Autonomous spatial disinfection of air and ground using pollution index

Abstract

A method is disclosed that includes continuously obtaining air purity and ground particle data from one or more sensors and / or one or more cameras of a robotic device in a queue of robotic devices; determining, based on air purity feedback from the one or more sensors of the robotic device, whether air impurities around the robotic device exceed an air purity threshold; modifying, based on the determination of whether the air impurities around the robotic device exceed the air purity threshold, an air purification mode of the robotic device; determining, based on ground particle feedback from the one or more sensors of the robotic device, whether ground particles around the robotic device exceed a ground particle threshold; and modifying, based on the determination of whether the ground particles around the robotic device exceed the ground particle threshold, a ground cleaning mode of the robotic device.

Description

[0001] introduce Various embodiments of this disclosure generally relate to systems and methods for air and ground disinfection, and more specifically, to systems and methods for autonomously disinfecting air and ground using one or more network-connected robotic devices. Summary of the Invention

[0002] According to certain aspects of this disclosure, systems and methods for autonomous disinfection of air and ground using pollution indices are disclosed.

[0003] A method includes: continuously acquiring air purity and ground particle data from one or more sensors and / or one or more cameras of a robot device in a robot device queue; determining whether air impurities around the robot device exceed an air purity threshold based on air purity feedback from one or more sensors of the robot device; modifying an air purification mode of the robot device based on the determination that air impurities around the robot device exceed the air purity threshold; determining whether ground particles around the robot device exceed a ground particle threshold based on ground particle feedback from one or more sensors of the robot device; modifying a ground cleaning mode of the robot device based on the determination that ground particles around the robot device exceed the ground particle threshold; and transmitting the operating state of the robot device to another robot device in the robot device queue based on the state of the air purification mode and the state of the ground cleaning mode of the robot device.

[0004] A system includes: at least one processor; and at least one memory, wherein the at least one processor: continuously acquires air purity and ground particle data from one or more sensors and / or one or more cameras of a robot device in a robot device queue; determines whether air impurities around the robot device exceed an air purity threshold based on air purity feedback from one or more sensors of the robot device; modifies an air purification mode of the robot device based on the determination that air impurities around the robot device exceed the air purity threshold; determines whether ground particles around the robot device exceed a ground particle threshold based on ground particle feedback from one or more sensors of the robot device; modifies a ground cleaning mode of the robot device based on the determination that ground particles around the robot device exceed the ground particle threshold; and transmits the operating state of the robot device to another robot device in the robot device queue based on the state of the air purification mode and the state of the ground cleaning mode of the robot device.

[0005] A non-transitory computer-readable medium storing instructions, which, when executed by at least one processor, cause the processor to perform the following methods: continuously acquiring air purity and ground particle data from one or more sensors and / or one or more cameras of a robot device in a robot device queue; determining whether air impurities around the robot device exceed an air purity threshold based on air purity feedback from one or more sensors of the robot device; modifying an air purification mode of the robot device based on the determination that air impurities around the robot device exceed the air purity threshold; determining whether ground particles around the robot device exceed a ground particle threshold based on ground particle feedback from one or more sensors of the robot device; modifying a ground cleaning mode of the robot device based on the determination that ground particles around the robot device exceed the ground particle threshold; and transmitting the operating state of the robot device to another robot device in the robot device queue based on the state of the air purification mode and the state of the ground cleaning mode of the robot device.

[0006] It should be understood that the foregoing general description and the following detailed description are merely exemplary and illustrative, and do not limit the claimed and disclosed embodiments. Attached Figure Description

[0007] The accompanying drawings, which are included in and form part of this specification, illustrate various exemplary embodiments and, together with the description, serve to explain the principles of the disclosed embodiments.

[0008] Figure 1 An exemplary networked computing system environment according to one or more embodiments is described.

[0009] Figure 2 Depicting Figure 1 A schematic block diagram of the framework of an Internet of Things (IoT) platform in a networked computing system environment.

[0010] Figure 3A A top view of a robotic device including an air purification system and a vacuum system according to an exemplary embodiment of the present disclosure is depicted.

[0011] Figure 3B A bottom view of a robotic device according to an exemplary embodiment of the present disclosure is depicted.

[0012] Figure 4 An exemplary block diagram of a robotic device according to an exemplary embodiment of the present disclosure is depicted.

[0013] Figure 5A and 5B An exemplary environment in which robotic devices may be employed according to one aspect of this disclosure is depicted.

[0014] Figure 6An exemplary method for operating a robotic device according to an exemplary embodiment is described.

[0015] Figure 7 Example systems that can perform the techniques presented in this article are described. Detailed Implementation

[0016] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description to provide a thorough understanding of the various described embodiments. However, it will be apparent to those skilled in the art that the various described embodiments can be practiced without these specific details. In other instances, well-known methods, processes, components, circuits, and networks have not been described in detail to avoid unnecessarily obscuring aspects of the embodiments.

[0017] Generally, this disclosure provides an "Internet of Things" (IoT) platform for enterprise performance management that uses real-time accurate models and visual analytics to provide intelligent, actionable recommendations for sustained peak performance of an enterprise or organization. Specifically, this disclosure provides an IoT platform for using autonomous robotic devices to provide real-time information on air quality and ground particulate matter present in various environments (e.g., offices, buildings, hospitals, houses, apartments, etc.) and to provide proactive measures for air and ground disinfection based on analysis of the real-time information. The IoT platform is scalable and portable, allowing deployment in any cloud or data center environment to provide an enterprise-wide, top-down view of the status of processes, assets, people, and safety. Furthermore, the IoT platform of this disclosure supports end-to-end capabilities to perform digital twins on process data and transform the output into actionable insights, as detailed in the following description. This disclosure also provides a cloud system having a secure, scalable infrastructure for collecting, aggregating, and storing data, allowing connected devices to communicate, and providing SaaS (Software as a Service) solutions, IaaS / PaaS (Infrastructure as a Service / Platform as a Service) and / or data lakes.

[0018] Figure 1 An exemplary networked computing system environment 100 according to this disclosure is illustrated. Figure 1 As shown, the networked computing system environment 100 is organized into multiple layers, including a cloud layer 105, a network layer 110, and an edge layer 115. As further detailed below, components of the edge 115 communicate with components of the cloud 105 via the network 110.

