8results about How to "Detect abnormalities in time" patented technology

The application discloses a tunnel construction process automatic monitoring method, and belongs to the technical field of tunnel engineering construction monitoring. The method comprises the following steps: step 1, automatic monitoring equipment is arranged at key positions of a tunnel opening area, an advanced support section, an excavation face, an initial support structure, a waterproof layer construction area and a secondary lining structure; step 2, each automatic monitoring equipment is started, and monitoring data is collected at a preset sampling frequency; step 3, after receiving the monitoring data, a central monitoring platform uses a big data analysis algorithm to deeply process the monitoring data; and step 4, the central monitoring platform feeds back monitoring results and early warning information after analysis to a tunnel construction control system in real time. The application realizes real-time data acquisition and analysis through 5G technology, especially the UWB positioning system collects data once per second, realizes instant acquisition and analysis of monitoring data, and compared with traditional manual monitoring, the data update frequency is improved by tens of times, and abnormal conditions in the construction process can be found in time.

This application relates to the field of temperature control equipment technology, specifically a coolant flow regulation and leak prevention device for semiconductor temperature control equipment. The device utilizes a liquid pump in a coolant regulation component to circulate coolant in cooling pipes, which connect the coolant regulation component and the semiconductor manufacturing equipment. Several sets of cooling pipes are equipped with flow meters and flow regulation devices, and the cooling pipes run through the semiconductor manufacturing equipment. The coolant regulation component includes a liquid pump and a variable frequency refrigeration compressor, both connected to a main controller. The leak prevention component includes several sets of leakage sensors corresponding to the cooling pipes and a first solenoid valve located on the cooling pipe at the inlet of the semiconductor manufacturing equipment. Each leakage sensor is externally connected to an alarm and a user controller, which is signal-connected to the main controller. This application enables real-time monitoring of the fluid state in the cooling pipes, ensuring the stability and reliability of the liquid supply during semiconductor manufacturing, thereby improving manufacturing efficiency and product quality.

This invention discloses a structural truss stress detection method based on an automated monitoring system. The method includes: receiving and executing acquisition commands from a data center using a sensor module; acquiring stress data at key nodes and connections of the structural truss in a photovoltaic system to obtain stress data acquisition results; and uploading the stress data acquisition results to the data center via a transmission module. The method also involves acquiring photovoltaic field data from the photovoltaic system using acquisition equipment in the transmission module and uploading this data to the data center; storing the stress data acquisition results and the photovoltaic field data in the data center's database; and fusing and analyzing the stress data acquisition results and the photovoltaic field data using a computer device in a control module to obtain analysis results. This invention enables multi-channel remote real-time monitoring using an automated monitoring system, effectively monitoring structural status and providing timely early warnings.

This invention relates to the field of medical technology, specifically to a method for monitoring anesthesia depth based on a neural network model. It includes the following steps: collecting physiological indicator data of patients under different degrees of anesthesia; preprocessing the physiological indicator data to obtain interference-free physiological indicator data; and transmitting the processed physiological indicator data to a data management system; constructing a neural network model; inputting the physiological indicator data into the neural network model through the data management system; and training the neural network model. This invention, by collecting physiological indicator data of patients under different degrees of anesthesia to establish a training dataset, and training the neural network model, enables the neural network to accurately predict the patient's anesthesia depth. This method of collecting and processing the training dataset provides sufficient data samples, allowing the neural network model to be trained on diverse data, thus improving the model's generalization ability.

This application discloses a battery type determination system and method based on weight loss curve modeling, relating to the field of battery type determination technology. The system includes a first acquisition module for acquiring real-time weight values ​​of the battery under test at fixed intervals 'a'; a curve fitting module for constructing a weight loss fitting curve for the battery under test using a fitting method; a second acquisition module for determining the liquid substance determination value of the battery under test based on weight loss characteristic values; a judgment module for determining whether to compensate the liquid substance determination value based on test confidence level; a compensation module for determining the compensation coefficient of the liquid substance determination value and obtaining the compensated liquid substance determination value; and a battery type determination module for determining the battery type of the battery under test. This application effectively overcomes the drawbacks of existing methods that rely on simplified processing based on the difference between initial and final mass.

This application provides a control method, device, smart glasses, medium, and program product for smart glasses. The control method includes: periodically acquiring eye-tracking images of the wearer according to a default cycle using an eye-tracking device during the wearing of the smart glasses; determining the wearer's eye usage data based on the eye-tracking images; adjusting the acquisition cycle of the eye-tracking images based on the eye usage data; and providing eye health reminders. By combining real-time eye usage information with eye health reminders, it is beneficial to improve the eye habits of smart glasses wearers and enhance eye protection. Simultaneously, by dynamically adjusting the acquisition cycle of the eye-tracking images based on eye usage information, eye-tracking image acquisition can be performed at a lower frequency when eye usage is healthy, thereby reducing the power consumption of eye monitoring.

This utility model discloses a prefabricated modular permeable well system, including a prefabricated well casing, a well ring, a well cover base, a well cover, an anomaly monitoring module, and several permeable bricks. The well ring is located at the upper opening of the well casing, and the well cover base is located at the upper opening of the well ring. The well cover is fitted onto the well cover base, and several permeable bricks are laid around the outer circumference of the well cover base. A permeable mortar layer is laid at the bottom of the permeable bricks, and the permeable mortar layer is laid around the outer circumference of the well ring. Several permeable through-tubes are arranged at circumferential intervals around the well ring, and the permeable through-tubes pass through the side wall of the well ring. The anomaly monitoring module includes a fiber optic grating sensor and an edge computing terminal. The fiber optic grating sensor is embedded in the well cover and is used to monitor the displacement and pressure data of the well cover in real time. The edge computing terminal is used to process the data collected by the fiber optic grating sensor. This utility model is easy to transport, replace and maintain. It can monitor the status of manhole covers in real time and detect abnormalities in a timely manner. It can also improve water permeability and effectively solve the problem of road water accumulation.

The invention discloses a data monitoring method and device, equipment and a medium, and the method comprises the steps: collecting the operation state data of each piece of monitored equipment according to a preset data collection mode corresponding to each piece of monitored equipment; each monitored device comprises a network device, a storage device, a server device, a middleware database, a big data cluster and a container cluster; acquiring basic environment data of each monitored device according to the environment data acquisition method corresponding to each monitored device; and carrying out data source integration on the basic environment data and the operation state data corresponding to each monitored device to obtain monitoring fusion data, and carrying out visual display on the monitoring fusion data. According to the invention, intelligent operation and maintenance monitoring of the monitored equipment of the cloud platform can be realized, so that the abnormity of the working state of the monitored equipment can be found in time, an administrator is helped to make a response and process more quickly, and the stability and safety of the system are improved.