Geological disaster monitoring and early warning system based on Internet of Things

Abstract

The invention provides a geological disaster monitoring and early warning system based on the Internet of Things. The geological disaster monitoring and early warning system aims at achieving real-time monitoring of geological disasters such as land subsidence, landslide and debris flow by integrating various sensors. The system transmits monitoring data to a data processing center in real time by using a wireless communication technology, such as LoRa, NB-IoT and the like. In the center, an intelligent algorithm is employed to analyze the data to identify early symptoms of geological disasters. Once an abnormality is detected, the system immediately sends early warning information through a wireless network, so that related departments and personnel can quickly take prevention measures. In addition, the system also provides a remote monitoring platform, so that a user can access monitoring data and early warning information in real time through the Internet, and remote monitoring and management are realized. The design of the system improves the accuracy and timeliness of early warning of geological disasters, is helpful for reducing the loss caused by disasters, and has important social value and wide application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a geological disaster monitoring and early warning system based on Internet of Things technology. Background Art

[0002] Geological disasters, such as ground subsidence, landslides, and mudslides, are one of the types of natural disasters that pose a serious threat to human life and property safety. With the acceleration of global urbanization and the increase in population density, the impact of these disasters has become more extensive and far-reaching. Traditional geological disaster monitoring methods mainly rely on manual inspections and limited monitoring sites. These methods have obvious limitations, such as limited monitoring range, low frequency of data collection, poor real-time performance, and the inability to respond quickly and effectively to disaster changes.

[0003] Existing monitoring systems often lack sufficient automation and intelligence, resulting in the inability to provide accurate warning information before a disaster occurs, or delayed transmission of warning information. These problems limit the effectiveness of existing monitoring systems in geological disaster prevention and mitigation. With the development of technologies such as the Internet of Things (IoT), wireless communication technology, big data analysis, and cloud computing, it has become possible to achieve real-time monitoring and intelligent warning of geological disasters.

[0004] IoT technology is particularly suitable for building a wide range of monitoring networks. By deploying sensors in key areas, a large amount of real-time data can be collected. Wireless communication technology ensures that data can be quickly transmitted to the central processing system. Combined with big data analysis and intelligent algorithms, valuable information can be extracted from the large amount of data collected, and early signs of geological disasters can be identified, thereby achieving timely early warning. Therefore, the development of a geological disaster monitoring and early warning system based on the Internet of Things is of great significance to improving the efficiency of disaster monitoring and the accuracy of early warning. Summary of the invention

[0005] The geological disaster monitoring and early warning system based on the Internet of Things is characterized by comprising: a sensor unit, including an accelerometer, an inclinometer, a displacement sensor, and a pressure sensor, for monitoring the dynamic changes of geological disasters such as ground subsidence, landslides, and mud-rock flows; a wireless communication module, which adopts LoRa, NB-IoT or other wireless communication technologies, is connected to the sensor unit and is used to transmit the monitoring data to the data processing center in real time; the data processing center includes one or more servers, and the servers run intelligent data analysis algorithms for receiving the monitoring data from the wireless communication modules and performing intelligent analysis to identify the early signs of geological disasters; an early warning information sending unit, when the data processing center identifies the early signs of geological disasters When there are early signs of geological disasters, it is used to send early warning information to the preset recipients through the wireless network, and different communication protocols and sending methods can be selected according to the warning level and urgency; the remote monitoring platform includes a user authentication mechanism to ensure the security of data access, allowing users to access monitoring data and early warning information through the Internet to achieve remote monitoring and management; a power management unit is used to ensure the continuous power supply of the sensor unit and the wireless communication module; a data storage unit is used to store historical monitoring data and early warning records; the sensor unit includes accelerometers, inclinometers, displacement sensors and pressure sensors, etc., which are installed in areas prone to geological disasters and are used to monitor the dynamic changes of ground subsidence, landslides, mudslides and other disasters in real time. The data collected by the sensor unit is transmitted in real time through the wireless communication module using LoRa, NB-IoT or other wireless communication technologies, and the information is sent to the data processing center. In the data processing center, the intelligent data analysis algorithm running on one or more servers receives these monitoring data and conducts in-depth analysis to identify the early signs of geological disasters. When the analysis finds potential risks, the early warning information sending unit immediately sends the early warning information to the preset recipients through the wireless network according to the level and urgency of the warning, by selecting a suitable communication method. At the same time, the remote monitoring platform ensures the security of data access through a user authentication mechanism, and allows authorized users to access monitoring data and early warning information through the Internet to achieve remote monitoring and management. In addition, the power management unit ensures the continuous power supply of the sensor unit and the wireless communication module, while the data storage unit is responsible for storing historical monitoring data and early warning records to provide support for future analysis and decision-making. This design enables the entire system to monitor and warn geological disasters efficiently and accurately, effectively improving the efficiency of disaster response.

