REAL-TIME AI MODEL VERIFICATION, CORRECTION, AND DISTRIBUTION ACROSS EDGE DEVICES USING VECTOR SPACE ANALYSIS

Abstract

The present disclosure provides a real-time AI model verification, correction, and distribution system across edge devices using vector space analysis, specifically an environmental monitoring system (100). The system includes a data acquisition unit (102) configured to capture environmental data from multiple monitoring devices (104). A verification unit (106) classifies the environmental data into regions and time intervals to generate verification vectors (108). A learning unit (110) adjusts an environmental prediction model based on verification vectors when variance exceeds a preset threshold. The system also features a distribution module (112) that transmits the updated prediction model to the monitoring devices for real-time environmental analysis.

Specification

Description:Field of the Invention


The present disclosure relates to environmental monitoring systems. Particularly, the present disclosure relates to real-time AI model verification, correction, and distribution across edge devices using vector space analysis for enhanced environmental prediction and monitoring accuracy.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Environmental monitoring systems are extensively utilized to collect and analyze data related to environmental conditions such as temperature, humidity, and pollution levels. Such systems commonly integrate various sensors and monitoring devices to capture relevant environmental data. The captured data is processed to generate meaningful insights that are crucial for decision-making in various sectors, including agriculture, industrial operations, and public safety. While environmental monitoring systems offer significant advantages, existing systems exhibit several limitations, particularly concerning the processing, verification, and transmission of environmental data.
Conventionally known environmental monitoring systems rely on data acquisition from monitoring devices and sensors, followed by analysis to detect any variations or changes in environmental conditions. However, such systems frequently encounter challenges in managing large amounts of data collected from multiple sources. Environmental data tends to vary based on several factors such as geographical region, time of day, and environmental conditions. Such systems face difficulties in accurately classifying data according to such variables, resulting in reduced effectiveness of environmental predictions.
An example of a conventional system includes basic sensor-based data collection systems, where sensors are used to gather data from various environmental sources. Such systems lack the capability to classify the data based on time intervals or regions. Consequently, such systems may not detect localized or time-sensitive changes in environmental conditions, leading to inaccurate analysis and delayed decision-making. Furthermore, such systems are often unable to adjust prediction models based on variations in data, resulting in predictions that may not reflect real-time environmental changes.
Another example of a conventional system involves verification units employed to process environmental data. Such systems often utilize fixed models that fail to account for fluctuating environmental factors. The reliance on static models can lead to a significant variance between predicted and actual environmental conditions. In many cases, such variance exceeds acceptable thresholds, making the predictions unreliable for practical applications. Additionally, the inability to dynamically adjust environmental models based on real-time data further exacerbates the problem of inaccurate environmental predictions.
Moreover, existing systems often lack an efficient mechanism to transmit updated prediction models to monitoring devices. In some instances, the transmission of data or models may experience delays, leading to outdated information being utilized for decision-making. Such limitations significantly impair the effectiveness of environmental monitoring, particularly in scenarios where timely and accurate data is critical.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and techniques for processing, verifying, and transmitting environmental data in environmental monitoring systems.
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
The present disclosure relates to environmental monitoring systems. Particularly, the present disclosure relates to real-time AI model verification, correction, and distribution across edge devices using vector space analysis for enhanced environmental prediction and monitoring accuracy.
An objective of the present disclosure is to provide an environmental monitoring system to efficiently capture, verify, and adjust environmental data based on specific criteria and thresholds. The system of the present disclosure aims to enhance environmental predictions and improve real-time monitoring across multiple regions.
In an aspect, the present disclosure provides an environmental monitoring system comprising a data acquisition unit to capture environmental data from a plurality of monitoring devices. A verification unit is arranged in communication with the data acquisition unit, and such a verification unit classifies the environmental data into multiple regions and time intervals to generate verification vectors. A learning unit is disposed in relation to the verification unit and adjusts an environmental prediction model based on verification vectors when the variance exceeds a preset threshold. A distribution unit is interconnected with the learning unit, wherein such a distribution unit transmits the adjusted environmental prediction model to the plurality of monitoring devices within the environmental monitoring system.
