1297 results about "FARR assay" patented technology

A vehicle device may execute one or more neural networks (and / or other artificial intelligence), such as based on input from one or more of the cameras and / or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

A vehicle dash cam may be configured to execute one or more neural networks (and / or other artificial intelligence), such as based on input from one or more of the cameras and / or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and / or to a safety manager.

The invention provides a photovoltaic operation fault diagnosis method and system, and the method comprises the steps: constructing a fault mode and influence factor database based on real-time monitoring data and historical record data, carrying out the feature extraction, obtaining a fault feature vector, and constructing a fault diagnosis model through a preset decision algorithm; performing label classification on the fault feature vector to obtain a label classification data set, and constructing a label classification model; constructing a fault propagation model based on the fault mode and the influence factor database; and based on the plurality of models, performing fault diagnosis analysis on the real-time monitoring data to obtain a fault diagnosis report. According to the method, the problem that the operation reliability of a photovoltaic system is poor due to the fact that only processing is carried out when abnormity occurs in the whole operation state and performance expression and further analysis and processing are not carried out on the propagation path and potential influence of the abnormity or the fault in the prior art is solved. The fault condition of the system is diagnosed and analyzed by constructing a plurality of models, and stable operation of the system is ensured.

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 provides an unmanned aerial vehicle intelligent routing inspection method for routing inspection of energy facilities, and the method comprises the steps: carrying a high-definition camera and a thermal sensor through an unmanned aerial vehicle, carrying out the routing inspection of the energy facilities, collecting a visual image and thermal imaging data in real time, and transmitting the data to a server side. And the server performs processing, analysis, risk classification and anomaly detection on the data by using various recognition training and deep learning models. The unmanned aerial vehicle acquires three-dimensional information by using a simultaneous positioning and map construction technology, constructs a three-dimensional map, and plans a routing inspection path by using an RRT algorithm, thereby realizing multiple functions. Potential risks are identified by using a deep learning technology, and early warning and automatic obstacle avoidance are realized. The edge calculation unit processes the data in real time and transmits the data to the ground control center, and further analyzes and predicts the risk trend. The system integrates a plurality of advanced modules, realizes efficient, intelligent and automatic inspection by using deep learning, edge computing and high-speed data transmission technologies, reduces cost and risks, and provides powerful support for safety management of energy infrastructures.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

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.

Disclosed herein are systems and methods for security monitoring and incident response using large language models. In one aspect, an exemplary method comprises: receiving input data from elements of Security Operations Center (SOC), generating and sending a query based on the received input data to a Large Language Model (LLM), parsing a response received from the LLM, and performing analysis to determine whether a threat has been identified. In one aspect, the method further comprises: when a threat is identified, collecting artifacts of the threat, and analyzing the threat further with involvement of security professionals, when a threat is not identified, determining whether additional data is needed, and when additional data is needed, determining a type of the additional data, when the type of additional data that is determined, collecting additional information from elements of the SOC, and when additional data is not needed, terminating the incident response.

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.

The invention relates to the technical field of data processing, in particular to a video monitoring data processing system based on cloud computing, which comprises a video quality synchronization module, an abnormal behavior identification module, a resource scheduling optimization module and a safety early warning trigger module, and synchronizes a video stream timestamp through a cloud parallel processing technology based on multi-source video data. According to the invention, the video stream timestamp and the image quality are precisely synchronized through the cloud parallel processing technology, the high quality of the data stream is ensured, the screening mechanism ensures that only the video stream meeting the quality standard is further analyzed, and the accuracy of subsequent abnormal behavior analysis is enhanced. The system not only can analyze and quickly identify abnormal behaviors deviating from a normal track in real time, but also can dynamically adjust, calculate and store resources according to actual resource use conditions, so that the processing speed and the cost efficiency are improved, the system can flexibly meet monitoring requirements of different scales, and the system is particularly suitable for a dynamic environment and has wide application prospects. And the early warning and response capabilities of video monitoring are improved.

The invention discloses pre-hospital emergency medical aid emergency help integrated equipment and system. The method comprises the following steps: S1, acquiring key vital sign data of a patient by using medical equipment; s2, calling basic information and medical history of the patient through equipment; s3, transmitting the collected data to a server of a remote data processing center in real time; s4, performing comprehensive analysis on the received vital sign data and the medical history information of the patient by applying a pre-trained deep learning model on the server, and generating a prediction report; s5, displaying a prediction result on a portable device or a remote monitoring station in real time by using a data visualization technology; s6, based on a visual prediction result and medical history analysis, guiding first-aid personnel to take targeted emergency medical measures; and S7, continuously monitoring the vital sign response of the patient and any health change related to the medical history, and updating the treatment strategy according to the further analysis of the deep learning model.

