A system for the temporal ordering of multi-channel data streams

Abstract

A system for the temporal ordering of multi-channel data streams, comprising the following: a multi-channel data reception module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect discrepancies in timestamps; a time synchronization module configured to align data packets using timestamp normalization, adaptive buffering, and AI-based predictive modeling; an AI-powered predictive modeling module trained to estimate missing or delayed timestamps and to reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adjust buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct order of the data packets and transmit them to a processing or analysis system; and a module for error detection and correction that is configured to detect inconsistencies in timestamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.

Description

The present invention relates to data processing systems, and more particularly to a system and method for efficient ordering of multi-channel streaming data in real time that ensures accurate synchronization across multiple data sources.With the increasing introduction of real-time data processing systems, huge amounts of multi-channel data streams are being generated in various industries such as healthcare, financial trading, IoT-based smart environments, and multimedia analysis. These data streams originate from different sources and often arrive with different time stamps, transmission delays or missing data points. The challenge is to maintain a correct temporal sequence while ensuring minimum processing delays. Conventional systems attempt to solve this problem using various techniques. However, they have several disadvantages that limit their efficiency and scalability.A common method is buffer-based ordering, in which incoming data packets are temporarily stored in a buffer before being processed in sequence. This approach, while allowing delayed packet reordering, results in significant latencies, particularly in high speed data environments. Moreover, static buffer sizes often result in inefficient memory usage, leading to either excessive data losses or unnecessary delays.Another conventional approach is time stamp based synchronization, in which the data packets are put in an order according to their time stamps. However, this method is very vulnerable to inaccuracies caused by clock drift, inconsistent time stamps on various devices, and delays in network transmission. In distributed systems where the data originates from different locations, these inconsistencies may result in a wrong order, which affects the reliability of the real-time analysis.Some systems attempt to use heuristic-based ordering methods that rely on predefined rules for rearranging the incoming data. However, these approaches lack flexibility because they do not account for variations in data transfer rates, network conditions, or dynamically changing workloads. This results in the data being frequently misorganized, resulting in erroneous analyses and decisions.To solve this problem, the present invention provides a system for time-ordering multi-channel data streams.The multi-channel stream temporal ordering system that can achieve accurate real-time synchronization of multi-channel streams by dynamically adapting to transmission delays, missing data packets, and time stamp inconsistencies.The system for time-arranging multi-channel data streams can minimize latency in data arrangement while ensuring high-speed processing. Thus, the system is suitable for applications requiring real-time decision making, such as financial trading, IoT-based monitoring, and healthcare analyses.The multi-channel stream temporal ordering system, which may develop an AI / ML based prediction model capable of analyzing historical patterns, learning from past data variations, and predicting the correct temporal ordering for non-consecutive or missing data packets.The multi-channel stream time-order system that can implement an adaptive buffer mechanism that dynamically adjusts the buffer sizes based on network conditions and incoming data rates, thereby reducing memory inefficiencies and avoiding unnecessary processing delays.The multi-channel stream temporal ordering system, which may provide fault tolerance and robustness, by incorporating fault detection and correction mechanisms that compensate for inaccuracies due to clock drift, inconsistent time stamps, and network delays.The system for multi-channel stream time ordering, supports scalability and interoperability across different areas and systems, and enables seamless integration with IoT networks, cloud-based analysis platforms, distributed computing environments, and real-time monitoring systems.The system for the temporal order of multi-channel data streams, which can improve the computational efficiency through the optimization of processing algorithms, ensures low resource consumption and at the same time maintains high accuracy in the temporal data order.The system for the temporal ordering of multi-channel data streams, which may be structures, formats and transmission protocols, thus being applicable to various industries such as video surveillance, sensor fusion and smart city applications.In one embodiment, a system for time-ordering multi-channel data streams is provided. The system includes a multi-channel data receiving module, a temporal synchronization module, an AI-based predictive modelling module, an adaptive buffer module, an ordering and output processing module, and an error detection and correction module, all of which operate in coordination to ensure accurate temporal ordering of multi-channel data streams. The system is designed for processing real-time data streams from multiple sources and addresses challenges such as time stamp inconsistencies, missing data packets, and data transmissions that are not in the proper order. The multi-channel data receiving module is configured to receive streaming data from various sources including IoT devices, financial trading systems, surveillance cameras, and healthcare surveillance platforms. This module extracts relevant temporal metadata, identifies deviations in the timestamps, and passes the data to the temporal synchronization module for further processing. The temporal synchronization module ensures the alignment of data packets by normalizing time stamps, correcting for deviations and applying synchronization techniques. It uses a hybrid approach that combines heuristic ordering and AI-driven sequence prediction to accurately reconstruct the original sequence of the incoming data. This module communicates with the AI-based predictive modeling module to improve the efficiency of ordering. The AI-based predictive modelling module is responsible for estimating missing or delayed time stamps and rearranging data packets that do not enter in the correct order. It uses machine learning techniques such as recurrent neural networks (RNNs) and reinforcement learning to analyze historical data patterns and predict the proper order of the data streams. By learning from past variations, this module accommodates real-time changes in data transfer rates and ensures consistent and accurate timing. The adaptive buffering module dynamically adjusts the buffer size to the real-time network conditions and the incoming data rates. It includes a sliding window mechanism that optimizes memory allocation and reduces unnecessary delays to prevent data losses while maintaining a balance between latency and accuracy. This module cooperates with the temporal synchronization and predictive modelling modules to improve the overall efficiency of the system. The ordering and output processing module is responsible for reconstructing the correct ordering of data packets and forwarding them to the intended processing or analysis system. It prioritizes the data based on predefined accuracy thresholds and ensures that the most critical and time critical information is processed without delays. This module interacts with all other modules to make final ordering decisions before the data is transferred for further use. The error detection and correction module identifies inconsistencies in time stamps, network delays, and missing data points. It employs anomaly detection techniques and correction algorithms to increase the reliability of the ordered data sequence. This module ensures fault tolerance and improves the accuracy of the overall system by validating and refining the results of the ordering and processing module. The seamless connection of these modules allows efficient and accurate real-time processing of multi-channel data streams. The system is scalable and adaptable to various applications where precise data ordering is of critical importance, including financial transactions, industrial IoT monitoring, real-time monitoring, and healthcare analyses. By integrating AI-controlled predictive modeling, adaptive buffering, and robust error correction mechanisms, the invention provides an advanced and reliable solution for managing the complexity of multi-channel data synchronization and ordering.The invention is explained again below with reference to the figure. The following shows:FIG. 1 shows a system for the temporal ordering of multichannel data streams.Figure 1 shows a system for time-ordering multi-channel data streams. The system includes a module for receiving multi-channel data, a module for time synchronization, an AI-based module for predictive modeling, an adaptive buffer module, a module for ordering and output processing, and a module for error detection and correction, all of which operate in coordination to ensure accurate time ordering of multi-channel data streams. The system is designed for processing real-time data streams from multiple sources and addresses challenges such as time stamp inconsistencies, missing data packets, and data transmissions that are not in the proper order. The multi-channel data receiving module is configured to receive streaming data from various sources including IoT devices, financial trading systems, surveillance cameras, and healthcare surveillance platforms. This module extracts relevant temporal metadata, identifies deviations in the timestamps, and passes the data to the temporal synchronization module for further processing. The temporal synchronization module ensures the alignment of data packets by normalizing time stamps, correcting for deviations and applying synchronization techniques. It uses a hybrid approach that combines heuristic ordering and AI-driven sequence prediction to accurately reconstruct the original sequence of the incoming data. This module communicates with the AI-based predictive modeling module to improve the efficiency of ordering. The AI-based predictive modelling module is responsible for estimating missing or delayed time stamps and rearranging data packets that do not enter in the correct order. It uses machine learning techniques such as recurrent neural networks (RNNs) and reinforcement learning to analyze historical data patterns and predict the proper order of the data streams. Through learning from past variations, this module accommodates real-time changes in data transfer rates and ensures consistent and accurate timing. The adaptive buffering module dynamically adjusts the buffer size to the real-time network conditions and the incoming data rates. It includes a sliding window mechanism that optimizes memory allocation and reduces unnecessary delays to prevent data losses while maintaining a balance between latency and accuracy. This module cooperates with the temporal synchronization and predictive modelling modules to improve the overall efficiency of the system. The ordering and output processing module is responsible for reconstructing the correct ordering of data packets and forwarding them to the intended processing or analysis system. It prioritizes the data based on predefined accuracy thresholds and ensures that the most critical and time critical information is processed without delays. This module interacts with all other modules to make final ordering decisions before the data is transferred for further use. The error detection and correction module identifies inconsistencies in time stamps, network delays, and missing data points. It employs anomaly detection techniques and correction algorithms to increase the reliability of the ordered data sequence. This module ensures fault tolerance and improves the accuracy of the overall system by validating and refining the results of the ordering and processing module. The seamless connection of these modules allows efficient and accurate real-time processing of multi-channel data streams. The system is scalable and adaptable to various applications where precise data ordering is of critical importance, including financial transactions, industrial IoT monitoring, real-time monitoring, and healthcare analyses. By integrating AI-controlled predictive modeling, adaptive buffering, and robust error correction mechanisms, the invention provides an advanced and reliable solution for managing the complexity of multi-channel data synchronization and ordering.List of reference characters100 System

