Geological disaster monitoring system and method based on iron tower big data

Abstract

The invention discloses a geological disaster monitoring system and method based on iron tower big data, and relates to the technical field of geological disaster monitoring. Comprising an iron tower monitoring node module, an edge calculation module, a data transmission module, a cloud big data processing and analysis module, a visualization and early warning issuing module and an emergency response and decision support module. According to the invention, communication iron tower resources are provided and a plurality of sensing devices are arranged, so that low-cost and large-scale deployment is realized, the monitoring coverage range is expanded, and the accuracy and reliability of geological disaster monitoring are improved at the same time; local preprocessing is carried out on the collected data through the edge computing module, and the data are transmitted to a cloud end in real time through a 4G / 5G network, so that real-time monitoring and quick response of the data are realized, and the timeliness of the monitored data is greatly improved; an intelligent analysis early warning system is established through a cloud big data processing and analysis module, and machine learning and an artificial intelligence algorithm are integrated to carry out multi-source data analysis and risk assessment, so that the early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and particularly to a geological disaster monitoring system and method based on iron tower big data. Background Art

[0002] Existing geological disaster monitoring systems mainly rely on traditional geological sensor networks, such as inclinometers, rain gauges, seismographs, etc. These devices are deployed in high-risk areas of geological disasters to monitor early signs of disasters such as landslides, collapses, debris flows, and ground subsidence. However, these systems have the following problems: High deployment cost: Traditional sensor networks require separate laying of power supply and communication lines, resulting in difficulties in large-scale deployment, especially in remote areas. Limited coverage: Due to the limited number of sensor devices, it is difficult for the monitoring system to comprehensively cover large areas of high-risk regions, resulting in the existence of monitoring blind spots. Poor data timeliness: The collected data usually needs to be checked manually at regular intervals or transmitted indirectly, and real-time monitoring cannot be achieved. Lack of intelligent analysis: Existing systems lack the deep integration of big data and artificial intelligence technologies and cannot efficiently analyze massive data, resulting in low accuracy of geological disaster early warnings.

[0003] Through the above analysis, the problems and defects existing in the prior art are as follows:

[0004] (1) High deployment cost: Traditional sensor networks require separate laying of power supply and communication lines, resulting in difficulties in large-scale deployment, especially in remote areas.

[0005] (2) Limited coverage: Due to the limited number of sensor devices, it is difficult for the monitoring system to comprehensively cover large areas of high-risk regions, resulting in the existence of monitoring blind spots.

[0006] (3) Poor data timeliness: The collected data usually needs to be checked manually at regular intervals or transmitted indirectly, and real-time monitoring cannot be achieved.

[0007] (4) Lack of intelligent analysis: Existing systems lack the deep integration of big data and artificial intelligence technologies and cannot efficiently analyze massive data, resulting in low accuracy of geological disaster early warnings. Summary of the Invention

[0008] In view of the problems existing in the prior art, the present invention is proposed.

[0009] Therefore, the problems to be solved by the present invention are as follows: Existing geological disaster monitoring systems have problems such as high deployment cost, limited coverage, poor data timeliness, and lack of intelligent analysis. Specifically, the traditional system requires separate laying of power supply and communication lines, resulting in difficult and costly deployment, limited number of sensor devices causing monitoring blind spots, manual regular inspection required for data collection and inability to achieve real-time monitoring, and low accuracy of early warnings due to lack of support from big data and artificial intelligence technologies.

[0010] To solve the above technical problems, the present invention provides the following technical solutions:

[0011] In a first aspect, an embodiment of the present invention provides a geological disaster monitoring system based on big data of iron towers, which includes an iron tower monitoring node module, an edge computing module, a data transmission module, a cloud big data processing and analysis module, a visualization and early warning publishing module, and an emergency response and decision support module;

[0012] The iron tower monitoring node module, connected to the edge computing module, is used to equip each iron tower with a variety of sensing devices, including tilt sensors, acceleration sensors, meteorological monitoring devices, and environmental sensors; these sensors collect data in real time through the power supply and communication network on the iron tower;

[0013] The edge computing module, connected to the data transmission module, is used to perform local preprocessing on the collected data, such as data denoising, compression, and preliminary analysis of abnormal events;

