35 results about "Surveillance Methods" patented technology

The invention discloses a high-voltage transmission line insulator health monitoring method and system, and the method comprises the steps: collecting multi-modal data to construct a data set, constructing a model containing an ITCN time sequence branch and a double-branch U-Net space branch, achieving the cross-modal fusion through an LTSFM module, and carrying out the cross-modal comparison pre-training and the optimization of a multi-task loss function (DiceLoss + WIoULoss + TSCL), thereby achieving the health monitoring of a high-voltage transmission line insulator. And finally, outputting health state classification, fault segmentation masks and degradation trend parameters, and triggering fault early warning when a threshold condition is met. According to the invention, the high-precision real-time monitoring of the weak degradation characteristics of the insulator can be realized, and the edge deployment efficiency and the complex environment adaptability are both considered.

The invention discloses a video monitoring method based on multi-target tracking analysis, which relates to the technical field of computer vision, and comprises the following steps: acquiring original trajectory data of each target, extracting key data and associated features of each target according to the original trajectory data, and acquiring vector data; the method comprises the following steps: distributing a unique identifier to each target, generating accurate original track data, extracting key data and associated features of the target, matching vector data with a rule base to identify an abnormal behavior of each target, triggering classification early warning according to the abnormal behavior, and dynamically optimizing the rule base according to different scenes. The rule base is dynamically optimized according to different conventional scenes and unconventional scenes, the practicability and pertinence of monitoring are enhanced according to classification early warning and historical track labeling functions triggered by abnormal behaviors, and the safety management requirements of various scenes are better met.

The invention relates to the technical field of artificial intelligence and channel disease monitoring, and discloses a channel revetment multi-dimensional disease intelligent monitoring method and system based on multi-source fusion, and the method comprises the steps: obtaining multi-source data of a target revetment region; constructing a three-level index system; performing DERU evaluation on each third-level index, and calculating a risk category score corresponding to each second-level index according to an evaluation result and a second-level index classification result; dividing the secondary indexes into a disease class and an environment class according to influence types, calculating a total risk value according to the risk class score of the secondary indexes of the disease class, and calculating an environment correction coefficient according to the risk class score of the secondary indexes of the environment class; calculating a health value score of the target bank protection area according to the total risk value and the environment correction coefficient; and outputting a disease intelligent monitoring result and a maintenance strategy according to the multi-source data and the health value score. According to the scheme, the health state of the revetment structure can be comprehensively, timely and accurately mastered, and identification monitoring and intelligent early warning of various diseases are realized.

This invention relates to the field of nursing technology and provides an internet-based remote monitoring method and system for health care. The method includes: screening for deviation events and identifying risk monitoring nodes, extracting sampling data only for these risk monitoring nodes; performing comparative analysis on computational parameters in chronological order, comparing the magnitude of parameter fluctuations with preset fluctuation thresholds based on historical data statistics, and identifying key parameters with significant fluctuations; and generating predictive intervention nodes through a preset assessment model. This invention avoids indiscriminate processing of all data by locating effective data segments with high-risk characteristics from the raw data, reducing computational load and data transmission pressure, and improving the targeting and efficiency of risk assessment. By identifying fluctuation nodes where key parameters change, it can more sensitively capture the initial signs of potential risks and improve the accuracy of risk identification.

The invention discloses a multi-modal health monitoring method and system based on large model driving, a terminal and a storage medium, and the method comprises the steps: collecting video stream data, audio data and physiological monitoring data of a target object, and carrying out the preprocessing of the collected data, and obtaining multi-modal health data; feature extraction is carried out on the multi-modal health data, and multi-modal health data fusion processing is carried out on the extracted features based on a multi-modal health large model; according to the fused health features, a dynamic intervention strategy is generated by means of intelligent agent decision logic driven by a large model; and performing voice reminding and health video playing based on the dynamic intervention strategy, and dynamically outputting multi-modal health monitoring data through a multi-modal interaction interface. According to the method, deep fusion of non-contact health monitoring and personalized health management is realized through technologies such as multi-modal sensor fusion, large-model-driven intelligent agent decision and natural interaction design, and the autonomous decision and personalized capabilities of the health monitoring technology are improved.

The application discloses a human health monitoring method and system based on proportion philosophy and individual self-adaption. A multi-level nested proportion model is constructed by non-invasively collecting multi-dimensional signals of a human body. A golden section 0.618 is taken as a harmonious anchor point to calculate an individual exclusive health benchmark zero point and a stable interval. Through real-time proportion comparison and fine ladder deviation analysis, the structure harmony and function state are judged. The health trend early warning is realized by combining multiple retests. The application breaks away from the limitation of unified medical indicators, realizes personalized, global and original health evaluation, can early identify imbalance and sub-health state, and is suitable for scenes such as daily monitoring, physical evaluation and health intervention effect judgment.

