847 results about "FARR assay" patented technology

An improved system and method of selectively transmitting asset data from one or more sensors associated with the vehicle to a backend server, which is configured to analyze the asset data and, if necessary for further analysis of the asset data (e.g., to determine whether a safety event has occurred) and / or to provide actionable data for review by a safety analyst, requests further asset data from a vehicle device.

The invention discloses a power load prediction method and device based on multi-factor influence, and particularly relates to the technical field of power load prediction. Historical weather data and power load data are collected, preprocessing and feature extraction are carried out, temperature change deviation features and power load lag features are extracted and input into a prediction model for training, the accuracy of a prediction result is evaluated, and when the prediction accuracy is low, the prediction result is subjected to prediction. The abnormal degree is further analyzed and predicted, and the power dispatching strategy is adjusted in time, so that the violent fluctuation of the load demand is dealt with, the stable operation of the power grid is ensured, the accuracy of power load prediction is effectively improved, and the risk of overload or collapse of the power grid is reduced especially in the case of extreme weather.

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention discloses a virtual power plant dispatching system and method, and particularly relates to the technical field of power dispatching. By monitoring the output characteristics of the distributed energy, whether nonlinear interference is generated or not is judged. And further analyzing the disturbance of the distributed energy on the power flow inversion path, judging the abnormal change of the coupling correlation degree between potential hidden resources, and evaluating the influence on the scheduling precision. And in combination with a node differentiation response mode of a distributed energy storage aggregation region and a flexible load community, analyzing the influence of a local grouping cooperation effect on the overall output boundary identification precision of the virtual power plant. Potential erosion of scheduling errors caused by demand side multi-dimensional elastic cooperation to scheduling precision is evaluated by analyzing spectral domain characteristics of demand side elastic gradients and transient disturbance under different spatial and temporal scales. Through comprehensive analysis, the internal regulation and control scheme of the virtual power plant is adjusted, and the overall scheduling efficiency and stability of the virtual power plant are improved.

Systems and methods are provided for stabilization of cells and retention of biomolecules therein for further analysis. Block copolymers are inserted into membranes of biological particles, cell membranes or organelles (e.g., organelle membranes) and polymerized to form an intra-biological particle (e.g., intracellular) polymer network.

Disclosed are embodiments of a system and method for baling recyclable materials and managing the baling of recyclable materials. The apparatus for baling includes a baler for compressing and baling recyclable materials, a scale, label printer, and a processor. Certain embodiments further comprise at least one digital camera, RFID dispenser, and an internet connection that allows the processor to transmit collected data to a remote database for further analysis.The disclosed method for baling recyclable materials comprising the steps of baling materials; weighing the bale automatically using a digital scale operably connected to the processor; recording the weight of the bale automatically; and printing a label which displays the recorded weight of the bale. Additionally, the method may comprise inputting information and / or automatically recording data, and / or automatically transmitting the data associated with a bale to a remote database.

The technology disclosed relates to a system for constructing a probeable output generative space of an agent-under-test (AUT) for a target input probe. An agent sampling logic is configured to induce an agent-under-test (AUT) to disclose a plurality of outputs in response to processing a target input probe. A space construction logic, having access to the plurality of outputs, is configured to construct a probeable output generative space based on the plurality of outputs. A space probing logic, having access to the probeable output generative space, is configured to probe the probeable output generative space for a query, and to make available results of the probing for further analysis.

The invention discloses a neurological critical patient monitoring system based on artificial intelligence and multi-modal data fusion, and relates to the technical field of intelligent monitoring, in the system, vital signs and medical record information of a neurological critical patient are obtained from existing medical monitoring equipment and an electronic medical record system and displayed in real time; different medical prediction models are utilized to perform different predictions according to the multi-modal monitoring data, and prediction results obtained by the different medical prediction models are displayed in real time; when the current key monitoring index of the neurological critical patient exceeds an early warning threshold value or prediction results obtained by different medical prediction models meet a preset early warning condition, a real-time early warning result display module is used for carrying out real-time early warning on medical staff; and finally, the data in the monitoring process is stored through a data storage and export module so as to be packaged and exported when further analysis is needed. The method is of great significance in improving the medical level of the neurointensive care unit, accelerating the illness response speed and improving the treatment efficiency.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