[0014] The data transmission module, connected to the cloud big data processing and analysis module, is used to utilize the communication base station function of the iron tower to upload the preprocessed data to the cloud data center in real time through the 4G / 5G network;

[0015] The cloud big data processing and analysis module, connected to the edge computing module, the data transmission module, the visualization and early warning publishing module, and the emergency response and decision support module, is used to deploy a big data analysis system in the cloud, integrate machine learning and artificial intelligence algorithms, and perform in-depth analysis of multi-source data, disaster risk assessment, and real-time early warning generation;

[0016] The visualization and early warning publishing module, connected to the iron tower monitoring node module, the edge computing module, and the cloud big data processing and analysis module, is used to visually present the analysis results through a geographic information system (GIS) platform and publish disaster early warning information to relevant departments and the public through multiple channels such as text messages, APPs, and broadcasts;

[0017] The emergency response and decision support module, connected to the cloud big data processing and analysis module, is used to integrate historical monitoring data and real-time data to provide decision support for emergency response departments, including evacuation route planning and personnel allocation suggestions for dangerous areas.

[0018] As a preferred solution of the geological disaster monitoring system based on big data of iron towers of the present invention, wherein: the iron tower monitoring node module:

[0019] Collects relevant data in real time through a variety of installed sensors, including surface tilt, acceleration change, rainfall, temperature and humidity, and combines environmental data at the same time.

[0020] As a preferred solution of the geological disaster monitoring system based on tower big data of the present invention, wherein: the edge computing module preprocesses the original data in the edge computing unit on the tower, including:

[0021] Abnormal data elimination: removing false alarms and outliers of sensors;

[0022] Data compression: reducing the redundancy of the uploaded data;

[0023] Preliminary analysis: detecting critical changes, such as the surface tilt angle exceeding the set threshold or the rainfall exceeding the warning value.

[0024] As a preferred solution of the geological disaster monitoring system based on tower big data of the present invention, wherein: the data transmission module:

[0025] Through the communication base station network of the tower, the preprocessed monitoring data is transmitted to the cloud in a low-latency manner.

[0026] As a preferred solution of the geological disaster monitoring system based on tower big data of the present invention, wherein: the cloud big data processing and analysis module further analyzes the received data by means of artificial intelligence algorithms and big data processing systems deployed in the cloud, including:

[0027] Time series analysis: performing trend analysis on the long-term monitoring data of tower nodes to extract potential disaster signs;

[0028] Multi-source data fusion: comprehensively evaluating the regional disaster risk according to the data of multiple tower nodes and combining multi-source data such as remote sensing, geology, mines, and engineering construction;

[0029] Intelligent prediction: predicting the possible time and location of future geological disasters through a machine learning model.

[0030] As a preferred solution of the geological disaster monitoring system based on tower big data of the present invention, wherein: the visualization and early warning release module:

[0031] According to the big data analysis results, conduct a hierarchical assessment of potential geological disaster risks and send early warning information to relevant parties through multiple channels.

[0032] As a preferred solution of the geological disaster monitoring system based on tower big data of the present invention, wherein: the emergency response and decision support module:

[0033] The system generates an emergency response plan according to the disaster risk level, including evacuation route planning and resource allocation suggestions.

[0034] Second aspect, an embodiment of the present invention provides a geological disaster monitoring method based on big data of iron towers, which includes using various sensing devices equipped on each iron tower through an iron tower monitoring node module, including tilt sensors, acceleration sensors, meteorological monitoring devices, and environmental sensors; these sensors collect data in real time through the power supply and communication network on the iron tower;

[0035] Performing local preprocessing on the collected data through an edge computing module, such as data denoising, compression, and preliminary analysis of abnormal events;

[0036] Using the communication base station function of the iron tower through a data transmission module to upload the preprocessed data to the cloud data center in real time through the 4G / 5G network;

[0037] Using a cloud big data processing and analysis module to deploy a big data analysis system in the cloud, integrating machine learning and artificial intelligence algorithms, for in-depth analysis of multi-source data, disaster risk assessment, and real-time early warning generation;

[0038] Visualizing the analysis results through a geographic information system (GIS) platform through a visualization and early warning release module, and releasing disaster early warning information to relevant departments and the public through multiple channels such as text messages, APPs, and broadcasts;

[0039] Integrating historical monitoring data and real-time data through an emergency response and decision support module to provide decision support for emergency response departments, including the planning of evacuation routes in dangerous areas and suggestions for personnel allocation.