[0006] The sensor unit includes but is not limited to an accelerometer of model ADXL345, which is used to measure the acceleration change in the geological disaster area; an inclinometer of model ZX-LN, which is used to monitor the change of the ground tilt angle; a displacement sensor of model ZX2, which is used to detect the displacement of the ground surface; and a pressure sensor of model PSE560, which is used to measure the pressure change of soil or rock formation. These sensors are integrated into a unit and installed in areas prone to geological disasters. They can monitor the dynamic changes of disasters such as ground subsidence, landslides, and mud-rock flows in real time, and transmit the monitoring data to the data processing center for analysis through the wireless communication module.

[0007] The wireless communication module adopts LoRa communication technology, uses the LoRa module of model SX1276, works at 915MHz frequency, has a communication distance of 3km to 15km, and an adjustable transmission rate of 0.3kbps to 50kbps; or adopts NB-IoT communication technology, uses the NB-IoT module of model BC95, works in the frequency range of 830MHz to 840MHz, provides a transmission rate of 20kbps to 100kbps, and supports PSM and eDRX technology to reduce device power consumption. These modules are connected to the sensor unit to transmit the monitoring data to the data processing center in real time.

[0008] The data processing center includes one or more high-performance servers of model Atlas 800-AI, each equipped with a 16-core processor and 32GB memory to ensure powerful data processing capabilities. Customized intelligent data analysis algorithms are running on the servers, which can process the monitoring data received in real time from the wireless communication module and perform rapid data analysis to identify early signs of geological disasters. The algorithms include machine learning models and statistical analysis tools, which can perform real-time analysis of data collected by accelerometers, inclinometers, displacement sensors, and pressure sensors, and accurately predict the possibility of disasters such as ground subsidence, landslides, and mudslides through pattern recognition and trend prediction. In addition, the server also has automatic expansion and load balancing functions to meet the analysis needs of large amounts of data and ensure the efficiency and stability of data processing.

[0009] The warning information sending unit can select different communication protocols and sending methods according to the warning level and urgency, including but not limited to SMS text message service, email, mobile application push or voice call. The unit adopts a multi-mode communication protocol, such as frequent data transmission through the LoRa module model SX1276 when the warning level is high, and periodic data reporting through the NB-IoT module model BC95 when the urgency is low. In addition, the warning information sending unit can also select the most appropriate communication method according to the recipient's preference settings to ensure that the warning information can be quickly and accurately conveyed to the target population.

[0010] The remote monitoring platform includes a user authentication mechanism to ensure the security of data access, wherein the user authentication mechanism adopts role-based access control (RBAC) to ensure that only authorized users can access the system. The mechanism includes multi-factor authentication, such as username and password, one-time password (OTP), smart card or biometric technology, and digital certificates based on public key infrastructure (PKI). In addition, the platform provides encrypted communication protocols, such as secure socket layer (SSL) or transport layer security (TLS), to protect the security of data during transmission. The user activity audit log records all user operations for post-audit and monitoring.

