Application of PM2.5, PM10 and inert gas in unmanned patrol car

Abstract

The invention discloses an application system of PM2.5, PM10 and inert gas on an unmanned patrol car, which comprises a vehicle-mounted platform on which a PM2.5 sensor, a PM10 sensor and an inert gas sensor group are mounted, a data processing unit performs data preprocessing by adopting Kalman filtering and median filtering algorithms, performs fusion correction on monitoring data through environmental parameters, and transmits the monitoring data to the unmanned patrol car. And pollution source identification is realized by using a neural network algorithm. The system integrates a laser radar and a camera to realize autonomous navigation, and a dynamic window method is combined with an artificial potential field method to perform path planning and obstacle avoidance. The communication module supports 4G and 5G networks and has the functions of data encryption transmission, network disconnection reconnection and graded alarm; according to the invention, synchronous and accurate monitoring of PM2.5, PM10 and inert gas is realized through a multi-sensor fusion technology, and the comprehensiveness, real-time performance and accuracy of monitoring are significantly improved. Compared with the prior art, the system has higher environmental adaptability, intelligent degree and cost effectiveness.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and intelligent robot technology, and in particular to the application of PM2.5, PM10 and inert gases in unmanned patrol vehicles. Background Technology

[0002] With the acceleration of industrialization and the improvement of urbanization, air pollution has become increasingly prominent. Particulate matter pollution such as PM2.5 and PM10, as well as emissions of various inert gases, pose a serious threat to public health and the ecological environment. Environmental monitoring, as a crucial foundation for pollution prevention and control, directly impacts the scientific validity and effectiveness of environmental management. While traditional fixed monitoring stations can provide continuous monitoring data, they suffer from limitations such as limited coverage, high deployment costs, and difficulty in responding to sudden environmental events. In recent years, with the rapid development of artificial intelligence, the Internet of Things, sensor technology, and mobile robotics, mobile platform-based environmental monitoring technology has gradually become a research hotspot, providing a new technological path to address the shortcomings of traditional monitoring methods.

[0003] Existing mobile environmental monitoring technologies suffer from the following main technical problems: First, the problem of monitoring blind spots manifests as numerous gaps between fixed monitoring points, making full coverage impossible, especially in complex environments such as industrial parks and urban fringe areas where accurate pollution distribution information is difficult to obtain. Second, the problem of data accuracy stems from insufficient anti-interference capabilities of existing monitoring equipment, significant influence of environmental factors on measurement results, and a lack of effective data fusion mechanisms between sensors during multi-parameter monitoring, resulting in monitoring accuracy failing to meet environmental management requirements. Third, the problem of equipment adaptability is reflected in the insufficient reliability of existing mobile monitoring equipment under complex terrain and severe weather conditions, limited autonomous navigation capabilities, and difficulty in adapting to diverse monitoring scenarios. Fourth, the problem of limited functionality is manifested in the fact that most existing systems only monitor specific pollutants, lacking multi-parameter simultaneous monitoring capabilities and failing to comprehensively reflect environmental quality. Finally, the problem of maintenance costs arises from the complex structure and low integration of existing equipment, resulting in high operation and maintenance costs that limit large-scale application.

[0004] To address the aforementioned technical challenges, the environmental monitoring field urgently needs a highly integrated, interference-resistant, and low-power multi-parameter monitoring system. This system should be able to achieve simultaneous and accurate monitoring of PM2.5, PM10, and inert gases by reconstructing the hardware and software architecture of unmanned vehicles, while possessing excellent environmental adaptability and autonomous navigation capabilities. It should improve data accuracy through multi-sensor fusion technology, utilize artificial intelligence algorithms for automatic identification and location of pollution sources, and achieve real-time monitoring and emergency response through remote monitoring and data transmission functions. This technical solution not only fills the gaps in traditional fixed monitoring but also significantly reduces monitoring costs, improves the efficiency and scientific rigor of environmental supervision, and provides more comprehensive and reliable technical support for environmental protection and public health. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of monitoring blind spots, data accuracy, equipment adaptability, single function, and maintenance cost in existing environmental monitoring technologies, and to provide a highly integrated, anti-interference, low-power multi-parameter environmental monitoring system that can achieve synchronous and accurate monitoring of PM2.5, PM10 and inert gases.

[0006] To achieve the above objectives, the technical solution adopted by this invention is an application system for PM2.5, PM10, and inert gases in an unmanned patrol vehicle. This system includes:

[0007] The vehicle platform is equipped with a PM2.5 sensor, a PM10 sensor, and an inert gas sensor group; a data processing unit electrically connected to the sensor group; a communication module electrically connected to the data processing unit; a lidar mounted on the vehicle platform; a camera mounted on the vehicle platform; an autonomous navigation controller electrically connected to the lidar and the camera respectively; and a power supply system that supplies power to the above components; wherein the PM2.5 sensor and the PM10 sensor are both laser scattering sensors, and the inert gas sensor group includes an electrochemical sensor and an infrared gas sensor.