[0040] Third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program instructions are executed by the processor, the steps of the geological disaster monitoring system based on big data of iron towers as in the first aspect of the present invention are implemented.

[0041] Fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program instructions are executed by the processor, the steps of the geological disaster monitoring system based on big data of iron towers as in the first aspect of the present invention are implemented.

[0042] The beneficial effects of the present invention are as follows: The geological disaster monitoring system based on iron tower big data provided by the present invention realizes low-cost large-scale deployment by utilizing existing communication iron tower resources and equipping various sensing devices, expands the monitoring coverage range, avoids the existence of monitoring blind spots, and improves the accuracy and reliability of geological disaster monitoring at the same time; through the edge computing module, the collected data is preprocessed locally and transmitted to the cloud in real time using 4G / 5G networks, realizing real-time monitoring and rapid response of the data, greatly improving the timeliness of the monitoring data, reducing the resource consumption of data transmission, and ensuring the efficient operation of the monitoring system; through the cloud big data processing and analysis module, an intelligent analysis and early warning system is established, integrating machine learning and artificial intelligence algorithms for multi-source data analysis and risk assessment, improving the accuracy of early warning, enhancing the intelligent level of the system, and ensuring the scientificity and forward-looking of geological disaster prevention and control. The present invention has achieved more remarkable effects in terms of monitoring coverage, data timeliness, and early warning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a structural block diagram of a geological disaster monitoring system based on iron tower big data;

[0045] Figure 2 It is a flowchart of the method for preprocessing raw data by the edge computing unit on the iron tower of the geological disaster monitoring system based on iron tower big data;

[0046] Figure 3 It is a flowchart of the method of the cloud big data processing and analysis module of the geological disaster monitoring system based on iron tower big data;

[0047] Figure 4 It is a flowchart of the geological disaster monitoring method based on iron tower big data.

[0048] In the figure: 100, iron tower monitoring node module; 200, edge computing module; 300, data transmission module; 400, cloud big data processing and analysis module; 500, visualization and early warning release module; 600, emergency response and decision support module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0050] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0051] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0052] Embodiment 1

[0053] Referring to Figures 1 to 4 , which is the first embodiment of the present invention. This embodiment provides a geological disaster monitoring system based on big data of iron towers, including

[0054] an iron tower monitoring node module 100, an edge computing module 200, a data transmission module 300, a cloud big data processing and analysis module 400, a visualization and early warning publishing module 500, and an emergency response and decision support module 600;

[0055] The iron tower monitoring node module 100 is connected to the edge computing module 200 and is used to equip each iron tower with a variety of sensing devices, including an inclination sensor, an acceleration sensor, a meteorological monitoring device, and an environmental sensor; these sensors collect data in real time through the power supply and communication network on the iron tower;

[0056] The edge computing module 200 is connected to the data transmission module 300 and is used to perform local preprocessing on the collected data, such as data denoising, compression, and preliminary analysis of abnormal events;

[0057] The data transmission module 300 is connected to the cloud big data processing and analysis module 400 and is used to utilize the communication base station function of the iron tower to upload the preprocessed data to the cloud data center in real time through the 4G / 5G network;

[0058] The cloud big data processing and analysis module 400 is connected to the edge computing module 200, the data transmission module 300, the visualization and early warning publishing module 500, and the emergency response and decision support module 600, and is used to deploy a big data analysis system in the cloud, integrate machine learning and artificial intelligence algorithms, and is used to perform in-depth analysis of multi-source data, disaster risk assessment, and real-time early warning generation;

[0059] The Visualization and Early Warning Release Module 500 is connected to the Tower Monitoring Node Module 100, the Edge Computing Module 200, and the Cloud Big Data Processing and Analysis Module 400, and is used to visually present the analysis results through the Geographic Information System (GIS) platform and release disaster early warning information to relevant departments and the public through multiple channels such as text messages, APPs, and broadcasts.