[0019] Network 110 can be any suitable network or combination of networks, and can support any suitable protocol for communication between components to and from the cloud 105 and various other components (e.g., components at the edge 115) within the networked computing system environment 100. Network 110 can include public networks (e.g., the Internet), private networks (e.g., networks within an organization), or combinations of public and / or private networks. Network 110 can be configured to provide Figure 1 The network 110 may include one or more networks that connect devices and / or components in the network layout to allow communication between devices and / or components. For example, the network 110 may be implemented as the Internet, a wireless network, a wired network (e.g., Ethernet), a local area network (LAN), a wide area network (WAN), Bluetooth, near field communication (NFC), or any other type of network that provides communication between one or more components of the network layout. In some embodiments, the network 110 may be implemented using a cellular network, satellite, licensed radio, or a combination of cellular, satellite, licensed radio, and / or unlicensed radio networks.

[0020] The components of cloud 105 include one or more computer systems 120 forming a so-called "Internet of Things" or "IoT" platform 125. It should be understood that "IoT platform" is an optional term describing a platform connecting any type of Internet-connected device and should not be construed as limiting the types of computing systems that can be used within IoT platform 125. Specifically, computer system 120 may include one or more processors of any type or number and one or more data storage devices, including memory for storing and executing applications or software modules of the networked computing system environment 100. In one embodiment, the processor and data storage device are embodied in server-grade hardware, such as an enterprise-grade server. For example, the processor and data storage device may include any type or combination of application servers, communication servers, web servers, supercomputing servers, database servers, file servers, mail servers, proxy servers, and / or virtual servers. Furthermore, one or more processors are configured to access memory and execute processor-readable instructions that, when executed by the processor, configure the processor to perform multiple functions of the networked computing system environment 100.

[0021] Computer system 120 also includes one or more software components of IoT platform 125. For example, the software components of computer system 120 may include one or more software modules for communicating with user devices and / or other computing devices via network 110. For example, software components may include one or more modules 141, models 142, engines 143, databases 144, services 145, and / or applications 146, which may be stored in / by computer system 120 (e.g., stored on memory), as described below. Figure 2 The details are as follows. The one or more processors may be configured to utilize one or more modules 141, models 142, engines 143, databases 144, services 145, and / or applications 146 when implementing the various methods described in this disclosure.

[0022] Therefore, computer system 120 can execute a cloud computing platform (e.g., IoT platform 125) with scalable resources for computing and / or data storage, and can run one or more applications on the cloud computing platform to perform the various computer-implemented methods described in this disclosure. In some embodiments, some of module 141, model 142, engine 143, database 144, service 145, and / or application 146 can be combined to form fewer modules, models, engines, databases, services, and / or applications. In some embodiments, some of module 141, model 142, engine 143, database 144, service 145, and / or application 146 can be separated into a larger number of separate modules, models, engines, databases, services, and / or applications. In some embodiments, some of module 141, model 142, engine 143, database 144, service 145, and / or application 146 can be removed while others can be added.

[0023] Computer system 120 is configured to receive data from other components of networked computing system environment 100 (e.g., components of edge 115) via network 110. Computer system 120 is further configured to use the received data to generate results. Information indicating the results may be transmitted to a user via network 110 through a user computing device. In some embodiments, computer system 120 may be referred to as a server system that provides one or more services, including providing information indicating the received data and / or (one or more) the results to the user. Computer system 120 is part of an entity that may include any type of company, organization, or institution implementing one or more IoT services. In some examples, the entity may be an IoT platform provider.

[0024] The components of edge 115 include one or more enterprises 160a-160n, each enterprise including one or more edge devices 161a-161n and one or more edge gateways 162a-162n. For example, a first enterprise 160a includes a first edge device 161a and a first edge gateway 162a, a second enterprise 160b includes a second edge device 161b and a second edge gateway 162b, and an nth enterprise 160n includes an nth edge device 161n and an nth edge gateway 162n. As used herein, enterprises 160a-160n can represent any type of entity, facility, or vehicle, such as, for example, a company, department, building, manufacturing plant, warehouse, real estate facility, laboratory, aircraft, spacecraft, automobile, ship, vessel, military vehicle, oil and gas facility, or any other type of entity, facility, and / or vehicle including any number of local devices.

[0025] Edge devices 161a-161n can refer to any of the various types of devices that can be found within enterprise 160a-160n. Edge devices 161a-161n are any type of device configured to access network 110 or be accessed by other devices through network 110, such as via edge gateways 162a-162n. Edge devices 161a-161n may be referred to as "IoT devices" in some cases, and therefore can include any type of network-connected (e.g., Internet-connected) device. For example, edge devices 161a-161n can include sensors, actuators, processors, computers, valves, pumps, pipes, vehicle parts, cameras, displays, doors, windows, security components, HVAC components, plant equipment, and / or any other device that can connect to network 110 to collect, transmit, and / or receive information. Each edge device 161a-161n includes one or more controllers, or communicates with additional controllers, which selectively control the respective edge device 161a-161n and / or send / receive information between the edge device 161a-161n and the cloud 105 via network 110. (Reference) Figure 2 The edge 115 may also include operational technology (OT) systems 163a-163n and information technology (IT) applications 164a-164n for each enterprise 161a-161n. OT systems 163a-163n include hardware and software for detecting and / or inducing changes through direct monitoring and / or control of industrial equipment (e.g., edge devices 161a-161n), assets, processes, and / or events. IT applications 164a-164n include networks, storage devices, and computing resources for the generation, management, storage, and delivery of data throughout and between organizations.

[0026] Edge gateways 162a-162n include devices for facilitating communication between edge devices 161a-161n and cloud 105 via network 110. For example, edge gateways 162a-162n include one or more communication interfaces for communicating with edge devices 161a-161n and with cloud 105 via network 110. The communication interfaces of edge gateways 162a-162n may include one or more cellular radios, Bluetooth, WiFi, near-field communication radios, Ethernet, or other suitable communication devices for transmitting and receiving information. Each gateway 162a-162n may include multiple communication interfaces for providing various forms of communication between edge devices 161a-161n, gateways 162a-162n, and cloud 105 via network 110. For example, communication with edge devices 161a-161n and / or network 110 can be achieved via wireless communication (e.g., WiFi, radio communication, etc.) and / or wired data connections (e.g., Universal Serial Bus, onboard diagnostic system, etc.) or other communication modes (e.g., local area network (LAN), wide area network (WAN) (such as the Internet), telecommunications network, data network, or any other type of network).

