9results about How to "Automatic recognition" patented technology

The present application belongs to the technical field of atmospheric dynamic structure identification, and discloses a method for automatically identifying the horizontal scale of atmospheric vortex and matching the vortex with a mesoscale convective system, which comprises the following steps: based on the two-dimensional coordinates of the vortex center of each extended height layer of a three-dimensional vortex at time t, a vortex range identification algorithm is used to identify the vortex range of each extended height layer of the three-dimensional vortex at time t; a vortex range smoothing processing algorithm is used to smooth the vortex range at each time t in the vortex life history time sequence; and it is determined whether the range of the mesoscale convective system coincides with the stable vortex range of any extended height layer of the three-dimensional vortex at the same time t. The present application automatically and accurately identifies the range of a large-scale and long-time atmospheric vortex, solves the problem that the traditional vortex identification cannot determine the horizontal range and the real influence scale, realizes the automatic matching and correlation analysis between the vortex and the mesoscale convective system, and constructs an automatic identification process which can be used in real-time or reanalysis business platforms.

This invention relates to the field of risk warning technology, and discloses a business risk intelligent warning method, system, and related equipment based on a rule engine. The method includes: establishing risk warning rules; in response to business data updates, obtaining a unique identifier for the target spatial object corresponding to the business data update; based on the unique identifier, determining the target level of the target spatial object in the spatial hierarchy, and the target rule conditions of the risk warning rules bound to the target level; according to the target level, querying and aggregating data items associated with the target spatial object from at least two different business domains to form a data set for risk analysis; matching and evaluating the data set with the target rule conditions; and in response to the matching and evaluation result satisfying the target rule conditions, triggering the execution of a corresponding response action to generate warning information for the target spatial object. This improves risk response efficiency and reduces false alarm / false negative rates.

The invention provides a semiconductor device parameter overall variation automatic identification method and system, and the method comprises the following steps: obtaining an FDC chart corresponding to a target parameter of each device according to an FDC system, and carrying out the reference standard deviation calculation and abnormal value filtering based on a historical data set of the FDC chart; and calculating a current standard deviation according to the current data of the FDC chart, judging whether the target parameter of each device is integrally degraded or not based on the reference standard deviation and the current standard deviation, and integrating and outputting analysis results of the overall degradation of the target parameters in all devices. According to the method, the overall deterioration of the parameters of the semiconductor equipment can be automatically, efficiently and accurately identified, so that the defects of an existing FDC system in the aspect of progressive fault early warning are overcome.

The application provides a time series load data enhancement generation method and device based on a hidden Markov model, relates to the technical field of power system data processing, divides and time-aligns historical and real-time original time series load data according to a key time period, extracts fluctuation characteristics, obtains historical and real-time fluctuation characteristics, models different fluctuation operation states in the historical fluctuation characteristics by using a hidden Markov model, establishes an online identification model of the fluctuation operation states, takes the real-time fluctuation characteristics as input, outputs the identified fluctuation operation states in real time, adaptively generates corresponding noise to inject into time-aligned real-time load data, and generates enhanced load data. The application adopts a training-free load data synthesis framework based on a hidden Markov model, can automatically and accurately identify periodic characteristics and different fluctuation operation states in load data without depending on a large amount of historical data, and completes data enhancement through adaptive noise injection.

The invention discloses a work scene investigation auxiliary system, method and tool, and the system comprises an image collection unit which comprises a camera and a light supplement lamp and is used for collecting multi-angle images of a work scene; the mechanical support and motion module comprises an electric control lifting rod, an electric control holder and a driver, and is used for carrying and controlling the image acquisition unit to perform multi-degree-of-freedom motion in the working space; the control and processing unit comprises a main control board and an image processor in communication connection with the main control board; wherein the main control board is configured with an automatic path planning algorithm, controls the mechanical support and motion module to move according to a predetermined track, and synchronously triggers the image acquisition unit to perform image acquisition; the image processor is configured with an image processing algorithm and is used for processing the acquired multi-angle image to generate a panoramic image and / or state change information of the operation scene; according to the system, the storage medium, the method and the tool, the operation and maintenance work efficiency and accuracy can be remarkably improved.

The invention discloses an intelligent safety management system and method for operation of a lifting section of a tower crane, and the system comprises a management platform which is used for storing an operation plan, confirming the capability before operation execution, recording operation process data, and carrying out operation acceptance; the action detection module is mounted on a jacking mechanism of the tower crane and is used for detecting the moving state of the jacking ladder cage in real time and generating a moving signal; the height detection module is mounted on the tower crane and used for detecting the lifting height of the tower crane in real time and generating height data; the video monitoring module comprises a camera and is used for acquiring a video image of a tower crane lifting section operation area; the management platform is in communication connection with the motion detection module, the height detection module and the video monitoring module. According to the method, a safety redundancy management and control system covering the human-technology-object full dimension is constructed, the whole-process, intelligent and refined safety management of the lifting section operation of the tower crane is achieved, and the construction safety level and the management efficiency are effectively improved.

A noise reduction earphone based on a convolutional neural network belongs to the technical field of noise reduction earphones and comprises a noise power generation and energy storage device, a microphone array, a preprocessing module, a convolutional neural network module, a noise reduction module and a loudspeaker which are connected in sequence. The microphone array, the preprocessing module, the convolutional neural network module, the noise reduction module and the loudspeaker are all connected with a storage battery of the noise power generation and energy storage device. The earphone provided by the invention can generate power by collecting noise without external power supply, and can automatically identify the noise, filter the noise and automatically play normal sound, thereby facilitating communication in a noise place and judging abnormal sound. The method is wide in application prospect, can be applied to places with high noise, such as pump rooms, factory buildings and workshops, and achieves the purposes of recognizing and filtering noise, carrying out voice communication and listening to abnormal sound.

The invention discloses an RFID-based device anti-substitution method and device, and relates to the technical field of energy power equipment, and the method comprises the steps: controlling an RFID reader to read the identification information stored by an RFID tag disposed on each power module in the equipment before the equipment is started; comparing all the read identification information with all legal identification information pre-stored in the control panel; if the comparison result shows that all the read identification information is matched with all the pre-stored legal identification information one by one, the control equipment is normally started; and if the comparison result shows that any identification information is not matched or missing, the control equipment terminates the starting process. According to the invention, access of a non-authentication module is completely eradicated from the source, electrical potential safety hazards caused by mismatching of module parameters and quality defects are directly eliminated, and the reliability and the service life of equipment operation are guaranteed. Meanwhile, the mandatory identity verification effectively prevents the behavior of replacing the module privately by the user.

The invention relates to the technical field of route distribution, and discloses a block chain node route distribution method, device and equipment and a medium, and the method comprises the steps: obtaining the historical load data of block chain nodes, and calculating the expected processing delay, the expected concurrent request amount and the network topology distance of each block chain node based on the historical load data; performing performance quantitative analysis on the expected processing delay, the expected concurrent request quantity and the network topology distance to obtain a processing delay metric value, a concurrent request metric value and a network topology distance metric value; determining a corresponding scene weight coefficient according to the business application scene of the block chain node; calculating a routing distribution cost characteristic value according to the processing delay metric value, the concurrent request metric value, the network topology distance metric value and the scene weight coefficient; and performing a routing distribution decision on the block chain node according to the routing distribution cost feature value. According to the invention, the accuracy of node routing distribution can be improved.