The standardized IFC entity data processing method comprises the following steps that 1, an original BIM model is obtained and preprocessed, and an IFC model data set is obtained; 2, aligning the geometric expression precision and the measurement unit, and performing sensitive information desensitization processing on the IFC model; 3, splitting the IFC model to obtain a single building component entity; step 4, performing standardization processing on the geometric expression information of the single building component entity to obtain standardized geometric expression information; step 5, format conversion and deduplication processing; step 6, generating a multi-view rendering strategy and visual data to obtain a multi-format data set which can be used for further analysis; according to the method, through format unification and geometric accuracy alignment of the multi-source BIM model, the problem that errors occur during combination or comparison of the IFC model due to the fact that different software uses different unit systems in the prior art is effectively solved.

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

The invention discloses an emergency decision deduction system and method, and relates to the technical field of emergency management and intelligent decision, and the method comprises the steps: receiving flood-related data, generating environment data and social data, building a space-time disaster matrix and a social vulnerability map based on the environment data and the social data, a coupling data body is determined based on analysis of a space-time disaster matrix and a social vulnerability map, and a disaster-social coupling network is determined based on the coupling data body. The disaster-social coupling network is further analyzed to construct an emergency decision-making row-known body comprising a candidate strategy set, a space-time knowledge graph, a multi-dimensional strategy evaluation matrix and an adaptive rule set, and the emergency decision-making row-known body is used for deduction. According to the invention, the method takes the construction of the emergency decision knowledge as a deduction core, and improves the dynamic adaptability and intelligent decision-making capability of a system to cope with complex disaster scenes.

The invention discloses a three-dimensional scene reconstruction and monitoring method based on multi-view fusion and deep learning. The method comprises the following steps: acquiring image data from different viewpoints through a plurality of cameras, and carrying out geometric calibration; denoising, correcting and enhancing the image; constructing a multi-scale convolutional neural network and a variational auto-encoder model, and extracting multi-level features; carrying out weighted fusion on the features, and carrying out three-dimensional reconstruction through sparse coding and a graph neural network; performing feature optimization by applying a dynamic graph convolutional network and a double attention mechanism, and performing model updating based on adversarial gradient descent; anomaly detection is carried out through multi-scale analysis and an adversarial variational auto-encoder, preliminary processing is carried out at a camera end by adopting an edge computing technology, and further analysis is carried out at a central server end through a heterogeneous graph neural network and sparse subspace clustering. According to the method, high-precision and intelligent three-dimensional scene reconstruction and monitoring are realized, and the method has a wide application prospect.

The invention provides a cage-rearing chicken abnormal behavior detection and early warning system and method based on image analysis, and relates to the technical field of computer vision. The ammonia concentration can be indirectly speculated through environmental pollution influence indexes, the cost of an ammonia concentration monitoring sensor can be saved, and the detection accuracy is improved. A stimulation influence index is introduced to further analyze the influence of burst noise intensity and particulate matter concentration on lower-layer cage rearing chickens, and the spatial relevance and the time dynamic nature of multi-point abnormal behavior detection results are analyzed, so that real-time dynamic evaluation can be carried out aiming at an instantaneous high-frequency stimulation event; the system is suitable for large-scale abnormal behavior recognition and management adjustment tasks in a high-density breeding scene, independent effects of different environment factors can be recognized, complex interaction effects of the environment factors can be revealed, early warning threshold values of cage rearing chickens of all grades are set, and early warning strategies of different grades are implemented based on the early warning threshold values.

The invention provides an identification detection method and system for a radio frequency connector, and the method comprises the steps: obtaining the initial appearance image data of the surface of the radio frequency connector, and carrying out the processing of the image data, and obtaining a first appearance image; extracting an appearance feature set and an identification area image from the first appearance image; performing character segmentation and feature extraction on the identification area image, obtaining a preliminary manufacturing information text in combination with a pre-established text recognition model, verifying the preliminary manufacturing information text and associating the preliminary manufacturing information text with the appearance feature set to generate an associated data set; comparing the appearance feature set with a preset standard appearance database to generate defect marking data, fusing the associated data set with the defect marking data to generate comprehensive analysis data, and generating a visual chart according to the comprehensive analysis data; and further analyzing performance index change and defect severity according to the visual chart, generating risk assessment data, and generating a final detection analysis file according to the risk assessment data.