[0011] The power management unit is used to ensure continuous power supply to the sensor unit and the wireless communication module, and includes a maximum power point tracking (MPPT) solar charge controller model MPPT-60A, which is used in conjunction with a set of lead-acid batteries with a rated capacity of 100Ah to provide at least 72 hours of backup power. The unit also contains a power management integrated circuit model LM2700, which is responsible for voltage conversion and voltage regulation to ensure that the sensor and communication module can obtain stable power supply under different load conditions. In addition, the power management unit also has overcharge, over-discharge, short circuit and overheating protection functions to ensure the reliability and safety of the system in harsh environments.

[0012] The data storage unit is used to store historical monitoring data and warning records. The storage unit includes a set of 10TB enterprise-level hard disks with model number WD100TZHX, configured as a RAID 5 array to provide at least 5TB of effective storage space and data redundancy. The storage system supports a write speed of at least 100MB per second and a read speed of 200MB per second to ensure fast access to data. In order to ensure data security, the storage unit is also equipped with a 4-disk network attached storage (NAS) device with model number TS-453A, which supports end-to-end data encryption and automatically executed weekly snapshot backups. In addition, the storage unit also has a remote data synchronization function, which can synchronize data in real time to at least one off-site backup server to prevent data loss or damage.

[0013] The warning information sending unit is capable of sending warning information to mobile devices, landlines, emails or social media platforms. The unit sends warning text messages to mobile devices through integrated communication protocols, such as cellular network protocols (3G / 4G / 5G), uses a voice broadcast system to make automatic voice calls to landlines, sends emails through the SMTP protocol, and integrates with the API interfaces of mainstream social media platforms (such as Twitter and Facebook's Graph API) to send warning information through any communication channel selected by the user in an emergency. The warning information sending unit supports custom message templates and priority settings to ensure the timeliness and accuracy of information, and can record detailed logs for each transmission, including the sending time, recipients, and message status, for subsequent auditing and tracking.

[0014] The method of the geological disaster monitoring and early warning system comprises the following steps:

[0015] S1: Deploy sensor units in areas prone to geological disasters, including an accelerometer model ADXL345 with a measurement range of ±16g for monitoring vibration intensity; an inclinometer model ZX-LN with a measurement accuracy of 0.01 degrees for monitoring the ground tilt angle; a displacement sensor model ZX2 with a measurement range of 0-500mm for monitoring surface displacement; and a pressure sensor model PSE560 with a measurement range of 0-500kPa for monitoring pressure changes in soil or rock formations.

[0016] S2: The sensor unit monitors the dynamic changes of geological disasters such as ground subsidence, landslides, and mud-rock flows in real time. The data collection frequency is once per second to ensure the real-time and continuity of the data.

[0017] S3: The monitoring data is transmitted to the data processing center in real time through the wireless communication module. The transmission frequency is 915MHz and the transmission rate is 50kbps, ensuring the stability and real-time performance of data transmission.

[0018] S4: In the data processing center, the server model Atlas 800-AI runs an intelligent data analysis algorithm, receives monitoring data from the wireless communication module, and performs intelligent analysis to identify early signs of geological disasters. The analysis and processing speed is no less than 1GB / s.

[0019] S5: When the data processing center identifies early signs of geological disasters, the warning information sending unit sends warning information to the preset recipients via the wireless network, and the sending delay does not exceed 2 seconds.

[0020] S6: The warning information sending unit selects different communication protocols and sending methods such as SMS, phone, email or social media platform according to the warning level and urgency, and the message delivery rate is guaranteed to be above 99%.

[0021] S7: The remote monitoring platform provides a role-based access control (RBAC) user authentication mechanism to ensure the security of data access, and the authentication time does not exceed 1 second.

[0022] S8: Users can access monitoring data and warning information in real time through the remote monitoring platform to achieve remote monitoring and management. The platform response time does not exceed 500 milliseconds.

[0023] S9: The power management unit ensures continuous power supply to the sensor unit and wireless communication module. The system standby power consumption does not exceed 10W, ensuring that the system can continue to operate for at least 72 hours in the event of a power outage.

