16 results about "Risk area" patented technology

The invention relates to the technical field of medical hygiene, and particularly discloses a hygiene visual intelligent monitoring and guiding system suitable for surgical hand disinfection, hand motion data is collected by fusing a multi-modal motion capture module with a depth camera and an inertial sensor, and a high-risk area identification module analyzes the risk of microbial residues in finger seams and fingernail edges. The three-dimensional motion modeling module constructs a biomechanical model to calculate a trajectory, the dynamic compliance guiding module marks deviation in real time through optical projection, the multi-source data fusion module integrates a risk value and a spectral data evaluation effect, and the intelligent decision engine module outputs an operation matching degree and a correction instruction based on a neural network. Omnibearing monitoring, accurate risk assessment, real-time action guidance and quantitative effect evaluation of the hand disinfection process of an operator are realized, the standardization and effectiveness of surgical hand disinfection operation are finally improved, and the infection risk of a surgical site is reduced.

The invention relates to the technical field of target detection, in particular to an adaptive detection method based on regional dynamic reasoning optimization, and the method comprises the steps: 1, presetting an abnormal target; step 2, carrying out risk level division on each abnormal target; step 3, setting a detection result without an abnormal target and contribution weights of the abnormal target of each risk level to risk scores, and determining risk score ranges and detection frequencies corresponding to risk areas of different levels; 4, starting a camera device, and dividing a detection picture obtained by the camera device into AxB grid regions; 5, performing risk area grade division on each grid area according to the corresponding risk score; 6, performing risk detection on each grid region based on the corresponding detection frequency according to the corresponding risk region grade, and outputting a risk detection result; and step 7, updating the risk score and the risk area grade of each grid area according to the risk detection result.

The invention discloses an intelligent pressure sore risk management and nursing decision support system and method. A patient risk file is established by collecting patient inspection index data, skin image data and Braden scale evaluation data, a normal index area is identified to form an index reference domain, and multi-part associated monitoring configuration is established; performing feature extraction on the inspection index data to form an index change curve, and performing high-risk combination mode recognition in combination with Braden scale assessment data to generate a grading risk assessment benchmark; executing time axis dynamic tracking on the skin image data to generate a skin state change sequence, and generating a burnishing positioning result through burnishing area detection; and matching the red pressing positioning result with the skin characteristic parameters to identify a risk area, executing risk level dynamic adjustment according to the risk area in combination with a grading risk assessment criterion to generate an injury degree value, matching personalized nursing measures to generate a nursing decision result, and providing intelligent decision support for clinical nursing.

The invention provides an intelligent turning-over assisting system and method for obese patients during hospital, and relates to the technical field of intelligent medical nursing, and the system comprises a split pressure sensing unit which is configured to monitor the regional pressure of different limb parts of the patients; the tissue hypoxia monitoring unit is configured to be used for monitoring tissue oxygenation degrees of different local tissues of the patient; the nursing decision module is configured to obtain risk area information for the pressure sores according to the area pressure and the tissue oxygenation degree, and the risk area information comprises the risk area position and the risk level; if it is judged that the patient does not have the operative wound, a first turning-over strategy for guiding the nursing mode of the patient is generated according to the risk area information, and the first turning-over strategy comprises the turning-over direction, the supporting part and the turning-over frequency. According to the system, pressure sore risk assessment is upgraded from subjective experience judgment of nurses to multi-dimensional physiological data driving, the problems of missed judgment and misjudgment caused by experience difference in traditional nursing are solved, and the system is particularly suitable for clinical scenes with complex body position pressure of obese patients.

The invention provides a multi-source data fusion-based old people falling risk dynamic assessment method and system, and belongs to the technical field of health monitoring and intelligent wearable equipment. The method comprises an activity area calibration step, a fall risk identification step, a data acquisition mode updating step and a risk early warning step. The method comprises the following steps of: classifying an activity area into a high-risk area, a medium-risk area and a low-risk area, and updating a multi-source monitoring data acquisition mode of the area when identifying that the old people pass through the areas with different risk levels; performing fall risk identification based on the multi-source monitoring data acquired by the updated data acquisition mode; and when a falling risk is identified, a risk early warning signal is sent out. The system is used for implementing the method. According to the technical scheme, the activity area can be dynamically calibrated, the risk level can be accurately divided, the data acquisition mode can be adjusted according to the risk level, and the pertinence and timeliness of multi-source data fusion assessment of the falling risk are improved.