The invention discloses a solid state disk health state monitoring method and system, and relates to the technical field of solid state disks, and the method comprises the steps: collecting monitoring data of a solid state disk, extracting write-in characteristics from the monitoring data, carrying out fluctuation analysis, and recognizing the over-write-in load characteristics of the solid state disk according to the fluctuation analysis result of the write-in characteristics; analyzing built-in heat transfer characteristics of the solid state disk based on the monitoring data, and analyzing the correlation degree between the heat transfer characteristics and the overwritten load to obtain abnormal temperature characteristics when the load is overwritten; and inputting the abnormal temperature features as a pre-constructed solid state disk health state evaluation model, predicting the health state of the solid state disk through the health state evaluation model, and formulating a data migration mechanism based on the health state. According to the method, the write-in quantity and the fluctuation condition of the write-in quantity in each unit time can be determined, the change of the write-in frequency is further analyzed, and the storage management strategy can be effectively optimized by identifying the over-write-in load characteristics.

System (100) for detecting anomalous commands in network traffic using a Large Language Model (LLM), comprising: a. a traffic capture module configured to monitor and extract command-level data from incoming network traffic over one or more communication protocols; b. a preprocessing and tokenization module configured to normalize, filter and transform the extracted commands into a structured format suitable for analysis; c. a contextual embedding module comprising a pre-trained or fine-tuned LLM configured to generate semantic embeddings of the structured commands; d. an anomaly detection module configured to compare the generated embeddings with a baseline profile of legitimate command behavior and identify deviations using machine learning algorithms; e. a threat classification module configured to categorize the identified anomalies based on the type and severity of the threat; and f. a response and logging module configured to initiate automatic remedial actions and record the events for further analysis, g. the system operates in real time to detect, classify, and respond to anomalous commands in a networked environment.

The invention provides an intelligent training system and method for a portable control console, and relates to the technical field of man-machine interaction and intelligent training, and the method comprises the steps of original trajectory data collection, operation feature extraction, capability score vector construction, user capability vector generation, capability trend analysis and personalized training path construction. The method comprises the following steps: acquiring attitude angular velocity, displacement acceleration and key data of a user in a training process through a portable console, extracting multi-dimensional operation characteristics, performing alignment and evaluation in combination with a scoring rule and a dynamic time warping method, and generating ability scores and user ability vectors; and further analyzing a capability development trend, identifying a capability short board, and matching a training task to generate a personalized path. The technical problems that an existing training system does not have portability, data acquisition dimensions in the training process are insufficient, capability evaluation is rough, and trend analysis and personalized training path recommendation are lacked are solved.

The invention provides a system and a method for automatically recording CPU abnormal restart reasons and storing logs and data, the system comprises a reset trigger module, a reset reason recording module, a data and log recording module and a reset execution module, each module circuit uses a discrete device, an FPGA or CPLD chip does not need to be used, the cost is saved, and the system reliability is improved. According to the method, restart reasons which can be automatically recorded comprise the condition of active restart or passive restart caused by program exception, the condition of manual operation of a reset key and the condition of sudden temporary failure of a power supply, and meanwhile, important data, state information and log information can be quickly stored through a memory by a CPU (Central Processing Unit) through a design mechanism before restart; and after restarting, the data is transferred to the nonvolatile storage device, so that data loss is prevented, and deep reasons of abnormal restarting can be further analyzed.

The invention belongs to the technical field of buffeting detection of aircraft model supporting systems, and discloses a wind tunnel experiment vibration analysis method based on a vibration control equation. According to the elastomer theory, an engineering model is simplified into a non-uniform cross-section beam, the constraint condition is that one end is fixedly connected, the other end is attached to a mass block, and non-uniform dynamic excitation loads are distributed on the surface, and a vibration control equation of the micro-unit is listed according to the Alembert principle; according to the Euler-Bernoulli theory, a relation equation of the bending moment and the deflection of the elastomer is constructed; a system vibration differential equation is obtained, the number of needed measurement points is determined according to the property of the equation, the equation is solved, a vibration analysis model of the system is obtained, and the vibration relation between local parts of the vibration system is further analyzed. The wind tunnel experiment vibration analysis method based on the vibration control equation lays a theoretical foundation for engineering application scenes of vibration control, vibration decoupling, vibration testing and the like of the wind tunnel experiment, and is a simple and efficient analysis calculation method.

