Beidou-based slope monitoring and early warning system evaluation method

Abstract

The invention discloses a Beidou-based slope monitoring and early warning system evaluation method, particularly relates to the technical field of system monitoring, and is used for solving the problem that a fault of an existing slope monitoring and early warning system is not found in time. Analyzing a time synchronization function of the Beidou system to realize data synchronization between the target sensors and analyzing automatic calibration conditions of the target sensors; on the basis that the data synchronism is normal and the sensor is reliable to use, the consistency of data fusion is further analyzed through an entropy theory, vibration influence evaluation and adaptability evaluation caused by a vehicle are analyzed, key monitoring is conducted for a high-risk time period, and through comprehensive analysis, the accuracy of data fusion is improved. The system can generate detailed early warning information for a specific monitoring scene, the safety of slope monitoring and the accuracy of early warning are enhanced, the data processing efficiency is improved through the staged processing flow, and disasters and accidents caused by inaccurate data or improper analysis are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the field of system monitoring technology, and more specifically, to a Beidou-based slope monitoring and early warning system evaluation method. Background Art

[0002] If a landslide or collapse occurs on the slope near the road, it will directly affect the safe passage of the road, so safety monitoring of the slope near the road is very important. The existing slope monitoring and early warning system, especially the technology based on the Beidou satellite navigation system, has been widely used to monitor slope stability, providing functions such as position monitoring, time synchronization and data transmission.

[0003] However, despite the advantages of slope monitoring and early warning systems in providing real-time data and early warnings, the evaluation of the system's own performance and the timely discovery of potential hazards have not received sufficient attention. Traditional methods often ignore the continuous evaluation of the stability and reliability of the slope monitoring and early warning system itself, which may lead to failures in the slope monitoring and early warning system not being discovered in time, increasing the risk of disasters such as landslides and endangering public safety.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a Beidou-based slope monitoring and early warning system evaluation method to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A slope monitoring and early warning system evaluation method based on Beidou includes the following steps:

[0008] S1: Determine that the Beidou system time synchronization function realizes data synchronization between target sensors, and the data synchronization between target sensors includes normal data synchronization and poor data synchronization;

[0009] S2: Analyze the automatic calibration of the target sensor to determine whether the use of the target sensor itself is reliable;

[0010] S3: When data synchronization is normal and the target sensor itself is reliable, the consistency of the data fusion between the Beidou system and the target sensor is analyzed through the entropy theory to evaluate the information loss and redundancy between the Beidou system and the target sensor;

[0011] S4: By analyzing the driving conditions of vehicles on the road, the influence of the vibration caused by the vehicle driving on the stability of the target sensor is evaluated; according to the influence of the vibration caused by the vehicle driving on the stability of the target sensor, the time period is divided into a high vibration risk time period and a low vibration risk time period;

[0012] S5: Evaluate the adaptability of the target sensor to the vibration caused by vehicle driving during the high vibration risk period;

[0013] S6: Through comprehensive analysis of the information loss and redundancy between the Beidou system and the target sensor and the adaptability of the target sensor to the vibration caused by vehicle driving, the degree of monitoring risk of the slope monitored by the target sensor is warned.

[0014] In a preferred embodiment, determining the Beidou system time synchronization function to achieve data synchronization between target sensors is specifically as follows:

[0015] Collect the timestamp data of all target sensors; obtain the reference timestamp provided by the Beidou system at each sampling moment; calculate the time deviation of each target sensor at each sampling moment; for each target sensor, calculate the average value of the time deviations of all its sampling moments;

[0016] Calculate the average time deviation of all target sensors, which is expressed as: ;in, is the average time deviation of all target sensors, is the number of target sensors, It is The average time deviation of target sensors;

[0017] Setting the Threshold ,Will and For comparison: , then the data synchronization between the target sensors is determined to be normal; when , then the data synchronization between the target sensors is determined to be poor.

[0018] In a preferred embodiment, the automatic calibration of the target sensor is analyzed to determine whether the use of the target sensor itself is reliable, specifically:

[0019] Setting a calibration monitoring interval; counting the number of times the target sensor triggers automatic calibration within the calibration monitoring interval, and marking the ratio of the number of times the target sensor triggers automatic calibration within the calibration monitoring interval to the time length corresponding to the calibration monitoring interval as the target calibration frequency;

[0020] Set the calibration frequency threshold; compare the target calibration frequency with the calibration frequency threshold to determine whether the target sensor itself is reliable:

[0021] When the target calibration frequency is greater than the calibration frequency threshold, it is determined that the use of the target sensor itself is unreliable; when the target calibration frequency is less than or equal to the calibration frequency threshold, it is determined that the use of the target sensor itself is reliable.

[0022] In a preferred embodiment, the consistency of the data fusion between the Beidou system and the target sensor is analyzed by entropy theory to evaluate the information loss and redundancy between the Beidou system and the target sensor, specifically:

[0023] Collect data from BeiDou system and target sensors; assume Represents the data of the Beidou system. Represents the data of the target sensor;

[0024] Calculate the joint probability distribution of BeiDou system data and target sensor data;

[0025] Divide continuous data into several intervals, count the joint occurrence frequency of each interval, and construct a joint frequency table; calculate the joint probability distribution ;

[0026] Count the edge frequencies of Beidou system data and target sensor data respectively;

[0027] Divide the marginal frequency by the total number of samples to get the marginal probability distribution and ;

[0028] The mutual information value is calculated using the mutual information formula, which is expressed as: ;in, is the mutual information value.