[0008] Furthermore, the data processing unit includes a data preprocessing module, a data fusion correction module, and a pollution source identification module. The data preprocessing module uses Kalman filtering and median filtering algorithms to process the raw sensor data. The data fusion correction module combines environmental parameters such as temperature, humidity, and air pressure to correct the monitoring data. The pollution source identification module uses a neural network algorithm to analyze the data and identify the type and location of pollution sources.

[0009] Furthermore, the sensor array is installed in a matrix layout at the front of the vehicle platform, and the PM2.5 and PM10 sensors are equipped with dust covers and temperature compensation devices.

[0010] Furthermore, the autonomous navigation controller uses a dynamic window method combined with an artificial potential field method to achieve path planning and obstacle avoidance. The lidar provides 360-degree environmental scanning to generate a three-dimensional point cloud map, and the camera identifies environmental elements through image recognition algorithms.

[0011] Furthermore, the communication module supports 4G and 5G network communication, uses encrypted protocols for data transmission, and has the functions of reconnecting after network disconnection and data caching.

[0012] Furthermore, the system also includes an alarm module, which is electrically connected to the data processing unit. It automatically triggers alarms based on preset thresholds, supports SMS, email, and mobile application push notifications, and has a tiered alarm function.

[0013] Furthermore, the system also includes a sensor calibration module, which automatically checks the sensor's zero point and range before each task is executed, calibrates the inert gas sensor using a built-in standard gas, and records the known air concentrations of PM2.5 and PM10 as reference values.

[0014] Furthermore, the data processing unit transmits the processed monitoring data to the remote monitoring center at 1.2-second intervals, and the remote monitoring center analyzes the data trends in real time and marks abnormal areas on the electronic map.

[0015] The beneficial effects of this invention are:

[0016] Through multi-sensor fusion technology, the system can simultaneously monitor PM2.5, PM10, and various inert gases, achieving synchronous monitoring of multiple parameters and significantly improving the comprehensiveness and practicality of monitoring. Employing high-precision laser scattering sensors and electrochemical infrared gas sensors, combined with multi-source data fusion technology and environmental parameter correction algorithms, the accuracy and reliability of monitoring data are effectively improved. An autonomous navigation system integrating lidar and cameras enables the unmanned patrol vehicle to drive safely and autonomously in complex environments, significantly enhancing the equipment's environmental adaptability and operational safety. A neural network-based intelligent data processing algorithm can analyze monitoring data in real time, automatically identifying pollution source types and locations, greatly improving the intelligence level and response speed of environmental monitoring. Real-time data transmission and remote monitoring are achieved through 4G and 5G networks, supporting remote control and tiered alarm functions, significantly improving monitoring efficiency and emergency response capabilities. The modular system design and automatic calibration function reduce equipment maintenance costs and improve system reliability and economy. Compared with existing technologies, this invention has significant advantages in monitoring accuracy, environmental adaptability, intelligence level, and cost-effectiveness, and can be widely applied in environmental monitoring, industrial safety, and emergency management, possessing significant social and economic benefits. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall structure of the unmanned patrol vehicle system of the present invention;

[0019] Figure 2 This is a schematic diagram of the sensor layout of the present invention;

[0020] Figure 3 This is a block diagram of the system functional modules of the present invention;

[0021] Figure 4 This is a schematic diagram of the system workflow of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0023] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0024] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0025] See Figures 1 to 4 As shown

[0026] This invention achieves an organic combination of multi-sensor fusion and edge computing through modular hardware design. The entire system adopts a layered architecture, comprising four main layers: a perception layer, a processing layer, a communication layer, and an application layer. The perception layer integrates a high-precision laser scattering sensor for PM2.5 and PM10 detection, an electrochemical sensor and an infrared gas sensor for inert gas monitoring, a lidar for environmental positioning, and a camera for real-time video monitoring. The processing layer is equipped with an edge computing unit responsible for data preprocessing, fusion and correction, and AI algorithm analysis. The communication layer enables remote data transmission via 4G / 5G networks, and the application layer provides a remote monitoring interface and alarm functions.

[0027] The core of the vehicle-mounted system lies in the precise integration of multiple sensors. PM2.5 and PM10 monitoring utilizes sensors based on the laser scattering principle. A laser beam illuminates particulate matter in the air, and the concentration of particulate matter is calculated based on the intensity of the scattered light. These sensors are mounted at the front of the unmanned patrol vehicle, equipped with dust covers and temperature compensation devices to ensure measurement accuracy under various environmental conditions. The inert gas sensor array includes both electrochemical and infrared sensors. The electrochemical sensors primarily detect gases such as oxygen and carbon monoxide, while the infrared sensors detect molecular gases such as carbon dioxide and methane. The sensor array employs a matrix layout, using multi-point sampling to improve the representativeness and accuracy of the detection.