The invention discloses a self-adaptive dynamic path planning method and system for an underwater operation robot, relates to the technical field of path planning, and aims to effectively reduce the collision risk and further improve the safety of the underwater operation robot by carefully analyzing and monitoring dynamic and static obstacles and establishing a collision prediction model. According to the method, the safety of equipment and environment is guaranteed, in combination with path consumption energy Xnn, a proper path is conveniently and preliminarily selected, the energy consumption is further reduced, the operation efficiency and cruising ability of the robot are improved, and according to the detour difference Rc, detour of a static obstacle appearing in the path is further analyzed so as to seek the shortest path for detour of the static obstacle; meanwhile, the running state of the dynamic obstacle is mastered in real time according to the obstacle running information, the dangerous dynamic obstacle is further locked, a collision prediction model is established, the environment change can be quickly coped with in the running process of the robot, and a foundation is laid for guaranteeing smooth completion of tasks.

The invention discloses a tobacco leaf bundle loosening degree detection method and system based on YOLOv8, and particularly relates to the technical field of tobacco leaf detection. An illumination sensor is installed in a detection area to monitor illumination intensity changes in real time, automatic camera equipment is combined to collect tobacco image data under different illumination conditions, the tobacco image data are preprocessed, shadow distribution abnormal features and color change amplitude features are extracted, a machine learning model for predicting the detection precision of a YOLOv8 model is trained, and the detection precision of the YOLOv8 model is predicted. According to a precision result output by the model, the detection is divided into a high-precision type and a low-precision type, the high-precision result is directly applied to production, and the low-precision result is used for improving the detection precision of the model by further analyzing an abnormal condition and dynamically adjusting a detection threshold value; the problem of abnormal detection precision caused by complex background and illumination change is effectively solved, the accuracy of tobacco classification is optimized, and the quality control of the production link is improved.

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 invention discloses a quality management control method and system based on a cosmetic production process, and relates to the technical field of cosmetic quality management.The method comprises the following steps that firstly, a conveying belt conveys a product to a detection area at a preset speed, and a visual detection system captures a high-definition image at a product sealing position through an industrial camera; establishing an ordered and searchable image data set for the captured image data, and extracting key features reflecting abnormal sealing of the product from the data set; and the extracted key features are further analyzed and processed under a detection window. According to the method, the deep learning model is introduced to intelligently evaluate the sealing light spot and heat distribution, the sealing abnormality coefficient is generated, pollution and defects are accurately distinguished, the misjudgment rate is reduced, and resource waste is reduced. Features are classified in real time, the conveying speed is dynamically adjusted, detection accuracy and production efficiency are both considered, system overload is avoided, and efficient intelligent quality control is provided for cosmetic production.

The invention discloses a user portrait intelligent analysis system and method based on data visualization. The method comprises the following steps: S1, constructing a user multi-source data set; s2, extracting association information among users based on the user multi-source data set, and constructing a user relationship network by using the user association information; s3, dynamically adjusting a community division result based on the similarity of the nodes and the distribution of edge weights; s4, performing feature extraction on the user nodes in each user community, and storing the user features to a tag management module in a classification tag form; s5, based on the user relation network and the community detection result, generating a visual chart through a data visualization module, and presenting the distribution and key features of the user community in a graphical form; and S6, based on the user portrait label and the community grouping result, using a machine learning algorithm to further analyze the user behavior data, and predicting future interest preference or behavior trend of the user. According to the invention, a more efficient tool is provided for enterprises in the aspects of user grouping and precision marketing.

The invention discloses an industrial control network threat trend detection method and system based on time sequence characteristics, and particularly relates to the technical field of industrial control networks. The method comprises the following steps: dynamically adjusting an existing threat detection model to improve the adaptability by introducing time series data after new equipment or system upgrading, inputting new data into an original model, and evaluating the model performance according to a reaction speed; if the performance is reduced, judging the characteristic consistency of the model by analyzing the drifting of the time series data characteristics; then, the stability of the model on different data sets is evaluated through hybrid verification; according to the evaluation results, the adaptability of the model is judged, the model is divided into a high-adaptability model or a low-adaptability model, for the low-adaptability model, a threshold value is dynamically adjusted, false alarm frequency abnormity is further analyzed and false alarms are filtered through a secondary detection mechanism, the adaptability of the model in data change is effectively improved, the false alarm risk is reduced, and the accuracy of the data change is improved. And the safety and the reliability of the system are enhanced.