[0029] In a preferred embodiment, by analyzing the driving conditions of vehicles on the road, the influence of the vibration caused by the vehicle driving on the stability of the target sensor is evaluated, and the time period is divided into a high vibration risk time period and a low vibration risk time period according to the influence of the vibration caused by the vehicle driving on the stability of the target sensor; specifically:

[0030] Determine the road monitoring area corresponding to the target sensor: set the radius with the target sensor as the center;

[0031] Get the geographic location and shape data of the road area;

[0032] Mark the overlapping area between the circle range and the road area as the road monitoring area;

[0033] Obtain the number of vehicles and the types of vehicles corresponding to the road monitoring area at each time point;

[0034] When the number of vehicles in the road monitoring area at a time point is greater than a preset number threshold, or there is a heavy vehicle at a time point, the time point is marked as a high vibration risk time point, otherwise, the time point is marked as a low vibration risk time point;

[0035] Based on the marked situations of the time points, a high vibration risk time period and a low vibration risk time period are determined.

[0036] In a preferred embodiment, the adaptability of the target sensor to the vibration caused by vehicle travel during the high vibration risk period is evaluated as follows:

[0037] Collect vibration data from target sensors during high vibration risk periods and vibration data during low vibration risk periods , Indicates a point in time;

[0038] Use Fast Fourier Transform to transform time domain data Convert to frequency domain and get the spectrum , represents frequency;

[0039] The data of the low vibration risk period Convert to frequency domain and get the spectrum ;

[0040] Calculating the spectrum The spectral power density ;

[0041] Calculating the spectrum The spectral power density ;

[0042] Defining the frequency range ; Calculate the spectrum analysis adaptability index within the defined frequency range, and its expression is: ;in, is the adaptability index for spectrum analysis.

[0043] In a preferred embodiment, in S6, by comprehensively analyzing the information loss and redundancy of the Beidou system and the target sensor and the adaptability of the target sensor to the vibration caused by vehicle driving, the monitoring risk level of the slope monitored by the target sensor is warned, specifically:

[0044] The mutual information value and the spectrum analysis adaptability index are normalized, and the normalized mutual information value and the spectrum analysis adaptability index are assigned preset proportional coefficients respectively, and the sensor monitoring hidden danger warning coefficient is calculated;

[0045] Set the sensor monitoring hidden danger warning threshold; compare the sensor monitoring hidden danger warning coefficient with the sensor monitoring hidden danger warning threshold:

[0046] When the sensor monitoring hidden danger warning coefficient is greater than the sensor monitoring hidden danger warning threshold, it is determined that a target sensor monitoring hidden danger warning signal is generated;

[0047] When the sensor monitoring hidden danger warning coefficient is less than or equal to the sensor monitoring hidden danger warning threshold, it is determined that a normal signal of the target sensor monitoring hidden danger is generated.

[0048] Technical effects and advantages of the Beidou-based slope monitoring and early warning system evaluation method of the present invention:

[0049] 1. By ensuring the time synchronization function of the Beidou system to achieve data synchronization between target sensors, this step can effectively ensure the consistency and reliability of data collection and reduce analysis errors caused by data asynchrony. In addition, by analyzing the automatic calibration of the target sensor, the stable operation of the sensor and accurate data feedback are ensured, which is particularly important for long-term slope monitoring.

[0050] 2. On the basis of normal data synchronization and reliable use of the sensor itself, the consistency of data fusion is further analyzed through entropy theory, which not only improves the accuracy of data processing, but also enhances the sensitivity to abnormal situations, so that the system can more effectively identify and respond to potential hidden dangers. On this basis, the vibration impact assessment and adaptability assessment caused by the vehicle enable the system to dynamically adjust the monitoring strategy according to the real-time traffic conditions and sensor response, and focus on monitoring high-risk time periods, thereby optimizing resource allocation and improving response efficiency; finally, through the comprehensive analysis of the above indicators, the system can generate detailed warning information for specific monitoring scenarios, which greatly enhances the safety of slope monitoring and the accuracy of warning in practical applications. This staged processing flow not only improves the efficiency of data processing, but also optimizes the use of monitoring resources, making the slope monitoring system more intelligent and automated. In addition, this method significantly improves the practicality and real-time performance of the warning system, provides more scientific and accurate warning support for slope monitoring, and is particularly suitable for changing natural environments and complex geographical conditions, effectively preventing disasters caused by inaccurate data or improper analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of a Beidou-based slope monitoring and early warning system evaluation method of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0053] Example: Figure 1 The present invention provides a Beidou-based slope monitoring and early warning system evaluation method, which includes the following steps:

[0054] S1: Determine that the Beidou system time synchronization function realizes data synchronization between target sensors. The data synchronization between target sensors includes normal data synchronization and poor data synchronization.