[0027] Edge gateways 162a-162n may also include a processor and memory for storing and executing program instructions to facilitate data processing. For example, edge gateways 162a-162n may be configured to receive data from edge devices 161a-161n and process that data before sending it to cloud 105. Therefore, edge gateways 162a-162n may include one or more software modules or components for providing the data processing services and / or other services or methods disclosed herein. Reference Figure 2 Each edge gateway 162a-162n includes edge services 165a-165n and edge connectors 166a-166n. Edge services 165a-165n may include hardware and software components for processing data from edge devices 161a-161n. As detailed above, edge connectors 166a-166n may include hardware and software components for facilitating communication between edge gateways 162a-162n and cloud 105 via network 110. In some cases, any of the edge devices 161a-n, edge connectors 166a-n, and edge gateways 162a-n can combine, omit, or separate their functions into any combination of devices. In other words, the edge devices, their connectors, and gateways do not necessarily have to be separate devices.

[0028] Figure 2The diagram illustrates a schematic block diagram of the framework 200 of the IoT platform 125 according to this disclosure. The IoT platform 125 of this disclosure is a platform for enterprise performance management that uses real-time accurate models and visual analytics to provide intelligent, actionable recommendations for sustained peak performance of enterprises 160a-160n. The IoT platform 125 is a scalable and portable platform, enabling deployment in any cloud or data center environment to provide an enterprise-wide, top-down view of the status of processes, assets, people, and security. Furthermore, the IoT platform 125 uses framework 200 to support end-to-end capabilities to perform digital twins on process data and transform the output into actionable insights, as detailed in the following description.

[0029] like Figure 2 As shown, the framework 200 of the IoT platform 125 includes multiple layers, such as an IoT layer 205, an enterprise integration layer 210, a data pipeline layer 215, a data insight layer 220, an application service layer 225, and an application layer 230. The IoT platform 125 also includes a core service layer 235 and an extensible object model (EOM) 250 including one or more knowledge graphs 251. Layers 205-235 further include various software components that collectively form each layer 205-235. For example, each layer 205-235 may include one or more of the following or a combination thereof: module 141, model 142, engine 143, database 144, service 145, and application 146. In some embodiments, layers 205-235 may be combined to form fewer layers. In some embodiments, some of layers 205-235 may be separated into separate, more numerous layers. In some embodiments, some of layers 205-235 may be removed while other layers may be added.

[0030] The IoT platform 125 is a model-driven architecture. Therefore, the Scalable Object Model 250 communicates with each layer 205-230 to contextualize site data of Enterprises 160a-160n using the Scalable Object Model (or “Asset Model”) and Knowledge Graph 251, in which the equipment (e.g., edge devices 161a-161n) and processes of Enterprises 160a-160n are modeled. The Knowledge Graph 251 of the EOM 250 is configured to store the model in a central location. The Knowledge Graph 251 defines a set of nodes and links that describe the real-world connections that enable intelligent systems. As used herein, the Knowledge Graph 251: (i) describes real-world entities (e.g., edge devices 161a-161n) organized in a graphical interface and their interrelationships; (ii) defines possible categories and relationships of entities in a schema; (iii) associates arbitrary entities with each other; and (iv) covers a wide range of subject areas. In other words, knowledge graph 251 defines a large network of entities (e.g., edge devices 161a-161n), the semantic types of entities, the attributes of entities, and the relationships between entities. Therefore, knowledge graph 251 describes a network of “things” related to a specific domain, enterprise, or organization. Knowledge graph 251 is not limited to abstract concepts and relationships, but may also contain instances of objects, such as, for example, documents and datasets. In some embodiments, knowledge graph 251 may include a Resource Description Framework (RDF) graph. As used herein, an “RDF graph” is a graph data model that formally describes the semantics or meaning of information. RDF graphs may also represent metadata (e.g., data describing data). Knowledge graph 251 may also include a semantic object model. A semantic object model is a subset of knowledge graph 251 that defines the semantics of knowledge graph 251; for example, a semantic object model defines a diagram of knowledge graph 251.

[0031] As used herein, EOM 250 is a collection of application programming interfaces (APIs) that enable the seed semantic object model to be extended. For example, the EOM 250 of this disclosure enables a client's knowledge graph 251 to be constructed based on constraints expressed in the client's semantic object model. Thus, knowledge graph 251 is generated by the client (e.g., an enterprise or organization) to create a model of edge devices 161a-161n of enterprise 160a-160n, and knowledge graph 251 is input into EOM 250 for visualization of said model (e.g., nodes and links).

[0032] The model describes the enterprise's assets (e.g., nodes) (e.g., edge devices 161a-161n) and their relationships with other components (e.g., links). The model also describes a graph (e.g., what the data is), and therefore is self-validating. For example, the model can describe the types of sensors installed on any given asset (e.g., edge devices 161a-161n) and the type of data each sensor is sensing. A Key Performance Indicator (KPI) framework can be used to bind the attributes of assets in the scalable object model 250 to the inputs of the KPI framework. Therefore, the IoT platform 125 is a scalable, model-driven end-to-end stack comprising: bidirectional model synchronization and secure data exchange between the edge 115 and the cloud 105, metadata-driven data processing (e.g., rules, computation, and aggregation), and model-driven visualization and applications. As used herein, "scalable" refers to the ability to extend the data model to include new attributes / columns / fields, new categories / tables, and new relationships. Therefore, the IoT platform 125 is scalable regarding edge devices 161a-161n and the applications 146 that process those devices 161a-161n. For example, when a new edge device 161a-161n is added to the enterprise system 160a-160n, the new device 161a-161n will automatically appear in the IoT platform 125 so that the corresponding application 146 can understand and use the data from the new device 161a-161n.

[0033] In some cases, asset templates are used to facilitate the configuration of instances of edge devices 161a-161n in the model using common structures. Asset templates define typical attributes of edge devices 161a-161n for a given enterprise 160a-160n for a specific type of device. For example, an asset template for pumps includes modeling pumps with inlet and outlet pressures, speeds, flow rates, etc. Templates may also include hierarchical or derived types of edge devices 161a-161n to accommodate variations in the base type of device 161a-161n. For example, a reciprocating pump is a specialization of the base pump type and will include additional attributes in the template. Instances of edge devices 161a-161n in the model are configured to use templates to match the actual physical devices of enterprise 160a-160n to define the expected attributes of device 161a-161n. Each attribute is configured as a static value (e.g., a capacity of 1000 BPH) or by referencing a time-series label that provides that value. Knowledge graph 251 can automatically map labels to attributes based on naming conventions, parsing and matching label and attribute descriptions and / or by comparing the behavior of time series data with expected behavior.

