US-12618581-B2 - Smart air control in a storage space

US12618581B2US 12618581 B2US12618581 B2US 12618581B2US-12618581-B2

Abstract

A processor may receive an air dataset associated with a smart environment having one or more storage objects. The processor may simulate the smart environment using the air dataset. The processor may apply an optimization criteria to the simulation of the smart environment. The processor may generate an optimum smart environment design associated with an improved air condition level of the smart environment and the optimization criteria.

Inventors

Venkata Vara Prasad Karri
Sri Harsha Varada
Sarbajit K. Rakshit
Venkatrama Siva Kumar Vemuri

Assignees

INTERNATIONAL BUSINESS MACHINES CORPORATION

Dates

Publication Date: 20260505
Application Date: 20220328

Claims (17)

1 . A computer-implemented method comprising: receiving, by a processor, an air dataset associated with a smart environment having one or more storage objects, the one or more storage objects configured within a robotic system used to mobilize the one or more storage objects; simulating the smart environment using the air dataset; applying an optimization criteria to the simulation of the smart environment, wherein the optimization criteria include an airflow criterion specifying a minimum airflow within the smart environment and an air quality criterion specifying an allotted concentration level of a chemical compound; generating an optimum smart environment design associated with an improved air condition level of the smart environment and the optimization criteria; and altering the smart environment based on the optimum smart environment design, wherein altering includes causing the robotic system to move at least one storage object of the one or more storage objects from an initial position to a secondary position.
2 . The method of claim 1 , further comprising: identifying one or more air devices in the smart environment; and instructing the one or more air devices in the smart environment to perform an action based, at least in part, on the one or more simulations and the optimization criteria, wherein the action is associated with the optimum smart environment design.
3 . The method of claim 1 , further comprising: analyzing the air dataset in real-time for one or more changes in the smart environment; re-simulating the smart environment based on the one or more changes; and updating, responsive to re-simulating the smart environment, the optimum smart environment design.
4 . The method of claim 1 , wherein the improved air condition level is greater than a preliminary air condition level.
5 . The method of claim 1 , further comprising: identifying one or more storage object components associated with the one or more storage objects in the smart environment; and instructing the one or more storage object components based, at least in part, on the one or more simulations and the optimization criteria, wherein instructing the one or more storage object components are associated with the optimum smart environment design.
6 . The method of claim 5 , wherein instructing the one or more storage object components includes: identifying one or more products are associated with the one or more storage objects; and arranging the one or more products on the one or more storage object components from an initial location to a secondary location, wherein arranging the one or more products is associated with the optimum smart environment design.
7 . A system comprising: a memory; and a processor in communication with the memory, the processor being configured to perform operations comprising: receiving an air dataset associated with a smart environment having one or more storage objects, the one or more storage objects configured within a robotic system used to mobilize the one or more storage objects; simulating the smart environment using the air dataset; applying an optimization criteria to the simulation of the smart environment, wherein the optimization criteria include an airflow criterion specifying a minimum airflow within the smart environment and an air quality criterion specifying an allotted concentration level of a chemical compound; generating an optimum smart environment design associated with an improved air condition level of the smart environment and the optimization criteria; and altering the smart environment based on the optimum smart environment design, wherein altering includes causing the robotic system to move at least one storage object of the one or more storage objects from an initial position to a secondary position.
8 . The system of claim 7 , further comprising: identifying one or more air devices in the smart environment; and instructing the one or more air devices in the smart environment to perform an action based, at least in part, on the one or more simulations and the optimization criteria, wherein the action is associated with the optimum smart environment design.
9 . The system of claim 7 , further comprising: analyzing the air dataset in real-time for one or more changes in the smart environment; re-simulating the smart environment based on the one or more changes; and updating, responsive to re-simulating the smart environment, the optimum smart environment design.
10 . The system of claim 7 , wherein the improved air condition level is greater than a preliminary air condition level.
11 . The system of claim 7 , further comprising: identifying one or more storage object components associated with the one or more storage objects in the smart environment; and instructing the one or more storage object components based, at least in part, on the one or more simulations and the optimization criteria, wherein instructing the one or more storage object components are associated with the optimum smart environment design.
12 . The system of claim 11 , wherein instructing the one or more storage object components includes: identifying one or more products are associated with the one or more storage objects; and arranging the one or more products on the one or more storage object components from an initial location to a secondary location, wherein arranging the one or more products is associated with the optimum smart environment design.
13 . A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations, the operations comprising: receiving an air dataset associated with a smart environment having one or more storage objects, the one or more storage objects configured within a robotic system used to mobilize the one or more storage objects; simulating the smart environment using the air dataset; applying an optimization criteria to the simulation of the smart environment, wherein the optimization criteria include an airflow criterion specifying a minimum airflow within the smart environment and an air quality criterion specifying an allotted concentration level of a chemical compound; generating an optimum smart environment design associated with an improved air condition level of the smart environment and the optimization criteria; and altering the smart environment based on the optimum smart environment design, wherein altering includes causing the robotic system to move at least one storage object of the one or more storage objects from an initial position to a secondary position.
14 . The computer program product of claim 13 , further comprising: identifying one or more air devices in the smart environment; and instructing the one or more air devices in the smart environment to perform an action based, at least in part, on the one or more simulations and the optimization criteria, wherein the action is associated with the optimum smart environment design.
15 . The computer program product of claim 13 , further comprising: analyzing the air dataset in real-time for one or more changes in the smart environment; re-simulating the smart environment based on the one or more changes; and updating, responsive to re-simulating the smart environment, the optimum smart environment design.
16 . The computer program product of claim 13 , further comprising: identifying one or more storage object components associated with the one or more storage objects in the smart environment; and instructing the one or more storage object components based, at least in part, on the one or more simulations and the optimization criteria, wherein instructing the one or more storage object components are associated with the optimum smart environment design.
17 . The computer program product of claim 16 , wherein instructing the one or more storage object components includes: identifying one or more products are associated with the one or more storage objects; and arranging the one or more products on the one or more storage object components from an initial location to a secondary location, wherein arranging the one or more products is associated with the optimum smart environment design.