Further, the environmental monitoring system enables the capture of air quality data from a plurality of air quality sensors positioned across different regions. Moreover, the verification unit compares environmental data from adjacent regions to detect anomalies. The verification vectors are based on environmental factors, including air pollution levels, temperature variations, and humidity. The learning unit dynamically adjusts the environmental prediction model in real-time as the verification vectors change. Moreover, the distribution unit notifies relevant authorities about abnormal environmental conditions when the variance exceeds a predefined limit.
Additionally, the plurality of monitoring devices in the environmental monitoring system includes traffic flow sensors, energy consumption monitors, and weather stations. Furthermore, the verification unit applies a learning method to analyze trends in environmental data across regions. The data acquisition unit captures historical data, and the verification unit compares the current environmental data against such historical data. Moreover, the learning unit prioritizes updates to monitoring devices in regions where the variance exceeds the threshold more frequently.

Brief Description of the Drawings


The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates an environmental monitoring system (100), in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates sequential diagram of an environmental monitoring system (100), in accordance with the embodiments of the present disclosure.
Detailed Description
The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The present disclosure relates to environmental monitoring systems. Particularly, the present disclosure relates to real-time AI model verification, correction, and distribution across edge devices using vector space analysis for enhanced environmental prediction and monitoring accuracy.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
As used herein, the term "data acquisition unit" refers to a unit that captures environmental data from various monitoring devices within the system. Such a data acquisition unit receives data from monitoring devices distributed across different regions to provide a comprehensive view of environmental conditions. The environmental data may include, but is not limited to, air quality, temperature, humidity, and other measurable factors. The data acquisition unit is responsible for gathering and transmitting such data to other components of the environmental monitoring system for further processing. The unit operates in continuous communication with the plurality of monitoring devices, ensuring that data is captured in real-time or at predefined intervals based on system requirements. The data acquisition unit may capture historical data for comparison purposes, allowing for trend analysis and anomaly detection. The unit operates in coordination with other elements of the system, including verification and learning units, to enhance environmental predictions. The data acquisition unit is integral to the system as it forms the primary data collection layer.
As used herein, the term "verification unit" refers to a unit responsible for classifying environmental data into specific regions and time intervals. Such a verification unit processes the data captured by the data acquisition unit and applies classification techniques to segregate the data based on predefined geographical and temporal parameters. The classification allows the environmental monitoring system to generate verification vectors, which are used to assess the accuracy and consistency of the captured data. The verification unit plays a critical role in detecting anomalies by comparing data from adjacent regions, thereby identifying irregularities or unexpected changes in environmental conditions. Said unit is connected to the data acquisition unit, ensuring continuous flow and verification of data as it is captured. The verification unit uses such verification vectors to provide inputs to the learning unit, ensuring that any significant deviations are detected and processed for further action within the system.
As used herein, the term "learning unit" refers to a unit that dynamically adjusts the environmental prediction model based on the verification vectors received from the verification unit. Such a learning unit operates by comparing the verification vectors against preset thresholds and determining if a variance exists. If the variance exceeds the predefined threshold, the learning unit modifies the environmental prediction model to reflect the updated conditions. The learning unit ensures that the prediction model remains accurate and responsive to changing environmental factors. The learning unit is capable of real-time adjustments, allowing the system to react swiftly to changes in environmental data. By processing data from multiple regions, the learning unit helps to optimize predictions across the entire environmental monitoring system. The adjustments made by the learning unit are essential for maintaining the accuracy and reliability of environmental forecasts and assessments, providing a dynamic and flexible response to environmental shifts.
