Abstract: Methods, systems, and apparatus, including wetness monitoring systems comprising a smart diaper comprising: a sensorial battery comprising at least two electrodes; and a transmitter, wherein the sensorial battery is configured to provide electric current to the transmitter in the presence of water or urine, and wherein the transmitter is configured to emit a signal comprising a unique identifier when the electric current is received from the sensorial battery; and a computing device comprising: a receiver; a user interface; one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving, via the receiver, the signal emitted by the transmitter, and providing, via the user interface, an indication that the smart diaper is wet.

Abstract: A modeling method for a low-loss transport technology with directed force application and slow landing is provided, including determining relevant parameters, such as friction coefficients between a crop and grid bars, performing theoretical analysis, modeling an auxiliary device for simulating a motion trail of the crop, and modeling a three-dimensional diagram according to structural parameters. In this way, the crop and the soil are effectively separated, and situations, such as high surface scratching rate and internal bruising caused due to a motion manner of a conventional low-loss transport device with directed force application and slow landing, in a separation process of the crop and the soil is greatly reduced.

Abstract: A diffusive gradients in thin films (DGT) test device for lake water, including: a floating ball and a plurality of water sampling units. A DGT detection device, a water quality detection device with a multi-parameter probe, and a water sampling device are disposed in each of the water sampling units. The water sampling device includes: a plurality of sampling bottles, each of the sampling bottles is provided with an inlet pipe and an exhaust pipe, each of the inlet pipes and exhaust pipes is provided with a one-way solenoid valve; a peristaltic pump, a water sample outlet of the peristaltic pump communicates with the inlet pipes of the plurality of sampling bottles via a multi-way connector; the exhaust pipes of the plurality of sampling bottles are connected to an exhaust manifold via a multi-way connector; a control device connected with the peristaltic pump and each of the one-way solenoid valves.

Abstract: A method includes generating a plurality of data pairs by prompting a generative AI model to output, for each of a plurality of service records, a data pair comprising a question and an answer. The question relates to an equipment issue indicated in the service record and the answer relates to a service task indicated in the service record. The method also includes fine-tuning the generative AI model using the plurality of data pairs and providing a service recommendation using the generative AI model.

Abstract: A method includes fine-tuning at least one large language model (LLM) using building domain data comprising information regarding equipment types, equipment parameters, and output conditions, facilitating generation of an input query for the at least one LLM by providing an interactive interface configured to guide input of a relevant equipment type, a relevant equipment parameter, a relevant output condition, and a request type by a user and generating the input query based on the input of the relevant equipment type, the relevant equipment parameter, the relevant output condition, and the request type, and providing a response to the input query as an output of the at least one LLM by using the input query as an input to the LLM.

Abstract: Systems and methods are disclosed relating to building management systems with building equipment servicing. For example, a system can include at least one machine learning model configured using training data that includes at least one of unstructured data or structured data regarding items of equipment. The system can provide inputs, such as prompts, to the at least one machine learning model regarding an item of equipment, and generate, according to the inputs, responses regarding the item of equipment, such as responses for detecting a cause of an issue of the item of equipment, performing a service operation corresponding to the cause, or guiding a user through the service operation.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment. The plurality of first unstructured service reports include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method includes receiving, by the processors, a second service request for servicing building equipment. The method includes generating, by the processors using the generative AI model, a user interface prompting a user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats.

Abstract: A method includes receiving, by one or more processors, a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment. The plurality of first unstructured service reports may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include training, by the one or more processors, a generative AI model using the plurality of first unstructured service reports. The method may include performing, by the one or more processors using the trained generative AI model, one or more actions with respect a second service request subsequent to training the generative AI model.

Abstract: A method for servicing building equipment using generative artificial intelligence models includes receiving a multi-modal data input characterizing operation of the building equipment using multiple modes of data, associating related data portions from each mode of the multi-modal data input to form a set of original analysis packages, training at least one data generator to generate artificial analysis packages using the original analysis packages, using the at least one data generator to generate a set of artificial analysis packages, and adjusting an output model using the set of artificial analysis packages and the set of original analysis packages. The output model is configured to generate a service relevant multi-modal data output for use in servicing the building equipment.