[0055] S2: Analyze the automatic calibration status of the target sensor to determine whether the use of the target sensor itself is reliable.

[0056] S3: When data synchronization is normal and the target sensor itself is reliable, the consistency of the data fusion between the Beidou system and the target sensor is analyzed through the entropy theory to evaluate the information loss and redundancy between the Beidou system and the target sensor.

[0057] S4: By analyzing the driving conditions of vehicles on the road, the influence of the vibration caused by the vehicle driving on the stability of the target sensor is evaluated; according to the influence of the vibration caused by the vehicle driving on the stability of the target sensor, the time period is divided into a high vibration risk time period and a low vibration risk time period.

[0058] S5: Evaluate the adaptability of the target sensor to the vibration caused by vehicle driving during the high vibration risk period.

[0059] S6: Through comprehensive analysis of the information loss and redundancy between the Beidou system and the target sensor and the adaptability of the target sensor to the vibration caused by vehicle driving, the degree of monitoring risk of the slope monitored by the target sensor is warned.

[0060] In S1, the Beidou system time synchronization function is determined to achieve data synchronization between target sensors, specifically:

[0061] In the slope monitoring and early warning system based on the BeiDou system, it is crucial to ensure data synchronization between multiple target sensors. In the slope monitoring and early warning system, multiple sensors such as displacement sensors, inclinometers and strain gauges are deployed at different locations to collect slope status data. These data must be consistent in time to ensure the accuracy of analysis and the effectiveness of warning. The high-precision time synchronization function provided by the BeiDou system is used to ensure that all sensor data is recorded and processed under the same time reference.

[0062] Collect the timestamp data of all target sensors. Each target sensor will record a timestamp at each sampling moment. The target sensor is in the The timestamp recorded when the sampling is performed is marked as .

[0063] Get the reference timestamp provided by the Beidou system at each sampling moment. The Beidou system reference time mark at the time of sampling is .

[0064] The collected time data were preprocessed, including outlier removal and data standardization, to ensure data quality and consistency.

[0065] Calculate the time deviation of each target sensor at each sampling moment: ;in, It is The target sensor is in the The time deviation of the subsampling.

[0066] For each target sensor, calculate the average of the time deviations of all its sampling moments: ;in, It is The average time deviation of target sensors, is the number of sampling times.

[0067] The average time deviation of all target sensors is calculated to evaluate the overall time synchronization, which is expressed as: ;in, is the average time deviation of all target sensors, is the number of target sensors.

[0068] Among them, the target sensor refers to the sensor used to monitor the slope in the slope monitoring and early warning system. The target sensor includes displacement sensors, inclinometers and strain gauges.

[0069] Setting the Threshold To distinguish between normal and bad data synchronization:

[0070] when , then the data synchronization between the target sensors is determined to be normal;

[0071] when , then the data synchronization between the target sensors is determined to be poor.

[0072] in, It is set by professional technicians in this field according to the average time deviation of all target sensors, the requirement standard for data synchronization and other actual conditions.

[0073] If data synchronization is assessed as poor, take the following actions to adjust it:

[0074] Check and correct the sensor's clock setting.

[0075] Optimize the reception quality of Beidou time synchronization signals.

[0076] Add data preprocessing steps to remove or correct abnormal data.

[0077] By calculating the deviation of each sensor relative to BeiDou time and taking the average value, the time synchronization of the entire system can be accurately evaluated. Considering the data of all sensors ensures the comprehensiveness and representativeness of the system evaluation. Through preprocessing and real-time calculation, synchronization problems can be discovered and corrected in a timely manner to ensure the real-time and validity of the data.

[0078] In S2, the automatic calibration of the target sensor is analyzed to determine whether the use of the target sensor itself is reliable, specifically:

[0079] In road slope monitoring, sensors with automatic calibration functions are usually used. This is because the slope monitoring environment is complex and changeable, including factors such as temperature, humidity and vibration that can affect the accuracy and stability of the sensor. The automatic calibration function can automatically adjust the sensor readings regularly or when an abnormality is detected to ensure the accuracy and reliability of the data. Automatic calibration can compensate for deviations caused by sensor aging or environmental changes, reduce the frequency and maintenance costs of manual calibration, and improve the overall efficiency and real-time performance of the monitoring system.

[0080] Set the calibration monitoring interval. The range of the calibration monitoring interval is set according to the actual situation. Generally, the end point of the calibration monitoring interval is set to the real-time time point.

[0081] The number of times the target sensor triggers automatic calibration within the calibration monitoring interval is counted, and the ratio of the number of times the target sensor triggers automatic calibration within the calibration monitoring interval to the time length corresponding to the calibration monitoring interval is marked as the target calibration frequency.

[0082] Too frequent target calibration may affect the reliability of the target sensor. Too frequent target calibration usually indicates that the target sensor may have low reliability. Frequent automatic calibration may be caused by drastic changes in the environment where the sensor is located. For example, external conditions such as temperature, humidity, and vibration fluctuate greatly, causing the sensor to need frequent adjustments to maintain accuracy. Continuous operation of the sensor in such an environment may cause higher wear and aging rates, affecting its long-term stability and reliability. Frequent calibration may also be caused by aging or hardware failure of the sensor itself. Aged sensors are more sensitive to external changes and therefore require more frequent calibration to maintain data accuracy.