As used herein, the term "distribution unit" refers to a unit responsible for transmitting the adjusted environmental prediction model to the plurality of monitoring devices within the system. Such a distribution unit operates in communication with the learning unit, ensuring that any adjustments made to the environmental prediction model are conveyed to all relevant monitoring devices in real-time. The distribution unit ensures that updated models are consistently applied across the monitoring devices, allowing the system to react to environmental changes in a coordinated manner. The distribution unit may also notify relevant authorities or external systems if abnormal environmental conditions are detected, particularly when variances exceed critical thresholds. The distribution of the updated prediction models enables monitoring devices to adjust their data collection and reporting processes based on the latest environmental conditions, thus contributing to the system's overall functionality. Said unit operates as the final stage in the system, ensuring the delivery and application of adjustments made to the environmental prediction model.
FIG. 1 illustrates an environmental monitoring system (100), in accordance with the embodiments of the present disclosure. In an embodiment, a data acquisition unit 102 captures environmental data from a plurality of monitoring devices 104 distributed across various regions. Said data acquisition unit 102 is responsible for continuously or periodically collecting real-time data from the monitoring devices 104. The data acquisition unit 102 may include interfaces to receive data from devices such as air quality sensors, temperature sensors, humidity sensors, or other environmental monitoring equipment deployed in different locations. Said data can be transmitted through wired or wireless communication networks, depending on the system design and layout. The data acquisition unit 102 processes incoming signals from said monitoring devices 104, converts them into a standard format, and stores the data temporarily for further processing. Additionally, the data acquisition unit 102 supports data synchronization across multiple regions to ensure consistency in data collection intervals. The data acquisition unit 102 manages the flow of data to subsequent system components, ensuring that each environmental factor is accurately captured from said monitoring devices 104 before being sent for classification and verification. The unit may further support fault detection, alerting the system to any malfunctions in said monitoring devices 104.
In an embodiment, a verification unit 106 is arranged in communication with the data acquisition unit 102, and such a verification unit 106 classifies the environmental data based on geographical regions and time intervals. The verification unit 106 segregates environmental data into predefined regions, enabling localized analysis of environmental conditions. Time intervals may be used to generate temporal data slices for trend analysis or for detecting sudden changes in environmental factors. The verification unit 106 processes the captured data to generate verification vectors 108, which represent the classified environmental data for further use in the system. The verification vectors 108 enable the system to compare environmental data between different regions and detect anomalies or irregularities in data patterns. The verification unit 106 may also compare current environmental data with historical data, providing a mechanism for anomaly detection over time. The verification unit 106 ensures that only relevant and classified data is transmitted to the next system components for further analysis.
In an embodiment, a learning unit 110 is disposed in relation to the verification unit 106 and processes the verification vectors 108 to adjust an environmental prediction model. The learning unit 110 compares the verification vectors 108 with a preset variance threshold and determines whether any changes in environmental data exceed said threshold. When the variance exceeds the threshold, the learning unit 110 updates the environmental prediction model to reflect the latest environmental conditions. The learning unit 110 may incorporate real-time data analysis, allowing dynamic adjustment of the prediction model to account for ongoing changes in environmental data. The learning unit 110 continuously refines the prediction model, ensuring its accuracy and relevance as new data becomes available. The learning unit 110 is also capable of analyzing long-term trends to predict future environmental changes based on historical data. The learning unit 110 works in conjunction with the verification unit 106 to receive updated verification vectors 108 as additional data is captured by the system.
In an embodiment, a distribution unit 112 is interconnected with the learning unit 110 and transmits the adjusted environmental prediction model to the plurality of monitoring devices 104. Said distribution unit 112 communicates the updated prediction model to each monitoring device 104 within the system, ensuring that all devices operate using the latest model data. The distribution unit 112 maintains a network connection with said monitoring devices 104, allowing for the seamless transfer of the prediction model across different regions. Additionally, said distribution unit 112 may transmit alerts or notifications to external entities, such as authorities or environmental management systems, when abnormal environmental conditions are detected. The distribution unit 112 ensures the system remains updated by efficiently disseminating prediction models to all relevant components. This allows said monitoring devices 104 to adapt their data collection parameters based on the latest environmental predictions, enhancing the system's responsiveness to dynamic environmental conditions.
