Productivity calculation method and device based on deep learning, equipment and storage medium

Abstract

The invention discloses a deep learning-based productivity calculation method, apparatus and device, and a storage medium. The method comprises the steps of collecting an initial score of each task ineach iterative allocation task period and an executive force evaluation factor of each employee; constructing a deep learning neural network model and training the deep learning neural network modelto obtain a pre-trained deep learning neural network model; inputting the initial score and the executive force evaluation factor into a pre-trained deep learning neural network model, allocating thetask according to the initial score to determine a target employee obtaining the task and a task score obtained by the target employee, and calculating the executive force of the target employee according to the executive force evaluation factor, and calculating the productivity of the target employee according to the task score and the executive force. Through the above mode, the method can improve the accuracy and calculation efficiency of productivity evaluation, enables the tasks to be distributed reasonably, is high in distribution efficiency, and can avoid the wrong distribution and missing distribution of the tasks.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a production capacity calculation method, device, equipment and storage medium based on deep learning. Background technique [0002] With the rapid development of Internet Technology (IT for short), the technical departments of various IT companies need to quickly respond to the needs of the business departments. In order to solve the shortage of self-owned technical resources, it is necessary to introduce external personnel to invest in project development. Tasks, assist in the completion of development, testing, and marketing. [0003] In order to do a good job of cost control and maximize the benefits of external employees, it is necessary to monitor and analyze the production capacity and output of existing technical resources according to the workload of the task during the project implementation process, analyze whether the technical resources are in short s...

AI Technical Summary

Problems solved by technology

However, the current method for judging whether the project team developers are sufficient usually relies on manual judgment by managers without specific quantitative tools. However, this method of manual judgment is highly subjective and difficult to quantify, and the efficiency and accuracy of judgment are relatively low. Low

[0004] At present, the production capacity of developers in statistical project teams is mostly calculated manually through time and task volume. However, on the one hand, through time calculation, it can only calculate the employee's on-the-job time, not the effective working time. A lot of on-the-job time cannot help increase production capacity. The calculation of quantity and time is difficult to estimate the production capacity; on the other hand, the calculation method of manual calculation is inefficient and error-prone

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