The invention relates to the technical field of heart rate monitoring, in particular to a portable heart rate anomaly early warning system based on edge computing, which comprises a signal acquisition module, a signal preprocessing module, a data fusion module, an anomaly detection module, a local alarm module and a cloud interaction module. According to the system, physiological signals are collected through a micro sensor array, filtering, denoising and feature extraction are carried out through edge computing nodes, comprehensive heart rate data are generated through a multi-modal data fusion algorithm, and the heart rate state is judged in combination with an anomaly detection model. And if abnormity is detected, the local alarm module gives out early warning and uploads the data to the cloud for further analysis. Real-time monitoring and low-delay processing are achieved, the accuracy and reliability of heart rate monitoring are improved, and accurate health early warning service is provided for a user.

The invention relates to the technical field of artificial intelligence, in particular to a network fault root cause automatic diagnosis method and device and electronic equipment. The method comprises the following steps: S1, receiving a network alarm event, and extracting a prompt word and a termination word from the network alarm event; s2, a pre-training large model is triggered for analysis according to the cue word, analysis of the pre-training large model is stopped according to the termination word, and the pre-training large model is obtained according to network field general knowledge big data training; s3, obtaining an analysis result of the pre-training large model, calling a sub-function according to the analysis result of the pre-training large model, and finding historical interaction data; s4, sending the sub-function calling result and the historical interaction data to a pre-training large model, and further analyzing the pre-training large model to obtain an analysis result; and S5, repeating the steps S3-S4 until the large model finds the network fault root cause of the network alarm event. Therefore, rapid and accurate diagnosis and positioning of network fault root causes in a dynamic network environment are realized.

The invention discloses a micro-through-hole quality detection method and system based on data model feedback, and relates to the technical field of semiconductor detection, and the method comprises the steps: obtaining micro-through-hole image data, carrying out the gray processing of the micro-through-hole image data, carrying out the binary conversion of a processed gray image, obtaining micro-through-hole binary image data, and carrying out the detection of the micro-through-hole quality. And obtaining two-dimensional binary image data of the micro-through hole according to the binary image data of the micro-through hole. According to the method, the micro-through hole is roughly detected through the shortest diameter of the micro-through hole, the monitoring efficiency is improved, resource waste is avoided, the point cloud spacing information is obtained through the minimum defect size information and the shortest diameter of the micro-through hole, dynamic adjustment of point cloud model construction is achieved, the accuracy of the model is improved, and the accuracy of the model is improved. The micro through hole is further analyzed through the contour curve function of the micro through hole instead of directly comparing the aperture of the micro through hole with the production standard, so that misjudgment of the micro through hole is avoided, and the quality detection efficiency of the micro through hole is improved.

The invention discloses a Presto task scheduling method and a Presto task scheduling device. According to the method, after a task request is received, tasks are allocated to matched clusters according to user information and task information in the request, then task types are further analyzed, execution modes of the tasks are determined, whether the tasks belong to task types needing to exclusively share resources or not is judged, and if the tasks belong to resource exclusive tasks, the tasks are allocated to the clusters. Tasks are allocated to reserved single tasks and special resource nodes, and when the tasks exclusively share working node resources, no other types of tasks are executed on the nodes, so that the tasks exclusively share the resources on specified cluster nodes, physical isolation of the working nodes in a cluster is realized, the performance problem caused by resource contention is avoided, and the service life of the cluster is prolonged. Therefore, the method is suitable for a complex query environment shared by multiple users, efficient execution of the important tasks can be ensured even when a large number of tasks are processed, and the overall reliability and stability of the system are improved.

