The invention provides a neural network improvement demand automatic generation method based on efficiency evaluation, and the method comprises the steps: S1, uploading the basic information of a first neural network model, and storing the basic information in a server; S2, constructing a first neural network model, loading a weight file of the first neural network model, and performing an inference test to obtain evaluation information of the first neural network model; S3, obtaining first performance information of the first neural network model according to the evaluation information, and analyzing the performance information to generate improvement information of the first neural network model; S4, optimizing the first neural network model, and performing training according to a training data set and a verification data set to obtain a second neural network model; and S5, performing an inference test on the second neural network model to obtain second performance information of thesecond neural network model, and performing comparative analysis on the second performance information and the first performance information to obtain a comparison result of the first neural networkmodel and the second neural network model. The invention further provides a neural network improvement demand automatic generation device based on efficiency evaluation.

Neural network improvement demand automatic generation method and device based on efficiency evaluation

The invention belongs to the technical field of deep learning, and particularly relates to a neural network architecture search method, a neural network application method, equipment and a storage medium. The method comprises the steps of searching a first neural network model through a reinforcement learning architecture search method, and performing network structure search on the first neural network model based on a preset jump connection number to obtain a second neural network model; and setting the structure of the second neural network model based on parameters of a pre-established evaluation network, and performing training on a test data set to obtain a trained neural network model. The neural network architecture in the method is higher in search speed, and the problem that network architecture search resources are consumed too much is effectively solved. The target image is processed through the neural network model obtained through training, various image processing taskscan be executed, and the recognition precision is higher during image processing.

Neural network architecture search method, neural network application method, equipment and storage medium

The invention aims to solve the problem that an existing rework and reproduction monitoring system cannot accurately monitor the rework and reproduction condition of an enterprise, provides a rework and reproduction monitoring method and system based on electric power flow type big data, and improves the rework and reproduction monitoring accuracy. The method comprises the following steps: generating a re-work and re-production monitoring map, and generating a plurality of fitting time periods; acquiring historical electric quantity data and real-time electric quantity data; fitting accordingto the historical power data of the fitting time periods to form a time/power consumption relation function of the enterprise in each fitting time period; calculating an estimation curve precision value of each time/power consumption relation function to determine a target estimation function; calculating an electric quantity consumption estimation value of the enterprise in the current time period according to the target estimation function; calculating the actual power consumption value of the enterprise in the current time period; scaling and filling a reworking and reproducing profile diagram of the enterprise according to the ratio of the actual power consumption value to the estimated power consumption value; according to the technical scheme of the invention, the method can achievethe more precise monitoring and reflection of the rework and reproduction conditions of a rework enterprise.

Rework and production monitoring method and system based on power flow type big data and readable medium

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Neural network architecture search method, neural network application method, equipment and storage medium

Rework and production monitoring method and system based on power flow type big data and readable medium