[0083] Set a calibration frequency threshold. According to historical data and the sensor manual, set a reasonable calibration frequency threshold to determine whether the sensor calibration frequency is within the normal range.

[0084] For example, if the sensor manufacturer recommends automatic calibration once a week, then the calibration frequency threshold = 1 / 7 times / day.

[0085] Compare the target calibration frequency with the calibration frequency threshold to determine whether the target sensor itself is being used reliably:

[0086] When the target calibration frequency is greater than the calibration frequency threshold, it is determined that the use of the target sensor itself is unreliable;

[0087] When the target calibration frequency is less than or equal to the calibration frequency threshold, it is determined that the use of the target sensor itself is reliable.

[0088] In S3, the consistency of the data fusion between the BeiDou system and the target sensor is analyzed by entropy theory, and the information loss and redundancy of the BeiDou system and the target sensor are evaluated. Specifically:

[0089] In the slope monitoring and early warning system, ensuring the consistency of the BeiDou system and the target sensor data is crucial for accurate warning and data reliability. The mutual information (MI) is used to evaluate the data fusion effect of the BeiDou system and the target sensor, and the degree of information loss and redundancy can be quantified.

[0090] In the slope monitoring and early warning system based on the BeiDou system, multiple sensors (such as surface displacement sensors, inclinometers, strain gauges, etc.) work together to monitor the stability of the slope in real time. In order to ensure the high accuracy and reliability of the data, it is necessary to evaluate the consistency between the BeiDou system and the sensor data and quantify the information loss and redundancy. Measuring the data fusion effect through mutual information can effectively identify and solve potential data consistency problems.

[0091] Sensors include but are not limited to surface displacement sensors, inclinometers, strain gauges, etc., which are used to monitor physical quantities such as surface deformation, inclination angle and strain value. Specifically:

[0092] Surface displacement sensors collect horizontal or vertical displacement data of the ground or target objects, usually expressed as displacement (meters) or speed (meters / second);

[0093] Inclinometers monitor the tilt angle of an object or structure. The output data is usually an angle value (degrees or radians) to reflect the directional change of the structure's tilt or displacement;

[0094] Strain gauges are used to measure the strain value of an object or structure under the action of external force. The output data is the strain amount (usually in microstrain or strain percentage), which can reflect the deformation of the structure.

[0095] These sensor data are collected through their respective sensor modules and input together with the positioning data obtained by the Beidou system (such as location information, speed, trajectory, etc.) for data analysis and processing. The physical quantity data monitored by the sensor is combined with the location information provided by the Beidou system through a data fusion algorithm, thereby achieving precise positioning and real-time monitoring of the target, ensuring the comprehensiveness and accuracy of the environmental monitoring data.

[0096] The unified processing method of GPS coordinate data and surface displacement sensor data is as follows: First, the initial geodetic coordinate data of the target, including longitude, latitude and elevation information, is obtained through the Beidou system or other global positioning systems (such as GPS). These data describe the initial position of the target on the earth's surface. The surface displacement sensor is used to monitor the relative displacement of the ground or target object. The data provided by the sensor is usually relative displacement (for example, horizontal displacement, vertical displacement, etc.), which usually represents the offset of the target relative to the initial position in meters or millimeters. The initial coordinate data provided by the GPS system is fused with the surface displacement sensor data. The specific operation is to update the actual position of the target through the displacement obtained by the surface displacement sensor and the initial coordinates provided by GPS. Specifically, the displacement data provided by the surface displacement sensor represents the change in the target relative to the initial position, and these data are added to the GPS initial coordinates to obtain the real-time position of the target in the geodetic coordinate system. This method can ensure the unification and compatibility between GPS coordinate data and surface displacement sensor data, and ensure the accuracy and real-time performance of the position data.

[0097] Mutual information is a statistic that measures the amount of information shared between two random variables. It quantifies the extent to which knowing the information about one variable reduces the uncertainty of another variable. The larger the mutual information value, the more information is shared between the two variables and the higher the data consistency.

[0098] Collect data from the BeiDou system and target sensors. Assumptions Represents the data of the Beidou system. Represents the data of the target sensor. For example, the high-precision positioning data provided by the Beidou system and displacement data recorded by surface displacement sensors .

[0099] In order to calculate the mutual information, it is necessary to first calculate the joint probability distribution of the Beidou system data and the target sensor data.

[0100] Discretize the data and divide the continuous data into several intervals. Count the joint occurrence frequency of each interval and construct a joint frequency table.

[0101] Divide the joint frequency by the total number of samples to get the joint probability distribution .

[0102] The edge frequencies of BeiDou system data and target sensor data are counted separately.

[0103] Divide the marginal frequency by the total number of samples to get the marginal probability distribution and , marginal probability distribution and Respectively and The value is and probability.

[0104] After obtaining the joint probability distribution and marginal probability distribution, the mutual information value is calculated using the mutual information formula, which is expressed as: ;in, is the mutual information value (representing the random variable and The mutual information between .