A surveillance apparatus (101) includes a first acquisition unit (104), a second acquisition unit (105), and a correlation unit (106). The first acquisition unit (104) acquires first moving body information MD1 including the position of a held apparatus (107) held by a predetermined moving body MB. The second acquisition unit (105) acquires second moving body information MD2 including the position and the type of a sensor moving body SMB being a moving body MB detected based on sensor information SI generated by a sensor apparatus (103). The correlation unit (106) correlates the first moving body information MD1 and the second moving body information MD2 that are related to a common moving body MB, based on the position of the held apparatus (107), and the position and the type of the sensor moving body SMB.

A plurality of hierarchical information management devices (4a, 4b, 4c) have a first hierarchical information management section that manages first hierarchical information (50a, 50b, 50c) that is information that expresses the information of the devices in a hierarchical structure, and the plurality of hierarchical information management devices (4a, 4b, 4c) each manage the first hierarchical information (50a, 50b, 50c) of different devices. The monitoring device (2) has a second hierarchical information management section that generates hierarchical information that is second hierarchical information that connects the first hierarchical information (50a, 50b, 50c) in a hierarchical structure based on a plurality of different first hierarchical information (50a, 50b, 50c) acquired from the plurality of hierarchical information management devices (4a, 4b, 4c), a display section that displays information, and a display processing section that performs processing to display a plurality of different monitoring screens that correspond to the operation states of the display monitoring targets of each hierarchical level in the second hierarchical information on the display section by switching the monitoring screens.

The application provides a kind of old patient sign monitoring method and system based on multi-modal perception, it is related to intelligent health monitoring technical field, including: acquisition and fusion physiological and behavioral data, construct unified representation and modal correlation matrix.Baseline features are calculated and tensor fusion is carried out, abnormal fragments are identified and dominant influence factors are traced back by encoding reconstruction.Risk level is mapped by deducing data evolution trajectory and calculating deviation from health benchmark.Simulate the timeliness of intervention scheme, determine the target scheme and generate monitoring report.The application realizes the deep correlation analysis and early warning of abnormality of multi-modal data, can automatically determine the risk dominant factor and generate individualized intervention scheme, improves the accuracy and initiative of old health monitoring.

The invention relates to the field of video monitoring, in particular to a video monitoring method for intelligent medical patient nursing, which comprises the following steps: determining a personnel state based on a personnel proportion value and a non-affected characterization value of a monitoring area, and determining whether to carry out frame optimization or not according to the personnel state; according to a comparison result of the association density corresponding to the preset video frame and the preset association density, determining that a data uploading mode is to upload the preset video frame and the corresponding compensation video frame or upload the preset video frame and the corresponding video paragraph; constructing a region sequence based on the stable number of the nursing personnel in the reference range corresponding to each monitoring region, and determining whether sequence adjustment is performed or not according to a comparison result of a stable number difference value of the nursing personnel in the region sequence and a preset number difference value; and carrying out data uploading on the finally obtained region sequence according to a sequence number ascending sequence. According to the invention, the video monitoring data processing efficiency of patient nursing is improved.

The invention discloses a public opinion monitoring method and system based on multi-modal data fusion. The public opinion risk management and control capability and the emergency response level in a complex public opinion environment are improved. The method comprises the following steps: collecting multi-modal data, and carrying out edge preprocessing on the collected multi-modal data to generate structured input data; performing feature extraction and projection on the multi-modal data to generate a unified event embedding representation; identifying event types based on a public opinion classification model, and performing risk assessment on events in combination with set parameters; constructing a public opinion knowledge graph, and inputting cue words into the pre-training language model to generate a response strategy; and triggering early warning based on a hierarchical risk threshold, and performing reinforcement learning fine tuning and strategy optimization on the pre-training language model based on event feedback and response results.

The invention discloses a platanus orientalis wadding monitoring method based on an image recognition technology, and relates to the technical field of environmental monitoring, and the method comprises the following steps: selecting a road containing platanus orientalis during the centralized outbreak of platanus orientalis wadding every year, and collecting the floating wadding through a floating wadding collection container; identifying the floating flocculus area in the floating flocculus collecting container by using an image identification technology; obtaining the weight of the floating catkins in each floating catkins collecting container; fitting the floating wadding area and the floating wadding weight through a floating wadding area-weight algorithm formula; quantifying floated floc grades, and dividing the floated floc of the platanus orientalis into four grades of'no ', 'light', 'medium 'and'heavy'. According to the method, the flouring grade standard can be quantified, the flouring phenomenon can be converted from subjective description to measurable scientific indexes, the subjective deviation of manual observation is eliminated, the data comparability of different areas and different time is ensured, and a unified and objective reference basis is provided for urban flouring treatment management, public health protection and scientific research.