The application discloses a kind of infectious disease monitoring system based on WebGIS in monitoring technical field, including signal connection's monitoring background, distributed field monitoring unit and mobile terminal, monitoring background includes display interface, for showing the login and query of infectious disease monitoring system;Identity verification module, for inputting username and password verification manager identity;Storage module, for storing map information and patient information;Data update module, for adding, modifying and deleting the personal information, health status and activity track of patient;Data query module, for manager to query the personal information, health status and activity track of patient and case change data;Online monitoring module, the geographic position of patient is acquired in real time;Risk analysis module, buffer zone analysis is carried out based on the activity track of patient, to determine whether the adjacent area of activity track exists risk;Measuring module, for measuring the length and area of target area, to determine the range of risk area.

This invention discloses a method, system, and electronic device for personnel operation assessment based on video temporal joint analysis, relating to the field of power safety technology. This application involves acquiring video from the work site and extracting frames; extracting the sequence of key human points from each frame; dynamically defining risk areas for the head, torso, and hands based on these key points; calculating hand movement speed, distance from the risk area, and movement consistency characteristics; triggering refined behavior recognition when hand movement characteristics indicate a tendency to enter the risk area; extracting multi-scale temporal features before and after the trigger; and judging specific risk behaviors using a pre-trained action recognition model. This invention, through a two-level mechanism of "trend triggering + refined recognition," achieves early warning and accurate identification of risk behaviors such as removing helmets and gloves, effectively improving the real-time performance and preventative capabilities of work safety monitoring. This application realizes a shift from post-event identification to pre-event warning, resulting in better safety monitoring performance.

The application discloses a community pension service management system and method based on risk analysis, and belongs to the technical field of pension service management. The application uses a YOLO algorithm to locate risk positions in a community, forms a community risk position set, constructs a community three-dimensional map, forms a risk area in the community three-dimensional map from the community risk position set, and labels risk information; exploratory data analysis is performed, a statistical model is constructed, and a risk area expansion factor is obtained; based on the risk area expansion factor and current weather data, the risk area of the community three-dimensional map and the corresponding risk information are adjusted; health data of a user is acquired, a scoring standard is set, and a health score is calculated; combined with individual conditions of the user, whether the user is currently suitable for going downstairs for exercise is evaluated based on the total health score, and a health evaluation result is obtained; when the health evaluation result of the user is suitable for exercise or cautious exercise, a planned exercise route of the user is adjusted, and risk information is prompted.

The application relates to the medical field, in particular to a method, device, equipment, medium and program product for intraoperative risk report generation. The method is used for solving the problems that the existing surgical video analysis can only identify process information, is difficult to continuously track risk events and predict risk levels. The method comprises the following steps: acquiring video data in a surgical process; determining a risk area in each frame of image according to the video data; generating a risk event instance according to the position and occurrence time of the risk area; performing feature matching between a newly detected risk area and an existing risk event instance in the surgical process, and when the matching is successful, the risk event instance is associated and updated; predicting a risk level at a future moment based on historical time sequence data of the risk event instance; and outputting a visual surgical report comprising the risk level. The scheme can continuously model and time sequence predict the intraoperative risk, improve the completeness and structural degree of the risk record, and is also convenient for postoperative review and quality evaluation.

The present invention proposes a method, device, electronic device, and storage medium for monitoring the operation of large-scale amusement facilities, including: constructing a three-dimensional model of the facility; establishing a basic failure model for the amusement facility; identifying key risk areas based on the model and the three-dimensional model; generating a multi-dimensional health assessment framework; testing key risk areas and monitoring operating parameters and structural health status; conducting automatic inspections of the amusement facility and collecting video images; deeply fusing non-destructive testing data, operating parameters, structural health data, and video images to obtain monitoring data; analyzing the monitoring data to automatically identify, diagnose, and predict faults; assessing the remaining service life of key risk areas of the amusement facility and conducting trend analysis in combination with historical data; quantitatively evaluating the health status of the facility, generating dynamic risk warnings and repair recommendations, and formulating a maintenance plan. The solution of the present invention significantly improves the safety and maintenance efficiency of the equipment.