[0105] The mutual information value can be used to evaluate the consistency and information sharing degree between the Beidou system and the target sensor data.

[0106] A larger mutual information value means:

[0107] The lower the degree of information loss: A larger mutual information value indicates that more information is shared between the BeiDou system and the target sensor, which means that less information is lost during the data transmission and fusion process.

[0108] The lower the redundancy: A larger mutual information value indicates less repeated information between the two. Low redundancy means that the information after data fusion is more effective and unique, without too much repeated data.

[0109] The smaller the mutual information value, the smaller the

[0110] The higher the degree of information loss: A smaller mutual information value indicates that less information is shared between the BeiDou system and the target sensor, which means that there is a greater information loss during the data transmission and fusion process.

[0111] The higher the redundancy: The smaller the mutual information value, the more duplicate information there is between the two. After data fusion, there is more redundant information, which leads to reduced data validity.

[0112] A large mutual information value means that the two data sources have a higher correlation when describing the same phenomenon. The positioning and time synchronization information provided by the Beidou system is highly consistent with the sensor data, indicating that the information is fully utilized during the transmission and fusion process. A high mutual information value means that the information after data fusion is of higher quality and contains more effective information rather than duplicate information. This enables the system to use this data more accurately in the analysis and decision-making process, reducing redundancy and improving efficiency.

[0113] For example, suppose in a slope monitoring system, the positioning data provided by the Beidou system is used and data from surface displacement sensors Perform mutual information analysis.

[0114] Mutual Information Value (Close to 1) indicates that the correlation between the BeiDou system and the sensor data is high. In this case, there is little information loss after data fusion, the redundancy is also low, and the data quality and analysis accuracy are high.

[0115] Mutual Information Value (Close to 0) indicates that the correlation between the BeiDou system and the sensor data is very low. In this case, there is a large amount of information loss after data fusion, there is a lot of redundant information, and the data quality and analysis accuracy are low, so the data collection and processing process needs to be further optimized.

[0116] As an important indicator for evaluating the consistency of the BeiDou system and the target sensor data, the mutual information value can quantify the degree of information sharing. By calculating and analyzing the mutual information value, the information loss and redundancy can be effectively evaluated, thereby optimizing the data fusion strategy and ensuring the efficiency and accuracy of the slope monitoring and early warning system.

[0117] In S4, by analyzing the driving conditions of vehicles on the road, the influence of the vibration caused by the vehicle driving on the stability of the target sensor is evaluated, and the time period is divided into a high vibration risk time period and a low vibration risk time period according to the influence of the vibration caused by the vehicle driving on the stability of the target sensor; specifically:

[0118] Vibrations caused by road vehicle movement may affect the stability of sensors in slope monitoring systems, leading to data errors or sensor failures.

[0119] Determine the road monitoring area corresponding to the target sensor: set a circle with the target sensor as the center, and all areas within the circle will be considered as potential monitoring areas.

[0120] Get the geographic location and shape data of a road region. A road region may include major roads, minor roads, and other transportation paths.

[0121] The overlapping area between the circle range and the road area is marked as the road monitoring area.

[0122] In the road monitoring area, the more the number of vehicles is or the larger the vibration influence coefficient of the vehicles is, the greater the influence of the vibration caused by the vehicle running on the stability of the target sensor is.

[0123] Among them, the vehicle vibration influence coefficient is a quantitative indicator used to describe the degree of vibration influence caused by different types of vehicles on the target sensor. The vibration influence coefficient of light vehicles (such as cars) is smaller, while the vibration influence coefficient of heavy vehicles (such as trucks and buses) is larger.

[0124] Obtaining the number of vehicles and the types of vehicles corresponding to the road monitoring area at each time point Obtaining the number of vehicles and the types of vehicles corresponding to the road monitoring area can be achieved based on image processing technology, which will not be described in detail here.

[0125] When the number of vehicles in the road monitoring area at a time point is greater than a preset number threshold, or there is a heavy vehicle at a time point, the time point is marked as a high vibration risk time point, otherwise, the time point is marked as a low vibration risk time point.

[0126] Based on the marked situations of the time points, a high vibration risk time period and a low vibration risk time period are determined.

[0127] For example, the determination of the high vibration risk time period and the low vibration risk time period may be implemented based on the following:

[0128] High vibration risk period: A series of consecutive or frequent high vibration risk time points constitute a high vibration risk period. For example, if multiple consecutive time points or they are frequently marked as high risk within a short period of time, this period is defined as a high vibration risk period.

[0129] Low vibration risk time period: Continuous low vibration risk time points constitute a low vibration risk time period.

[0130] The preset number threshold is a limit that may cause significant vibration when the number of vehicles reaches or exceeds the threshold at a given time point.

[0131] In S5, the adaptability of the target sensor to the vibration caused by the vehicle running during the high vibration risk period is evaluated, specifically:

[0132] In slope monitoring and early warning systems, it is critical to ensure that target sensors (such as displacement sensors, inclinometers, and strain gauges) can provide accurate and reliable data during high vibration risk periods. Vibrations caused by vehicle travel may affect sensor stability and data quality.