In an embodiment, the data acquisition unit 102 is configured to capture air quality data from a plurality of air quality sensors positioned across different regions. Such sensors may be distributed over large geographical areas, including urban and rural environments, to measure various parameters related to air quality. The air quality sensors are capable of detecting pollutants such as particulate matter (PM2.5, PM10), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and volatile organic compounds (VOCs), among others. The data acquisition unit 102 receives readings from each air quality sensor and processes the data to ensure consistency and accuracy. The data may be collected in real-time or at predefined intervals, depending on the system's requirements. Each air quality sensor may communicate with the data acquisition unit 102 wirelessly or via wired connections, with the unit aggregating the data for further classification and analysis by subsequent components of the system. The data acquisition unit 102 may also account for factors like sensor calibration and environmental interference, adjusting the captured data to provide a reliable representation of air quality across the monitored regions. The captured air quality data is then forwarded to the verification unit 106 for further processing, where it is analyzed and classified according to various environmental parameters.
In an embodiment, the verification unit 106 is further configured to compare environmental data from adjacent regions to detect anomalies. Such comparisons enable the system to identify irregularities that may indicate unusual or unexpected environmental changes. The verification unit 106 processes the environmental data received from the data acquisition unit 102 and applies a comparative analysis between neighboring regions. This analysis may include identifying significant deviations in temperature, air quality, humidity, or other environmental factors. When an anomaly is detected, the system may flag the data for further scrutiny or initiate alerts for potential environmental hazards. The comparison can occur across multiple time intervals, allowing the verification unit 106 to monitor trends over time and detect gradual or sudden shifts in environmental conditions. By detecting anomalies between adjacent regions, the verification unit 106 enhances the system's capability to identify localized environmental issues, such as pollution spikes or temperature fluctuations, which may not be apparent when analyzing each region independently. The verification unit 106 is integral to maintaining system accuracy and identifying anomalies in environmental data.
In an embodiment, the verification vectors 108 are generated based on environmental factors including air pollution levels, temperature variations, and humidity. Such factors are collected from the data acquisition unit 102, which receives data from the plurality of monitoring devices 104 distributed across various regions. The verification vectors 108 provide a structured representation of the environmental data, enabling further analysis and prediction within the system. The verification unit 106 processes the captured environmental data and assigns vector components to represent the different environmental factors. Air pollution levels may include particulate matter concentrations, gas pollutants, and other contaminants, while temperature variations track changes in regional temperature over time. Humidity data reflects the amount of moisture in the air, contributing to an understanding of overall weather patterns. The verification vectors 108 allow the system to handle large volumes of environmental data in a compact and efficient manner. These vectors are used by the learning unit 110 to adjust the environmental prediction model when discrepancies in the data are detected.
In an embodiment, the learning unit 110 is configured to dynamically adjust the environmental prediction model in real-time as the verification vectors 108 change. The learning unit 110 receives the verification vectors 108 from the verification unit 106, continuously monitoring the changes in environmental data from multiple regions. As the verification vectors 108 fluctuate, the learning unit 110 identifies patterns, trends, or anomalies that may impact the accuracy of the environmental prediction model. When significant changes occur, the learning unit 110 dynamically adjusts the prediction model to account for the new data, ensuring that the model remains current and reliable. The adjustments made by the learning unit 110 may include recalibrating the weight of different environmental factors, such as placing more emphasis on areas where pollution levels are rising or temperature variations are abnormal. Real-time adjustments ensure that the prediction model reflects the most up-to-date environmental conditions, allowing the system to provide accurate forecasts and alerts.
In an embodiment, the distribution unit 112 is further configured to notify relevant authorities about abnormal environmental conditions when said variance exceeds a predefined limit. Said distribution unit 112 is responsible for monitoring the outputs of the learning unit 110, which detects significant variances in environmental data that may indicate potential hazards. When such variances exceed the preset threshold, the distribution unit 112 triggers a notification process, contacting relevant authorities or external systems to alert them about the detected abnormal conditions. The notification may be delivered through various communication channels, including SMS, email, or automated system alerts. The distribution unit 112 ensures that authorities are promptly informed of environmental anomalies, enabling them to take appropriate action to mitigate risks, such as issuing public warnings or deploying environmental response teams. The distribution unit 112 is essential in enabling the system to operate as a comprehensive monitoring tool, integrating both data analysis and real-world action.
