Machine learning-based intelligent material scheduling method and system for intelligent construction site

A machine learning and intelligent material technology, applied in the field of machine learning, can solve problems such as reducing work efficiency, increasing enterprise costs, and different delivery times, and achieving the effect of improving construction efficiency and improving economy.

Pending Publication Date: 2021-03-12
深圳市中科数建科技有限公司
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the one hand, the materials required by each type of work are different. If various materials cannot be delivered to each type of work in a timely and appropriate amount, it will inevitably reduce work efficiency and increase enterprise costs; on the other hand, due to the existence of process problems, the delivery time of materials will inevitably Different, if the delivery time cannot be reasonably arranged according to the process, it will also lead to a decline in construction efficiency
[0003] At present, most of the product technologies related to material distribution in China focus on simplicity and practicality, and are usually only suitable for application in simple working conditions, and it is difficult to meet the needs of complex working conditions such as construction sites.

Method used

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  • Machine learning-based intelligent material scheduling method and system for intelligent construction site
  • Machine learning-based intelligent material scheduling method and system for intelligent construction site
  • Machine learning-based intelligent material scheduling method and system for intelligent construction site

Examples

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Embodiment 1

[0051] see figure 1 As shown, the first aspect of the present invention provides a machine learning-based smart construction site smart material scheduling method, which specifically includes the following steps:

[0052] S1: Collect historical data and obtain a training set;

[0053] The historical data includes: historical construction progress and historical demand status data of various materials;

[0054] The demand status of various materials is provided by each type of work according to the construction progress. The construction progress is also divided into types of work, and each type of work has a workload that needs to be completed. The state variable s is defined as the state of material requirements and the progress of construction completion.

[0055] S2: Establish a deep reinforcement learning network based on the A3C algorithm

[0056] The structure of deep reinforcement learning network is as follows figure 2As shown, including: a global network and n l...

Embodiment 2

[0096] see Figure 4 As shown, the present invention also provides a machine learning-based intelligent construction site intelligent material scheduling system, including: a processor and a memory coupled to the processor, the memory stores a computer program, and the computer program is processed by the Implement the method steps of the machine learning-based intelligent material scheduling method for a smart construction site described in Embodiment 1 when the machine is executed.

[0097] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but ...

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Abstract

The invention discloses a machine learning-based intelligent material scheduling method and system for an intelligent construction site. The method comprises the following steps: acquiring construction progress and demand state data of each material in the construction site; and inputting the data into a pre-trained deep reinforcement learning network to obtain a material scheduling scheme. According to the method and system, the construction progress and the material demand state of real-time reaction of each work type are taken as input, and factors such as working procedures among the worktypes, material remaining conditions (whether urgent needs exist or not) and an optimal distribution path (enabling the material distribution distance to be as short as possible) are considered, so that material distribution is arranged in real time, and intelligent material scheduling is achieved; and the result output in real time is visualized by using a visual interface, so that enterprise personnel can conveniently distribute materials according to the displayed result.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a machine learning-based intelligent material scheduling method and system for a smart construction site. Background technique [0002] In the process of building construction, due to the existence of multiple types of work and different processes, this brings many problems to the intelligent scheduling of materials. On the one hand, the materials required by each type of work are different. If various materials cannot be delivered to each type of work in a timely and appropriate amount, it will inevitably reduce work efficiency and increase enterprise costs; on the other hand, due to the existence of process problems, the delivery time of materials will inevitably Different, if the delivery time cannot be reasonably arranged according to the process, it will also lead to a decline in construction efficiency. [0003] At present, most of the product technolo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/08G06N3/04G06N3/08
CPCG06Q10/06312G06Q50/08G06N3/08G06N3/045
Inventor 杨之乐赵世豪郭媛君冯伟王尧
Owner 深圳市中科数建科技有限公司
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