Automated multi-dimensional data processing system

A computer-implemented method, comprising:

• receiving unstructured project-related data from heterogeneous sources;
• applying parsing logic using pattern matching and contextual analysis to identify project components and planning dimensions within the unstructured project-related data;
• normalizing the unstructured project-related data using schema-aligned transformation rules based on the project components and the planning dimensions within a unified schema to generate normalized data;
• mapping the normalized data to a dimensional calculations model to identify project execution tasks and associated constraint types;
• applying the dimensional calculations model to integrate spatial, temporal, and regulatory parameters, aligning the normalized data with spatial zones, temporal phases, and operational constraints to generate a constraint-mapped project representation;
• applying a trained model to the constraint-mapped project representation to evaluate constraint relationships and determine task interdependencies and resource availability;
• extracting task interdependencies among project execution tasks identified by the trained model based on the constraint-mapped project representation, the constraint relationships comprising resource availability, zone-phase allocation, and temporal sequence constraints;
• applying a structured inference layer comprising deterministic rule-based logic and executable inference rules, the structured inference layer operable to implement the deterministic rule-based logic on trained-model output vectors to resolve task interdependencies into actionable scheduling and resource constraints not derivable by the trained-model output vectors alone;
• generating a task execution sequence based on the resolved task interdependencies and resource availability;
• automatically generating a machine-structured project execution plan including task sequence and resource assignment instructions based on the resolved task interdependencies, spatial zones, temporal phases, and regulatory constraint parameters derived from the constraint-mapped project representation;
• iteratively refine the machine-structured project execution plan by applying a feedback process that evaluates one or more machine-computed performance criteria and automatically adjusts the task execution sequence or resource assignments in response to evaluated machine-computed performance criteria, wherein the machine-structured project execution plan is updated in real time as new constraint data are ingested;
• and
• generating, via the computing device processor, updated machine-readable execution instructions incorporating refinements generated by the feedback process for interactive presentation through a visualization interface.

A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a computing system, causes the computing system to:

• receive unstructured project-related data from heterogeneous sources;
• apply parsing logic using pattern matching and contextual analysis to identify project components and planning dimensions within the unstructured project-related data;
• normalize the unstructured project-related data using schema-aligned transformation rules based on the project components and the planning dimensions within a unified schema to generate normalized data;
• map the normalized data to a dimensional calculations model to identify project execution tasks and associated constraint types;
• apply the dimensional calculations model to integrate spatial, temporal, and regulatory parameters, aligning the normalized data with spatial zones, temporal phases, and operational constraints to generate a constraint-mapped project representation;
• apply a trained model to the constraint-mapped project representation to evaluate constraint relationships and determine task interdependencies and resource availability;
• extract task interdependencies among project execution tasks identified by the trained model based on the constraint-mapped project representation, the constraint relationships comprising resource availability, zone-phase allocation, and temporal sequence constraints;
• apply a structured inference layer comprising deterministic rule-based logic and executable inference rules, the structured inference layer operable to implement the deterministic rule-based logic on trained-model output vectors to resolve task interdependencies into actionable scheduling and resource constraints not derivable by the trained-model output vectors alone;
• generate a task execution sequence based on the resolved task interdependencies and resource availability;
• automatically generate a machine-structured project execution plan including task sequence and resource assignment instructions based on the resolved task interdependencies, spatial zones, temporal phases, and regulatory constraint parameters derived from the constraint-mapped project representation;
• iteratively refine the machine-structured project execution plan by applying a feedback process that evaluates one or more machine-computed performance criteria and automatically adjusts the task execution sequence or resource assignments in response to evaluated machine-computed performance criteria, wherein the machine-structured project execution plan is updated in real time as new constraint data are ingested;
• and
• generate, via the computing device processor, updated machine-readable execution instructions incorporating refinements generated by the feedback process for interactive presentation through a visualization interface.