In an embodiment, the plurality of monitoring devices 104 comprises traffic flow sensors, energy consumption monitors, and weather stations. These devices are distributed across different regions to capture a broad range of environmental and infrastructural data. Traffic flow sensors monitor vehicle movement and density on roads, providing data that may correlate with air pollution levels or changes in temperature caused by human activity. Energy consumption monitors track the usage of electrical power in various regions, potentially indicating environmental strain or increased emissions from energy production facilities. Weather stations capture a wide array of atmospheric data, including temperature, humidity, precipitation, and wind speed, all of which contribute to the overall environmental data collected by the system. The plurality of monitoring devices 104 operates as an interconnected network, providing the data acquisition unit 102 with comprehensive and region-specific environmental information. Each type of device serves a unique purpose, contributing to the system's ability to monitor and analyze environmental factors.
In an embodiment, the verification unit 106 applies a learning method to analyze trends in environmental data across said regions. Said learning method enables the verification unit 106 to assess environmental changes over time and identify patterns that may not be immediately apparent through basic data analysis. The verification unit 106 processes data collected from the plurality of monitoring devices 104 and applies the learning method to detect recurring trends, anomalies, or gradual environmental shifts. Such trends may relate to seasonal changes in air pollution, long-term temperature increases, or fluctuations in humidity levels. By analyzing the data in this manner, the verification unit 106 can provide valuable insights into the environmental conditions of each monitored region, contributing to the accuracy and reliability of the environmental prediction model. The learning method enhances the system's ability to recognize long-term environmental changes that may require adjustments to the prediction model or external interventions.
In an embodiment, the data acquisition unit 102 is configured to capture historical data, and the verification unit 106 is configured to compare the current environmental data against said historical data. The data acquisition unit 102 not only captures real-time data but also stores historical data for later analysis. Historical data includes past readings of air quality, temperature, humidity, and other environmental factors collected over time. By comparing current environmental data with historical data, the verification unit 106 can identify deviations from long-term trends, allowing for more accurate detection of anomalies or changes in environmental conditions. Such comparisons enable the system to account for cyclical patterns, seasonal variations, or other natural phenomena that may affect the environmental data. The verification unit 106 uses historical data as a benchmark, enabling the system to detect outlier events, such as sudden pollution spikes or abnormal temperature shifts, which may not align with established trends.
In an embodiment, the learning unit 110 is configured to prioritize updates to monitoring devices 104 in regions where the variance exceeds the threshold more frequently. The learning unit 110 continuously monitors verification vectors 108 to determine regions where environmental data shows significant variances. When variances exceed a certain threshold more frequently, the learning unit 110 prioritizes updates to the monitoring devices 104 in those regions to ensure accurate and up-to-date data collection. This prioritization process allows the system to focus on areas where environmental conditions are changing rapidly or unpredictably, ensuring that the data acquisition unit 102 receives the most current information from the monitoring devices 104 in those regions. The learning unit 110 may adjust the data collection intervals or parameters for such monitoring devices 104 to provide more granular data in areas with frequent variances, allowing the system to better predict environmental trends and respond to emerging conditions.
