Digital Engineering Information System

20250076871

18/823608

September 03, 2024

Described herein is the Digital Information Management System (DIMS) which uses advances in AI and/or machine learning to automate data management. In some embodiments, DIMS offers self-diagnosis in the interest of providing effective, timely maintenance, and continuing operation while systems are down. Embodiments of anti-malware are also described, covering common attack vectors in an Internet of Things sensor system, and bolstered by AI and/or machine learning. Data processing and presentation is further enhanced by AI and/or machine learning, to validate, verify, and extrapolate/interpolate values in a variety of formats. Embodiments of visualization subsystems are also described, which include a variety of analytics and recommendations to further automate and increase the efficiency of data analysis across a wide range of assets and/or phenomena. Notification connects users to DIMS with tailored updates, notifications, predictive maintenance alerts, queries, and other specified information.

1. A data management system comprising: data acquisition units, wherein each data acquisition unit is connected to a central processing subsystem, configured to analyze and transmit data to a visualization subsystem user interface; one or more AI or machine learning models, wherein the AI or machine learning model(s) are configured to: predict timelines of longevity for continued asset function, predict future measurements from the data acquisition units, interpolate missing data, notify relevant parties of data management issues, and diagnose issues in the data management system at all points of data acquisition, processing, transmission, and presentation, protect data security through automated responses that are reinforced through machine learning of common hacking attempts, transform the data into optimized formats for presentation, analyze transmitted signals against background noise, perform secondary calculations and statistical analytics, and filter, sort, or generate visual dashboards or reports; wherein the system includes a notification subsystem configured to generate and send to users automated notifications that are adjustable based on user-defined preferences, including system diagnostics, predictive maintenance alerts, or other analytics.

2. The system of claim 1, wherein data interpolation or extrapolation compares predicted measurements to locally stored values in the data acquisition unit(s) following data transmission outages.

3. The system of claim 1, wherein a self-diagnosis result will trigger an alternate protocol of operation for the data management system, or trigger maintenance operations for scheduled/unscheduled maintenance scenarios.

4. The system of claim 1, wherein the system's malware defense further comprises web application firewalls, rerouting, pattern recognition, adaptive thresholds, predictive models, anomaly detection, data encryption algorithms, secure VPN tunnels, cloud service providers, authentication schemes, firewalls, domain name system security, network segmentation, and intrusion detection and intrusion protection system, IP address hopping, and intrusion detection and protection facilitated by machine learning.

5. The system of claim 1, wherein data transmission includes the functions of aggregation, compression, encryption, packetization and segmentation, routing and switching, flow control techniques such as buffering, windowing, and congestion avoidance algorithms, error correction techniques, and data security measures.

6. The system of claim 1, wherein the notification subsystem is configured to send alert notifications to operators, users, or maintenance personnel across multiple devices, the notifications comprising an explanation of any actual or potential issue, a proposed maintenance schedule, an option to modify or reject the proposed maintenance, and a follow-up notification upon successful resolution.

7. The system of claim 1, wherein data acquisition, transmission, processing, and presentation each includes the functions of data encryption and decryption, verification, validation, transformation into a readable format, central indexing, and backup storage in the event of a central processing subsystem failure.

8. The system of claim 1, wherein the system's data security protection includes AI/machine learning-driven encryption, which continuously optimizes the balance between resource efficiency and security.

05

edspap.20250076871