The invention discloses a bridge anti-icing control system based on multi-source sensing data analysis and a control method thereof, and relates to the technical field of bridge anti-icing. The method comprises the following steps: marking a position where an icing event easily occurs in a bridge as a risk area, collecting risk factor data of the risk area, constructing a risk model used for evaluating the occurrence probability of the icing event, and calculating the probability of the icing event in the risk area in a current risk factor data state by using the risk model. According to a probability calculation result, the risk area type is divided into a high risk area, a medium risk area or a low risk area, corresponding anti-icing control strategies are made for areas of different risk levels, and icing detection, early warning and automatic control measures are organically combined to form a complete anti-icing solution, so that the anti-icing efficiency is improved. Therefore, bridge icing can be effectively prevented and treated, the safety and reliability of the bridge running in winter are improved, and the influence of the bridge icing on traffic is reduced.

The present invention relates to the technical field of personnel behavior monitoring, and specifically, to an emergency call method, device, equipment and medium based on artificial intelligence. The present invention marks and grades the more dangerous areas in the hospital in advance, and configures key monitoring groups of different age groups based on the grades, and configures corresponding cameras for monitoring the area. When multiple elderly people enter the vicinity of the corresponding high-risk area, the key monitoring objects are screened out based on the human parameter monitoring equipment worn by the elderly. After the key monitoring objects enter the high-risk area, the camera corresponding to the high-risk area is used to zoom and focus on monitoring the body posture of the elderly when passing through the high-risk area, and then detect whether the elderly have fallen, etc. Since the entire process is automatically analyzed and intelligently determined through high-definition video monitoring, Internet of Things sensors and big data, the personnel monitoring task volume in the nursing home or sanatorium is effectively reduced, and the labor cost expenditure is reduced.

The invention discloses a risk area classification early warning system, device and method based on an HIS system, relates to the technical field of hospital construction and management, and solves the problems that existing risk assessment early warning is only designed for individual properties of patients and is not designed according to specific conditions in a hospital and conditions of the patients, and the risk assessment early warning is not designed for dynamically changing conditions in the hospital during use. And the technical problem of lack of accurate and targeted early warning is solved. The risk area classification early warning system based on the HIS system comprises a risk area classification system and an early warning system, and the early warning system is connected with the risk area classification system and a hospital HIS data system. A specific area in a hospital is divided into different dynamic risk areas according to corresponding risk types through the risk area classification system, the risk types of patients are judged through the early warning system, information of the dynamic risk areas is received, whether the patients are in the dynamic risk areas or not is judged, and early warning prompts are sent in a targeted mode.

A construction method for an individualized virtual laparoscopic intraoperative risk feedback model and a feedback method for an individualized virtual laparoscopic intraoperative risk feedback model, belonging to the field of medical robotics. The construction method for an intraoperative risk feedback model comprises: constructing a multi-dimensional risk assessment model based on individual patient characteristics that comprehensively considers organ morphology, a physical distance of a mechanical arm, clinical risk levels of organs, and historical risk accumulation, so as to quantify risk scores for different organs during virtual laparoscopic surgery; improving a conventional attention mechanism on the basis of the risk scores, and designing a risk attention module; embedding the risk attention module into an encoder and a decoder of a conventional U-Net segmentation model to obtain an RU-Net segmentation model; and using the RU-Net segmentation model to achieve organ segmentation, highlight risk areas, and dynamically generate visual feedback such as adaptive transparency adjustment and risk warnings. The present application improves the comprehensiveness and accuracy of intraoperative risk analysis, and enables dynamic real-time visual feedback, significantly improving the reliability of virtual laparoscopic surgery.

A patient monitoring and nursing method and system based on AI multi-scenario linkage is disclosed. The method includes: (1) installing monitoring terminals at the bedside, ward entrance, corridor wall, toilet wall, toilet cubicle wall and main entrance of medical institution respectively; (2) incorporating a pre-trained patient identification model, balance assessment algorithm and companion identification model, and completing patient identity confirmation, gait feature extraction, balance status quantification score, physical fitness status assessment and identification of whether there are family members accompanying the patient through real-time image data collected by the monitoring terminal; (3) triggering an alarm when the patient is detected to be out of bed and the balance score is ≤6 points, without family members accompanying the patient, appearing in the high-risk area of ​​the corridor wall / toilet wall and with an unstable gait, near the main entrance without a caregiver and without triggering the discharge registration process; (4) the monitoring terminal transmits the alarm signal, the patient's real-time location, risk level and on-site image to the server through wireless communication, and the server pushes it to the nurse's early warning terminal simultaneously.