The present invention discloses an advanced environmental monitoring system (100) that leverages real-time AI model verification, correction, and distribution across edge devices using vector space analysis. The system includes a data acquisition unit (102) responsible for collecting environmental data from a wide array of monitoring devices (104) located across diverse regions. This data is processed by a verification unit (106), which classifies it into specific regions and time intervals, generating verification vectors (108). These vectors allow for precise regional analysis, identifying environmental variances that could affect predictive accuracy. The learning unit (110) is configured to compare these vectors against preset thresholds, and when variances are detected, the system automatically adjusts the environmental prediction model. This dynamic learning process ensures that predictions remain accurate even in fluctuating environmental conditions, such as changes in temperature, humidity, or pollution levels. Once adjustments are made, the distribution module (112) transmits the updated model back to the network of monitoring devices (104). The system ensures that each edge device within the environmental monitoring framework receives real-time updates, allowing for continuous environmental data analysis and prediction at the device level. By using AI-driven vector space analysis, the system enhances the precision and reliability of environmental monitoring, making it suitable for applications in urban planning, agriculture, disaster management, and climate research. This intelligent model adjustment mechanism mitigates errors and ensures that environmental predictions are accurate and responsive to changing conditions, significantly improving decision-making processes in real-time monitoring environments.The present invention discloses an advanced environmental monitoring system (100) that leverages real-time AI model verification, correction, and distribution across edge devices using vector space analysis. The system includes a data acquisition unit (102) responsible for collecting environmental data from a wide array of monitoring devices (104) located across diverse regions. This data is processed by a verification unit (106), which classifies it into specific regions and time intervals, generating verification vectors (108). These vectors allow for precise regional analysis, identifying environmental variances that could affect predictive accuracy. The learning unit (110) is configured to compare these vectors against preset thresholds, and when variances are detected, the system automatically adjusts the environmental prediction model. This dynamic learning process ensures that predictions remain accurate even in fluctuating environmental conditions, such as changes in temperature, humidity, or pollution levels. Once adjustments are made, the distribution module (112) transmits the updated model back to the network of monitoring devices (104). The system ensures that each edge device within the environmental monitoring framework receives real-time updates, allowing for continuous environmental data analysis and prediction at the device level. By using AI-driven vector space analysis, the system enhances the precision and reliability of environmental monitoring, making it suitable for applications in urban planning, agriculture, disaster management, and climate research. This intelligent model adjustment mechanism mitigates errors and ensures that environmental predictions are accurate and responsive to changing conditions, significantly improving decision-making processes in real-time monitoring environments.
FIG. 2 illustrates sequential diagram of an environmental monitoring system (100), in accordance with the embodiments of the present disclosure. The sequential diagram illustrates the workflow of an environmental monitoring system (100), showing the interactions between the components. The monitoring devices (104) first capture environmental data, which is then sent to the data acquisition unit (102). The data acquisition unit processes and forwards this data to the verification unit (106), which classifies it into regions and time intervals. Based on this classification, the verification unit generates verification vectors (108). These vectors are subsequently sent to the learning unit (110), where the environmental prediction model is adjusted if the variance exceeds a preset threshold. The updated prediction model is then transmitted to the distribution unit (112). Finally, the distribution unit transmits the adjusted environmental prediction model back to the monitoring devices (104), ensuring that they are functioning with the latest environmental data predictions. This process outlines how the system operates in a continuous feedback loop, facilitating dynamic updates and improving environmental monitoring across multiple regions.
In an embodiment, the data acquisition unit 102 is configured to capture environmental data from a plurality of monitoring devices 104. Such configuration allows real-time or interval-based data collection from diverse environmental monitoring sources positioned across various regions. Each monitoring device 104 may include sensors designed to measure air quality, temperature, humidity, and other environmental parameters. The data acquisition unit 102 serves as the primary interface between the monitoring devices 104 and the environmental monitoring system 100, providing a centralized point for receiving raw environmental data. By gathering data from a plurality of sources, the data acquisition unit 102 enables the system 100 to form a comprehensive view of environmental conditions across different geographical areas. Additionally, said unit 102 supports communication with both wired and wireless monitoring devices 104, facilitating seamless integration across a variety of environments. The capability of aggregating and synchronizing data from multiple sources aids in ensuring that the environmental data is accurate, timely, and representative of the conditions in each monitored region.