The invention relates to the technical field of atomization rate control, in particular to an intelligent atomization compliance monitoring method and system for children. The method comprises the following steps: firstly, acquiring expiration flow velocity, inspiration flow velocity and multi-direction motion acceleration time sequence data of a child in a historical monitoring period, and reflecting breathing and behavior action conditions; dynamically analyzing the numerical characteristics of the expiration flow velocity and the inspiration flow velocity to divide a respiratory cycle, and analyzing the cycle length, the inspiration flow velocity cumulant and the like to construct a respiratory characteristic vector; screening a target monitoring period based on the breathing feature vector and the period number similarity; further analyzing the motion acceleration change and accumulation characteristics in the target monitoring period, and quantifying the behavior compliance expression degree; the behavior compliance expression degree and the breathing feature vector are deeply fused, a'breathing state-behavior cooperation 'joint evaluation model is established, the breathing and behavior states of children are dynamically recognized, the medicine atomization rate is accurately adjusted, and effective deposition of lung medicine is improved.

The invention relates to the technical field of medical health monitoring, in particular to a portable intelligent breathing mask and a remote monitoring method thereof. The method comprises the steps that airflow, humidity, blood oxygen and temperature data in the mask wearing process of a user are obtained, and time sequence alignment and feature fusion are carried out; inputting the fused physiological feature sequence into an embedded neural network model to recognize respiratory behaviors, and generating an abnormal probability value; carrying out compression coding based on the parameters, setting an uploading period according to a self-adaptive formula, and uploading the data to a cloud end; the cloud model further analyzes and generates health assessment parameters and a risk trend sequence; and dynamically adjusting an early warning threshold according to an evaluation result, and updating a local model and a sampling strategy in combination with user feedback. The portable breathing mask has the functions of fusing multi-modal data analysis, edge-cloud collaborative recognition, self-adaptive uploading scheduling, personalized early warning regulation and control and the like, and the intelligent level and practicability of the portable breathing mask in remote health monitoring are improved.

The invention discloses a disaster emergency system based on big data, which comprises a data collection module, a fire-fighting early warning module and a fire-fighting emergency module, and is characterized in that the data collection module is used for collecting visual data and environmental comprehensive data in a monitored target area in real time, and inputting comprehensive information of all people entering a building into the system; the fire-fighting early warning module is used for detecting whether a fire occurs in a target area or not, further analyzing the spreading trend and the danger degree of the fire and adjusting the operation mode of the spraying system according to an analysis result, and the fire-fighting emergency module is used for obtaining the positions of trapped persons in a building, shunting the trapped persons and sending the trapped persons to the fire-fighting early warning module. The system comprises a data collection module and a distribution module, an evacuation route is planned for each distribution group, the data collection module comprises a data input module, a visual module and a sensor module, the data input module is used for inputting comprehensive information of personnel entering the building into the system, and the system has the advantage of improving evacuation efficiency and accuracy.

The invention provides an intelligent management system for encrypted archives, and relates to the technical field of intelligent archive management.The intelligent management system comprises the steps that operation data of a user and encoding data of an encoder are obtained and analyzed to obtain reconstruction errors, and then the reconstruction errors are further analyzed to obtain abnormal operation evaluation coefficients; dividing the users into normal, suspicious and abnormal types based on the abnormal operation evaluation coefficient, and obtaining historical access data of the suspicious users; analyzing to obtain a historical behavior judgment coefficient; and further judging the suspicious user through the historical behavior judgment coefficient, and finally judging the suspicious user as a normal or abnormal user, thereby breaking through the limitation of the traditional single judgment, realizing the intelligent association of the cross-cycle behavior track, more accurately judging the access behavior mode of the user, and greatly reducing the misjudgment rate.

The invention discloses a warehousing identification and storage management system based on an intelligent refrigeration house, and relates to the technical field of refrigeration house goods identification and storage, and the system comprises a goods identification and storage module, a real-time dynamic adjustment module and a storage planning management module. According to the method, the storage location is screened through the preset rule, and the storage scheme is intelligently determined according to the matching condition of the storage space of the storage location and the cargo quantity, so that the utilization rate of the storage location is improved, and the suitability of cargo storage is guaranteed; meanwhile, the storage planning management module calculates the cargo use frequency by using historical data, classifies the cargos, particularly further analyzes the storage demand conflict condition for high-frequency storage cargos, optimizes the storage planning by calculating the selection value of the screened storage location, more reasonably arranges the storage location according to the cargo characteristics and the use rule, and improves the storage efficiency. And the overall utilization efficiency of refrigeration house resources is improved.