[0060] The Emergency Response and Decision Support Module 600 is connected to the Cloud Big Data Processing and Analysis Module 400 and is used to integrate historical monitoring data and real-time data to provide decision support for emergency response departments, including the planning of evacuation routes in dangerous areas and suggestions for personnel allocation.

[0061] It should be noted that the configuration of various sensors in the Tower Monitoring Node Module 100 is based on the analysis of geological disaster characteristic parameters. The tilt sensor (accuracy ±0.005°, sampling frequency 10Hz) is used to monitor the surface displacement in real time; the three-axis acceleration sensor (sampling rate 200Hz, range ±2g) detects ground vibrations; the automatic weather station includes a rain gauge (accuracy 0.1mm, maximum measurement intensity 300mm / h) and temperature and humidity sensors (temperature accuracy ±0.1°C, humidity accuracy ±2%RH) to monitor environmental parameters. All sensors reach the IP67 protection level to ensure stable operation in harsh environments.

[0062] Furthermore, the Tower Monitoring Node Module 100:

[0063] Relevant data is collected in real time through a variety of installed sensors, including surface tilt, acceleration changes, rainfall, temperature and humidity, and at the same time combined with environmental data such as wind speed and vibration.

[0064] Furthermore, the Edge Computing Module 200 preprocesses the original data in the edge computing unit on the tower, including:

[0065] S101, Abnormal data elimination: Remove sensor false alarms and outliers;

[0066] S102, Data compression: Reduce the redundancy of uploaded data;

[0067] S103, Preliminary analysis: Detect critical changes, such as the surface tilt angle exceeding the set threshold or the rainfall exceeding the warning value.

[0068] Furthermore, the Data Transmission Module 300:

[0069] Transmits the preprocessed monitoring data to the cloud in a low-latency manner through the communication base station network of the tower.

[0070] Furthermore, the Cloud Big Data Processing and Analysis Module 400 further analyzes the received data through the artificial intelligence algorithm and big data processing system deployed in the cloud, including:

[0071] S201, Time Series Analysis: Conduct trend analysis on the long-term monitoring data of the iron tower nodes to extract potential disaster signs;

[0072] S202, Multi-source Data Fusion: Based on the data of multiple iron tower nodes, combined with multi-source data such as remote sensing, geology, mining, and engineering construction, comprehensively evaluate the regional disaster risks;

[0073] S203, Intelligent Prediction: Predict the possible time and location of future geological disasters through machine learning models.

[0074] It should be noted that the cloud big data processing and analysis module 400 adopts a three-layer analysis architecture of time series analysis, multi-source data fusion, and intelligent prediction to achieve a comprehensive assessment and early warning of geological disaster risks.

[0075] Furthermore, the visualization and early warning release module 500:

[0076] According to the big data analysis results, conduct a hierarchical assessment of potential geological disaster risks, and send early warning information to relevant parties through multiple channels such as text messages and APP push.

[0077] Furthermore, the emergency response and decision support module 600:

[0078] The system generates an emergency response plan according to the disaster risk level, including evacuation route planning and resource allocation suggestions.

[0079] It should be noted that the application fields of the present invention cover multiple fields such as geological disaster monitoring and early warning, communication iron tower safety monitoring, and infrastructure disaster prevention management, and are mainly used in the following scenarios:

[0080] 1 Geological disaster monitoring of communication iron towers:

[0081] The communication iron towers installed in mountainous areas and high landslide-prone areas monitor the geological change data in real time and identify potential risks in advance.

[0082] Ensure the stability of communication infrastructure during disasters and prevent communication interruptions caused by the collapse of iron towers.

[0083] 2 Disaster prevention monitoring of mountain roads and bridges:

[0084] Applied to the bridge and road infrastructure in high-risk mountainous areas, monitor the surface deformation, landslide possibility, and other environmental impacts in real time.

[0085] 3 Geological disaster early warning system:

[0086] For large-scale monitoring of high-incidence areas of geological disasters such as landslides, debris flows, and settlements, providing scientific basis and timely warnings for the government, enterprises, and the public.

[0087] 4 Smart grid security guarantee:

[0088] Apply this system in cross-regional high-voltage transmission towers to avoid the paralysis of the power transmission network caused by geological disasters.

[0089] 5 Public safety emergency management:

[0090] Provide technical support for disaster prevention and mitigation, and be used for disaster emergency command and rapid response to public safety.

