63results about How to "Predictive" patented technology

The invention discloses a high-speed rail catenary geometric parameter detection non-contact compensation and Kalman filtering correction method. The method mainly comprises the steps that firstly, a video camera is triggered to collect video images at equal intervals through an encoder on a wheel set; the regions where target light spots appear in the images are predicted according to prediction strategies; the positions of the target light spots in the images are located according to the centroid method and the image morphology method; the angle of roll vibration is detected through an angle sensor, and compensation is performed on the vibration through coordinate transformation; contact wire height and pull-off values are figured out according to mapping transformation of the light spots from the position of an image coordinate system to the position of a world coordinate system; finally, detecting values are corrected through the Kalman filter equations. According to the method, the defects that the system is low in detecting accuracy, poor in real-time processing performance and the like are effectively overcome, the processing efficiency of the system is improved, and the requirements of high-speed rail catenary on-line detection for real-time performance and accuracy are well satisfied.

The invention discloses a multi-phase gait switching control system and control method for a power-assisted exoskeleton robot. The multi-phase gait switching control system is characterized in that the multi-phase gait switching control system includes a sensor module, a signal acquisition and processing module, a gait switching motion control module and a motor driving module; and sensors comprise inertial measurement units, plantar pressure sensors and motor encoders. With the multi-phase gait switching control system and control method for the power-assisted exoskeleton robot of the invention adopted, the output of lower hip joint and knee-joint motor power-assisted torque under various of phase gaits and smooth switching between the phase gaits can be planned under a condition that the exoskeleton robot bears a heavy object, and the coordination and flexibility of the movement of the limbs of the weight-bearing exoskeleton equipment can be realized.

The invention provides a traffic jam avoiding prompting system based on a collective intelligence network. The traffic jam avoiding prompting system consists of a webpage traffic hot spot digging module, a microblog traffic hot spot digging module, a road condition rule predicting module, a jam hot spot verification and management module, a real-time traffic hot spot server, a traveling route planning module based on hot spot avoiding, a traffic hot spot and avoiding route display module and intelligent mobile phone end application software. The traffic jam avoiding prompting system faces the collective intelligence sensing network, is combined with the traditional traffic information sources, carries out digging and prediction on the urban traffic hot spots on the basis of the data activation technology, and further provides the traffic jam hot spot display and avoiding prompt service for intelligent terminal users. The traffic jam avoiding prompting system has the advantages that the data is from the multi-source collective intelligence network, the accuracy is good, the checking comprehensive rate is high, the hot spots are captured and fed back in real time, high efficiency and practicability are realized, the design is good, the operation is stable, the installation is convenient, the traffic jam avoiding prompting system can become a tool capable of providing wide and practical intelligent traffic information for urban residents, and the efficient information prompt is provided for the users to realize the smooth traveling.

The invention discloses a method for determining carbon sinking capacity of a mangrove forest. The method comprises the following steps: collecting surface soil of the lower covert of the mangrove forest and bare land nearest to the mangrove forest in an area to be measured and mangrove plant leaves; determining a ratio of organic carbon in the soil and stable carbon isotopes (delta <13>C) of the plant leaves; taking the bare land as the background value, and calculating a ratio of mangrove-sourced carbon in the soil organic carbon in the area to be detected and in the artificial mangrove area with known ages of the mangrove according to a relevant formula; obtaining a multiplier index of the organic carbon of the mangrove plants according to the age of mangrove forest, establishing an index multiplier equation, obtaining a mangrove forest organic carbon multiplier rate model by virtue of derivation, and calculating the carbon sinking capacity of the target mangrove forest at a specific age of the forest according to the content of the organic carbon in the surface soil under the lower convert of the target mangrove species. The method is simple, easy and free from damaging the forest; and the obtained result has a prediction function, and the carbon sinking capacity of different species with different ages in different environments can be accurately determined, so that comparative data can be provided to the research of the global variation.