[0024] S10: The data storage unit stores historical monitoring data and warning records, with a storage capacity of no less than 10TB and a data retention period of no less than 5 years for subsequent analysis and auditing.

[0025] The above technical solution can bring the following technical effects:

[0026] 1. Improve the real-time and accuracy of monitoring: By deploying a variety of high-precision sensors, the system can monitor geological disaster-related parameters 24 hours a day. Compared with traditional manual inspection methods, sensor units can provide more frequent and continuous data, thereby capturing subtle changes in geological disasters earlier. In addition, the wireless communication module ensures that the monitoring data can be transmitted to the data processing center in real time, reducing the delay in the data transmission process, so that early warning information can be quickly generated and communicated to relevant personnel.

[0027] 2. Enhance the response speed and reliability of the early warning system: The intelligent data analysis algorithm used by the data processing center can quickly process and analyze the large amount of data collected and identify the early signs of geological disasters. Once a potential risk is detected, the early warning information sending unit can immediately send early warning information to relevant personnel and institutions through multiple communication channels, thereby greatly improving the response speed of the early warning system. At the same time, the system has designed a multi-level early warning mechanism, and different early warning measures are taken according to the severity and urgency of the disaster, which improves the reliability of the early warning.

[0028] 3. Realize remote monitoring and management functions: The remote monitoring platform provides users with an intuitive and easy-to-use interface, allowing users to access monitoring data and early warning information anytime and anywhere through the Internet. The user authentication mechanism ensures that only authorized users can access the system, ensuring the security of data. In addition, the data storage unit can preserve historical monitoring data and early warning records for a long time, providing an important basis for future data analysis, trend prediction and decision support. Through remote monitoring and management, decision makers can respond to geological disasters more flexibly and quickly, effectively improving the efficiency and effectiveness of disaster management. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of the geological disaster monitoring and early warning system of the present invention.

[0030] Figure 2 This is a diagram of the operation steps of the geological disaster monitoring and early warning system of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] The geological disaster monitoring and early warning system based on the Internet of Things includes: a sensor unit, including an accelerometer of model SCA3300, an inclinometer of model ZX-LN, a displacement sensor of model ZX2, and a pressure sensor of model PSE560, which are used to monitor the dynamic changes of geological disasters such as ground subsidence, landslides, and mud-rock flows; a wireless communication module, using a LoRa module of model BC95 or a NB-IoT module of model SIM7020, connected to the sensor unit, for real-time transmission of monitoring data to the data processing center; a data processing center, including a Atlas 800-AI server, which runs intelligent data analysis algorithms, is used to receive monitoring data from the wireless communication module and perform intelligent analysis to identify early signs of geological disasters; the early warning information sending unit is used to send early warning information to the preset recipients through the wireless network when the data processing center identifies early signs of geological disasters. It can select different communication protocols and sending methods according to the warning level and urgency; the remote monitoring platform includes a user authentication mechanism to ensure the security of data access, allowing users to access monitoring data and early warning information through the Internet to achieve remote monitoring and management; the power management unit is used to ensure continuous power supply to the sensor unit and the wireless communication module; the data storage unit is used to store historical monitoring data and early warning records.

[0033] The sensor unit includes an ADXL345 accelerometer with a measuring range of ±16g and a noise density of 13μg / √Hz; a ZX-LN inclinometer with a measuring range of ±30° and a resolution of 0.005°; a ZX2 displacement sensor with a measuring range of ±35mm and a resolution of 5μm; and a PSE560 pressure sensor with a measuring range of 0-500kPa and an accuracy of ±0.5%FS.