[0091] It should also be noted that the relevant evidence of the technical effects obtained in the embodiments of the present invention

[0092] 1 High-precision disaster monitoring data support:

[0093] Through the multi-sensor layout of the tower monitoring nodes, this system realizes the real-time collection of multi-dimensional parameters such as surface tilt, vibration, and rainfall. Experimental data shows that compared with traditional monitoring equipment, the data collection accuracy has increased by more than 30%.

[0094] 2 Low-latency data processing and transmission:

[0095] The edge computing unit preprocesses the data, greatly reducing the data transmission redundancy. Experiments show that the latency of this system is less than 100 milliseconds, greatly improving the real-time performance of monitoring.

[0096] 3 Accuracy of disaster risk prediction:

[0097] The machine learning model deployed in the cloud, based on time series analysis and multi-source data fusion, can predict the geological disaster risk within the next 72 hours. Multiple simulation tests show that its prediction accuracy rate reaches 92%, far higher than that of traditional early warning systems.

[0098] 4 Effectiveness of dynamic emergency response:

[0099] The emergency plan automatically generated by the system can quickly generate evacuation routes and resource allocation suggestions. Through actual simulations, it is found that the response speed is 40% higher than that of manual plans, effectively shortening the rescue preparation time after the disaster occurs.

[0100] 5 Environmental adaptability and deployment flexibility:

[0101] The monitoring nodes adopt high-performance edge computing units and low-power sensors, which can adapt to complex environments such as high altitudes, low temperatures, and strong winds and rains, and the actual operation stability reaches 99%.

[0102] 6 Multi-level early warning mechanism:

[0103] Provide graded warning signals according to different risk levels to avoid overreaction in low-risk situations or underestimation of impacts in high-risk situations. Through field verification, it is found that this mechanism has significantly reduced false alarms and missed alarms.

[0104] 7 Full-region risk management capabilities:

[0105] In a regional network covered by multiple nodes, the system can provide comprehensive geological disaster assessment capabilities through the integration and analysis of multi-source data. Experiments show that in the integration and analysis of multiple monitoring tower areas, the regional risk judgment accuracy of the system has been improved by 25%.

[0106] This embodiment also provides a computer device applicable to the geological disaster monitoring system based on tower big data, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the geological disaster monitoring system based on tower big data as proposed in the above embodiment.

[0107] This computer device can be a terminal, and this computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, operator networks, NFC near-field communication, or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0108] This embodiment also provides a storage medium, on which a computer program is stored, and when this program is executed by a processor, it implements the geological disaster monitoring system based on tower big data as proposed in the above embodiment.

[0109] In summary, the geological disaster monitoring system based on iron tower big data provided by the present invention realizes low-cost large-scale deployment by utilizing existing communication iron tower resources and equipping with a variety of sensing devices, expands the monitoring coverage range, avoids the existence of monitoring blind spots, and improves the accuracy and reliability of geological disaster monitoring at the same time; through the edge computing module, the collected data is locally pre-processed and transmitted to the cloud in real time through the 4G / 5G network, realizing real-time monitoring and rapid response of the data, greatly improving the timeliness of the monitoring data, reducing the resource consumption of data transmission, and ensuring the efficient operation of the monitoring system; through the cloud big data processing and analysis module, an intelligent analysis and early warning system is established, integrating machine learning and artificial intelligence algorithms for multi-source data analysis and risk assessment, improving the accuracy of early warning, enhancing the intelligent level of the system, and ensuring the scientificity and forward-looking of geological disaster prevention and control. The present invention has achieved more remarkable effects in terms of monitoring coverage, data timeliness, and early warning accuracy.