In an embodiment, the verification unit 106 is arranged in communication with the data acquisition unit 102 and is configured to classify the environmental data into multiple regions and time intervals. The verification unit 106 receives data from the data acquisition unit 102 and processes it by dividing it into distinct regional categories and predefined time intervals. Such classification allows the system to handle large datasets more efficiently by focusing on specific regions and times, enabling localized environmental analysis. By organizing data geographically and temporally, the verification unit 106 facilitates a more structured and focused approach to analyzing environmental conditions. Further, the classification into regions helps identify patterns and correlations within each area, while the time interval aspect allows for tracking changes over time. This structure also supports easier detection of trends, enabling the system 100 to pinpoint when and where significant environmental shifts occur. The verification unit 106 thereby enhances the system's ability to manage environmental data at both the regional and temporal levels.
In an embodiment, the learning unit 110 is disposed in relation to the verification unit 106 and is configured to adjust the environmental prediction model based on the verification vectors 108 when variance exceeds a preset threshold. The learning unit 110 continuously monitors the verification vectors 108 generated by the verification unit 106, analyzing them against the environmental prediction model in place. When the learning unit 110 detects that the variance in the verification vectors 108 surpasses a specified threshold, it initiates adjustments to the prediction model, allowing the system 100 to refine its predictive capabilities. Such adjustments ensure that the prediction model stays relevant and accurate in response to changing environmental conditions. By recalibrating the model based on real-time or historical data patterns, the learning unit 110 enhances the system's ability to predict future environmental outcomes. This dynamic updating process allows the system to maintain a high degree of accuracy in its predictions, improving its overall responsiveness to environmental shifts.
In an embodiment, the distribution unit 112 is interconnected with the learning unit 110 and is configured to transmit the adjusted environmental prediction model to the plurality of monitoring devices 104 within the environmental monitoring system 100. The distribution unit 112 ensures that the updates made to the environmental prediction model by the learning unit 110 are disseminated to all monitoring devices 104 operating in the system. The communication between the distribution unit 112 and the monitoring devices 104 allows the entire system to function with the most current environmental predictions. The distribution unit 112 may transmit data across both wired and wireless networks, enabling seamless distribution of updated models across varied geographical locations and devices. By providing up-to-date model data to all monitoring devices 104, the distribution unit 112 supports the system's adaptive functionality, allowing for continuous improvement in environmental monitoring and data collection strategies. The unit plays a crucial role in ensuring consistency and coherence across the entire system.
In an embodiment, the data acquisition unit 102 is configured to capture air quality data from a plurality of air quality sensors positioned across different regions. These sensors are strategically placed in both urban and rural areas to monitor key air quality parameters such as particulate matter (PM2.5, PM10), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). By capturing data from multiple air quality sensors, the data acquisition unit 102 provides a detailed and accurate representation of the air quality across a wide range of environments. Said unit 102 collects and processes data continuously or at defined intervals, depending on the system's settings, ensuring that the system 100 has up-to-date information on current air quality conditions. Furthermore, the data acquisition unit 102 supports real-time data transmission from these air quality sensors, enabling immediate analysis and integration wit












I/We Claims


1. An environmental monitoring system (100) comprising:
a data acquisition unit (102) configured to capture environmental data from a plurality of monitoring devices (104);
a verification unit (106) arranged in communication with said data acquisition unit (102), said verification unit (106) configured to classify said environmental data into multiple regions and time intervals to generate verification vectors (108);
a learning unit (110) disposed in relation to said verification unit (106), said learning unit (110) configured to adjust an environmental prediction model based on said verification vectors (108) when variance exceeds a preset threshold; and
a distribution module (112) interconnected with said learning unit (110), said distribution module (112) configured to transmit the adjusted environmental prediction model to said plurality of monitoring devices (104) within said environmental monitoring system (100).
2. The environmental monitoring system (100) of claim 1, wherein said data acquisition unit (102) is configured to capture air quality data from a plurality of air quality sensors positioned across different regions.
3. The environmental monitoring system (100) of claim 1, wherein said verification unit (106) is further configured to compare the environmental data from adjacent regions to detect anomalies.
