EXPLOSIVE DETECTING DEVICE

Abstract

The present invention relates to an AI-based device to detect explosives in chemicals utilizes a multi-sensor array which includes technologies such as gas chromatography, mass spectrometry, electrochemical, and optical sensors for the high-accuracy detection of explosives, including but not limited to TNT, RDX, PETN, and ammonium nitrate. It has an AI engine that processes sensor data using deep learning and pattern recognition models and references an active database of known and emerging explosive chemical signatures. Cross-sensor data fusion allows it to minimize false alarms and increase confidence in detection. This supports a portable and fixed installation that allow for an energy-efficient power management system to optimize battery usage. Ensure adaptability to environmental changes, which supports field operations, drones, vehicle installations, and high-security installations.

Specification

Description:The AI-based explosive chemical detecting device 100 is provides a solution that avails highly advanced sensors and artificial intelligence for improving the security operations of an organization. The device captures real-time detection, analysis, and alerting in bringing together various sensor technologies with AI-driven pattern recognition, which makes it highly effective at detecting known and improvised explosive compounds for prompt detection and prevention.

The device includes a sensor array 101 made specifically with different types of sensors, such as gas chromatography, mass spectrometry, electrochemical, and optical sensors. These multiple sensors are specialized for detecting chemical compounds in trace amounts in the environment. The aggregation of diverse sensor technologies increases the device's chance of detecting a trace with high accuracy, while decreasing the chances of false positives to a minimum, and enhances overall sensitivity towards the smallest traces of explosives, such as TNT, RDX, PETN, and ammonium nitrate.

The AI engine 102 is developed to consume deep learning models and pattern recognition algorithms that process data that flows into it through sensors. This AI engine 102 compares detected chemical signatures against a dynamic database 103 of known and emerging explosive compounds. It upgrades the system by learning from new inputs therefore it recognizes newly discovered and modified explosives, which in the form of improvised explosive devices. This keeps the detection device relevant and effective against an evolving security threat.
The user interface 104 provides an operator with real-time data and alerts, and it displays the chemical signature, concentration levels, probability of explosive detection, and recommended action as based on the detected threat. An explosive detection alerts are simultaneously transmitted through the wireless communication module 105 to centralized security systems, mobile devices and a remote monitoring center. This is real-time alerting allow the security operative to respond quickly and to effectively co-ordinate countermeasures.

The device relies on cross-sensor data fusion technology. This is collecting information from the different sensors in the array. It exploits cross-correlation for improved accuracy since it cross correlates the information across sensors, reducing false positives and increasing the degree of confidence in identification of explosive chemicals. Such fusion allows the system to maintain high sensitivity while diminishing interference from many potential interferes, in the complex environment in which it is deployed.

The device is integrated with power supply 106 and an energy-efficient power management system to optimize battery usage. Depending on environmental conditions and the potential chances of explosive detection, the power management system adjusts activity for sensors and AI processing accordingly. It also manages to preserve power while keeping the device running for a much more extended period, which is useful for field deployments.

This device uses environment sensors that measure the external conditions, like temperatures, humidity, and pressures. Such sensors allow the device to change the sensitivity settings of any changing environmental conditions, and with that, the device is succeed in becoming adaptive and responsive to any operating environment.

Sensor Array 101:
The device equipped with an array of multi-sensors that are able to detect the widest possible range of explosives. These sensors are:
- Gas Chromatography (GC): This function separates and identifies volatile chemical compounds from air samples. It is suitable for detecting chemicals contained in trace amounts.
- Mass Spectrometry (MS): It calculates the mass-to-charge ratio of ions. It is highly sensitive toward detecting even the smallest concentrations of explosive material.
- Electrochemical Sensors: Detect electrochemical reactions, ideal for liquid and vapour explosives.
- Optical Sensors: Detect specific wavelengths of light emitted or absorbed by chemical compounds, useful for identifying explosives that are optically different from others.
These sensors together offer full-range detection capability, overcoming traditional systems limitations that operate based on a single kind of sensor.

AI Engine 102:
An AI engine 102 is a neural network-type structure used in processing gathered and collected data from the sensor array. The AI engine 102 is able to do the following:
-Data Fusion: integrated with sensor inputs to cross-check chemical signatures to avoid false positives
Pattern recognition: detection of patterns as studied through deep learning models, bringing up known and unknown explosive chemicals that turn out to have recognizable patterns.
- Continuous learning with new data: The AI engine 102 is updated with new data that changes its knowledge base in regard to emerging threats and new types of explosives.

