A system and method are described for carrying out machine learning-based automated laundry machine error/degraded status detection and maintenance by use of acoustic sensor data rendered by the laundry machines and applied to a plurality of machine learning models for use with acoustic data rendered by the laundry machines.

Devices, systems, and methods for autonomously queueing a plurality of folded household laundry articles in a packing queue are described. A packing system includes a rotating platform configured to rotate a folded laundry article for directional placement in the packing queue, at least one packing queue platform receiving the folded laundry article into a packing queue disposed thereon, and a double ended conveyor. The double ended conveyor includes a retrieving end and a depositing end. The conveyor is configured to be mounted to a gantry for traveling along the length of the queue platform and cantilevering the retrieving end over the rotating platform for retrieving the folded laundry article and the depositing end over the packing queue platform for depositing the folded laundry article onto either a surface of the packing queue platform or another folded laundry article of the plurality of household laundry articles.

Disclosed is an artificial intelligence (AI)-based self-control air conditioner. The AI-based self-control air conditioner includes a communication unit configured to receive an image including member data for identifying the member from an image acquisition apparatus corresponding to a group including at least one member, and a processor configured to recognize the member data from the received image, to acquire operation data including an operation condition of an air conditioner, which is desired by the member, based on the recognized data, to store member information including the member data and the operation data in a database, and to acquire and analyze the operation condition of the air conditioner, which is desired by the member, with respect to at least one member from a plurality of pieces of member information corresponding to the group stored in the database, wherein the air conditioner is autonomously driven according to control of the processor. Accordingly, the air conditioner learns members itself and controls an operation in an optimum state to reduce power consumption and is driven according to an operation condition set for each member to enhance personal convenience of the member.

Laundry washing machine (1) has an outer casing (2), a washing tub (3), arranged inside the casing (2), a rotatable drum (4), arranged in axially rotating manner inside the washing tub (3) and is designed to receive laundry to be washed. The machine further has a detergent dispensing assembly (12), designed for supplying laundry detergent into the washing tub (3), a water softening system (14), designed to receive fresh water from a water mains (13) and reduce the hardness degree of the fresh water in order to supply softened water to the detergent dispensing assembly (12) and/or to the washing tub (3), during one or more softened water laundry washing phases. The machine further has a control panel (28) configured to allow operator to input information associated with a laundry washing course having one or more softened water laundry washing phase/s; and a controller (15) configured to determine a time saving value (tSi) of one or more laundry washing phases of the washing courses to be performed, based on the softened water laundry washing phase/s of said laundry washing course.

A drive apparatus includes: a drive unit; a controller that obtains an application including a plurality of blocks, and executes the application to control the drive unit in accordance with the plurality of blocks; a first sensor; and a second sensor. Each of the plurality of blocks includes a parameter and an end condition. When, during execution of a first block, the first driving state detected by the first sensor meets the end condition of the first block and the second driving state detected by the second sensor meets a parameter change condition, the controller: changes the parameter of a second block that is executed after the first block; and controls the drive unit in accordance with the second block including the changed parameter.

Techniques are described controlling and managing machines to provide laundry services. A request may be received from a mobile device of a user, the request indicating that the mobile device is in proximity to a laundry machine such as a washer or dryer. In some cases, the request is sent by the mobile device based on detecting, at the mobile device, a wireless signal transmitted from the laundry machine. In some cases the request may include a code that identifies the laundry machine, the code being displayed by the machine and/or encoded in a scannable code displayed by the machine. In response to the request, a signal may be sent to activate the laundry machine and enable the user to use the laundry machine for a predetermined period of time (e.g., a cycle). In some cases, the activation signal is sent if the user's payment is successfully processed.

An autonomously operated system is configured to load at least one unbound deformable article into a container. The system includes a refillable cartridge configured to receive therein the article, at least one extendable conveyor being configured to extend into the refillable cartridge and deposit the article within the refillable cartridge, a driven lifter configured to selectively lower and raise the refillable cartridge relative to the at least one extendable conveyor, one or more sensors configured output a signal indicative of a fill height of the refillable cartridge, and at least one controller configured to, in response a received signal form the one or more sensors, instruct the driven lifter to at least one of raise and lower the refillable cartridge to at least one of one or more loading heights for receiving the article, and lower the refillable cartridge to an unloading position within the container.

A system and method are described for carrying out machine learning-based automated laundry machine error/degraded status detection and maintenance by use of acoustic sensor data rendered by the laundry machines and applied to a plurality of machine learning models for use with acoustic data rendered by the laundry machines.

A method for controlling an electrical appliance, and a non-transitory computer-readable storage medium. The control method comprises: receiving a voice control instruction (S101); when the identity of a user who issued the voice control instruction is determined to be a child, acquiring a child knowledge graph (S102); when determined that a control object is a first electrical appliance, determining whether a knowledge expression corresponding to the first electrical appliance exists in the child knowledge graph (S103); if a knowledge expression corresponding to the first electrical appliance doesn't exist in the child knowledge graph, then not responding to the voice control instruction; otherwise, carrying out processing on the voice control instruction according to the knowledge expression corresponding to the first electrical appliance in the child knowledge graph (S104).

A laundry device has a drive system. The drive system contains a plurality of actuators for executing synchronized adjusting actions, a plurality of decentralized control modules assigned to the respective actuators and are connected to one another via a data bus, and a central controller for controlling the actuators via the data bus and the decentralized control modules. The central controller is configured to send a preconditioning message with information relating to preconditioning an adjusting action of at least one portion of the actuators via the data bus to the decentralized control modules. The central controller or one of the decentralized control modules is configured to send a trigger signal to the decentralized control modules via a trigger line after the sending of the preconditioning message. The trigger signal prompts the decentralized control modules to control the actuators in a temporally synchronized manner according to the preconditioned adjusting action.