Intelligence driven method and system for multi-factor optimization of schedules and resource recommendations for smart construction

One or more non-transitory computer-readable storage media storing one or more instructions programmed which, when executed by one or more computing devices, cause:

• receiving, by a server device, a schedule request for a construction activity for a construction project from a client device, the schedule request identifying at least one construction objective;
• accessing, by the server device, a plurality of input data sets, wherein the plurality of input data sets includes data sets from a data feed database comprising a plurality of data feeds from various sources, and wherein each data feed includes data segments comprising metadata indicating a data type, and wherein the plurality of input data sets includes at least one camera feed, wherein the at least one camera feed includes metadata or tags identifying all segments of the at least one camera feed;
• training a plurality of ensembles of neural network machine learning models using new training data, including data relating to the construction project;
• applying, by the server device, the trained plurality of ensembles of neural network machine learning models to each of the plurality of input data sets to generate a plurality of intermediary data sets that each identifies factors that impact the construction project, wherein each trained ensemble of the neural network machine learning models of the trained plurality of ensembles of the neural network machine learning models generates an intermediary data set, according to function objectives of a respective ensemble of the neural network machine learning models, from particular segments of all segments that are associated with the function objectives of the respective ensemble of the neural network machine learning models;
• determining, by the server device and based on the plurality of intermediary data sets, task data using at least one knowledge and semantic graph model, wherein an analytical aggregator uses a correlation analyzer to correlate various outputs of each ensemble of the neural network machine learning models to generate the at least one knowledge and semantic graph model;
• generating, by the server device and from the task data, a schedule for the construction activity, the schedule achieving the at least one construction objective and satisfying tolerance levels associated with a baseline schedule;
• and notifying, by the server device, the schedule based on the at least one construction objective identified from the schedule request.

A computing system, comprising:

• one or more computer systems comprising one or more hardware processors and storage media;
• instructions stored in the storage media and which, when executed by the computing system, cause the computing system to perform:
• receiving, by a server device, a schedule request for a construction activity of a construction project from a client device, the schedule request identifying at least one construction objective;
• accessing, by the server device, a plurality of input data sets, wherein the plurality of input data sets includes data sets from a data feed database comprising a plurality of data feeds from various sources, and wherein each data feed includes data segments comprising metadata indicating a data type, and wherein the plurality of input data sets includes at least one camera feed, wherein the at least one camera feed includes metadata or tags identifying all segments of the at least one camera feed;
• training a plurality of ensembles of neural network machine learning models using new training data, including data relating to the construction project;
• applying, by the server device, the trained plurality of ensembles of neural network machine learning models to each of the plurality of input data sets to generate a plurality of intermediary data sets that each identifies factors that impact the construction project, wherein each trained ensemble of the neural network machine learning models of the trained plurality of ensembles of the neural network machine learning models generates an intermediary data set, according to function objectives of a respective ensemble of the neural network machine learning models, from particular segments of all segments that are associated with the function objectives of the respective ensemble of the neural network machine learning models;
• determining, by the server device and based on the plurality of intermediary data sets, task data using at least one knowledge and semantic graph model, wherein an analytical aggregator uses a correlation analyzer to correlate various outputs of each ensemble of the neural network machine learning models to generate the at least one knowledge and semantic graph model;
• generating, by the server device and from the task data, a schedule for the construction activity, the schedule achieving the at least one construction objective and satisfying tolerance levels associated with a baseline schedule;
• and notifying, by the server device, the schedule based on the at least one construction objective identified from the schedule request.