Working platform workload prediction method and system based on load prediction

A load forecasting and work platform technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve problems such as inaccurate workload forecasting and low work efficiency, and achieve the effect of improving task allocation and facilitating pricing

Inactive Publication Date: 2020-11-10
武汉空心科技有限公司
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a work platform workload prediction method and system based on load prediction, to train and optimize the SOM network through known data, to group the tasks to be allocated in the task pool according to the workload, and to obtain the wor

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  • Working platform workload prediction method and system based on load prediction

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Embodiment Construction

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0031] see figure 1 Shown, the present invention is a kind of work platform workload forecasting method based on load forecasting, comprises the following steps:

[0032] Step S1: Initialize the known data sources and computing topology datasets generated by computing tasks;

[0033] Step S2: training the optimized SOM network according to the topology dataset;

[0034] Step S3: Get the tasks to be assigned in the task pool and group them according to the workloa...

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Abstract

The invention discloses a working platform workload prediction method and system based on load prediction, and relates to the technical field of computer software. The method comprises the following steps: initializing a known data source calculation topology data set to train an optimized SOM network; obtaining to-be-allocated tasks in the task pool and grouping the to-be-allocated tasks according to the workload; enabling the SOM network model to acquire a training neural network model of each cluster by using the workload characteristics of each cluster in the neural network learning task pool; when a user publishes a new task, enabling the SOM network model to firstly obtain an initial workload of the user and determine a cluster to which the user belongs according to the initial workload of the user; and predicting the workload of the new task by utilizing the training neural network model of the cluster to which the new task belongs. The SOM network is trained and optimized through the known data, the training neural network model of each cluster is obtained according to the load characteristics of the clusters to complete prediction of the workload of the new task, pricing of the task is facilitated, the task is reasonably allocated, and the working efficiency of employees is improved.

Description

technical field [0001] The invention belongs to the technical field of computer software, and in particular relates to a workload prediction method and system of a work platform based on load prediction. Background technique [0002] The work platform is an Internet platform that provides various work management related services in a crowdsourcing mode. The contracting party publishes the work task requirements to the work platform, and the platform decomposes the tasks and finds the matching sub-tasks from the platform talent pool according to the skill requirements of each sub-task, and assigns the sub-tasks to the appropriate sub-tasks; The party starts working after receiving the assigned subtasks, and submits the work results to the platform after the subtasks are completed; the contracting party receives and reviews the task delivery results. When the contract issuing party releases the task, it entrusts the task fee on the platform, and after the task is delivered an...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06N3/08
CPCG06Q10/04G06Q10/0631G06N3/08
Inventor 王琦
Owner 武汉空心科技有限公司
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