The invention relates to an urban surveillance method, comprising a training phase (100), intended to create training data; a detection phase (200), in which at least one detection vehicle (20) equipped with at least one sensor is mobile in an area to be monitored (22), the at least one sensor records data relating to the area to be monitored (22), then the recorded detection data is processed by a computer detection device (28), configured to detect elements (24) present in the area to be monitored using the training data; and an analysis phase (300), in which the area to be monitored (22) and the elements (24) present in the area to be monitored (22) are analyzed by a computer analysis device (38); characterized in that the training phase (100) and the detection phase (200) are distinct;and in that during the analysis phase (300), the computer analysis device (38) establishes a score for the area to be monitored (22), taking into account various parameters, including the different elements (24) present in the area to be monitored (22). The invention also relates to an urban surveillance system. Abbreviated figure: 1;

The invention belongs to the technical field of disease prognosis prediction, and provides a prognosis prediction method for multidrug-resistant tuberculosis in order to solve the problem that an existing prognosis monitoring method for a multidrug-resistant tuberculosis patient has certain limitation. By integrating handmade radiology features and deep learning derived imaging features extracted from baseline, two-month and six-month continuous CT scanning, processing data by adopting a gated recurrence unit (GRU) network, and combining with a multi-drug-resistant tuberculosis prognosis prediction system, a treatment result of a multi-drug-resistant tuberculosis patient is predicted. According to the prediction method provided by the invention, the high-risk patient can be accurately identified in the early stage of the treatment process, and powerful support is provided for personalized treatment decision making, intervention opportunity optimization and reasonable public health resource allocation. Moreover, the kit has higher sensitivity and specificity, and the potential in the aspects of multi-drug-resistant tuberculosis treatment result prediction and risk stratification accuracy is improved.

A surveillance apparatus (100) includes a detection unit (102) processing an image in which an area around a road is captured and detecting that the relative distance between a person and a vehicle included in the image is in a state of being equal to or less than a reference value, and an output unit (104) selecting a type of information to be output, by using an attribute of the person detected to be in the state, and outputting information of the selected type.

This invention discloses a primer design method and primer set. Through in-depth analysis of influenza surveillance network data nationwide, this application makes significant contributions to improving the accuracy and sensitivity of influenza virus identification. The primer revisions not only achieved significant results in experiments but also provided valuable insights for future influenza surveillance and research. A deeper understanding of the genetic characteristics and epidemiological patterns of influenza viruses is crucial for developing scientifically effective prevention and control strategies. In disease prevention and public health management, continuous improvement of surveillance methods and strategies will bring greater benefits to timely responses to infectious diseases such as influenza.

The invention provides a monitoring device, a monitoring method, and a computer program product for monitoring. The monitoring device includes: a detection unit that detects a person from each of a plurality of chronological images generated by an imaging unit; a tracking unit that tracks the person in one or more images in which the detected person is displayed among the plurality of images; and a determination unit that, on the basis of the tracking result, determines whether or not the position of the detected person on the image is included in a first range on the image corresponding to the prohibited region over the entire first period. And determines whether or not the position of the detected person on the image is included in a second range on the image provided around the first range over a second period that is longer than the first period. When the position of the detected person on the image is included within the first range throughout the first period or within the second range throughout the second period, it is determined that the detected person has entered the prohibited area.

This monitoring system for monitoring a vehicle (10) or a monitoring target operating in the vehicle (10) comprises: a reliability management unit (1186) for managing a reliability indicating a security protection status of the monitoring target, in accordance with a vehicle event of the vehicle (10); and a function restricting unit (1163) for restricting at least some functions of the monitoring target in accordance with the reliability.

The invention provides a monitoring system, a monitoring method, and a program. The time of setting position information of an abnormal monitoring object device for a moving body is saved, and the moving body can move to a position suitable for confirming the device condition irrespective of the map information specification of the moving body. A monitoring system is provided with: a first storage unit for storing map information in a building; a moving body control unit that controls the movement of the moving body on the basis of the map information stored in the first storage unit; the beacon terminal is arranged at a position corresponding to the equipment and is used for transmitting a beacon signal containing equipment identification information capable of uniquely determining the equipment; a second storage unit that associates and stores device identification information of the device and the closest coordinate point of the device in the map information stored in the first storage unit; and a transmission unit that transmits the closest coordinate point of the device and the device identification information of the device, which are stored in the second storage unit, to the moving object control unit. The moving body control unit moves the moving body on the basis of a nearest coordinate point of the device and a beacon signal including device identification information of the device.