[0034] The modeling phase includes an onboarding process for synchronizing the model between Edge 115 and Cloud 105. For example, the onboarding process can include a simple onboarding process, a complex onboarding process, and / or a standardized rollout process. A simple onboarding process includes: Knowledge Graph 251 receiving raw model data from Edge 115 and running a context discovery algorithm to generate a model. The context discovery algorithm reads the context of the edge naming conventions of Edge devices 161a-161n and determines what the naming conventions refer to. For example, Knowledge Graph 251 might receive “TMP” during the modeling phase and determine that “TMP” is related to “temperature”. The generated model is then published. A complex onboarding process includes: Knowledge Graph 251 receiving raw model data, historical data from receiving points, and survey data from receiving sites. Knowledge Graph 251 can then use these inputs to run the context discovery algorithm. The generated model can be edited and then published. The standardized rollout process includes: manually defining a standard model in Cloud 105 and pushing the model to Edge 115.

[0035] The IoT layer 205 includes one or more components for device management, data ingestion, and / or command / control of edge devices 161a-161n. The components of the IoT layer 205 enable data to be ingested from various sources into the IoT platform 125 or otherwise received at the IoT platform 125. For example, data can be ingested from edge devices 161a-161n via a process history recorder or a laboratory information management system. The IoT layer 205 communicates via network 110 with edge connectors 165a-165n mounted on edge gateways 162a-162n, and the edge connectors 165a-165n securely transmit the data to the IoT platform 205. In some embodiments, only authorized data is transmitted to the IoT platform 125, and the IoT platform 125 only accepts data from authorized edge gateways 162a-162n and / or edge devices 161a-161n. Data can be transmitted from edge gateways 162a-162n to the IoT platform 125 via direct streaming and / or via bulk delivery. Furthermore, in the event of any network or system outage, data transmission will resume once communication is re-established, and any data lost during the outage will be refilled from the source system or from the cache of the IoT platform 125. The IoT layer 205 may also include components for accessing time series, alarms and events, and transaction data via various protocols.

[0036] Enterprise Integration Layer 210 includes one or more components for event / messaging, file upload, and / or REST / OData. These components enable IoT Platform 125 to communicate with third-party cloud applications 211, such as any one or more applications operated by the enterprise and associated with its edge devices. For example, Enterprise Integration Layer 210 connects to enterprise databases, such as visitor databases, customer databases, financial databases, patient databases, etc. Enterprise Integration Layer 210 provides standard application programming interfaces (APIs) to third parties for accessing IoT Platform 125. Enterprise Integration Layer 210 also enables IoT Platform 125 to communicate with the OT systems 163a-163n and IT applications 164a-164n of the enterprise 160a-160n. Therefore, Enterprise Integration Layer 210 enables IoT Platform 125 to receive data from third-party applications 211, rather than directly from edge devices 161a-161n, or in combination with receiving data directly from edge devices 161a-161n.

[0037] Data pipeline layer 215 includes one or more components for APIs of data cleaning / enrichment, data transformation, data computation / aggregation, and / or data flow. Therefore, data pipeline layer 215 can preprocess received data and / or perform initial analysis. Data pipeline layer 215 performs advanced data cleaning routines, including, for example, data correction, quality balance reconciliation, data conditioning, component balancing, and simulation, to ensure that the desired information is used as the basis for further processing. Data pipeline layer 215 also provides advanced and rapid computation. For example, cleaned data is run through an enterprise-specific digital twin. The enterprise-specific digital twin may include a reliability advisor component, which contains a process model for determining current operations and a failure model for triggering any early detections and determining appropriate solutions. The digital twin may also include an optimization advisor component, which integrates real-time economic data with real-time process data, selects the correct feed for the process, and determines optimal process conditions and product yield.

[0038] The data pipeline layer 215 can also use models and templates to define computational and analytical methods, and to define how these methods relate to assets (e.g., edge devices 161a-161n). For example, a pump template can define pump efficiency calculations so that a standard efficiency calculation is automatically performed for the pump each time it is configured. Computational models define various types of computations, the type of engine that should run the computation, input and output parameters, preprocessing requirements and prerequisites, schedules, etc. Actual computational or analytical logic can be defined in the template or referenced from it. Therefore, computational models can be used to describe and control the execution of various different process models. Computational templates can be linked to asset templates so that when an asset (e.g., edge devices 161a-161n) instance is created, any associated computational instances are also created, and the input and output parameters of the computational instances are linked to the appropriate properties of the asset (e.g., edge devices 161a-161n).

[0039] The IoT Platform 125 supports a variety of analytical models, including, for example, first-principles models, empirical models, engineering models, user-defined models, machine learning models, built-in functions, and / or any other type of analytical model. Fault models and predictive maintenance models will now be described with examples, but any type of model can be applied.

[0040] Failure models are used to compare current and projected enterprise 160a-160n performance to identify problems or opportunities, as well as their underlying causes or drivers. The IoT platform 125 includes a rich set of hierarchical symptom-failure models to identify anomalous conditions and their potential consequences. For example, the IoT platform 125 can work down from higher-level conditions to understand contributing factors and determine the potential impact of lower-level conditions. For a given enterprise 160a-160n, there may be multiple failure models focusing on different aspects such as processes, equipment, controls, and / or operations. Each failure model can identify problems and opportunities in its domain and may also focus on the same core problem from different perspectives. A holistic failure model can be layered at the top to synthesize the different perspectives from each failure model into a comprehensive assessment of the situation and pinpoint the true root cause.

[0041] When a fault or opportunity is identified, the IoT platform 125 can offer recommendations on the best corrective actions to take. Initially, these recommendations are based on expert knowledge pre-programmed into the system by process and equipment experts. The recommendation service module presents this information in a consistent manner, regardless of its source, and supports workflows for tracking, closing, and logging recommendation follow-ups. As existing recommendations are validated (or not validated) or as users and / or analytics learn new causal relationships, recommendation follow-ups can be used to improve the system's overall knowledge over time.

[0042] Each model can be used to accurately predict what will happen before it occurs and to explain the state of the installation foundation. Therefore, the IoT platform 125 enables operators to quickly initiate maintenance measures in the event of an anomaly. The digital twin architecture of the IoT platform 125 can utilize a variety of modeling techniques. These modeling techniques may include, for example, rigorous models, fault detection and diagnosis (FDD), descriptive models, predictive maintenance, prescriptive maintenance, process optimization, and / or any other modeling techniques.

