6results about How to "Simple classification" patented technology

The invention discloses a flat wire motor stator winding, which comprises a wire inserting stator and a winding wound on the stator, and is characterized in that the wire inserting stator comprises a stator iron core provided with M stator slots at equal intervals, and a flat wire motor corresponding to the pole number P is provided, and the stator slots are divided into 2n slot layers along the radial direction of the stator iron core; the windings comprise three phases of phase windings, and each phase winding comprises a first winding unit and a second winding unit which are the same and formed by hairpin coils; the first winding unit comprises a plurality of different-layer hairpin coils with spans of q, q-1 and q + 2, the different-layer hairpin coils are arranged between two adjacent slot layers of the stator slot in a crossing manner, the span between two adjacent different-layer hairpin coils is q, and q is M / P; and each phase winding comprises a parallel branches. According to the invention, wire outgoing can be carried out on one side of the iron core, wire outgoing can also be carried out on two ends of the iron core, the method adapts to a winding scheme of integer hairpin coil number and non-integer hairpin coil number of a single branch, and combination of pole-to-slot ratio and parallel branch number can be flexibly carried out.

The application discloses a vehicle classification method and system, the method comprises the following steps: S1, acquiring a plurality of real-time data of an observation area, and acquiring corresponding point cloud data set D according to the real-time data; S2, matching the point cloud data in the point cloud data set D with target vehicle data predicted by an extended Kalman filtering algorithm, and adding corresponding target vehicles into a target vehicle set O; S3, using a DBSCAN algorithm to perform cluster analysis on the point cloud data that are not matched successfully in the step S2, and adding corresponding target vehicles into the target vehicle set O; S4, acquiring the distance between any two point cloud data, and taking the maximum distance as the vehicle length; S5, according to the size of the vehicle length and a threshold value, configuring a size label for the vehicle, configuring a large vehicle label for the target vehicle with the vehicle length greater than or equal to the threshold value, and configuring a small vehicle label for the target vehicle with the vehicle length less than the threshold value. The application can realize vehicle size classification, and has the characteristics of small calculation amount and simple classification.

The application provides a vehicle detection data classification method and system, a computer and a readable storage medium. The method comprises the following steps: preprocessing vehicle detection data to generate input text; inputting an input vector into an ERNIE model to convert the input vector into a first word vector, and performing sequence feature processing on the input text to generate a second word vector; performing splicing processing on the first word vector and the second word vector to generate a word vector matrix, and inputting the word vector matrix into a DPCNN model; optimizing the DPCNN model through an equal-length convolution function, performing maximum pooling processing on the equal-length convolution function through the optimized DPCNN model to generate a maximum feature value; and outputting a predicted classification label corresponding to the input text according to the maximum feature value. Through the above method, the classification of vehicle detection data can be quickly completed, thereby greatly shortening the time consumed for classifying vehicle detection data.

The invention discloses a flat wire motor stator winding, and belongs to the technical field of motor stator winding, the flat wire motor stator winding comprises a stator, a plurality of stator grooves are arranged in the stator, the flat wire motor stator winding also comprises winding pairs formed by first windings and second windings, and the winding pairs are continuously arranged along the circumferential direction of the stator grooves, so that the winding pairs form even number layers in the radial direction in the stator grooves; taking the first layer and the second layer as a first layer pair, taking the third layer and the fourth layer as a second layer pair, and sequentially arranging; two ends of the first winding and the second winding are respectively arranged on an odd-numbered layer and an even-numbered layer of the same layer pair; and the winding pairs of different layer pairs are connected along the circumferential direction of the stator. According to the scheme, the types of line types of the hairpin winding are few, the investment of a production line on hairpin forming equipment and tools can be reduced, and the line types are simple in classification and easy to manage; the motor stator winding is reasonable in structure, and the connection mode and process are simple to realize.

The utility model relates to the technical field of chopping and scattering equipment, in particular to solid waste chopping and scattering equipment which comprises a chopping box and a scattering barrel, a feeding pipeline is arranged on the end face of the front end of the chopping box, and a chopping device is installed in the chopping box. The cutting device comprises a first air cylinder, a first fodder chopper set, a second air cylinder, a second fodder chopper set, an electric telescopic rod and a push plate, the cutting device cuts solid waste in a large-bag shape into laminated small blocks, cutting of the solid waste is achieved, follow-up treatment or recovery is facilitated, and the cutting efficiency is improved. Small blocky solid waste can be transported, classified, stored and further treated more easily, so that the whole treatment process is accelerated, then the output end of a first motor drives a driving shaft to rotate at a high speed, the driving shaft drives a scattering rod to scatter the solid waste entering a scattering barrel, and after the cut solid waste is evenly scattered, an electromagnetic plate is powered on, so that the solid waste is evenly scattered. And metal impurities remained in the solid waste can be adsorbed on the electromagnetic plate, so that the metal impurities in the solid waste are removed.

The present invention discloses a multi-scale fusion optimization method for classifying diabetic nephropathy based on routine test data, and relates to the technical field of diabetic nephropathy classification. The method comprises inputting the dimension of blood routine test from the perspective of L1_1, inputting the dimension of urine routine test from the perspective of L1_2, fitting the blood routine test and urine routine test respectively using a double loss function, and extracting an abstract feature layer; the abstract feature layer is nonlinearly combined through L3; the three layers of neural networks L4, L5, and L6 are jointly learned, and finally the blood routine test dimension and urine routine test dimension information are output respectively through L7_1 and L7_2. The present invention discloses a multi-scale fusion optimization method for classifying diabetic nephropathy based on routine test data, which takes the neural network of a multilayer perceptron as the basic model, constructs a shared fusion structure, and adopts a shared fusion training method to make the blood routine test dimension richer than the information obtained by direct splicing, so that the model training effect is better.