[0133] Collect vibration data from target sensors during high vibration risk periods and vibration data during low vibration risk periods , Indicates a point in time.

[0134] Among them, the data sampling frequency should be high enough to capture the vibration frequency caused by vehicle driving.

[0135] Use Fast Fourier Transform (FFT) to transform time domain data Convert to frequency domain and get the spectrum ; ; Represents frequency.

[0136] The data of the low vibration risk period Convert to frequency domain and get the spectrum ; .

[0137] Calculating the spectrum The spectral power density : .

[0138] Calculating the spectrum The spectral power density : .

[0139] Define a frequency range , the frequencies in this range are the most important for evaluating the suitability of the sensor. Usually, this range should include the main vibration frequencies caused by vehicle driving.

[0140] Within the defined frequency range, after calculating the sum of the spectral power densities of the high vibration risk time period and the low vibration risk time period, the spectrum analysis adaptability index is calculated based on the sum of the spectral power densities of the high vibration risk time period and the low vibration risk time period. The expression is: ;in, is the adaptability index for spectrum analysis.

[0141] The adaptability of the target sensor to the vibration caused by vehicle driving is evaluated based on the calculated spectrum analysis adaptability index:

[0142] The larger the spectrum analysis adaptability index is, the worse the adaptability of the target sensor to the vibration caused by vehicle driving during the high vibration risk period, for example:

[0143] Good adaptability: If the spectrum analysis adaptability index is close to 1, it means that the spectrum power density of the target sensor in the high vibration risk period is similar to that in the low vibration risk period, and the adaptability is good.

[0144] Poor adaptability: If the spectrum analysis adaptability index is significantly greater than 1, it means that the spectrum power density of the target sensor increases significantly during the high vibration risk period and the adaptability is poor.

[0145] Very poor adaptability: If the spectrum analysis adaptability index is greater than a certain threshold (for example, 2), it means that the spectrum power density of the target sensor increases significantly during the high vibration risk period, and the adaptability is very poor.

[0146] Among them, the frequency range can be dynamically adjusted according to the changes in real-time data and historical data and evaluation thresholds to adapt to different environments and working conditions.

[0147] Spectral analysis can accurately evaluate the sensor's response to vibrations of different frequencies, thereby improving data accuracy. The spectrum analysis adaptability index can help identify potential problems with sensors in high-vibration environments and take timely measures to correct or replace them. By evaluating the adaptability of different sensors, the selection and configuration of sensors can be optimized to ensure the reliability and stability of the system. The spectrum analysis adaptability index quantifies the adaptability of target sensors during high-vibration risk periods, providing a comprehensive, accurate and real-time risk assessment method.

[0148] In S6, by comprehensively analyzing the information loss and redundancy between the Beidou system and the target sensor and the adaptability of the target sensor to the vibration caused by vehicle driving, the monitoring risk level of the slope monitored by the target sensor is warned, specifically:

[0149] The mutual information value and the spectrum analysis adaptability index are normalized, and after the normalized mutual information value and the spectrum analysis adaptability index are assigned preset proportional coefficients respectively, the sensor monitoring hidden danger warning coefficient is calculated.

[0150] The specific implementation method of calculating the sensor monitoring hidden danger warning coefficient is not specifically limited here. Any method that can assign preset proportional coefficients to the normalized mutual information value and the spectrum analysis adaptability index respectively to obtain the sensor monitoring hidden danger warning coefficient is acceptable. In order to implement the technical solution of the present invention, the present invention provides a specific method for calculating the sensor monitoring hidden danger warning coefficient.

[0151] The expression of the sensor monitoring hidden danger warning coefficient is: ;in, is the early warning coefficient of hidden dangers monitored by sensors, are the preset proportional coefficients of the mutual information value and the spectrum analysis adaptability index, respectively, and Both are greater than 0.

[0152] From the expression of the sensor monitoring hidden danger warning coefficient, it can be seen that the larger the sensor monitoring hidden danger warning coefficient, the greater the monitoring hidden danger degree of the target sensor monitoring slope. The sensor monitoring hidden danger warning coefficient is obtained by combining the mutual information value and the spectrum analysis adaptability index, which are directly related to the sensor's response to vibration and environmental changes. The mutual information value reflects the degree of information sharing between the sensor data and the actual environmental state, while the spectrum analysis adaptability index evaluates the performance of the sensor under vibration of a specific frequency. The combined high value of the two indicates that the sensor's response may be unstable or unreliable in a complex environment, increasing the risk of false alarms or missed alarms. A higher sensor monitoring hidden danger warning coefficient means that there is a large deviation or inconsistency in spectrum response or information sharing, which may be caused by technical limitations, damage, calibration failure, or drastic changes in the external environment of the sensor. In this case, the reliability of the monitoring data is reduced, thereby increasing the risk that the monitoring system fails to warn of real hidden dangers in a timely manner.

[0153] The purpose of setting the preset proportional coefficient is to ensure that when comprehensively evaluating the monitoring hazards of the sensor, different indicators (mutual information value and spectrum analysis adaptability index) appropriately affect the final warning coefficient according to their importance and influence. The preset proportional coefficient is usually set by the following method:

[0154] By analyzing historical monitoring data, we can identify which factors are more frequently or significantly associated with monitoring hazards. For example, if the spectrum analysis adaptability index shows higher prediction accuracy in historical events, it may be assigned a higher preset scaling factor.