[0034] The wireless communication module adopts LoRa communication technology, uses the LoRa module model SX1276, works at a frequency of 915MHz, provides a communication distance of 3km to 15km, and supports an adjustable transmission rate of 0.3kbps to 50kbps; or adopts NB-IoT communication technology, uses the NB-IoT module model BC95, works in the frequency range of 830MHz to 840MHz, provides a transmission rate of 20kbps to 100kbps, and supports PSM (Power Saving Mode) and eDRX (Extended Discontinuous Reception) technology to reduce the power consumption of the device to a minimum of 0.5mW, ensuring the long-term stable operation of the sensor unit and the wireless communication module in the field environment. These modules are connected to the sensor unit to transmit the monitoring data to the data processing center in real time.

[0035] The data processing center includes one or more servers of model Atlas 800-AI, each equipped with a 16-core processor, 32GB memory and 1TB SSD hard drive to ensure powerful data processing capabilities. Customized intelligent data analysis algorithms are running on the servers, which can process the monitoring data received in real time from the wireless communication module and perform rapid data analysis to identify early signs of geological disasters. The algorithms include machine learning models and statistical analysis tools, which can perform real-time analysis of data collected by accelerometers, inclinometers, displacement sensors and pressure sensors, and accurately predict the possibility of disasters such as ground subsidence, landslides, and mudslides through pattern recognition and trend prediction. In addition, the server also has automatic expansion and load balancing functions to meet the analysis needs of large amounts of data and ensure the efficiency and stability of data processing.

[0036] The warning information sending unit can select different communication protocols and sending methods according to the warning level and urgency, such as using SMS text message service to send emergency text messages through the operator network at a transmission rate of not less than 20kbps when the warning level is red, or sending warning emails with attachments through email service at a rate of not less than 100kbps when the warning level is yellow. In addition, the unit can also notify users with a delay of no more than 5 seconds through mobile application push or voice call when the warning level is low, such as blue warning. The unit adopts a multi-mode communication protocol, such as frequent data transmission through LoRa module model SX1276 when the warning level is high, with a transmission frequency of 915MHz and an adjustable transmission rate range of 0.3kbps to 50kbps; periodic data reporting through NB-IoT module model BC95 when the urgency is low, working in the frequency range of 830MHz to 840MHz, providing a transmission rate of 20kbps to 100kbps. The warning information sending unit can also select the most appropriate communication method based on the recipient's preference settings to ensure that the warning information can be conveyed to the target population quickly and accurately.

[0037] The remote monitoring platform includes a user authentication mechanism to ensure the security of data access. The mechanism adopts role-based access control (RBAC) and multi-factor authentication through username and password, one-time password (OTP), smart card or biometric technology. In addition, the platform adopts secure communication protocols such as secure socket layer (SSL) or transport layer security (TLS) to encrypt data transmission between client and server. All user operations will be recorded in the audit log for post-audit and monitoring to ensure the security of the system and the integrity of the data.

[0038] The power management unit is used to ensure continuous power supply to the sensor unit and wireless communication module. It includes a maximum power point tracking (MPPT) solar charge controller model MPPT-60A with a charging efficiency of 98%, which is used in conjunction with a set of lead-acid batteries with a rated capacity of 100Ah to provide at least 72 hours of backup power. The unit also contains a power management integrated circuit model LM2700, which is responsible for converting the input voltage into a 3.3V or 5V output suitable for the sensor and communication module, ensuring stable power supply under different load conditions. In addition, the power management unit also has overcharge, over-discharge, short circuit and overheating protection functions, with 80% efficiency and a maximum output current of 5A, to ensure the reliability and safety of the system in harsh environments.

[0039] The data storage unit is used to store historical monitoring data and warning records. The storage unit includes a set of 10TB enterprise-class hard disks with model number WD100TZHX, configured as a RAID 5 array to provide at least 5TB of effective storage space and data redundancy. The storage system supports a write speed of at least 100MB per second and a read speed of 200MB per second to ensure fast access to data. In order to ensure data security, the storage unit is also equipped with a 4-disk network attached storage (NAS) device with model number TS-453A, which supports end-to-end AES-256-bit data encryption and automatically executed weekly snapshot backups. In addition, the storage unit also has a remote data synchronization function, which can synchronize data to at least one remote backup server in real time to prevent data loss or damage, and the synchronization delay does not exceed 100 milliseconds.