[0110] Embodiment 2

[0111] Referring to Figure 4 , which is the second embodiment of the present invention. This embodiment provides a geological disaster monitoring method based on iron tower big data, including,

[0112] S301, through the iron tower monitoring node module, each iron tower is equipped with a variety of sensing devices, including tilt sensors, acceleration sensors, meteorological monitoring devices, and environmental sensors; these sensors collect data in real time through the power supply and communication network on the iron tower;

[0113] S302, through the edge computing module, the collected data is locally pre-processed, such as data denoising, compression, and preliminary analysis of abnormal events;

[0114] S303, through the data transmission module, using the communication base station function of the iron tower, the pre-processed data is uploaded to the cloud data center in real time through the 4G / 5G network;

[0115] S304, through the cloud big data processing and analysis module, using the big data analysis system deployed in the cloud, integrating machine learning and artificial intelligence algorithms, for in-depth analysis of multi-source data, disaster risk assessment, and real-time early warning generation;

[0116] S305, through the visualization and early warning release module, the analysis results are visually presented through the geographic information system GIS platform, and disaster early warning information is released to relevant departments and the public through multiple channels such as text messages, APPs, and broadcasts;

[0117] S306, through the emergency response and decision support module, integrating historical monitoring data and real-time data, providing decision support for emergency response departments, including evacuation route planning and personnel allocation suggestions for dangerous areas.

[0118] It should be noted that the main application scenarios of this method include:

[0119] Geological disaster monitoring of communication towers: Real-time monitoring of geological change data in mountainous areas and high landslide-prone areas to identify potential risks in advance.

[0120] Disaster prevention monitoring of mountain roads and bridges: Real-time monitoring of infrastructure in high-risk mountainous areas.

[0121] Geological disaster warning system: Providing large-scale monitoring and warning for areas with high incidence of geological disasters.

[0122] Intelligent power grid security guarantee: Protecting high-voltage transmission towers across regions from the impact of geological disasters.

[0123] Public safety emergency management: Providing technical support and emergency command for disaster prevention and mitigation.

[0124] It should also be noted that the technical effects of this method are reflected in the following aspects:

[0125] Improved monitoring data accuracy: Compared with traditional monitoring equipment, the data acquisition accuracy is increased by more than 30%.

[0126] Data transmission efficiency: The system response delay is controlled within 100 milliseconds.

[0127] Prediction accuracy: The prediction accuracy of geological disaster risks within the next 72 hours reaches 92%.

[0128] System stability: The operating stability in complex environments reaches 99%.

[0129] Reduced misreporting rate: The multi-level warning mechanism significantly reduces false alarms and missed reports.

[0130] Risk assessment ability: In the integrated analysis of multiple monitoring tower areas, the regional risk judgment accuracy of the system is increased by 25%.

[0131] Embodiment 3

[0132] This is the third embodiment of the present invention. This embodiment provides a geological disaster monitoring method based on tower big data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0133] To verify the actual effect of the geological disaster monitoring system based on big data from communication towers, a geological disaster-prone area of approximately 50 square kilometers was selected in a mountainous area for a 6-month system test from January to June 2024. There are 12 communication towers distributed in this area, with an average altitude of 2,100 meters, an annual rainfall of 1,200 - 1,500 mm, a geological structure mainly composed of metamorphic rocks and granite, well-developed joints, and historical records of multiple landslides and debris flow disasters.

[0134] Before the start of the experiment, a comprehensive renovation and upgrade was carried out on the 12 towers. The following equipment was installed on each tower: (1) a high-precision tilt sensor (accuracy ±0.005°, sampling frequency 10 Hz) for monitoring surface displacement; (2) a three-axis accelerometer (sampling rate 200 Hz, range ±2g) for detecting ground vibrations; (3) an automatic weather station, including a rain gauge (accuracy 0.1 mm, maximum measurement intensity 300 mm / h) and temperature and humidity sensors (temperature accuracy ±0.1 °C, humidity accuracy ±2%RH); (4) a ground acoustic wave sensor (frequency range 0.5 - 100 Hz) for monitoring underground vibrations. All sensors adopt an industrial protection grade (IP67) and are powered by the existing power supply system of the tower, equipped with a UPS backup power supply to ensure continuous operation for 24 hours.

[0135] A high-performance edge computing unit (CPU: Intel i7-12700, RAM: 32GB, SSD: 1TB) was deployed in the machine room of each tower base station. An improved Kalman filtering algorithm was used to perform real-time noise reduction processing on the original data. A multi-level early warning threshold system was set: level 1 early warning (tilt angle change > 0.8° / hour, surface acceleration > 0.15g, hourly rainfall > 80 mm / hour), level 2 early warning (tilt angle change > 0.5° / hour, surface acceleration > 0.1g, hourly rainfall > 50 mm / hour), level 3 early warning (tilt angle change > 0.3° / hour, surface acceleration > 0.05g, hourly rainfall > 30 mm / hour). An improved compressive sensing algorithm was used for data compression, with a compression rate reaching 85% while keeping the data accuracy loss within 1%.

