering a federal learning update process to realize protocol dynamic adaptation and continuous analysis of the stock power Internet of Things terminal.