Description

BACKGROUND Aspects of the present disclosure relates generally to the field of artificial intelligence (AI), and more particularly to techniques for air quality. Conversations about air pollutants and their effect on air quality often revolve around discussions of outdoor environments. While air pollutants associated with outdoor environments are important to consider, indoor pollutants, or those air pollutants associated with a bounded environment (e.g., a building) may also pose a significant concern for people occupying those indoor/bounded environments. In some situations, the air quality in an enclosed or bounded environment can be worse (e.g., have more pollutants) than the air quality associated with an outdoor environment or area immediately surrounding a bounded environment. SUMMARY Embodiments of the present disclosure include a method, computer program product, and system for optimizing worker safety in a smart environment. A processor may receive an air dataset associated with a smart environment having one or more storage objects. The processor may simulate the smart environment using the air dataset. The processor may apply an optimization criteria to the simulation of the smart environment. The processor may generate an optimum smart environment design associated with an improved air condition level of the smart environment and the optimization criteria. The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS The drawings included in the present disclosure are incorporated into, and form part of, the specification. They illustrate embodiments of the present disclosure and, along with the description, serve to explain the principles of the disclosure. The drawings are only illustrative of certain embodiments and do not limit the disclosure. FIG. 1 illustrates a block diagram of an example air management system, in accordance with aspects of the present disclosure. FIG. 2 illustrates a flowchart of an example method for managing air conditions in a smart environment, in accordance with aspects of the present disclosure. FIG. 3A illustrates a cloud computing environment, in accordance with aspects of the present disclosure. FIG. 3B illustrates abstraction model layers, in accordance with aspects of the present disclosure. FIG. 4 illustrates a high-level block diagram of an example computer system that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein, in accordance with aspects of the present disclosure. While the embodiments described herein are amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the particular embodiments described are not to be taken in a limiting sense. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure. DETAILED DESCRIPTION Aspects of the present disclosure relate generally to the field of artificial intelligence, and more particularly to managing the air conditions of a smart environment (e.g., storage space. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context. In storage spaces there can be various configurations of objects. For example, a warehouse may have a variety of shelves or racks, that may be specifically configured to hold/store various products (e.g., products that have expiration dates or need to be stored under particular conditions to maintain quality. As items are moved to or from the warehouse the amount and location of available storage space changes. For example, some products are removed from a shelf and the shelf becomes empty while other shelves within the warehouse are filled. This continuous change in the location of various products can impact how air flows throughout the warehouse. In situations where air flow is impacted, particularly in a large open space such as a warehouse, various issues may arise regarding the condition of the air (e.g., air condition). These air conditions include, but are not limited to: controlling a contaminant source (e.g., contaminants arising from materials and machinery within the warehouse), whether there is an appropriate level of fresh air within the warehouse (e.g., as dictated by worker safety standards and potential pollutants), is there sufficient air filtration to remove pollutants from the air, and whether there is an appropriate humidity management (e.g., warehouses sufficiently airconditioned to control humidity). As such there is a desire for a solution that will ensure spaces (e.g., smart environments), such as warehouses, have sufficient air conditions despite ch