[0136] Data was transmitted in real-time through the dedicated 5G network channel of the tower to the Alibaba Cloud elastic computing server (96-core CPU, 512GB memory, 10TB storage). A deep learning model based on the LSTM + Attention mechanism was deployed in the cloud for time series analysis, and a random forest algorithm was combined for multi-source data fusion. This algorithm comprehensively considered more than 20 characteristic variables such as real-time monitoring data within 72 hours, historical disaster records, geological parameters, and meteorological data, and constructed a disaster risk prediction model with an accuracy rate of 92%.

[0137] Experimental data table:

[0138]

[0139]

[0140] The results of six months of field tests show that the geological disaster monitoring system based on big data of iron towers has achieved significant breakthroughs in multiple key indicators:

[0141] In terms of data acquisition accuracy: The data acquisition accuracy of the three groups of iron tower nodes has been improved by 31.5%, 30.8% and 31.2% respectively, with an average increase of more than 30%. This improvement is mainly due to the configuration of high-precision sensors and the improved Kalman filtering algorithm, enabling the system to capture more subtle geological change signals.

[0142] In terms of system response performance: The response delay of this system is controlled between 95 - 102 milliseconds, while the traditional system requires 15 seconds, an improvement of about 150 times. Through preprocessing by edge computing and transmission through a dedicated 5G channel, the real-time nature of the data is ensured, winning precious time for disaster early warning.

[0143] In terms of prediction accuracy: The prediction accuracy rate of the system reaches 91.8% - 92.2%, far exceeding 75.5% of the traditional system. This high accuracy rate is attributed to the precise modeling of time series data by the LSTM + Attention deep learning model and the comprehensive analysis of various influencing factors by the multi-source data fusion algorithm.

[0144] In terms of emergency response efficiency: The average time for the system to generate an emergency plan is 26 - 28 minutes, an improvement of about 40% compared to the traditional manual plan (45 minutes). The automatically generated plan includes the optimal evacuation route based on real-time road conditions and the optimal allocation plan of rescue resources.

[0145] In terms of system stability: The operation stability of the three groups of iron tower nodes all reaches more than 98.9%, while the traditional system is only 92.5%. Even under extreme weather conditions - low temperature of -20°C and heavy rain, the system can still operate stably.

[0146] In terms of early warning reliability: The false alarm rate of the system is only 2.1% - 2.3%, significantly lower than 8.5% of the traditional system. The establishment of a multi-level early warning mechanism effectively reduces false alarms and missed alarms, improving the credibility of early warning.

[0147] In terms of risk assessment ability: The risk assessment accuracy of the system reaches 94.5% - 95.2%, an improvement of about 25% compared to the traditional system (70.2%). This improvement stems from the system's ability to integrate and analyze data from multiple monitoring points, enabling more accurate assessment of regional disaster risks.

[0148] These data fully demonstrate the technical advantages of the present invention in practical applications. It not only achieves high-precision, low-latency, and high-reliability in geological disaster monitoring, but also greatly improves the early warning accuracy and emergency response efficiency through intelligent means, providing comprehensive technical support for geological disaster prevention and control.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A geological disaster monitoring system based on tower big data, characterized by: include, Tower monitoring node module (100), edge computing module (200), data transmission module (300), cloud big data processing and analysis module (400), visualization and warning release module (500), emergency response and decision support module (600); The tower monitoring node module (100) is connected to the edge computing module (200) and is used to equip each tower with a variety of sensing devices, including tilt sensors, acceleration sensors, meteorological monitoring devices and environmental sensors; these sensors collect data in real time through the power supply and communication network on the tower; The edge computing module (200) is connected to the data transmission module (300) and is used to perform local preprocessing on the collected data, such as data denoising, compression and preliminary analysis of abnormal events; The data transmission module (300) is connected to the cloud big data processing and analysis module (400) and is used to utilize the communication base station function of the tower to upload the pre-processed data to the cloud data center in real time through the 4G / 5G network; The cloud-based big data processing and analysis module (400) is connected to the edge computing module (200), the data transmission module (300), the visualization and warning release module (500), and the emergency response and decision support module (600), and is used to deploy a big data analysis system through the cloud, integrate machine learning and artificial intelligence algorithms, and perform in-depth analysis of multi-source data, disaster risk assessment, and real-time warning generation; The visualization and warning release module (500) is connected to the tower monitoring node module (100), the edge computing module (200), and the cloud big data processing and analysis module (400), and is used to visualize the analysis results through a geographic information system (GIS) platform, and release disaster warning information to relevant departments and the public through multiple channels such as text messages, APPs, and broadcasts; The emergency response and decision support module (600) is connected to the cloud big data processing and analysis module (400) and is used to integrate historical monitoring data and real-time data to provide decision support for the emergency response department, including evacuation route planning and personnel deployment suggestions in dangerous areas.

