5results about How to "Risk prevention" patented technology

This invention relates to the field of bid document management technology, and in particular to an automatic bid document review method and system based on natural language processing. The method includes: identifying target sections; determining historical performance and financial information; judging whether false performance exists; analyzing the complete sentences containing target keywords; calculating material scores; determining price deviation; and determining the review result. This invention achieves multi-dimensional and comprehensive review of bid documents by intelligently analyzing multiple sections such as qualifications, risks, materials, and pricing. This effectively improves the comprehensiveness and depth of the review. Utilizing natural language processing technology, it can accurately identify and extract key information from bid documents, overcoming the subjectivity and limitations of traditional manual review. Quantitative assessment based on risk level significantly improves the accuracy and relevance of risk identification. By comprehensively weighing material scores and price deviation, it significantly improves review efficiency.

This invention discloses an intelligent visual prosthesis system and encoding method. The system includes an external sensing processing unit and an internal implanted stimulation unit. The external sensing processing unit includes at least an environment sensing unit and a first data processing unit, and the internal implanted stimulation unit includes at least a stimulation signal generation unit and an electrode array. The environment sensing unit is used to acquire a target image. The first data processing unit is used to match the current scene to a preset lighting scene mode according to the image features or ambient illuminance of the target image, independently configure anchor points for the corresponding image grayscale value boundaries for the matched lighting scene mode, and generate a grayscale-charge density mapping relationship corresponding to the lighting scene mode. Then, the grayscale value of each pixel of the target image is mapped to the corresponding target charge density, and a stimulation command is generated and output to the stimulation signal generation unit. The stimulation signal generation unit is used to generate an electrical stimulation pulse sequence according to the stimulation command and a preset reference pulse width and output it to the electrode array.

The application relates to the technical field of emergency management, and discloses an emergency response and early warning system for a sudden combustion and explosion event, which comprises a multi-source data access module, which is used for collecting target equipment data and environment benchmark equipment data of energy storage equipment or a centralized charging site in real time, including temperature, current, voltage, fan state and smoke / odor sensor data, and pre-processing the collected target equipment data and environment benchmark equipment data; an abnormal index preset module, which is used for presetting a composite abnormal index representing early signs of thermal runaway and risk grading standards; and a same-site benchmarking module, which is based on the collected target equipment data and environment benchmark equipment data. The same-site benchmarking module is adopted, the environment benchmark data and abnormal acceleration change trend calculation are carried out, the technical effect of identifying the composite abnormal index in an extremely early stage is achieved, early warning is realized, and the problem that the safety window period for the operation party to take initial disposal actions is short is solved.

This invention discloses an intelligent CTD sampling control method and system combining tidal current prediction, relating to the field of marine observation technology. The method includes: generating tidal current predictions by fusing historical tidal elements and numerical model outputs, and planning candidate sampling windows; real-time acquisition of multi-source observation data to form standardized observation vectors, calculating the deviation from the predicted values ​​and fusing the vertical shear force intensity factor to perform rolling corrections on the predictions and update the sampling windows; comparing real-time observation data with safety thresholds to generate operational access instructions, issuing control instructions under permissible conditions, and dynamically adjusting sampling parameters and triggering resampling based on profile data quality coefficients; synchronously monitoring abnormal events, triggering emergency recovery, and deferring the task to subsequent candidate windows. By constructing intelligent closed-loop control, introducing vertical shear force early warning, and a parallel processing architecture, adaptive, highly safe, and highly reliable CTD sampling operations under complex sea conditions are achieved.

The invention discloses a robot navigation method fusing U-net semantic segmentation, and relates to the technical field of computer vision and robots. Comprising the steps of S1, collecting an original image of a front road scene in real time; s2, performing semantic segmentation processing on the original image to generate a semantic segmentation prediction map and a probability distribution map; and S3, based on the probability distribution diagram, analyzing the spatial distribution and boundary trend of the passable area, and calculating a structured control parameter at the next moment. Accurate recognition of passable areas, static boundaries and dynamic obstacles in a complex road environment is realized, an upper and lower double-layer hardware architecture design is adopted, a semantic perception task with a high computing power requirement is separated from a motion control task with a high real-time requirement, the upper industrial personal computer is responsible for image processing and decision making, and the real-time performance of the robot is improved. The lower STM32 single-chip microcomputer is responsible for PWM signal output and motor driving, and then fast response and stability of bottom layer control are ensured under the condition that efficient operation of the deep learning model is ensured.