6 results about "Class type" patented technology

The invention discloses a method and device for transmitting big data between a C language and a Flutter, and relates to the technical field of big data.The method for transmitting the big data between the C language and the Flutter comprises the steps that a structural body is declared in the C language, a class consistent with the data type of the structural body is defined in the Flutter, and the structural body in the C language and the class defined in the Flutter are mapped; configuring an external function interface package in the Flutter, wherein the external function interface package is used for enabling the Flutter application to call a native code library written by a C language; calling a C function in the Flutter, and obtaining a pointer address returned by the function; the obtained pointer address is converted into a corresponding Pointer type in a Flutter; and converting the Pointer type into a mapping class type defined in the Flutter, so as to obtain the representation of the C structural body in the Flutter. The problem that in the prior art, the efficiency of big data transmission between the C language and the Flutter is low is solved, and efficient and stable data interaction and sharing are achieved.

Disclosed is a method that includes generating a prediction consistency value that indicates a consistency of prediction of an object in an input image with respect to class prediction values for the object in an input image from classification models to which the input image is input, and identifying a class of the object. Identifying the class of the object includes, in response to a class type being determined, based on the prediction consistency value, of the object being determined to correspond to a majority class, identifying a class of the object based on a corresponding class prediction value output for the object from a majority class prediction model, and in response to the class type of the object being determined to correspond to a minority class, identifying the class of the object based on another corresponding class prediction value output for the object from a minority class prediction model.

A system receives a packet via a network device in a network fabric. This packet contains at least one traffic class value for a traffic class type and one endpoint class value for an endpoint class type. The system extracts the traffic class and endpoint class values ​​from the packet and determines a hierarchical class structure that specifies the following: priorities associated with class types, including a first priority associated with the traffic class type and a second priority associated with the endpoint class type; and bandwidth allocation ratios for values ​​in each class type, including first bandwidth allocation ratios for traffic class type values ​​and second bandwidth allocation ratios for endpoint class type values.The system determines a bandwidth allocation for the packet based on the extracted values ​​and the hierarchical class structure and forwards the packet based on the determined bandwidth allocation for the packet.

The application provides a kind of municipal maintenance work order overtime degree prediction method and system, the method first obtains municipal maintenance work order data and divides discrete feature data therein into fixed class type data and fixed sequence type data, then label, one-hot encoding and leave-one-out method encoding are used to encode fixed class type data and fixed sequence type data respectively, then the continuous feature data in municipal maintenance work order data is normalized, and the normalized and encoded data is used as training set sample, then SMOTE+Tomeklinks method is used for data balancing, the balanced training set sample is trained to establish gradient boosting tree model, and the gradient boosting tree model is adjusted using bayesian optimization algorithm, then the learning curve is used to test the generalization performance of the gradient boosting tree model, the gradient boosting tree model that passes the generalization performance test is used as the optimal gradient boosting tree model, and then the overtime degree of municipal maintenance work order is predicted, which effectively improves the efficiency of work order processing.

Disclosed is a method that includes generating a prediction consistency value that indicates a consistency of prediction of an object in an input image with respect to class prediction values for the object in an input image from classification models to which the input image is input, and identifying a class of the object. Identifying the class of the object includes, in response to a class type being determined, based on the prediction consistency value, of the object being determined to correspond to a majority class, identifying a class of the object based on a corresponding class prediction value output for the object from a majority class prediction model, and in response to the class type of the object being determined to correspond to a minority class, identifying the class of the object based on another corresponding class prediction value output for the object from a minority class prediction model.

The application relates to a multi-image optical phase truncation asymmetric security compression method, relates to the field of image encryption technology, and solves the problems of insufficient nonlinear characteristics, low key security and poor transmission efficiency in the existing optical image encryption process. In the phase truncation asymmetric security compression method, the initial key is encrypted through an elliptic curve algorithm and a hash algorithm, the truncated private key is subjected to class type permutation and scrambling, and the private key is processed as an initial value for generating a composite secondary chaotic phase mask, so that the security of the encryption process is greatly improved; and the method of multi-image encryption solves the problem of low transmission efficiency, and greatly improves the transmission efficiency.