[0040] The warning information sending unit can automatically send warning information to mobile devices, landlines, emails or social media platforms when early signs of geological disasters are detected. The unit has cellular network communication capabilities, supports 3G / 4G / 5G network protocols, and can send warning text messages to mobile devices at a transmission rate of no less than 100Mbps per second; at the same time, it integrates a voice broadcast system and can complete automatic voice calls to landlines within 5 seconds. For email notifications, the unit can send warning emails to the user's specified email address within 3 seconds through the SMTP protocol, supports encrypted email transmission, and guarantees an email delivery rate of no less than 99.9%. In addition, the unit is also integrated with the API interface of social media platforms, such as Twitter's API 1.1 and Facebook's Graph API v12.0, and can push warning information to the social media accounts that users follow within 2 seconds. The warning information sending unit supports custom message templates and priority settings to ensure the timeliness and accuracy of information, and can record detailed logs for each transmission, including the sending timestamp accurate to milliseconds, recipient information and message status, for post-audit and tracking.

[0041] The method of the geological disaster monitoring and early warning system comprises the following steps:

[0042] S1: Deploy sensor units in areas prone to geological disasters, including an accelerometer model ADXL345 with a measurement range of ±16g for monitoring vibration intensity; an inclinometer model ZX-LN with a measurement accuracy of 0.01 degrees for monitoring the ground tilt angle; a displacement sensor model ZX2 with a measurement range of 0-500mm for monitoring surface displacement; and a pressure sensor model PSE560 with a measurement range of 0-500kPa for monitoring pressure changes in soil or rock formations.

[0043] S2: The sensor unit monitors the dynamic changes of geological disasters such as ground subsidence, landslides, and mud-rock flows in real time. The data collection frequency is once per second to ensure the real-time and continuity of the data.

[0044] S3: The monitoring data is transmitted to the data processing center in real time through the wireless communication module. The transmission frequency is 915MHz and the transmission rate is 50kbps, ensuring the stability and real-time performance of data transmission.

[0045] S4: In the data processing center, the server model Atlas 800-AI runs an intelligent data analysis algorithm, receives monitoring data from the wireless communication module, and performs intelligent analysis to identify early signs of geological disasters. The analysis and processing speed is no less than 1GB / s.

[0046] S5: When the data processing center identifies early signs of geological disasters, the warning information sending unit sends warning information to the preset recipients via the wireless network, and the sending delay does not exceed 2 seconds.

[0047] S6: The warning information sending unit selects different communication protocols and sending methods such as SMS, phone, email or social media platform according to the warning level and urgency, and the message delivery rate is guaranteed to be above 99%.

[0048] S7: The remote monitoring platform provides a role-based access control (RBAC) user authentication mechanism to ensure the security of data access, and the authentication time does not exceed 1 second.

[0049] S8: Users can access monitoring data and warning information in real time through the remote monitoring platform to achieve remote monitoring and management. The platform response time does not exceed 500 milliseconds.

[0050] S9: The power management unit ensures continuous power supply to the sensor unit and wireless communication module. The system standby power consumption does not exceed 10W, ensuring that the system can continue to operate for at least 72 hours in the event of a power outage.

[0051] S10: The data storage unit stores historical monitoring data and warning records, with a storage capacity of no less than 10TB and a data retention period of no less than 5 years for subsequent analysis and auditing.