Dynamic Database 103:
The device carries a dynamic database 103 of known explosive material on which the AI engine 102 makes comparisons during detection. The database has all chemical signatures for:
- Traditional explosive substances, such as TNT, RDX, and PETN Homemade explosives with non-traditional chemical compositions. New risks arising from new or changed chemical agents.

User Interface 104 and Alarms
The user interface 104 gives an instant response to the operator. The interface includes:
Display Screen: Status information for the device, such as identified compounds, confidence level, and steps to take.
Audio and Visual Alarms: Once an explosive agent is identified, it triggers immediate alarms throughout the device which alert security officers.
-Control Interface: These options are for altering the sensitivity levels, starting/stop detection and viewing logs.

Wireless Communication Module 105
The device is equipped with a wireless communication module 105. This module allows for:
-Wi-Fi and Bluetooth connectivity: This includes a real-time alert and data from detection to remote systems for real-time monitoring by central security teams in one place.
-GPS Integration: The location of an event that is detected by GPS integration, which is pretty critical to mobile and field-based operations.

Portability and Scalability Design
This makes the device highly portable, capable of handheld use and being used as part of much larger system integration, such as on a drone and in a vehicle. This design is adapted to be used as a fixed installation at airports, government buildings, and checkpoints.

Power Supply 106 and Energy Management
A recharged lithium-ion battery allows to be deployed for extended field durations. The device is further integrated with a power management system that adjusts its energy usage based on the sensor's activity and the data processing demands.

2. Device Operating Process
The following steps depict how the AI-based explosives chemical detecting device 100 operate:

Step 1: System Initialization
- When powered up, the device performs a self-test-check of all sensors and the AI engine 102.
- After initialization, sensors then begin sampling the surrounding environment for traces of chemicals.

Step 2: Sample Accumulation
The sensor array 101 is continuously taking samples from air and surfaces.
- Different sensors in the array capture different chemical signatures, for example, including VOCs, ions, or wavelengths of light given off by explosive materials.

Step 3: Data Collection
- The module of data collection converts raw inputs from the sensors to digital data. Such conversions ensure that all signals collected are standardized and normalized for further processing.

Step 4: Data Pre-processing
- The data is cleaned and filtered to get rid of all the 'noise' and information that is irrelevant to the task, such as environmental contaminants.
- The data are normalized by use of algorithms. Subsequently, the AI engine 102 performs uniform analysis on standardized data.

Step 5: Feature Extraction
The device performs feature extraction on pre-processed data. During this process, distinctive chemical signatures distinct to explosive material are found. For instance, mass-to-charge ratio of ions present in a sample in mass spectrometry happens to be one such signature.

Step 6: AI engine Evaluation
It compares the features with known explosive profiles stored in the database because it uses an AI engine 102 to pass through all features.
The AI algorithm is able to perform pattern recognition and anomaly detection, which is identifying various kinds of explosive materials, including altered and chemical compositions.

Step 7: Decision Making
The AI engine 102 is able to detect chemical to matches the explosive profiles. The AI engine 102 declares the chemical explosive if any explosive is detected.
- The device immediately gives an alarm indication.
- If there is no match, the device continues to search for the environment.

Step 8: Alarms and Threat Activation
- Once an explosive has been sensed, the device sends audio and visual alarm to the user.
- The wireless communication module 105 also transmits it to the remote monitoring unit which enables the security personnel to act in time.

Step 9: Data Logging
- The detection events, including chemical signatures, timestamps, and the results of the decision, are logged in case of further analysis. This data is used for audit trails and for developing the AI engine 102.

3. Implementation Plans
Fixed Installations:
It is positioned at fixed check points in very secure environments, such as airports, government buildings, and military bases. Here, the device is always scanning the surroundings since it does not need a person to scan his/her body.

Handheld Portable Device
Being portable and designed to be carried around by security officers performing their operations in the field, this device is applied to scan people, cars, and luggage and make real-time detection. This makes it applicable for public events, border checkpoints, and military patrols.

Drone or Vehicle Integration
- The device is installed on drones or unmanned vehicles for wide area surveillance. That is useful in dangerous places and places that are inaccessible and even impossible for human operators.

Method of performing the invention:
Step 1: Optimized Sensor Configuration
The sensor array is designed for environments in which explosives are likely to be found based on the kind of chemical signatures one is most likely to encounter, such as:
Gas Chromatography (GC) for airborne volatile organic compounds (VOCs).
Mass Spectrometry (MS), high sensitivity detection of ionized particles in trace amounts.
Electrochemical sensors, liquid and vapour-based explosives like nitroglycerin.
Unique light absorption explosives for all sensors in a critical environment such as an airport and military base must be active and cross-referenced through the AI engine 102 is covered.