[0043] The rigorous model can be transformed from process design simulation. In this way, process design is integrated with feed conditions and production requirements. Process changes and technological improvements provide business opportunities for more efficient maintenance scheduling and resource deployment within the context of production needs. Fault detection and diagnosis include a common rule set specified based on industry experience and domain knowledge, which can be easily incorporated and used with equipment models. Descriptive models identify problems, and then predictive models can determine possible damage levels and maintenance options. Descriptive models can include models for defining the operating windows of edge devices 161a-161n.

[0044] Predictive maintenance includes predictive analytics models developed based on rigorous and statistical models, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS). Machine learning methods can be used to train models for fault prediction. Predictive maintenance can leverage FDD-based algorithms to continuously monitor individual control and equipment performance. Predictive modeling is then applied to selected condition indicators that deteriorate over time. Prescriptive maintenance involves determining the optimal maintenance options and when to implement them based on actual conditions rather than time-based maintenance schedules. Prescriptive analysis can select the appropriate solutions based on company capital, operational, and / or other requirements. Process optimization determines optimal conditions by adjusting setpoints and schedules. Optimized setpoints and schedules can be directly sent to the underlying controllers, enabling an automated closed loop from analysis to control.

[0045] The Data Insights Layer 220 includes one or more components for a Time Series Database (TDSB), a relational / document database, a data lake, a blob (binary large object), files, images and videos, and / or APIs for data querying. When raw data is received at the IoT platform 125, it can be stored as time series tags or events in hot storage (e.g., in a TSDB) to support interactive queries and in cold storage for archiving purposes. Data can be further sent to the data lake for offline analytics development. The Data Pipeline Layer 215 can access data stored in the database of the Data Insights Layer 220 to implement the analytics detailed above.

[0046] Application service layer 225 includes one or more components for rules engines, workflows / notifications, KPI frameworks, BI, machine learning, and / or APIs for application services. Application service layer 225 is capable of building applications 146a-d. Application layer 230 includes one or more applications 146a-d of IoT platform 125. For example, applications 146a-d may include building application 146a, factory application 146b, aviation application 146c, and other enterprise applications 146d. Application 146 may include a general application 146 for document management, asset management, autonomous control, and / or any other customized applications. Document management may include KPI frameworks and flexible user interface (UI) builders. Asset management may include asset performance and asset health. Autonomous control may include energy optimization and predictive maintenance. As detailed above, the general application 146 can be scalable, allowing each application 146 to be configured for different types of enterprises 160a-160n (e.g., building application 146a, factory application 146b, aviation application 146c, and other enterprise applications 146d).

[0047] Application layer 230 also enables visualization of enterprise 160a-160n performance. For example, dashboards provide advanced overviews with a downward cut to support deeper investigations. Suggested overviews provide users with prioritized actions to address current or potential problems and opportunities. Data analytics tools, in particular, support data exploration to aid in troubleshooting and process improvement.

[0048] The core service layer 235 includes one or more services of the IoT platform 125. Core services 235 may include data visualization, data analytics tools, security, scaling, and monitoring. Core services 235 may also include services for: tenant provisioning, single sign-on / public portal, self-service management, UI library / UI tiles, identity / access / rights, logging / monitoring, usage metering, API gateway / development portal, and IoT platform 125 streams.

[0049] Figure 3A A top view of a robotic device 361a including an air purification system and a vacuum system according to exemplary embodiments of the present disclosure is depicted. In one embodiment, the robotic device 361a is an edge device 161a, as described above regarding... Figure 1 and Figure 2As described, robot device 361a can be a fully autonomous IoT device that connects to other devices via wired and / or wireless networks (e.g., network 110). For example, robot device 361a can connect to IoT platform 125 formed by one or more computer systems 120 of cloud 105. As described above, network 110 can be implemented as the Internet, wireless network, wired network (e.g., Ethernet), local area network (LAN), wide area network (WAN), Bluetooth, near field communication (NFC), or any other type of network that provides communication between one or more components of a network layout.

[0050] like Figure 3A As shown, the robot device 361a may include one or more sensors 301, one or more cameras 302, and an air filtration system 304. Figure 3A Each of the sensors 301 depicted may include one or more types of sensors 301. For example, sensor 301 may be an air quality sensor, proximity sensor, dust sensor, optical sensor, temperature sensor, pressure sensor, light sensor, air pollution sensor, weight measurement sensor, GPS sensor, and / or infrared sensor. Sensors 301 and camera 302 can be used to identify characteristics of the environment surrounding the robotic device 361a, enabling the device to navigate throughout a room and across multiple rooms in small and / or large buildings. Furthermore, the robotic device 361a may include several ultraviolet C (UV-C) windows 306 through which UV-C light can be projected. For example, the robotic device may include one or more UV-C lamps that provide disinfection for air, water, and non-porous surfaces. When open, the UV-C lamps provide disinfection by radiating light through the UV-C windows 306. Window 306 may be designed as an opening in the frame of the robotic device. Additionally, each window may include a retractable opaque cover that can be opened or closed independently of each other. Depending on which surfaces and / or areas need to be disinfected at any given time, the cover can be manually controlled and / or automatically controlled by robotic device 361a. The opening may or may not be covered by a transparent material. The placement of sensor 301, camera 302, air filtration system 304, and UV-C window 306 is not limited to the locations depicted in the figures.

[0051] The robotic device may also include a display panel 307. This display panel provides key information, such as device settings that can be viewed from a distance. The robotic device may include input buttons 308. These input buttons may be mechanical buttons or can be activated by touch input.

[0052] Air filtration system 304 can use a certified high-efficiency particulate air (HEPA) filter to purify any polluted outside air. Air filtration system 304 may also include a specialized air filter designed to neutralize several household odors, such as those from pets, the kitchen, and smoke. Another specialized air filter is designed to reduce volatile organic compounds (VOCs) and neutralize fumes from renovation and home improvement products. Users can choose which specialized air filter to use at any time. A VOC sensor can continuously monitor air quality and provide real-time feedback on air quality using display panel 307. For example, display panel 307 may include color-coded air quality indicators: a green indicator to indicate good air quality; a yellow indicator to indicate moderate air quality; and a red indicator to indicate poor air quality.