[0028] The intelligent data processing system comprises three key components. The data preprocessing module filters and denoises the collected raw data. The system employs a Kalman filter to eliminate sensor noise, median filtering to remove outliers, and a moving average algorithm to smooth data fluctuations. The preprocessed data undergoes standardization to ensure comparability between data from different sensors. The data fusion and correction module compensates for monitoring data by incorporating environmental parameters. The system collects environmental parameters such as temperature, humidity, and air pressure in real time. An established correction model compensates for PM2.5, PM10, and inert gas data, eliminating the influence of environmental factors on measurement results. The correction algorithm, based on a regression model built from extensive experimental data, effectively improves monitoring accuracy. The pollution source identification module uses machine learning algorithms to analyze data patterns. The system identifies the characteristic fingerprints of different pollution sources through a trained neural network model, combining spatiotemporal information to determine the type and location of the pollution sources. The identification results are automatically marked on an electronic map, providing decision support for environmental management.

[0029] The autonomous navigation system integrates LiDAR and cameras to achieve precise positioning and path planning in complex environments. LiDAR provides 360-degree environmental scanning, generating high-precision 3D point cloud maps for obstacle detection and localization. Cameras provide visual information, using image recognition algorithms to identify environmental elements such as traffic signs and road markings. The obstacle avoidance algorithm employs a dynamic window method combined with an artificial potential field method to achieve real-time path planning and obstacle avoidance. The system updates the environmental model in real time based on LiDAR and camera data, calculating the optimal driving path to ensure the safe operation of the unmanned patrol vehicle in complex environments.

[0030] The remote monitoring system enables real-time data transmission and remote control via 4G / 5G networks. Data transmission employs encryption protocols to ensure information security, supports network reconnection after disconnection, and data caching. The monitoring center can view the location, monitoring data, and operational status of the unmanned patrol vehicles in real time, and supports remote task allocation and route adjustment. The alarm system automatically triggers alarms based on preset thresholds, supporting multiple alarm methods such as SMS, email, and app push notifications. The system has a tiered alarm function, automatically selecting the appropriate alarm level and handling procedure based on the degree of pollution.

[0031] Example 1: Environmental Monitoring Application in Industrial Parks

[0032] An industrial park has deployed unmanned patrol vehicles equipped with the technology of this invention for environmental monitoring. Before each daily mission, the unmanned patrol vehicle first performs a sensor calibration procedure. The system automatically checks the zero point and range of the PM2.5 and PM10 sensors, and calibrates the inert gas sensor using a built-in standard gas to ensure that the measurement accuracy meets the requirements. During the calibration process, the system records the PM2.5 and PM10 values ​​with known air concentrations as baseline values. During the mission execution phase, the unmanned patrol vehicle travels along a preset route. The lidar continuously scans the surrounding environment for precise positioning, and the camera monitors the surrounding scene in real time to identify potential pollution sources. The onboard sensor system collects environmental data once per second, including PM2.5 and PM10 concentrations, as well as the concentrations of inert gases such as oxygen, carbon monoxide, and carbon dioxide. The data processing system compares and corrects the sensor data with the calibration values ​​of fixed monitoring stations, integrates multi-source data through a fusion algorithm, and transmits the processed data to a remote pan-tilt unit every 1.2 seconds. The pan-tilt unit system analyzes the data trends in real time, automatically identifies abnormal areas, and marks them on an electronic map. During a monitoring operation, the system detected an abnormally high PM2.5 concentration and excessive carbon monoxide concentration in the southeast corner of the park. Through AI algorithm analysis, the system determined that there was a source of combustion pollution in the area, immediately triggered an alarm, and pushed detailed information to the management personnel. Subsequent investigation confirmed that there was indeed illegal burning in the area, verifying the effectiveness of the system.

[0033] Example 2: Application of Urban Atmospheric Environment Inspection

[0034] Urban environmental protection departments are using this invention's technology for atmospheric environmental inspections. The unmanned patrol vehicle is equipped with a complete set of monitoring equipment, enabling it to autonomously navigate and continuously monitor complex urban traffic environments. Upon system startup, a routine calibration is performed. The system uses GPS to determine its current location and automatically retrieves environmental baseline data for the area to calibrate the sensors. After calibration, the unmanned patrol vehicle begins its inspection along a smartly planned route, prioritizing key monitoring areas such as schools, hospitals, and residential areas. During monitoring, the system continuously records changes in the concentrations of PM2.5, PM10, and various inert gases. When passing through industrial areas, the system detects increased concentrations of sulfur dioxide and nitrogen oxides, with AI algorithms automatically identifying this as an impact of industrial emissions. Near residential areas, the system detects peak PM2.5 concentrations at specific times, which, combined with time information, is attributed to the impact of morning and evening traffic rush hours. Data is transmitted in real-time to the environmental monitoring center, creating a dynamic urban air quality distribution map. Monitoring personnel can monitor the city's air quality in real time, providing a scientific basis for environmental decision-making. In the three months since its operation, the system has detected 15 pollution anomalies with an accuracy rate of 92%, significantly improving environmental supervision efficiency.