[0155] Through field or simulation experiments, test the performance of the early warning system under different preset proportional coefficient configurations, and select the coefficient configuration that can most accurately predict hidden dangers.

[0156] Using statistical or machine learning methods, such as regression analysis or optimization algorithms, the preset scaling factors are automatically adjusted to maximize the effectiveness and accuracy of the early warning system.

[0157] Through these methods, the setting of preset proportionality factors is intended to ensure that each indicator is weighted according to its contribution to the accuracy of the monitoring system, so that the early warning system can effectively identify and respond to potential monitoring hazards.

[0158] Set the sensor monitoring hidden danger warning threshold. The setting of the sensor monitoring hidden danger warning threshold is usually based on historical data analysis and risk tolerance. First, by collecting and analyzing historical monitoring data, determine the range of the sensor monitoring hidden danger warning coefficient when actual hidden dangers occurred in the past. Set the sensor monitoring hidden danger warning threshold. Above the sensor monitoring hidden danger warning threshold, it may indicate an actual monitoring hidden danger. The sensor monitoring hidden danger warning threshold should also take into account the operational safety margin, which is usually set as a percentage of the threshold of past events to increase the accuracy and timeliness of the warning.

[0159] Compare the sensor monitoring hidden danger warning coefficient with the sensor monitoring hidden danger warning threshold:

[0160] When the sensor monitoring hidden danger warning coefficient is greater than the sensor monitoring hidden danger warning threshold, it is determined that a target sensor monitoring hidden danger warning signal is generated. This indicates that the hidden danger level displayed by the target sensor under the current monitoring conditions is higher than the normal operating safety range, and there may be actual or potential hidden danger risks, reflecting the poor credibility of the actual state of the slope.

[0161] According to the target sensor monitoring hidden danger warning signal, the following measures can be taken:

[0162] Perform an immediate inspection of the sensor and its monitoring environment to identify any equipment failures, installation issues, or external environmental influences.

[0163] Review and analyze data during the abnormal period to verify the authenticity of the alarm.

[0164] Increase the frequency of data collection from sensors to more closely monitor possible changes or deterioration.

[0165] Additional sensors are added at the same monitoring point or in nearby areas to cross-validate data and enhance the density of the monitoring network.

[0166] When the sensor monitoring hidden danger warning coefficient is less than or equal to the sensor monitoring hidden danger warning threshold, it is determined that a normal signal of hidden danger monitoring by the target sensor is generated. This indicates that the performance of the target sensor under the current monitoring conditions is within an acceptable safety range, there is no obvious hidden danger risk, and the credibility of reflecting the actual state of the slope is good.

[0167] By comprehensively analyzing the information loss and redundancy of the Beidou system and the target sensor, as well as the adaptability of the target sensor to the vibration caused by vehicle driving, the calculated sensor monitoring hidden danger warning coefficient provides a scientific and systematic method to evaluate the health status and potential risks of the slope monitoring system. This method enables the monitoring system to automatically generate accurate warning signals based on real-time data and dynamic analysis, greatly improving the response speed and accuracy of the monitoring system. It can effectively prevent or mitigate disaster risks caused by environmental changes or sensor performance degradation. Through real-time monitoring and early warning, managers can take corresponding preventive measures or emergency measures before the hidden danger develops to the dangerous stage, such as reinforcing the slope, diverting traffic, or promptly repairing monitoring equipment, thereby ensuring public safety and infrastructure integrity. In addition, this method reduces manual intervention and operation and maintenance costs through automated processing, and improves the economic benefits and technical level of the entire monitoring system.

[0168] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0169] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0170] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0172] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0173] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0175] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0176] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0177] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A Beidou-based slope monitoring and early warning system evaluation method, characterized in that: The steps include: S1: Determine that the Beidou system time synchronization function realizes data synchronization between target sensors, and the data synchronization between target sensors includes normal data synchronization and poor data synchronization; S2: Analyze the automatic calibration of the target sensor to determine whether the use of the target sensor itself is reliable; S3: When data synchronization is normal and the target sensor itself is reliable, the consistency of the data fusion between the Beidou system and the target sensor is analyzed through the entropy theory to evaluate the information loss and redundancy between the Beidou system and the target sensor; S4: By analyzing the driving conditions of vehicles on the road, the influence of the vibration caused by the vehicle driving on the stability of the target sensor is evaluated; according to the influence of the vibration caused by the vehicle driving on the stability of the target sensor, the time period is divided into a high vibration risk time period and a low vibration risk time period; S5: Evaluate the adaptability of the target sensor to the vibration caused by vehicle driving during the high vibration risk period; S6: Through comprehensive analysis of the information loss and redundancy between the Beidou system and the target sensor and the adaptability of the target sensor to the vibration caused by vehicle driving, the degree of monitoring risk of the slope monitored by the target sensor is warned.