[0053] Air filtration system 304 can use a certified HEPA filter to capture and eliminate up to 99.97% of harmful micro-allergens from the air passing through the filter. These micro-allergens can be as small as 0.3 microns. Examples of allergens can include pollen, dust, pet dander, dust mite fragments, and / or smoke particles. The air purifier can help reduce 99.9% of certain airborne viruses, bacteria, and mold spores. Additionally, the robotic device may include an activated carbon pre-filter, which helps capture larger particles and helps reduce odors and VOCs.

[0054] Figure 3B A bottom view of a robotic device 361a according to an exemplary embodiment of the present disclosure is depicted. Figure 3B Each sensor 301 depicted may include one or more types of sensors. For example, sensor 301 may be an air quality sensor, a proximity sensor, a dust sensor, an optical sensor, a temperature sensor, a pressure sensor, a light sensor, an air pollution sensor, and / or an infrared sensor. Figure 3B As shown, the robotic device includes wheels 303. The wheels are controlled by electric motors, and the wheels 303 can rotate to always allow the robotic device 361a to move in all directions. In one embodiment, the robotic device 361a includes a vacuum system 305 for cleaning floors and carpets. The vacuum system may include rotating brushes and filters. Additionally, the vacuum system may include weight measurement sensors for determining the amount of dust and / or debris collected. All data collected by the sensors can be transmitted via wired and / or wireless networks (e.g., network 110) to a centralized management system (e.g., cloud 105, BMS (Building Management System)).

[0055] Figure 4An exemplary block diagram of a robotic device 361a according to one aspect of this disclosure is depicted. In one embodiment, the robotic device 361a includes a processor 401, a communication interface 402, one or more motors 403, a vacuum system 404, an air purification system 405, a UV-C control 406, one or more sensors 301, and / or one or more cameras 302. The communication interface includes the ability to enable the robotic device 361a to communicate with other devices (including communication with an external server and / or cloud 105) via a network 110. The network 110 may be implemented as the Internet, a wireless network, a wired network (e.g., Ethernet), a local area network (LAN), a wide area network (WAN), Bluetooth, near field communication (NFC), or any other type of network that provides communication between one or more components of a network layout. The one or more motors can control all aspects of the device, including wheels 303, the vacuum system 404, and the air filtration system 405. The UV-C control 406 can control the operation of a UV-C lamp that disinfects surfaces by radiating light through a UV-C window 306.

[0056] According to one embodiment, the communication interface 402 can be connected to an augmented reality (AR) device that allows a user to visualize the operation of the robotic device 361a and view simulations of various particles detected in the air. For example, the AR device will display the number of particles in the air based on the amount of impurities detected in the air by the sensors of the robotic device 361a.

[0057] Figure 5A An exemplary environment in which robotic devices can be employed according to one aspect of this disclosure is depicted. For example... Figure 5A As shown, the building can have multiple rooms 501. A cluster of robotic devices 361a, 361b, and 361c can be arranged as follows: Figure 5A The locations shown span separate rooms, or they can be located in the same room, as... Figure 5B As shown in the diagram. Each of the robot devices 361a, 361b, and 361c can communicate with each other via communication interface 562. Communication interface 562 can be any device connected to network 110 via a wireless network and / or a wired network. For example, communication interface 562 can be a wireless router and / or gateway 162a, as described above. Figure 1 and Figure 2 The subject of discussion.

[0058] The cluster of robot devices 361a, 361b, and 361c can communicate with each other to provide information about the surrounding environment of each device. For example, if robot device 361a detects air impurities and / or ground particles, it can communicate with other robot devices (e.g., devices 361b and 361c) via communication interface 562 to request robot devices 361b and 361c to activate their own air purification mode and / or ground cleaning mode.

[0059] Furthermore, robot device 361a can determine that the room it is in contains various airborne impurities that it may struggle to clean without assistance. This determination can be made by comparing airborne impurity data obtained through sensors and cameras with a predetermined threshold. If the airborne impurities exceed the predetermined threshold, robot device 361a can request assistance from other robot devices (e.g., devices 361b and 361c). These other robot devices can then reposition themselves to the room where robot device 361a is located, such as... Figure 5B As shown in the image.

[0060] Furthermore, robot device 361a can determine that the room it is in contains various types of floor particles that it may struggle to clean without assistance. This determination can be made by comparing floor particle data obtained through sensors and cameras with a predetermined threshold, and if the number of floor particles exceeds the threshold, robot device 361a can request assistance from other robot devices (e.g., devices 361b and 361c). Figure 5B As shown, a cluster of robot devices 361b and 361c can move to assist robot device 361.

[0061] According to one embodiment, the pollution index for a specific room 501 and / or building (e.g., a group of rooms 501) can be determined based on a variety of factors, such as occupancy level, noise, temperature, humidity, and occupant stay time. These factors can be identified using sensors 301 and cameras 302 of robotic devices 361a, 361b, and 361c. The pollution index provides an overall score for the pollution level of a specific room, office, and / or building.

[0062] Furthermore, machine learning algorithms can be used to determine the contamination index. Machine learning algorithms can be trained strategies (e.g., if the machine learning model is trained using reinforcement learning techniques), analytical models, neural networks, and / or generally, models that take inputs (e.g., feature sets) and output a target (e.g., target location) based on a trained function. For example, a machine learning algorithm can take multiple inputs from sensors and cameras of a robotic device and combine these inputs with external health risk indicators and human resources (HR) information about the number of individuals reporting illness. The machine learning algorithm can then identify the contamination risk of a specific area and the time periods during which the contamination risk of a specific area exceeds a predetermined threshold based on the combination of inputs. According to one embodiment, during periods of high infectious disease risk and / or after a space has been heavily occupied by residents (indicating a high contamination risk in an area), a UV-C system can be set to automatically turn on after an area has been unoccupied for a period of time (e.g., based on a recommendation calculated by the system). Therefore, according to one embodiment, the devices and methods of this disclosure can use artificial intelligence and machine learning algorithms to automatically disinfect areas at high risk of exposing residents to infectious diseases.

[0063] Figure 6 An exemplary method for operating a robotic device according to an exemplary embodiment is depicted. According to one embodiment, an exemplary method 600 for operating a robotic device may include one or more of the following steps. In step 605, the method includes receiving or acquiring data from one or more sensors and / or one or more cameras of the robotic device. For example, the data may include information obtained from air quality sensors, proximity sensors, dust sensors, optical sensors, temperature sensors, pressure sensors, light sensors, air pollution sensors, weight measurement sensors, GPS sensors, and / or infrared sensors. The weight measurement sensor can determine the amount of dust and / or debris collected by the device, and this information can be used to analyze the air quality near the device.

