CN-121982819-A - Forest fire prevention intelligent monitoring system and method based on end Bian Yun cooperative architecture

CN121982819ACN 121982819 ACN121982819 ACN 121982819ACN-121982819-A

Abstract

The embodiment of the application provides a forest fire prevention intelligent monitoring system and method based on a Bian Yun cooperative framework, and belongs to the technical field of fire prevention monitoring. The system comprises an intelligent monitoring terminal, an edge computing node and a cloud platform, wherein the intelligent monitoring terminal is deployed in a monitoring area and used for collecting environment data and image data, the intelligent monitoring terminal comprises an Internet of things mainboard, a sensor, a power supply module and a dual-mode communication module, the edge computing node is in communication connection with the intelligent monitoring terminal and used for carrying out real-time fire identification and preliminary early warning locally based on data collected by the intelligent monitoring terminal, the cloud platform is in communication connection with the edge computing node and used for receiving results generated by the edge computing node and the environment data collected by the intelligent monitoring terminal, and integrating geographic information system data, meteorological data and historical fire records to carry out comprehensive research and judgment and big data analysis to generate a dynamic fire risk assessment result and an optimal resource scheduling scheme. The embodiment of the application realizes intelligent monitoring of forest fires based on the end Bian Yun cooperative architecture.

Inventors

ZHONG YIXIU
LI CHANGXI
LIANG ZEXIAO
ZHANG XIAOHUAN

Assignees

广东九联开鸿科技发展有限公司
广东九联科技股份有限公司

Dates

Publication Date: 20260505
Application Date: 20260211

Claims (10)

1. Forest fire prevention intelligent monitoring system based on end Bian Yun cooperated architecture, its characterized in that, the system includes: the intelligent monitoring terminal is deployed in a monitoring area and used for collecting environment data and image data and comprises an Internet of things mainboard, a sensor, a power supply module and a dual-mode communication module; The edge computing node is in communication connection with the intelligent monitoring terminal and is used for carrying out real-time fire identification and preliminary early warning locally based on data acquired by the intelligent monitoring terminal; The cloud platform is in communication connection with the edge computing nodes, and is used for receiving results generated by the edge computing nodes and environment data acquired by the intelligent monitoring terminal, integrating geographic information system data, meteorological data and historical fire records, carrying out comprehensive research and judgment and big data analysis, and generating a dynamic fire risk assessment result and an optimal resource scheduling scheme.
2. The system of claim 1, further comprising a visualization terminal in communication with the cloud platform for receiving and visually displaying a fire risk map, pre-warning information, resource distribution and scheduling schemes, and supporting a linked unmanned aerial vehicle device for fire detection and fire suppression treatments.
3. The system of claim 1, wherein the cloud platform is further integrated with a blockchain certification module for trusted recording and certification of all links from acquisition, identification to reporting of fire data.
4. The system of claim 1, wherein the intelligent monitoring terminal uses Hi3861 as a main control chip, is powered by a solar panel and a lithium battery in combination, and is integrated with a wide-angle camera, a smoke sensor and a temperature and humidity sensor.
5. The system of claim 4, wherein the sensor comprises an infrared sensor, a smoke sensor and a temperature and humidity sensor; the smoke sensor reads the detection voltage by adopting a multiple average algorithm; the detection voltage is converted into smoke concentration through the following formula; ; Wherein, the The resistance value of the sensor is represented, 3.3 is a reference voltage, vol is a detection voltage obtained by conversion according to the read original data, and 0.5 is a fixed parameter representing the resistance value connected in series with the sensor; ; Wherein, the Is the resistance of the sensor in clean air, Is the previously calculated resistance of the sensor, 11.5428 is a coefficient related to the sensor characteristics, 0.6549 is an index related to the sensor characteristics.
6. The system of claim 1, wherein the real-time fire recognition and preliminary pre-warning are performed locally based on data collected by the intelligent monitoring terminal, comprising: When at least one of the infrared sensor, the smoke sensor and the temperature and humidity sensor is abnormal, acquiring data acquired by other sensors in the abnormal area; if the data collected by other sensors are not abnormal, outputting fire early warning and giving the fire early warning to low confidence; if the data acquired by other sensors are abnormal, judging whether the types of the other sensors are consistent with the types of the sensors acquiring the abnormal data; When the infrared sensor, the smoke sensor and the temperature and humidity sensor in the same area acquire abnormal data, outputting fire early warning and giving the fire early warning to high confidence; When two or more identical sensors in the same area collect abnormal data, a fire early warning is output and given to the middle confidence.
7. The system of claim 6, wherein after the cloud platform receives the fire pre-warning generated by the edge computing node, the cloud platform obtains alarm signals, GPS locations, site snapshots, and sensor data of an intelligent monitoring terminal of an area where the fire pre-warning is located through an NB-IoT network.
8. The system of claim 1, wherein the merging of geographic information system data, weather data, historical fire records for comprehensive research and big data analysis to generate dynamic fire risk assessment results comprises: Acquiring fire early warning output by the edge computing node, Unifying the fire early warning position, the geographic information system data and the meteorological data to the same coordinate system; obtaining a fire development process benchmark according to the history fire records which are most matched with the fire early warning; And correcting the fire development process reference based on the change amount of the meteorological data to obtain the dynamic fire risk assessment result.
9. The system of claim 1, wherein the integrating of the geographic information system data, the weather data, the historical fire record for comprehensive research and big data analysis generates an optimal resource scheduling scheme, comprising: determining a resource scheduling goal, the resource scheduling goal being one of minimizing total response time, maximizing risk coverage, minimizing scheduling cost, and balancing resource usage, And generating a scheduling path and a scheduling mode of each fire extinguishing resource according to the constraint of the total quantity of the fire extinguishing resources, the constraint of the fire extinguishing task demands and the constraint of the fire extinguishing time window.
10. A forest fire prevention intelligent monitoring method based on a co-architecture of a terminal Bian Yun, which is applied to the forest fire prevention intelligent monitoring system based on a co-architecture of a terminal Bian Yun as set forth in any one of claims 1 to 9, wherein the method includes: S1, acquiring environmental data and image data of a forest region in real time through an intelligent monitoring terminal; s2, operating a fire disaster recognition algorithm at the edge computing node to process the image data in real time and perform primary fire disaster recognition; S3, if the fire condition is identified, generating an early warning signal, and uploading the early warning signal and key environment data to the cloud platform through a wireless communication network; S4, after the cloud platform receives the data, merging multi-source data comprising GIS and meteorological data to carry out comprehensive research and judgment, and generating a fire risk assessment result and a resource scheduling scheme; s5, uploading fire key information to a blockchain network for certification; and S6, sending alarm and decision support information to the command center to guide the fire extinguishing action.