Step 2: Real-Time AI Processing
The AI engine 102 is set up and trained on a large, extensive database of known explosive chemical signatures. As real-time sensor data is collected, the AI engine 102 uses:
- It uses deep learning that identifies the chemical signature of explosives from known spectra through the recognition of patterns.
- Uses anomaly detection algorithms to identify unknown or modified forms of explosive compounds.
This AI process is able to constantly update through the detection of false positives and negatives, as thresholds of detection change dynamically as well.

Step 3: Alert and Remote Monitoring
The alert system of this device is activated upon detecting the presence of the explosive with a high degree of confidence, that is, with more than 90% probability. The alerts include:
- A light alert through LEDs or a screen notification
- An audio alert in the form of alarms or voice prompts
- Remote alerts sent wirelessly to a central monitoring where real-time information is accessed by security personnel.
The user interface 104 assists the operators in observing detection history, updating the sensitivity settings, and getting changes that result from the learning process of the AI engine 102.

Step 4: Portable and Fixed Deployments
The device is carried by personnel in public settings and high traffic areas for mobile use. Drones and vehicles carrying the device perform wide area scans and locations that are out of reach. In fixed installation, such as security checkpoint, the device is able to continuously scan people and objects going through the checkpoint.
, Claims:1. An AI-based explosives chemical detecting device 100 comprising:
a sensor array 101 for detecting explosive compounds, where the sensor array includes including gas chromatography, mass spectrometry, electrochemical, and optical sensors;
an AI engine 102 used to process sensor data using deep learning and pattern recognition models;
a dynamic database 103 of known and emerging explosive chemical signatures, wherein the AI engine 102 continuously updates detection algorithms;
user interface 104 for providing real-time feedback and alerts;
wireless communication module 105 for transmitting detection data and alerts to remote monitoring unit;
power supply 106 for portable and fixed installation use; and
wherein the device performs real-time detection, analysis, and alerting of explosive compounds by cross-referencing sensor data with the database of known chemical profiles.

2. A method for detecting explosives using an AI-based explosives chemical detecting device as claimed in claim 1, comprising:
- Collecting samples from the environment using a multi-sensor array 101;
- Pre-processing the data by filtering and normalizing sensor inputs;
- Extracting chemical signatures and passing them to an AI engine 102;
- Comparing the extracted features with known explosive profiles stored in a dynamic database103;
- Triggering alerts when an explosive is detected;
- Transmitting alerts wirelessly to remote monitoring unit; and
- Wherein the method provides real-time explosive detection through AI-driven analysis and multi-sensor data fusion.

3. The AI-based explosives chemical detecting device as claimed in claim 1, wherein the AI engine 102 employs a deep learning model trained on large datasets of explosive chemical compounds, enabling the detection of both known and explosives, including improvised explosive devices (IEDs), by using pattern recognition and anomaly detection.

4. The device as claimed in claim 1, further comprising a cross-sensor data fusion module that combines data from multiple sensors, including gas chromatography, mass spectrometry, electrochemical, and optical sensors, to enhance detection accuracy by reducing false positives and increasing the confidence level of explosive identification.
5. The device as claimed in claim 1, wherein the sensor array is capable of detecting trace amounts of explosive chemicals, including TNT, RDX, PETN, and ammonium nitrate, at concentrations as low as parts per billion (ppb) for ensuring sensitivity to minimal explosive residues in the air or on surfaces.

6. The AI-based explosives chemical detecting device as claimed in claim 1, wherein the user interface 104 provides real-time data including the detected chemical's signature, concentration levels, probability of being an explosive, and recommended actions for the operator, based on the severity of the threat detected.

7. The device as claimed in claim 1, wherein the wireless communication module 105 is configured to send real-time alerts to centralized security systems, individual mobile devices, and remote monitoring centers, enabling remote threat detection and coordination of security responses.

8. The device as claimed in claim 1, wherein the AI engine 102 continuously learns from new detection data, updating its dynamic database 103 of explosive signatures with newly discovered or modified explosive compounds, ensuring that the system remains effective against evolving threats.

9. The AI-based explosives chemical detecting device as claimed in claim 1, wherein portable design allows for deployment in field operations, including handheld use by security personnel, integration into drones or vehicles, and fixed installations at high-security locations such as airports, border checkpoints, and military facilities.

10. The device as claimed in claim 1, further comprising an energy-efficient power management system that optimizes battery usage by adjusting sensor activity and AI processing based on environmental conditions and the probability of explosive detection.

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