[0064] In step 610, the method includes: detecting whether airborne impurities are detected based on data obtained from the one or more sensors and the one or more cameras. If airborne impurities are detected, the method includes: operating an air purification mode of the robotic device (e.g., step 615). The air purification mode includes operating an air filtration system. The air filtration system may include drawing in air from the outside through an air intake system and treating the outside air using a HEPA filtration system. The clean air is then output into the room. The airflow can be automatically adjusted by changing the airflow path using the air filtration system 304.

[0065] In step 620, the method includes: detecting whether ground particles are detected based on data obtained from the one or more sensors and the one or more cameras. If ground particles are detected, the method includes: operating a ground cleaning mode of the robotic device (e.g., step 625). The ground cleaning mode may include: operating a vacuum system and using a weight measurement sensor to determine how much dust and debris the robotic device has collected. The vacuum system may also include a rotating brush and a general filter for cleaning any collected dust.

[0066] In step 630, the method includes updating the state of the device. For example, the processor may continuously acquire data from the one or more sensors and the one or more cameras, and update the state of the device (e.g., cleaning completed) when no airborne impurities and / or no ground particles are detected near the robotic device.

[0067] In step 635, the method includes transmitting the status of the robotic device to other devices. For example, data obtained from the sensors and cameras may be transmitted to other robotic devices and / or to network 110 and / or cloud 105. The acquired data may be transmitted to a centralized system to improve the efficiency of air filtration and floor cleaning in other buildings throughout the system. Furthermore, the acquired data may be collected for use by EOM 250 to provide better data management and improve data visualization.

[0068] Figure 7 An example system 700 capable of performing the techniques presented herein is described. Figure 7 This is a simplified functional block diagram of a computer that can be configured to perform the techniques described herein, according to exemplary embodiments of the present disclosure. Specifically, the computer (or “platform,” as it may not be a single physical computer infrastructure) may include a data communication interface 760 for packet data communication. The platform may also include a central processing unit (“CPU”) 720 in the form of one or more processors for executing program instructions. The platform may include an internal communication bus 710, and may also include program storage and / or data storage devices, such as ROM 730 and RAM 740, for various data files processed and / or sent by the platform; however, system 700 may receive programs and data via network communication. System 700 may also include input and output ports 750 for connecting input and output devices, such as a keyboard, mouse, touchscreen, monitor, display, etc. Of course, various system functions can be implemented in a distributed manner across multiple similar platforms to distribute the processing load. Alternatively, these systems can be implemented through appropriate programming of a single computer hardware platform.

[0069] The general discussion of this disclosure provides a brief, general description of a suitable computing environment in which this disclosure may be implemented. In one embodiment, any of the disclosed systems, methods, and / or graphical user interfaces may be executed or implemented by a computing system consistent with or similar to what is depicted and / or explained in this disclosure. Although not required, aspects of this disclosure are described in the context of computer-executable instructions, such as routines executed by data processing devices (e.g., server computers, wireless devices, and / or personal computers). Those skilled in the art will understand that aspects of this disclosure can be practiced with other communications, data processing, or computer system configurations, including: Internet devices, handheld devices (including personal digital assistants (“PDAs”), wearable computers, all forms of cellular or mobile phones (including Voice over IP (“VoIP”) phones), dumb terminals, media players, gaming devices, virtual reality devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, minicomputers, mainframes, etc. In practice, the terms “computer,” “server,” and similar terms are generally used interchangeably herein and refer to any of the foregoing devices and systems as well as any data processor.

[0070] Various aspects of this disclosure can be embodied in a dedicated computer and / or data processor specifically programmed, configured, and / or constructed to implement one or more computer-executable instructions as detailed herein. While aspects of this disclosure (such as certain functions) are described as being implemented specifically on a single device, this disclosure can also be practiced in a distributed environment where functions or modules are shared among different processing devices linked by communication networks such as local area networks (“LANs”), wide area networks (“WANs”), and / or the Internet. Similarly, the techniques presented herein involving multiple devices can be implemented in a single device. In a distributed computing environment, program modules can reside in local and / or remote memory storage devices.

[0071] The aspects of this disclosure can be stored and / or distributed on non-transitory computer-readable media, including magnetically or optically readable computer disks, hardwired or pre-programmed chips (e.g., EEPROM semiconductor chips), nanotechnology memories, biological memories, or other data storage media. Alternatively, computer-implemented instructions, data structures, screen displays, and other data according to the aspects of this disclosure can be distributed on the Internet and / or other networks (including wireless networks), distributed for a period of time on propagation signals on propagation media (e.g., one or more electromagnetic waves, sound waves, etc.), and / or they can be provided on any analog or digital network (packet switching, circuit switching, or other schemes).

[0072] The various programmatic aspects of this technology can be considered as “products” or “artifacts” typically in the form of executable code and / or related data carried on or contained in a type of machine-readable medium. “Storage” media include any or all tangible memory, such as various semiconductor memories, tape drives, disk drives, etc., of computers, processors, etc., or their associated modules, which can provide non-transitory storage for software programming at any time. All or part of the software can sometimes be transmitted via the Internet or various other telecommunications networks. For example, such transmission can load software from one computer or processor into another, such as from a management server or host computer of a mobile communication network into a server's computer platform and / or from a server into a mobile device. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as those used through physical interfaces between local devices, through wired and optical terrestrial networks, and through various air links. Physical elements carrying such waves (such as wired or wireless links, optical links, etc.) can also be considered as media carrying software. As used herein, unless limited to non-transitory tangible "storage" media, terms such as "computer or machine readable medium" refer to any medium involved in providing instructions to a processor for execution.

[0073] "One or more" includes functions implemented by one element, functions implemented by more than one element (e.g., in a distributed manner), multiple functions implemented by one element, multiple functions implemented by multiple elements, or any combination of the above.

[0074] It should also be understood that although the terms first, second, etc., are used in certain circumstances herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the various embodiments described, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0075] The terminology used in the description of the various embodiments described herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an,” and “the” are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and covers any and all possible combinations of one or more of the associated listed items. It will be further understood that, when used in this specification, the terms “comprising,” “including,” “containing,” and / or “having” specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0076] As used herein, depending on the context, the term "if" may optionally be interpreted as "when" or "based on" or "in response to determination" or "in response to detection". Similarly, depending on the context, the phrase "if it is determined" or "if [the stated condition or event] is detected" may optionally be interpreted as "based on determination" or "in response to determination" or "based on the detection of [the stated condition or event]" or "in response to the detection of [the stated condition or event]".