The invention relates to the technical field of monitoring management, and particularly discloses a nursing personnel safety state monitoring method and system, and the method comprises the steps: obtaining a courtyard video in real time based on a public area camera, carrying out the recognition of the courtyard video, and determining the spatial feature value of each space unit in a virtual model; according to the space characteristic values of all the space units, environment characteristic values of all the positions are determined in the virtual model; obtaining individual information of nursing personnel, and determining own characteristic values according to the individual information; determining a monitoring area of each nurse according to the environment characteristic value and the self characteristic value, and sending the monitoring area to a public area camera; the abnormal conditions in different rooms are determined in real time, then the abnormal conditions of different positions in the hospital area are determined, then the abnormal conditions of the nursing personnel are obtained, the monitoring area is determined by combining the abnormal conditions of the nursing personnel and the abnormal conditions of all the positions in the hospital area, and the monitoring efficiency is improved while the monitoring effect is almost not reduced. And the cost is greatly reduced.

This invention relates to the interdisciplinary field of medical informatics, health economics, and public health management, and discloses a personalized liver cancer screening and monitoring method and system. The method includes: acquiring real-world clinical data of patients with chronic hepatitis B; calculating the aMAP score for each patient using an aMAP risk prediction model based on the real-world clinical data, and classifying patients into different risk groups according to the aMAP score and a preset score threshold; constructing a personalized screening and monitoring decision tree model, and setting differentiated screening and monitoring strategies according to risk groups; constructing a decision tree Markov analysis model to conduct cost-effectiveness analysis on different screening and monitoring strategies, and obtaining the optimal screening and monitoring strategy corresponding to different risk groups based on the analysis results. This provides quantitative support for health decision-making, promotes the scientific and refined allocation of resources, helps improve screening compliance and early diagnosis rates among people with chronic liver disease, thereby improving the prognosis of liver cancer and reducing the burden on public health.

The present application belongs to the technical field of disease prognosis prediction, and aims at the problem that the existing prognosis monitoring method for multidrug-resistant tuberculosis patients has certain limitations. The present application provides a multidrug-resistant tuberculosis prognosis prediction method, which integrates the manually made radiology features and the deep learning derived imaging features extracted from the baseline, two-month and six-month continuous CT scans, adopts a gated recurrent unit (GRU) network to process the data, and combines a multidrug-resistant tuberculosis prognosis prediction system to predict the treatment results of multidrug-resistant tuberculosis patients. The prediction method provided by the present application can accurately identify high-risk patients in the early stage of the treatment process, and provides strong support for personalized treatment decisions, optimization of intervention timing and reasonable allocation of public health resources. Moreover, the present application has higher sensitivity and specificity, and improves the potential of the accuracy of multidrug-resistant tuberculosis treatment result prediction and risk stratification.

Digital surveillance methods and systems are described. One example method relates to detecting disruptions to a computer network associated with an organization. Another example method relates to detecting abnormal activities on a computer network. The disclosed embodiments include techniques to analyze a unique and varied collection of publicly available signals associated with specific organizations to allow abnormal behaviors of a digital network to be detected.

The present invention relates to the field of community video surveillance technology, and discloses a method for video surveillance of abnormal behavior of community personnel based on human factors engineering. The method first divides community surveillance video data based on preset activity areas to obtain monitoring data of each sub-area, including personnel trajectory and environmental interaction data; then determines the abnormality level of each sub-area based on the movement frequency of the personnel trajectory data and the interaction intensity of the environmental interaction data; then calculates key monitoring nodes based on the abnormality level, and determines the scanning order and scanning duration according to dynamic path adjustment; then generates observation anchor points and redundant anchor points at the sub-area boundaries; and finally, associates and integrates them to generate a monitoring path. The method of the present invention improves the accuracy, flexibility, and environmental adaptability of community abnormal behavior monitoring through multi-dimensional data processing and dynamic optimization, and is suitable for community safety management.

A monitoring system is disclosed for monitoring the performance of an enclosed system for processing pharmaceutical ingredients. The enclosed system comprises one or more inlets for receiving one or more pharmaceutical ingredients and one or more outlets, and the monitoring system comprises a first sensor and a processing unit operatively connectable to the first sensor. Also disclosed are methods for monitoring the performance of the enclosed system for processing pharmaceutical ingredients, and a room comprising the enclosed system and the monitoring system.

The application relates to the technical field of artificial intelligence, can be applied to the field of digital medicine, and discloses an old population disease monitoring method and device, an electronic device and a storage medium. The method comprises the following steps: synchronously collecting physiological data, behavior data and environment data of a target old population; performing data cleaning processing and feature extraction on the collected data at an edge end to obtain multi-modal fusion features; inputting the multi-modal fusion features into a preset personalized health baseline model to perform real-time evaluation on the health risk of the target old population, so as to generate a risk evaluation result; triggering a corresponding early warning mechanism according to a risk level corresponding to the risk evaluation result; based on a warning triggering result, corresponding risk data is pushed to a family end, a community end and a hospital end through a preset standardized data interface, and corresponding risk intervention progress data and risk intervention result data are received. The method improves the comprehensiveness, safety and collaboration of old population disease monitoring.