2. The method for evaluating a slope monitoring and early warning system based on Beidou according to claim 1 is characterized in that: Determine the BeiDou system time synchronization function to achieve data synchronization between target sensors, specifically: Collect the timestamp data of all target sensors; obtain the reference timestamp provided by the Beidou system at each sampling moment; calculate the time deviation of each target sensor at each sampling moment; for each target sensor, calculate the average value of the time deviations of all its sampling moments; Calculate the average time deviation of all target sensors, which is expressed as: ;in, is the average time deviation of all target sensors, is the number of target sensors, It is The average time deviation of target sensors; Setting the Threshold ,Will and For comparison: , then the data synchronization between the target sensors is determined to be normal; when , then the data synchronization between the target sensors is determined to be poor.

3. The method for evaluating a slope monitoring and early warning system based on Beidou according to claim 2 is characterized in that: Analyze the automatic calibration of the target sensor to determine whether the target sensor itself is reliable, specifically: Setting a calibration monitoring interval; counting the number of times the target sensor triggers automatic calibration within the calibration monitoring interval, and marking the ratio of the number of times the target sensor triggers automatic calibration within the calibration monitoring interval to the time length corresponding to the calibration monitoring interval as the target calibration frequency; Set the calibration frequency threshold; compare the target calibration frequency with the calibration frequency threshold to determine whether the target sensor itself is reliable: When the target calibration frequency is greater than the calibration frequency threshold, it is determined that the use of the target sensor itself is unreliable; when the target calibration frequency is less than or equal to the calibration frequency threshold, it is determined that the use of the target sensor itself is reliable.

4. The method for evaluating a slope monitoring and early warning system based on Beidou according to claim 3 is characterized in that: The entropy theory is used to analyze the consistency of the data fusion between the Beidou system and the target sensor, and to evaluate the information loss and redundancy between the Beidou system and the target sensor. Specifically: Collect data from BeiDou system and target sensors; assume Represents the data of the Beidou system. Represents the data of the target sensor; Calculate the joint probability distribution of BeiDou system data and target sensor data; Divide continuous data into several intervals, count the joint occurrence frequency of each interval, and construct a joint frequency table; calculate the joint probability distribution ; Count the edge frequencies of Beidou system data and target sensor data respectively; Divide the marginal frequency by the total number of samples to get the marginal probability distribution and ; The mutual information value is calculated using the mutual information formula, which is expressed as: ;in, is the mutual information value.

5. The method for evaluating a slope monitoring and early warning system based on Beidou according to claim 4 is characterized in that: By analyzing the driving conditions of vehicles on the road, the influence of the vibration caused by the vehicle driving on the stability of the target sensor is evaluated. According to the influence of the vibration caused by the vehicle driving on the stability of the target sensor, the time period is divided into a high vibration risk time period and a low vibration risk time period; specifically: Determine the road monitoring area corresponding to the target sensor: set the radius with the target sensor as the center; Get the geographic location and shape data of the road area; Mark the overlapping area between the circle range and the road area as the road monitoring area; Obtain the number of vehicles and the types of vehicles corresponding to the road monitoring area at each time point; When the number of vehicles in the road monitoring area at a time point is greater than a preset number threshold, or there is a heavy vehicle at a time point, the time point is marked as a high vibration risk time point, otherwise, the time point is marked as a low vibration risk time point; Based on the marked situations of the time points, a high vibration risk time period and a low vibration risk time period are determined.

6. The method for evaluating a slope monitoring and early warning system based on Beidou according to claim 5 is characterized in that: Evaluate the adaptability of the target sensor to the vibration caused by vehicle driving during the high vibration risk period, specifically: Collect vibration data from target sensors during high vibration risk periods and vibration data during low vibration risk periods , Indicates a point in time; Use Fast Fourier Transform to transform time domain data Convert to frequency domain and get the spectrum , represents frequency; The data of the low vibration risk period Convert to frequency domain and get the spectrum ; Calculating the spectrum The spectral power density ; Calculating the spectrum The spectral power density ; Defining the frequency range ; Calculate the spectrum analysis adaptability index within the defined frequency range, and its expression is: ;in, is the adaptability index for spectrum analysis.

7. The method for evaluating a slope monitoring and early warning system based on Beidou according to claim 6 is characterized in that: In S6, by comprehensively analyzing the information loss and redundancy of the Beidou system and the target sensor and the adaptability of the target sensor to the vibration caused by vehicle driving, the monitoring risk level of the slope monitored by the target sensor is warned, specifically: The mutual information value and the spectrum analysis adaptability index are normalized, and the normalized mutual information value and the spectrum analysis adaptability index are assigned preset proportional coefficients respectively, and the sensor monitoring hidden danger warning coefficient is calculated; Set warning thresholds for potential hazards in sensor monitoring; Compare the sensor monitoring hidden danger warning coefficient with the sensor monitoring hidden danger warning threshold: When the sensor monitoring hidden danger warning coefficient is greater than the sensor monitoring hidden danger warning threshold, it is determined that a target sensor monitoring hidden danger warning signal is generated; When the sensor monitoring hidden danger warning coefficient is less than or equal to the sensor monitoring hidden danger warning threshold, it is determined that a normal signal of the target sensor monitoring hidden danger is generated.

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