7results about How to "Automatic Identification" patented technology

The application provides a marine rainstorm diagnosis method and system based on a multi-source remote sensing South China Sea summer monsoon index, relates to the meteorological monitoring technical field, and comprises the following steps: acquiring multi-source remote sensing data such as a South China Sea wind field, a temperature field and a humidity field, calculating a monsoon comprehensive index field, and performing time-space gradient analysis to identify an abnormal region. Based on the spatial position and evolution trend of the abnormal region, the intensity of kinetic energy input and thermal energy output is calculated respectively, and whether the energy budget is in a positive feedback enhancement state is determined by comparison. Finally, the rainstorm diagnosis result is generated by coupling the evolution path of the index field and the energy state, and more accurate and timely diagnosis and early warning of the South China Sea marine rainstorm are realized.

The invention relates to the technical field of digital management, and discloses a cloud-side collaborative digital platform based on regional production and operation integrated management and control, which comprises an infrastructure layer used for constructing a unified hardware resource pool and an automatic operation and maintenance base for supporting a cloud-side collaborative architecture, global unified management and automatic operation and maintenance of a bottom server, storage and network hardware equipment are realized; the platform layer is used for converging global data and integrating an application development module and an AI module to form a common tool chain and data sharing service covering low-code development, micro-service and model full-life-cycle management; and the application layer is used for calling the common technical capability and the data sharing service provided by the platform layer, and constructing and deploying a business application system serving for integrated management and control of regional production and operation. According to the invention, a single-point operation and maintenance mode is converted into a new integrated intelligent management and control mode with cross-domain cooperation, active early warning and accurate decision making, and the safety, operation efficiency and risk resistance of regional production and operation are improved.

The present application relates to a method and device for determining the side of transition of wheel load transition of a motor train unit passing a turnout. The method first acquires continuous left and right track irregularity data and the turnout point mileage from the comprehensive detection of the train. The turnout switch area mileage interval is determined by extending a preset distance forward and backward from the turnout point mileage, and the left and right track irregularity data to be calculated in the interval is extracted. The two sets of data are respectively subjected to second-order difference processing, and the sliding root mean square value is calculated window by window according to the preset sliding window size and step, to obtain the left and right track characteristic sequences. The maximum values of the two sequences are extracted and compared to determine the side of transition of wheel load transition. The present application only uses conventional detection data and turnout point mileage, without the need for additional sensors or manual calculation, and can accurately, efficiently and automatically identify the side of transition of wheel load transition, providing direct data support for the fine analysis and treatment of turnout car shaking.

The invention relates to the technical field of image processing, and discloses a hepatobiliary calculus operation video processing method, which comprises the following steps: acquiring an operation video of a hepatobiliary calculus operation; extracting images in the operation video by adopting a first time window to obtain a first operation image sequence; determining a plurality of image segmentation rules associated with the operation image sequence by using the operation images in the first operation image sequence, wherein the pre-trained calculus model and the plurality of image segmentation rules are used for identifying consistent calculus images in adjacent operation images in the first operation image sequence; the time range is obtained based on video time corresponding to the operation images in the first operation image sequence. According to the technical scheme, the operation efficiency and accuracy are improved, and the auxiliary operation capability of doctors is enhanced.

The application discloses a hyperspectral image target intelligent detection method based on deep learning and belongs to the field of remote sensing image processing. The method comprises the following steps: selecting a background training set with large difference and representativeness between each other through a coarse detection and de-correlated background pixel selection strategy; generating sufficient target training sets by using a target spectrum band random replacement target training sample data enhancement strategy; mixing and disordering the background and target training sets, and inputting the background and target training sets into upper and lower branches of a multi-depth-multi-branch target detection network respectively for training; inputting a to-be-detected pixel and a prior target into the upper and lower branches of the trained network respectively for target detection, and outputting the network to form a final detection result. By using the application, target pixels in a hyperspectral image can be detected and highlighted, and background pixels can be effectively suppressed, so that a detection result with high detection precision, high background suppression degree and high target-background separation degree is obtained.

The invention relates to the field of buildings, in particular to a system and method for assisting in recognizing quality assurance data of reinforcing steel bar raw materials, and the system comprises a database module, a picture processing module and a counterfeit checking rule module, the database module is used for storing and managing project information, personnel role information, a reinforcing steel bar authenticity verification ledger, an AI authenticity verification recognition model, algorithm rules and manufacturer information; the picture processing module is used for shooting and preprocessing a picture of a reinforcing steel bar raw material quality guarantee, and extracting information in the picture by applying an icon algorithm and a table recognition algorithm; and the authenticity verification rule module performs authenticity verification judgment on the extracted information according to a preset authenticity verification rule. The method can automatically and quickly recognize the character, icon and table information in the steel bar raw material quality assurance data, improves the recognition efficiency, and can achieve the precise recognition of the steel bar raw material quality assurance data through the training of a large number of samples through the deep learning algorithm, and reduces the error rate of manual auditing.

This invention relates to the field of industrial equipment safety monitoring technology, and discloses a data-driven intelligent early warning method and system for the degradation risk of organic heat transfer fluids. The method includes: acquiring multimodal monitoring data of the organic heat transfer fluid, performing preprocessing and feature extraction to obtain entropy yield and acoustic emission activity index; inputting the entropy yield, acoustic emission activity index, and medium quality parameters into a mechanism classification neural network model to obtain the determination probability of the degradation mechanism; according to the determination probability, inputting the feature vector into the corresponding target branch neural network to obtain an instantaneous risk index; predicting the time required for the medium quality parameters and entropy yield to reach a safety threshold, taking the minimum value as the predicted failure time, and calculating the degradation resilience value in conjunction with the planned maintenance time; and determining the comprehensive risk level based on the instantaneous risk index and the degradation resilience value. This solution can achieve early detection of degradation trends and distinguish different degradation mechanisms, improving assessment accuracy and making maintenance strategies more targeted.