[0077] The systems, apparatuses, devices, and methods disclosed herein are described in detail by way of example and with reference to the accompanying drawings. The examples discussed herein are merely illustrative and are provided to aid in the explanation of the apparatuses, devices, systems, and methods described herein. Unless expressly specified as mandatory, features or components shown in the drawings or discussed above should not be considered mandatory for any particular implementation of any of these apparatuses, devices, systems, or methods. For ease of reading and clarity, certain components, modules, or methods may be described only in conjunction with specific drawings. Any identification of particular techniques, arrangements, etc., in this disclosure is either related to the specific example presented or is merely a general description of such techniques, arrangements, etc. Identification of specific details or examples is not intended and should not be construed as mandatory or limiting unless specifically specified otherwise. Any combination or sub-combination of components that is not specifically described should not be construed as indicating that any combination or sub-combination is impossible. It will be understood that modifications may be made to the disclosed and described examples, arrangements, configurations, components, elements, apparatuses, devices, systems, methods, etc., and such modifications may be desired for a particular application. Furthermore, for any method described, whether or not it is described in conjunction with a flowchart, it should be understood that, unless otherwise stated or required by context, any explicit or implicit ordering of the steps performed in the execution of the method does not imply that these steps must be performed in the order presented, but may instead be performed in a different order or in parallel.

[0078] Throughout this disclosure, references to components or modules generally refer to items that can be logically combined to implement a function or a set of related functions. Similar reference numerals are generally intended to refer to the same or similar components. Components and modules can be implemented in software, hardware, or a combination of software and hardware. The term "software" is used broadly to include not only executable code (e.g., machine-executable or machine-interpretable instructions) but also data structures, data storage, and computational instructions stored in any suitable electronic format, including firmware and embedded software. The terms "information" and "data" are used broadly and include various types of electronic information, including executable code; content such as text, video data, and audio data; and various codes or symbols. The terms "information," "data," and "content" are sometimes used interchangeably when the context permits.

[0079] The description and examples are intended to be illustrative only, and the true scope and spirit of this disclosure are indicated by the following claims.

Claims

1. A method for operating a queue of robotic devices, comprising: Air purity and ground particle data are continuously acquired from one or more sensors and / or one or more cameras of the robotic devices in the robot device queue, wherein the network of the robot device queue, the attributes of the robot device queue, and the relationships between the robotic devices in the queue are defined in a knowledge graph; Based on air purity feedback from one or more sensors of the robotic device, it is determined whether air impurities around the robotic device exceed an air purity threshold. Based on the determination of whether the air impurities around the robot exceed the air purity threshold, the air purification mode of the robot is modified. Based on ground particle feedback from one or more sensors of the robot device, determine whether the ground particles around the robot device exceed a ground particle threshold; Based on the determination of whether the ground particles around the robot exceed a ground particle threshold, the floor cleaning mode of the robot is modified; and Based on the fact that the air impurities around the robot device exceed a second air purity threshold and / or the ground particles around the robot device exceed a second ground particle threshold, a request is sent to the second robot device in the robot device queue to reposition to a position within a predetermined distance from the robot device.

2. The method according to claim 1, further comprising: Based on the fact that the air impurities around the robot device exceed a second air purity threshold, a request is sent to a second robot device in the robot device queue to modify the air purification mode of the second robot device, the second air purity threshold being based on air purity feedback from one or more sensors of the robot device.

3. The method according to claim 1, further comprising: If the ground particles around the robot device exceed a second ground particle threshold, a request is sent to a second robot device in the robot device queue to modify the ground cleaning mode of the second robot device, the second ground particle threshold being based on ground particle feedback from one or more sensors of the robot device.

4. The method according to claim 1, further comprising: Simultaneously modify the air purification mode and the floor cleaning mode.

5. The method of claim 1, wherein modifying the air purification mode includes automatically adjusting the airflow.

6. The method according to claim 1, wherein, Modifying the air purification mode includes starting, changing, or stopping the air purification mode.

7. The method according to claim 1, wherein, Modifying the floor cleaning mode includes starting, changing, or stopping the floor cleaning mode.

8. The method according to claim 1, further comprising: Air purity and ground particle data obtained from the one or more sensors and / or one or more cameras will be transmitted to a second device.

9. The method of claim 8, wherein the second device is another robotic device.

10. The method of claim 8, wherein the second device is an external server.

11. A system for operating a queue of robotic devices, comprising: At least one processor; and At least one memory, The at least one processor executes instructions to perform operations including the following: Air purity and ground particle count are continuously obtained from one or more sensors and / or one or more cameras of the robotic devices in the robotic device queue, wherein the network of the robotic device queue, the attributes of the robotic device queue, and the relationships between the robotic devices in the queue are defined in a knowledge graph; Based on air purity feedback from one or more sensors of the robotic device, it is determined whether air impurities around the robotic device exceed an air purity threshold. Based on the determination of whether the air impurities around the robot exceed the air purity threshold, the air purification mode of the robot is modified. Based on ground particle feedback from one or more sensors of the robot device, determine whether the ground particles around the robot device exceed a ground particle threshold; Based on the determination of whether the ground particles around the robot exceed a ground particle threshold, the floor cleaning mode of the robot is modified; and Based on the fact that the air impurities around the robot device exceed a second air purity threshold and / or the ground particles around the robot device exceed a second ground particle threshold, a request is sent to the second robot device in the robot device queue to reposition to a position within a predetermined distance from the robot device.

12. The system according to claim 11, wherein, The operation further includes: sending a request to a second robot device in the robot device queue to modify the air purification mode of the second robot device based on the fact that air impurities around the robot device exceed a second air purity threshold, the second air purity threshold being based on air purity feedback from the one or more sensors of the robot device.

13. The system according to claim 11, wherein, The operation further includes: sending a request to a second robot in the robot queue to modify the ground cleaning mode of the second robot based on ground particles around the robot device exceeding a second ground particle threshold, the second ground particle threshold being based on ground particle feedback from the one or more sensors of the robot device.

14. The system according to claim 11, wherein, The operation also includes: modifying the floor cleaning mode, including starting, changing, or stopping the floor cleaning mode.

15. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1-10.

16. A computer program product comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1-10.

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