4. The environmental monitoring system (100) of claim 1, wherein said verification vectors (108) are generated based on environmental factors including air pollution levels, temperature variations, and humidity.
5. The environmental monitoring system (100) of claim 1, wherein said learning unit (110) is configured to dynamically adjust the environmental prediction model in real-time as the verification vectors (108) change.
6. The environmental monitoring system (100) of claim 1, wherein said distribution module (112) is further configured to notify relevant authorities about abnormal environmental conditions when said variance exceeds a predefined limit.
7. The environmental monitoring system (100) of claim 1, wherein said plurality of monitoring devices (104) comprises traffic flow sensors, energy consumption monitors, and weather stations.
8. The environmental monitoring system (100) of claim 1, wherein said verification unit (106) applies a machine learning algorithm to analyze trends in environmental data across said regions.
9. The environmental monitoring system (100) of claim 1, wherein said data acquisition unit (102) is configured to capture historical data, and said verification unit (106) is configured to compare the current environmental data against said historical data.
10. The environmental monitoring system (100) of claim 1, wherein said learning unit (110) is configured to prioritize updates to monitoring devices (104) in regions where the variance exceeds the threshold more frequently.




The present disclosure provides a real-time AI model verification, correction, and distribution system across edge devices using vector space analysis, specifically an environmental monitoring system (100). The system includes a data acquisition unit (102) configured to capture environmental data from multiple monitoring devices (104). A verification unit (106) classifies the environmental data into regions and time intervals to generate verification vectors (108). A learning unit (110) adjusts an environmental prediction model based on verification vectors when variance exceeds a preset threshold. The system also features a distribution module (112) that transmits the updated prediction model to the monitoring devices for real-time environmental analysis.
, Claims:I/We Claims


1. An environmental monitoring system (100) comprising:
a data acquisition unit (102) configured to capture environmental data from a plurality of monitoring devices (104);
a verification unit (106) arranged in communication with said data acquisition unit (102), said verification unit (106) configured to classify said environmental data into multiple regions and time intervals to generate verification vectors (108);
a learning unit (110) disposed in relation to said verification unit (106), said learning unit (110) configured to adjust an environmental prediction model based on said verification vectors (108) when variance exceeds a preset threshold; and
a distribution module (112) interconnected with said learning unit (110), said distribution module (112) configured to transmit the adjusted environmental prediction model to said plurality of monitoring devices (104) within said environmental monitoring system (100).
2. The environmental monitoring system (100) of claim 1, wherein said data acquisition unit (102) is configured to capture air quality data from a plurality of air quality sensors positioned across different regions.
3. The environmental monitoring system (100) of claim 1, wherein said verification unit (106) is further configured to compare the environmental data from adjacent regions to detect anomalies.
4. The environmental monitoring system (100) of claim 1, wherein said verification vectors (108) are generated based on environmental factors including air pollution levels, temperature variations, and humidity.
5. The environmental monitoring system (100) of claim 1, wherein said learning unit (110) is configured to dynamically adjust the environmental prediction model in real-time as the verification vectors (108) change.
6. The environmental monitoring system (100) of claim 1, wherein said distribution module (112) is further configured to notify relevant authorities about abnormal environmental conditions when said variance exceeds a predefined limit.
7. The environmental monitoring system (100) of claim 1, wherein said plurality of monitoring devices (104) comprises traffic flow sensors, energy consumption monitors, and weather stations.
8. The environmental monitoring system (100) of claim 1, wherein said verification unit (106) applies a machine learning algorithm to analyze trends in environmental data across said regions.
9. The environmental monitoring system (100) of claim 1, wherein said data acquisition unit (102) is configured to capture historical data, and said verification unit (106) is configured to compare the current environmental data against said historical data.
10. The environmental monitoring system (100) of claim 1, wherein said learning unit (110) is configured to prioritize updates to monitoring devices (104) in regions where the variance exceeds the threshold more frequently.

Documents