4 results about "Local disease" patented technology

The present application relates to the technical field of image processing, in particular to a kind of agricultural disease detection method based on multi-scale feature extraction, comprising: obtaining target disease image, according to the mode of ladder convolution processing, the hierarchical branch of target disease image is constructed, under any hierarchical branch, with the mode of each level series coupling, determine the local disease feature of target disease image;Local disease feature is extracted by channel attention, and the local disease feature after attention recalibration is obtained;Local disease feature is mapped into semantic sequence space, and local disease feature is spatially attenuated and weighted to determine the disease distribution characteristics of crop disease under multi-scale fusion;At least one disease category is obtained based on the spatial position corresponding to disease distribution characteristics, and the average time and average accuracy of disease detection are combined to determine the disease detection effect.The precision and efficiency of disease identification are improved.

The invention provides a hyaluronic acid gel microneedle as well as a preparation method and application thereof.The hyaluronic acid gel microneedle is prepared from sodium hyaluronate as a raw material, the sodium hyaluronate is dissolved to obtain HA hydrogel, and the HA hydrogel is soaked in an organic solvent and dried after being circularly frozen and unfrozen to obtain the hyaluronic acid gel microneedle. The natural polymer sodium hyaluronate is used as a main raw material, so that the hydrogel is good in biocompatibility, free of toxic and harmful effects on the environment and biodegradable. The micro-needle body and the backing layer are both hyaluronic acid, have biocompatibility and degradability, have great application prospects in the biological medicine fields of drug delivery, microsampling, biosensing, local disease treatment, medical cosmetology and the like, and can be used for preparing carriers for loading drugs.

The invention discloses an arch bridge main arch ring bottom surface disease distribution diagram drawing method. Comprising a local coordinate system, a disease position recording standard, an overall coordinate system, a disease position description standard and a disease pattern drawing standard, is used for recording and drawing irregular line and surface diseases, and can be used for calculating engineering quantities including length, width and area. According to the method, on one hand, a set of local coordinate system based on the horizontal projection plane and facilitating recording of the disease position is established, on the other hand, a three-dimensional curved surface is unfolded into a two-dimensional curved surface to establish an overall coordinate system reflecting the actual size of the main arch ring, and local disease coordinates are reflected at the actual position of the main arch ring of the arch bridge through coordinate transformation; and accurate drawing of the three-dimensional curved surface disease is realized.

The application discloses a subway tunnel structure intelligent three-dimensional modeling and disease diagnosis method and system, relates to the technical field of intelligent operation and maintenance of urban rail transit infrastructure, adopts master-slave cooperative inspection units to collect laser radar point clouds, high-definition images, infrared thermal images, ultrasonic wave data and environmental sensing data, pre-identifies suspected disease areas after time synchronization and processing, constructs a self-adaptive separable four-parameter model M(beta, theta, delta, epsilon), wherein beta is a structure form parameter set, theta is a structure posture parameter set, delta is a local disease parameter set, and epsilon is an environmental correlation parameter set, and completes model initialization based on a design BIM model and historical data. The LSTM time sequence prediction model of the application realizes 12-month structure state change prediction based on a historical optimal parameter set, generates targeted maintenance suggestions, supports predictive maintenance decision-making, and reduces operation risks and maintenance costs caused by sudden diseases.