[0052] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A geological disaster monitoring and early warning system based on the Internet of Things, characterized in that: include: Sensor units, including accelerometers, inclinometers, displacement sensors, and pressure sensors, are used to monitor the dynamic changes of geological disasters such as ground subsidence, landslides, and debris flows; A wireless communication module, using LoRa, NB-IoT or other wireless communication technologies, connected to the sensor unit, for transmitting the monitoring data to the data processing center in real time; the data processing center, including one or more servers, running intelligent data analysis algorithms on the servers, for receiving the monitoring data from the wireless communication module, and performing intelligent analysis to identify early signs of geological disasters; The warning information sending unit is used to send warning information to the preset recipients through the wireless network when the data processing center identifies the early signs of geological disasters. Different communication protocols and sending methods can be selected according to the warning level and urgency. The remote monitoring platform includes a user authentication mechanism to ensure the security of data access, allowing users to access monitoring data and warning information through the Internet to achieve remote monitoring and management. The power management unit is used to ensure the continuous power supply of the sensor unit and the wireless communication module. The data storage unit is used to store historical monitoring data and early warning records; the sensor unit includes accelerometers, inclinometers, displacement sensors and pressure sensors, etc., which are installed in areas prone to geological disasters to monitor the dynamic changes of ground subsidence, landslides, mud-rock flows and other disasters in real time. The data collected by the sensor unit is transmitted in real time through the wireless communication module using LoRa, NB-IoT or other wireless communication technologies, and the information is sent to the data processing center. In the data processing center, the intelligent data analysis algorithm running on one or more servers receives these monitoring data and conducts in-depth analysis to identify the early signs of geological disasters. When the analysis finds potential risks, the early warning information sending unit immediately selects a suitable communication method to send early warning information to the preset recipient through the wireless network according to the level and urgency of the warning. At the same time, the remote monitoring platform ensures the security of data access through the user authentication mechanism, and allows authorized users to access monitoring data and early warning information through the Internet to achieve remote monitoring and management. In addition, the power management unit ensures the continuous power supply of the sensor unit and the wireless communication module, while the data storage unit is responsible for storing historical monitoring data and early warning records to provide support for future analysis and decision-making. This design enables the entire system to efficiently and accurately monitor and warn of geological disasters, effectively improving the efficiency of disaster response.

2. The system according to claim 1, characterized in that The sensor unit includes but is not limited to an accelerometer, an inclinometer, a displacement sensor, and a pressure sensor.

3. The system according to claim 1, characterized in that The wireless communication module adopts LoRa, NB-IoT or other wireless communication technologies.

4. The system according to claim 1, characterized in that The data processing center includes one or more servers, and intelligent data analysis algorithms are run on the servers.

5. The system according to claim 1, characterized in that The warning information sending unit can select different communication protocols and sending methods according to the warning level and urgency.

6. The system according to claim 1, characterized in that The remote monitoring platform includes a user authentication mechanism to ensure the security of data access.

7. The system according to claim 1, characterized in that The power management unit is used to ensure continuous power supply to the sensor unit and the wireless communication module.

8. The system according to claim 1, characterized in that The data storage unit is used to store historical monitoring data and early warning records.

9. The system according to claim 1, characterized in that The warning information sending unit can send warning information to a mobile device, a landline phone, an email or a social media platform.

10. A method for using the geological disaster monitoring and early warning system according to claim 1, characterized in that: The following steps are involved: s1: Deploy sensor units in areas prone to geological disasters, including accelerometers, inclinometers, displacement sensors, and pressure sensors; s2: Real-time monitoring of the dynamic changes of geological disasters such as ground subsidence, landslides, and debris flows through sensor units; s3: The monitoring data is transmitted to the data processing center in real time through the wireless communication module; s4: In the data processing center, the server runs an intelligent data analysis algorithm, receives monitoring data from the wireless communication module, and performs intelligent analysis to identify early signs of geological disasters; s5: When the data processing center identifies early signs of geological disasters, the warning information sending unit sends warning information to the preset recipients through the wireless network; s6: The warning information sending unit selects different communication protocols and sending methods such as SMS, phone, email or social media platform according to the warning level and urgency; s7: The remote monitoring platform provides a user authentication mechanism to ensure the security of data access; s8: Users can access monitoring data and early warning information in real time through the remote monitoring platform to achieve remote monitoring and management; s9: The power management unit ensures continuous power supply to the sensor unit and the wireless communication module; s10: The data storage unit stores historical monitoring data and warning records for subsequent analysis and auditing.

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