Abstract: A method includes training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment and outcome data indicating outcomes of the plurality of first service requests. The generative AI model may be trained to identify one or more patterns or trends between characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests. The method may include receiving a second service request for servicing building equipment. The method may include automatically determining, using the generative AI model, one or more responses to the second service request based on characteristics of the second service request and the one or more patterns or trends between the characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests identified using the generative AI model.

Abstract: A method for servicing building equipment using generative artificial intelligence models includes creating a set of analysis components that describe expected behaviors of the building equipment, combining multiple analysis components that satisfy a similarity criterion to form a concise set of analysis components, prompting a generative artificial intelligence model using the concise set of analysis components, and performing an automated action for servicing the building equipment based on the response of the generative artificial intelligence model.

Abstract: A method includes receiving, by one or more processors, an unstructured service report corresponding to a service request handled by one or more technicians for servicing building equipment. The unstructured service report may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include automatically generating, by the one or more processors using a generative AI model, a structured service report in the predetermined format for delivery to a customer associated with the building equipment. The structured service report may include additional content generated by the generative AI model and not provided within the unstructured service report.

Abstract: A method includes receiving, by one or more processors, unstructured service data corresponding to one or more service requests handled by technicians for servicing building equipment of a building. The method may include detecting, by the one or more processors, an identifier of the building equipment, a space of the building, or a customer associated with the building using the unstructured service data. The method may include retrieving, by the one or more processors based on the identifier of the building equipment, the space, or the customer, additional data associated with the building equipment, the space, or the customer from one or more additional data sources separate from the unstructured service data. The method may include training, by the one or more processors, a generative AI model using training data including the unstructured service data and the additional data.

Abstract: A method includes receiving, by one or more processors, an unstructured service report corresponding to a service request handled by one or more technicians for servicing building equipment. The unstructured service report may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include extracting, by the one or more processors, a set of standards from one or more technical documents associated with the building equipment. The method may include automatically generating, by the one or more processors using a generative AI model, a structured service report in the predetermined format for delivery to a customer associated with the building equipment. Generating the structured service report may include standardizing the structured service report to comply with the set of standards extracted from the one or more technical documents associated with the building equipment.

Abstract: A method includes training, by one or more processors, a generative AI model using training data including a plurality of first service requests indicating a plurality of first problems associated with building equipment and a plurality of first actions performed in response to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request indicating a second problem associated with building equipment. The method may include automatically determining, by the one or more processors using the generative AI model, one or more second actions to perform based on characteristics of the second service request. The method may include automatically initiating, by the one or more processors, the one or more second actions to address the second problem associated with the building equipment.

Abstract: A method including training, by one or more processors, a generative AI model using first operating data from building equipment and a plurality of first service reports indicating a plurality of first problems associated with the building equipment. The method may include predicting, by the one or more processors using the generative AI model, one or more future problems likely to occur with the building equipment based on second operating data from the building equipment. The method may include automatically initiating, by the one or more processors, one or more actions to prevent the one or more future problems from occurring or mitigate an effect of the one or more future problems.

Abstract: The present disclosure provides a video generation method, an apparatus, a device and a storage medium. The method includes: firstly, when a selection operation for a target music is received, an atmosphere effect corresponding to the target music is displayed on a capturing page, and when a trigger operation for a capturing control on the capturing page is received, a target result video is generated based on the displayed atmosphere effect, where the audio in the target result video is obtained based on the target music, and the atmosphere effect corresponding to the target music is determined based on characteristics of the target music.

Abstract: A method for training a fault probability model using warranty claim data includes obtaining, by a processing circuit, a first data set for failed building devices based on warranty claim data associated with the building devices; receiving, by the processing circuit, design change data associated with the building devices and determining a design change date based on the design change data; comparing, by the processing circuit, a manufacturing date for each of the failed building devices with the design change date; removing, by the processing circuit, any building devices from the first data set in response to the manufacturing date preceding the design change date to create an updated first data set; generating, by the processing circuit, a training data set comprising the updated first data set; and training, by the processing circuit, a fault probability model using the training data set to produce a trained model.