2. The geological disaster monitoring system based on tower big data as claimed in claim 1, characterized in that: The tower monitoring node module (100): Through the installation of a variety of sensors, relevant data is collected in real time, including surface tilt, acceleration changes, rainfall, temperature and humidity, and combined with environmental data.

3. The geological disaster monitoring system based on tower big data as claimed in claim 2, characterized in that: The edge computing module (200) pre-processes the original data at the edge computing unit on the tower, including: Abnormal data removal: remove sensor false alarms and outliers; Data compression: reduce the redundancy of uploaded data; Primary analysis: Detect critical changes, such as the surface tilt angle exceeding a set threshold or rainfall exceeding a warning value.

4. The geological disaster monitoring system based on tower big data as claimed in claim 3, characterized in that: The data transmission module (300): The pre-processed monitoring data is transmitted to the cloud with low latency through the tower’s communication base station network.

5. The geological disaster monitoring system based on tower big data as claimed in claim 4, characterized in that: The cloud-based big data processing and analysis module (400) further analyzes the received data using an artificial intelligence algorithm and a big data processing system deployed in the cloud, including: Time series analysis: Conduct trend analysis on long-term monitoring data of tower nodes to extract potential disaster signs; Multi-source data fusion: Based on the data from multiple tower nodes, combined with remote sensing, geology, mining, engineering construction and other multi-source data, a comprehensive assessment of regional disaster risks is conducted; Intelligent prediction: Use machine learning models to predict the time and location of possible future geological disasters.

6. The geological disaster monitoring system based on tower big data as claimed in claim 5, characterized in that: The visualization and warning release module (500): Based on the results of big data analysis, potential geological disaster risks are assessed at a graded level and early warning information is sent to relevant parties through multiple channels.

7. The geological disaster monitoring system based on tower big data as claimed in claim 6, characterized in that: The emergency response and decision support module (600): The system generates an emergency response plan based on the disaster risk level, including evacuation route planning and resource allocation recommendations.

8. A method for monitoring geological disasters based on tower big data, based on the geological disaster monitoring system based on tower big data according to any one of claims 1 to 7, characterized in that: Also includes, Through the tower monitoring node module, each tower is equipped with a variety of sensor devices, including tilt sensors, acceleration sensors, meteorological monitoring equipment and environmental sensors; these sensors collect data in real time through the power supply and communication network on the tower; The collected data is pre-processed locally through the edge computing module, such as data denoising, compression, and preliminary analysis of abnormal events; The data transmission module utilizes the communication base station function of the tower to upload the pre-processed data to the cloud data center in real time through the 4G / 5G network; The cloud-based big data processing and analysis module uses the cloud-based big data analysis system to integrate machine learning and artificial intelligence algorithms for in-depth analysis of multi-source data, disaster risk assessment, and real-time warning generation; The analysis results are visualized through the Geographic Information System (GIS) platform through the visualization and warning release module, and disaster warning information is released to relevant departments and the public through multiple channels such as SMS, APP, and broadcasting; The emergency response and decision support module integrates historical monitoring data and real-time data to provide decision support for emergency response departments, including evacuation route planning and personnel deployment recommendations in dangerous areas.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the geological disaster monitoring system based on tower big data described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the geological disaster monitoring system based on tower big data described in any one of claims 1 to 7 are implemented.

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