[0035] Example 3: Application of Emergency Environmental Monitoring

[0036] Following a leak at a chemical plant, the emergency management department immediately deployed unmanned patrol vehicles equipped with the technology of this invention for on-site environmental monitoring. Due to the complex environment and inherent safety risks, the remote monitoring capabilities of the unmanned patrol vehicles played a crucial role. During the emergency response phase, the unmanned patrol vehicles quickly completed startup and calibration procedures, establishing a communication connection with the emergency command center via a 4G network. Multiple gas sensors on board the vehicles simultaneously detected toxic and harmful gases and particulate matter pollution, while lidar and camera systems ensured safe operation even in smoky environments. During monitoring, the system detected severely excessive concentrations of volatile organic compounds such as benzene and toluene, along with a significant increase in PM2.5 concentration. Data was updated every 1.2 seconds, allowing the command center to monitor the pollution spread in real time. Based on the monitoring data, emergency personnel promptly adjusted evacuation areas and response plans, effectively controlling the impact of the accident. In this emergency monitoring operation, the unmanned patrol vehicles worked continuously for 8 hours, obtaining over 20,000 valid data points, providing vital data support for accident response and environmental recovery, and fully demonstrating the practical value of this invention in emergency environmental monitoring.

[0037] As can be seen from the above embodiments, this invention, through the organic combination of innovative technologies such as multi-sensor fusion, intelligent data processing, autonomous navigation, and remote monitoring, achieves high-precision, real-time monitoring of harmful gases, PM2.5, PM10, etc., in both conventional and complex environments. It has broad application prospects and significant value in fields such as environmental monitoring, industrial safety, and emergency management.

[0038] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0039] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A system for the application of PM2.5, PM10, and inert gases in an unmanned patrol vehicle, characterized in that... include: The vehicle-mounted platform is equipped with a PM2.5 sensor, a PM10 sensor, and an inert gas sensor array; the data processing unit is electrically connected to the sensor array. A communication module is electrically connected to the data processing unit; a lidar is mounted on the vehicle platform. The camera is mounted on the vehicle platform; An autonomous navigation controller is electrically connected to the lidar and camera respectively; a power supply system supplies power to the above components; wherein the PM2.5 sensor and PM10 sensor are both laser scattering sensors, and the inert gas sensor group includes an electrochemical sensor and an infrared gas sensor.

2. The system according to claim 1, characterized in that: The data processing unit includes a data preprocessing module, a data fusion correction module, and a pollution source identification module. The data preprocessing module uses Kalman filtering and median filtering algorithms to process the raw sensor data. The data fusion correction module combines environmental parameters such as temperature, humidity, and air pressure to correct the monitoring data. The pollution source identification module uses a neural network algorithm to analyze the data and identify the type and location of pollution sources.

3. The system according to claim 1, characterized in that: The sensor array is installed in a matrix layout at the front of the vehicle platform, and the PM2.5 sensor and PM10 sensor are equipped with dust covers and temperature compensation devices.

4. The system according to claim 1, characterized in that: The autonomous navigation controller uses a dynamic window method combined with an artificial potential field method to achieve path planning and obstacle avoidance. The lidar provides 360-degree environmental scanning to generate a three-dimensional point cloud map, and the camera identifies environmental elements through image recognition algorithms.

5. The system according to claim 1, characterized in that: The communication module supports 4G and 5G network communication, uses encrypted protocols for data transmission, and has functions for reconnecting after network disconnection and data caching.

6. The system according to claim 2, characterized in that: The system also includes an alarm module, which is electrically connected to the data processing unit. It automatically triggers alarms based on preset thresholds, supports SMS, email, and mobile application push notifications, and has a tiered alarm function.

7. The system according to claim 1, characterized in that: The system also includes a sensor calibration module, which automatically checks the sensor zero point and range before each task is executed, calibrates the inert gas sensor using a built-in standard gas, and records the PM2.5 and PM10 values ​​of known air concentrations as reference values.

8. The system according to claim 1, characterized in that: The data processing unit transmits the processed monitoring data to the remote monitoring center at 1.2-second intervals. The remote monitoring center analyzes the data trends in real time and marks abnormal areas on the electronic map.

Images