Claims

A system for time ordering multi-channel data streams, comprising: a multi-channel data receiving module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect deviations in time stamps; a temporal synchronization module configured to match data packets using time stamp normalization, adaptive buffering, and AI-based prediction modeling; an AI-based predictive modeling module trained to estimate missing or delayed time stamps and reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adapt buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct ordering of the data packets and to transmit them to a processing or analysis system; and an error detection and correction module configured to detect inconsistencies in time stamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.The system of claim 1, wherein the multi-channel data receiving module is configured to be connectable to multiple sources including IoT devices, sensors, financial trading platforms, surveillance cameras, and healthcare surveillance systems.The system of claim 1, wherein the temporal synchronization module employs a hybrid approach that combines heuristic-based ordering, time stamp correction, and AI-controlled sequence prediction to ensure accurate real-time synchronization.The system of claim 1, wherein the AI-based predictive modeling module uses machine learning techniques including reinforcement learning, recurrent neural networks (RNNs), and deep learning based anomaly detection to predict and correct out-of-order data.The system of claim 1, wherein the adaptive buffer module includes a dynamically adjustable sliding window mechanism to manage variable data arrival rates and minimize latency.The system of claim 1, wherein the ordering and output processing module uses a priority-based execution mechanism to determine whether to process, delay, or discard data packets based on predefined accuracy thresholds.The system of claim 1, wherein the system is implemented in a distributed cloud environment to support high-speed, large-scale multi-channel data processing.The system of claim 1, wherein the error detection and correction module uses anomaly detection techniques to detect and correct inconsistencies in time stamps and transmission sequences.

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