Description

Forest fire prevention intelligent monitoring system and method based on end Bian Yun cooperative architecture Technical Field The application relates to the technical field of fire prevention monitoring, in particular to a forest fire prevention intelligent monitoring system based on a terminal Bian Yun cooperative framework and a forest fire prevention intelligent monitoring method based on a terminal Bian Yun cooperative framework. Background The current forest fire prevention still depends on manual inspection, infrared sensing and satellite remote sensing, and has the obvious defects that forest guards inspect under the limitation of topography and weather, efficiency suddenly drops at night or in severe environments, the false alarm rate of an infrared sensor is as high as 30% -40%, fire sources and high-temperature industrial activities cannot be distinguished, and satellite remote sensing data delay is as long as several hours, so that real-time response requirements are difficult to meet. In addition, most of the existing systems lack of advanced integration of AI and the Internet of things technology, the phenomenon of data islanding is serious, early warning decisions depend on manual experience, and therefore resource scheduling efficiency is low. Forest fires not only cause direct economic losses (such as forest damage and saving expenses), but also cause long-term ecological problems such as water and soil loss and reduced biodiversity. The traditional mode of 'post-suppression' is difficult to suppress the risks, and the realization of pre-management and control of risks through intelligent means is needed. Disclosure of Invention The embodiment of the application aims to provide a forest fire prevention intelligent monitoring system and method based on a Bian Yun collaborative architecture, which aim to realize early warning, quick response and accurate positioning of fire, reduce delay and bandwidth consumption through edge calculation, improve identification precision through artificial intelligence, guarantee data credibility through block chains, and finally construct a highly reliable, low-cost and intelligent all-weather forest fire prevention system so as to solve part of technical problems mentioned in the background art. To achieve the above object, a first aspect of the present application provides a forest fire prevention intelligent monitoring system based on a coordinated architecture of ends Bian Yun, the system comprising: The intelligent monitoring terminal is arranged in a monitoring area and used for collecting environment data and image data, and comprises an Internet of things mainboard, a sensor, a power supply module and a dual-mode communication module, an edge computing node, a cloud platform and a dynamic fire risk assessment scheme, wherein the edge computing node is in communication connection with the intelligent monitoring terminal and used for carrying out real-time fire risk identification and preliminary early warning locally based on data collected by the intelligent monitoring terminal, the cloud platform is in communication connection with the edge computing node and used for receiving results generated by the edge computing node and the environment data collected by the intelligent monitoring terminal, and integrating geographic information system data, meteorological data and historical fire risk records to carry out comprehensive research and judgment and big data analysis to generate the dynamic fire risk assessment result and the optimal resource scheduling scheme. Optionally, the system further comprises a visual terminal, wherein the visual terminal is in communication connection with the cloud platform, and is used for receiving and visually displaying a fire hazard map, early warning information, resource distribution and scheduling schemes and supporting linkage unmanned aerial vehicle equipment to perform fire scene reconnaissance and fire extinguishing treatment. Optionally, the cloud platform is further integrated with a blockchain evidence storage module, which is used for carrying out trusted recording and evidence storage on all links from acquisition, identification and reporting of the fire data. Optionally, the intelligent monitoring terminal adopts hua Hi3861 as a main control chip, is powered by a solar panel and a lithium battery in a combined way, and is integrated with a wide-angle camera, a smoke sensor and a temperature and humidity sensor. Optionally, the sensor comprises an infrared sensor, a smoke sensor and a temperature and humidity sensor; the smoke sensor reads the detection voltage by adopting a multiple average algorithm; the detection voltage is converted into smoke concentration through the following formula; Wherein, the The resistance value of the sensor is represented, 3.3 is a reference voltage, vol is a detection voltage obtained by conversion according to the read original data, and 0.5 is a fixed parameter repres