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 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 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 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: 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 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: 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.

Abstract: A method for generating a training data set includes receiving, by a processing circuit, warranty claim data associated with one or more building devices or building device components; processing, by the processing circuit, the warranty claim data using natural language processing to generate a training data set comprising one or more causes and solutions associated with failure of the one or more building devices or the building device components; and training, by the processing circuit, a component reliability model using the training data set to produce a trained model.

Abstract: A method, apparatus and system for detecting carbon emission-involved gas from a ruminant is provided, including: continuously receiving ear tag information of the ruminant in a monitoring range; determining an operation state of a feed supply system based on the ear tag information; and determining, based on collected emitted gas from the ruminant during eating, an emission rate of carbon emission-involved gas in the emitted gas when it is determined that the feed supply system is in a continuous operation state. In the method, apparatus and system, the ear tag information of the ruminant is detected to determine whether there is a ruminant in a detection area and feeding information of the ruminant, then the operation state of the feed supply system is controlled, and an intelligent information technology is used to acquire the emission rate of the carbon emission-involved gas matching the identity of the ruminant.

Abstract: A method for affecting operation of building equipment includes providing a plurality of reliability models that model failure probabilities of components of the building equipment as functions of equipment runtime, providing associations of the components with a plurality of subsystems of the building equipment, calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure based on the reliability models for the components and the associations, and initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure.