Traffic engineering geological disaster monitoring and early warning system

Abstract

The invention provides a traffic engineering geological disaster monitoring and early warning system, and the system comprises a data collection module which is used for collecting environment data related to a traffic engineering region in real time; the data transmission module is used for transmitting the acquired environment data to the data processing and analyzing module; the data processing module is used for carrying out cleaning, normalization, feature selection and preprocessing on the input environment data; the disaster prediction module is used for constructing a disaster prediction model based on a support vector machine algorithm, performing disaster prediction on the environment data input in real time through the disaster prediction model, and outputting a prediction result; the early warning decision module is used for carrying out risk assessment according to an output result of the disaster prediction model and triggering early warning; and the alarm execution module is used for executing corresponding emergency response operation according to the early warning signal and notifying related departments and personnel to deal with disasters and deal with the disasters. According to the invention, accurate disaster monitoring and early warning can be provided under different environments and conditions, and disaster response efficiency and public safety guarantee are improved.

Description

Technical Field

[0001] The invention belongs to the field of geological disasters, and in particular relates to a traffic engineering geological disaster monitoring and early warning system. Background Art

[0002] With the rapid development of transportation infrastructure construction, especially in mountainous and hilly areas, transportation projects are facing increasingly severe threats from geological disasters. Traditional disaster warning methods rely on manual monitoring and post-analysis, and have problems such as delayed response and insufficient accuracy. To solve these problems, the use of automated monitoring and intelligent early warning technology can obtain environmental data in real time and conduct efficient analysis, warn of disasters in advance, and avoid or reduce the impact of disasters on transportation projects and personnel. Summary of the invention

[0003] The purpose of the present invention is to provide a traffic engineering geological disaster monitoring and early warning system based on environmental data. The system collects environmental data in real time through a variety of sensors, and combines advanced support vector machine (SVM) algorithms and other data processing technologies to accurately predict the risk of geological disasters, trigger early warnings in a timely manner and implement emergency response measures, thereby improving traffic safety and reducing disaster losses.

[0004] The technical solution of the present invention is as follows:

[0005] A traffic engineering geological disaster monitoring and early warning system, the system comprising:

[0006] A data acquisition module, used to collect environmental data related to the traffic engineering area in real time, including precipitation, temperature, humidity, soil moisture, earthquake data, soil type, slope, and ground vibration;

[0007] A data transmission module, used to transmit the collected environmental data to the data processing and analysis module;

[0008] Data processing module, used for cleaning, normalizing, feature selection and preprocessing of input environmental data;

[0009] The disaster prediction module builds a disaster prediction model based on the support vector machine algorithm, uses the disaster prediction model to predict disasters based on real-time input environmental data, and outputs the prediction results;

[0010] The early warning decision module conducts risk assessment based on the output of the disaster prediction model and triggers early warnings;

[0011] The alarm execution module performs corresponding emergency response operations according to the early warning signal and notifies relevant departments and personnel to respond to and handle the disaster.

[0012] Furthermore, the data acquisition module includes a plurality of sensors, specifically including:

[0013] Soil moisture sensor, used to monitor soil moisture changes in real time;

[0014] Meteorological sensors to monitor precipitation, temperature, and humidity;

[0015] Seismic sensors, which monitor vibrations or ground displacement data;

[0016] GPS positioning sensor is used to obtain the location of the disaster.

[0017] Furthermore, the data transmission module includes:

[0018] A wireless communication unit, used to transmit the collected environmental data to the data processing module via wireless signals, the wireless communication unit supports multiple communication protocols, including Wi-Fi, Bluetooth, ZigBee and cellular networks;

[0019] The fault diagnosis and retransmission unit is used to monitor abnormal conditions during data transmission in real time, automatically diagnose faults and retransmit lost or erroneous data.

[0020] Furthermore, the data processing module includes:

[0021] Data cleaning unit, used to delete or fill missing data, remove abnormal data, and perform outlier detection;

[0022] Feature engineering unit, used to extract and select features from raw data and construct features related to the occurrence of geological disasters;

[0023] Standardization units are used to standardize or normalize data to ensure that different feature values ​​have the same scale.

[0024] Furthermore, the disaster prediction module includes:

[0025] A disaster prediction model building unit, used for building a disaster prediction model;

[0026] A disaster type prediction unit is used to determine the specific type of disaster based on the input environmental data and historical disaster data, and output the risk probability of the relevant disaster;

[0027] The real-time updating unit is used to regularly update the disaster prediction model based on the newly collected data.

[0028] Furthermore, the disaster prediction model constructed based on the support vector machine algorithm is specifically as follows:

[0029] The support vector machine model is trained using historical disaster data and environmental data as input to obtain the optimal hyperplane;

[0030] Select a kernel function, which is a radial basis function kernel, to map data to a high-dimensional feature space for classification;

[0031] The cross-validation method was used to evaluate the model performance, and the hyperparameters C and gamma of SVM were adjusted by the grid search method to obtain the disaster prediction model.

[0032] Furthermore, the early warning decision module includes:

[0033] A risk assessment unit, which classifies risks according to the disaster probability output by the disaster prediction model and a set threshold, and outputs a disaster risk level;

[0034] The early warning trigger unit triggers corresponding early warning measures when the disaster risk level exceeds the preset threshold.

[0035] Furthermore, the alarm execution module includes:

[0036] Traffic control units, used to restrict traffic flow through traffic lights and electronic signs;

[0037] The emergency response coordination unit is used to coordinate and trigger emergency response measures, send evacuation instructions and disaster prevention measures to relevant personnel and departments, and warn the public through broadcasting and alarm systems.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] The traffic engineering geological disaster monitoring and early warning system provided by the present invention collects environmental data in real time through multiple sensors and combines the support vector machine algorithm to predict disasters, which can accurately assess the risk of geological disasters and issue early warnings in a timely manner. The system has real-time data transmission, fault diagnosis and retransmission mechanisms to ensure the integrity and accuracy of the data. The early warning decision module automatically conducts risk assessment and response based on the disaster prediction results, triggers traffic control and emergency response measures, and minimizes disaster losses. The system has a high degree of automation, intelligence and self-adaptation, and can provide accurate disaster monitoring and early warning under different environments and conditions, improving disaster response efficiency and public safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the description and claims, serve to illustrate the embodiments of the invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method.

[0041] Figure 1 A schematic diagram of the module framework of the present invention is shown. DETAILED DESCRIPTION

[0042] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0043] The embodiment of the present invention provides a traffic engineering geological disaster monitoring and early warning system, including the following modules:

[0044] Data acquisition module: collects environmental data through various sensors, including precipitation, temperature, humidity, soil moisture, earthquake data, soil type, slope, ground vibration and other key factors; the module includes multiple sensors, each of which has its specific function, as described below:

[0045] Soil moisture sensor: used to monitor soil moisture changes, which is an important indicator for determining the occurrence of geological disasters (such as landslides and mud-rock flows). Using resistive or capacitive sensors, soil moisture data can be used to estimate soil saturation and the probability of landslides with an accuracy of ±2%. Data range: 0% to 100%.

[0046] Meteorological sensors: including temperature and humidity sensors, precipitation sensors, wind speed and direction sensors, etc. The temperature and humidity sensors use capacitive or resistive probes and can accurately measure the temperature (±0.5°C) and humidity (±3%RH) in the air. The precipitation sensor is a rain gauge that provides precipitation data by detecting the intensity and duration of precipitation with an accuracy of ±0.1mm.

[0047] Seismic sensor: used to monitor earthquake vibration and ground displacement data in real time, using MEMS (micro-electromechanical system) accelerometer and displacement sensor, which can monitor ground vibration, displacement, tilt and other data. The sensor has an operating frequency of 0.1Hz to 500Hz, an accuracy of ±0.01g, and can identify small vibrations and sense earthquake events in a timely manner.

[0048] GPS positioning sensor: used to obtain the latitude, longitude and altitude of the disaster site in real time, and provide high-precision positioning data based on GNSS (Global Navigation Satellite System). The accuracy can reach ±5 meters, and the positioning frequency is 1Hz to 10Hz. It is used to accurately mark the location of the disaster and assist in the analysis of the disaster impact range.

[0049] Data transmission module: transmits data to the data processing module through the wireless communication network; this module includes a wireless communication unit and a fault diagnosis and retransmission unit.

[0050] Wireless communication unit: used to transmit the collected data to the data processing module via wireless signals. The wireless communication unit supports a variety of communication protocols, including Wi-Fi, ZigBee, cellular networks (such as 4G, 5G), etc. The choice of communication method is optimized according to environmental conditions and transmission distance to ensure that data transmission is not interfered by the outside world and can adapt to different application scenarios.

[0051] Fault diagnosis and retransmission unit: This unit is used to monitor abnormal conditions (such as communication interruption, data loss, signal interference, etc.) during data transmission in real time. When the system detects a transmission failure, it automatically triggers the retransmission mechanism to ensure that the lost data is restored. The unit uses a timing-based fault detection algorithm (such as a hash-based data integrity check) to identify and retransmit lost or erroneous data to ensure the integrity and accuracy of system data.

[0052] Data processing module: pre-processes the input environmental data by cleaning, normalizing, and selecting features to prepare for subsequent disaster prediction; this module includes the following units:

[0053] Data cleaning unit: Cleans the collected raw data, mainly including processing missing values, outliers and redundant data. Missing values ​​can be filled by interpolation methods (such as linear interpolation, Lagrange interpolation, etc.), and outliers are identified and removed by Z-score detection method, IQR method, etc. to ensure the quality of the data set. The goal of the data cleaning unit is to process the data generated by the sensor into structured and reliable data input.

[0054] Feature Engineering Unit: This unit is responsible for extracting and selecting features from data, and constructing features related to geological disasters based on environmental data and historical disaster data. For example, disaster risk features are generated based on the relationship between soil moisture and precipitation, and earthquake vibration and slope. Feature selection is performed based on methods such as information gain, chi-square test or correlation coefficient analysis to ensure that key features that contribute to disaster prediction are selected.

[0055] Standardization unit: In order to ensure the consistency of data scales between features and avoid a feature dominating the model due to dimensional differences, the standardization unit standardizes or normalizes the data. For example, using the Z-score standardization method or the Min-Max normalization method, the value of each feature is converted into dimensionless standard data to ensure that the features in the data set have the same scale.

[0056] Disaster prediction module: Use the support vector machine algorithm to model historical disaster data and real-time data to predict the probability of disasters; specifically include:

[0057] Disaster prediction model building unit: Use historical disaster data and environmental data as input to train the support vector machine (SVM) model. By optimizing the model's hyperplane parameters (such as C, gamma, etc.), the classification interval is maximized to obtain accurate prediction results. This unit uses the Lagrange multiplier method to solve the optimal hyperplane, and maps low-dimensional data to high-dimensional space through kernel functions to enhance the model's nonlinear fitting ability.

[0058] The SVM formula is:

[0059]

[0060] Among them, w is the normal vector of the hyperplane, b is the bias, and x i is the input feature, y i is a label indicating whether a disaster occurs or not.

[0061] Disaster type prediction unit: This unit predicts the type of disaster (such as landslide, mudslide, earthquake, etc.) based on the input environmental data (such as temperature, precipitation, soil moisture, etc.) and historical disaster data, and outputs the risk probability of the disaster.

[0062] Real-time update unit: As new environmental data is continuously input, the disaster prediction model will be updated regularly. The update process is done by retraining the model or fine-tuning the model using incremental learning technology. The real-time update mechanism ensures that the model can adapt to changing environmental conditions and improve the accuracy of disaster prediction.

[0063] Early warning decision module: Based on the risk assessment results output by the disaster prediction model, it determines whether it exceeds the preset threshold and triggers an early warning; the module has a risk assessment unit and an early warning trigger unit.

[0064] Risk Assessment Unit: Classify disasters based on the probability of occurrence output by the disaster prediction model and the preset risk threshold. The goal of this unit is to assess the severity of disasters based on the prediction results and classify disasters into different risk levels (such as low risk, medium risk, and high risk).

[0065] The risk assessment formula is as follows:

[0066]

[0067] Among them, P (disaster) is the probability of disaster occurrence, and T1 and T2 are the set risk thresholds.

[0068] Early warning trigger unit: When the risk level exceeds the set threshold, the early warning measures are automatically triggered. This unit determines the relationship between the risk level and the early warning threshold and issues an early warning signal in a timely manner.

[0069] Alarm execution module: executes emergency response operations according to warning signals, including traffic control and evacuation instructions, to ensure traffic safety and timely evacuation of personnel, including:

[0070] Traffic control unit: When the disaster risk is assessed as medium or high, the system automatically triggers traffic control measures. Through traffic lights, electronic signs, warning signs and other means, road access is controlled to reduce the impact of disasters on traffic. The traffic control unit can be integrated with the intelligent transportation system to automatically adjust the lights, restrict traffic or guide vehicles to avoid the disaster area.

[0071] Emergency Response Coordination Unit: When a disaster occurs, the Emergency Response Coordination Unit automatically triggers emergency response measures, including sending evacuation instructions, disaster prevention measures, and emergency rescue notifications. The unit can issue warning information to the public through broadcast systems, mobile applications, SMS platforms, etc., and send emergency response instructions to relevant departments and personnel through mobile apps. The Emergency Response Coordination Unit can work in conjunction with relevant emergency management agencies to ensure rapid coordination of emergency rescue forces after a disaster occurs.

[0072] The traffic engineering geological disaster monitoring and early warning system of the present invention can realize accurate prediction and timely response to geological disasters through real-time data collection, intelligent analysis and prediction, and precise early warning and response mechanisms of multiple sensors.

[0073] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A traffic engineering geological disaster monitoring and early warning system, characterized in that: The system comprises: A data acquisition module, used to collect environmental data related to the traffic engineering area in real time, including precipitation, temperature, humidity, soil moisture, earthquake data, soil type, slope, and ground vibration; A data transmission module, used to transmit the collected environmental data to the data processing and analysis module; Data processing module, used for cleaning, normalizing, feature selection and preprocessing of input environmental data; The disaster prediction module builds a disaster prediction model based on the support vector machine algorithm, uses the disaster prediction model to predict disasters based on real-time input environmental data, and outputs the prediction results; The early warning decision module conducts risk assessment based on the output of the disaster prediction model and triggers early warnings; The alarm execution module performs corresponding emergency response operations according to the early warning signal and notifies relevant departments and personnel to respond to and handle the disaster.

2. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The data acquisition module includes multiple sensors, specifically including: Soil moisture sensor, used to monitor soil moisture changes in real time; Meteorological sensors to monitor precipitation, temperature, and humidity; Seismic sensors, which monitor vibrations or ground displacement data; GPS positioning sensor is used to obtain the location of the disaster.

3. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The data transmission module comprises: A wireless communication unit, used to transmit the collected environmental data to the data processing module via wireless signals, the wireless communication unit supports multiple communication protocols, including Wi-Fi, Bluetooth, ZigBee and cellular networks; The fault diagnosis and retransmission unit is used to monitor abnormal conditions during data transmission in real time, automatically diagnose faults and retransmit lost or erroneous data.

4. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The data processing module comprises: Data cleaning unit, used to delete or fill missing data, remove abnormal data, and perform outlier detection; Feature engineering unit, used to extract and select features from raw data and construct features related to the occurrence of geological disasters; Standardization units are used to standardize or normalize data to ensure that different feature values ​​have the same scale.

5. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The disaster prediction module includes: A disaster prediction model building unit, used for building a disaster prediction model; A disaster type prediction unit is used to determine the specific type of disaster based on the input environmental data and historical disaster data, and output the risk probability of the relevant disaster; The real-time updating unit is used to regularly update the disaster prediction model based on the newly collected data.

6. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The disaster prediction model constructed based on the support vector machine algorithm is specifically as follows: The support vector machine model is trained using historical disaster data and environmental data as input to obtain the optimal hyperplane; Select a kernel function, which is a radial basis function kernel, to map data to a high-dimensional feature space for classification; The cross-validation method was used to evaluate the model performance, and the hyperparameters C and gamma of SVM were adjusted by the grid search method to obtain the disaster prediction model.

7. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The early warning decision module includes: A risk assessment unit, which classifies risks according to the disaster probability output by the disaster prediction model and a set threshold, and outputs a disaster risk level; The early warning trigger unit triggers corresponding early warning measures when the disaster risk level exceeds the preset threshold.

8. The traffic engineering geological disaster monitoring and early warning system according to claim 1 is characterized in that: The alarm execution module comprises: Traffic control units, used to restrict traffic flow through traffic lights and electronic signs; The emergency response coordination unit is used to coordinate and trigger emergency response measures, send evacuation instructions and disaster prevention measures to relevant personnel and departments, and warn the public through broadcasting and alarm systems.

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