The invention discloses an infant posture intelligent detection and early warning monitoring system which is characterized in that the system comprises an information terminal, a processing side end and a display terminal, and the information terminal comprises a camera and monitors the posture and state of an infant in real time; images and posture information acquired by the camera are transmitted to the processing side end through wireless signals to be analyzed and processed; the processing side end comprises a small computing device with an AI function and a server with an AI processing capability; the small computing device performs preliminary data processing, and then sends the processed data or unprocessed original data to a server with stronger AI processing capability for further analysis; the server analyzes the posture data of the baby through a deep learning algorithm, detects abnormal postures and makes early warning judgment, and an analysis result is transmitted to the display terminal through a wireless signal; the display terminal comprises a plurality of display devices. According to the invention, intelligent detection and real-time early warning of baby postures can be realized, and the stability is good.

The invention discloses an intelligent number asking method based on a large language model, and relates to the field of artificial intelligence application, a question proposed by a user is analyzed and understood through an LLM, a number asking API matched with the question is searched according to the question of the user, then a system inputs the searched API, query parameters of the API and the question of the user into the LLM together, and the query parameters of the API and the question of the user are input into the LLM; the LLM further analyzes user questions and API query parameters, then generates an API call request body and provides the API call request body to the system; when the number query API cannot be found, performing a query task by using a query SQL; the problem that the length of the cue word exceeds the context length limit of the LLM due to the fact that the number of APIs for query access is too large and API description is too long is effectively solved, and the situation that when the LLM is pre-trained, some knowledge is not learned or expired, and consequently the problem cannot be solved or mistakenly solved when the number asking requirement of a user is processed is avoided by adding a special label into the cue word.

The invention relates to the technical field of data statistics, in particular to an intelligent water meter missing data complementation method and system.The intelligent water meter missing data complementation method includes the steps that initial segmentation points of a water consumption data curve are determined by analyzing the slope change condition of the water consumption data curve, and the variation index of the initial segmentation points is further analyzed to determine segmentation points of the water consumption data curve; performing clustering analysis on all segmentation points in the water consumption data curve of the user in all days, and constructing a standard water consumption data curve of the user based on a clustering result; and respectively using a Bezier curve fitting method and a regression analysis method, and combining the change trend of the standard water consumption data curve to carry out data completion on the missing data segment. Through the method and the system provided by the invention, complementation data which conform to the overall trend and are in smooth transition can be obtained, the integrity and the accuracy of the data are remarkably improved, and a more reliable data basis is provided for subsequent water resource management and decision support.

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 invention provides a risk early warning method of an intelligent seal management system, and relates to the technical field of information, and the method comprises the steps: carrying out the semantic analysis of a seal use subject text in a seal use approval sheet through a natural language processing method, extracting a keyword, judging whether the seal use subject belongs to the common range of a corresponding seal related business, and carrying out the early warning of the risk. If the seal deviates from the common category and belongs to high-heat or associated core business, early warning is triggered, and the risk level is improved; on the basis of the seal using frequency early warning, further analyzing whether the seal using behavior triggering the early warning occurs outside the normal working time, and if high-frequency abnormal seal using occurs in the non-working time, improving the early warning level and triggering the risk prompt with the higher priority. The risk of abuse of the seal is effectively prevented, and the safety and standardization of seal use management are 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 relates to a method and a system for analyzing a cholestenoma image based on deep learning, and provides an efficient image processing mode for cholestenoma, which comprises the steps of quantum feature screening of image data, topological segmentation, generative adversarial network enhancement, graph convolutional network spatial correlation analysis, fuzzy logic classification and the like. Through quantum state conversion and feature screening, contrast and resolution of image data are significantly improved, noise is removed, and key features are extracted. Topological data segmentation is combined with multi-scale processing, and the focus area boundary is effectively recognized. The generative adversarial network is used for enhancing lesion feature data, and the graph convolutional network is used for further analyzing spatial correlation features to ensure that description of a lesion region is more refined. And finally, the existence probability and the classification result of the focus are obtained through fuzzy logic classification. The method aims at improving the information extraction density of the bile adipoma image.