Railway debris flow early warning method based on fluid characteristics and collaborative monitoring

Abstract

The application discloses a railway debris flow early warning method based on fluid characteristics and cooperative monitoring, adopts a dynamics model to simulate a debris flow valley needing to be monitored and early warned; monitors rainfall data and soil humidity data of a source area of the debris flow valley, monitors mud water level data and infrasound vibration data of a flow-through area of the debris flow valley, and monitors video data of an accumulation area of the debris flow valley; and according to monitoring results, triggers first, second and third level early warnings respectively. The application is used to solve problems in the prior art, such as low cooperation ability among various sensors in a debris flow monitoring process along a railway, and inability to realize multi-level early warning of debris flow disasters along the railway, and realizes accuracy of debris flow monitoring and early warning along the railway, reduces a false alarm rate, and simultaneously realizes the purpose of graded early warning.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning along railway lines, specifically to a railway debris flow early warning method based on fluid characteristics and collaborative monitoring. Background Technology

[0002] The occurrence and development of debris flows are caused by a combination of factors, including precipitation, soil conditions, and topography. Reliable debris flow monitoring and early warning along railway lines are crucial for reducing disaster losses and ensuring train safety. Current debris flow monitoring methods mostly rely on sensors to monitor meteorological variables such as rainfall, infrasound, or image signals. Early warning methods are typically based on empirical statistics or debris flow early warning models of physical processes, using the monitoring results. However, existing monitoring methods are merely simple combinations of one or a few sensor detection methods, lacking synergy. Furthermore, the motion state of trains is not considered during the early warning process, resulting in limited applicability of debris flow disaster warnings for moving trains along railway lines. This poses challenges to emergency response, mitigation, and decision-making regarding debris flow disasters along railway lines.

[0003] Specifically, existing technologies employ one or more sensors to monitor mudslide levels, rainfall, air temperature, vibration signals, or image signals, using precipitation thresholds, sound, or image recognition to determine the occurrence or imminent formation of debris flows. However, this approach suffers from high false alarm rates, low reliability, and inconsistent timeliness. While monitoring methods using a simple combination of multiple sensors can enhance the reliability of the results, they still suffer from inconsistent monitoring data, low automation in result comparison, and lack of coordination among monitoring elements. Furthermore, early warning methods based solely on monitoring results or employing fluid kinematics cannot accurately reflect the dynamic changes in advance warning time as debris flows develop. In summary, existing debris flow monitoring and early warning methods are not yet effectively applicable to railway lines and cannot provide stable and accurate debris flow warnings for trains. Summary of the Invention

[0004] This invention provides a railway debris flow early warning method based on fluid characteristics and collaborative monitoring to solve the problems in the existing technology of low collaborative ability among sensors and inability to achieve multi-level early warning of debris flow disasters along railway lines. This method improves the accuracy of debris flow monitoring and early warning along railway lines, reduces the false alarm rate, and achieves the purpose of graded early warning.

[0005] This invention is achieved through the following technical solution:

[0006] A railway debris flow early warning method based on fluid characteristics and collaborative monitoring includes:

[0007] Identify debris flow gullies along the railway line that require monitoring and early warning;

[0008] A dynamic model was used to simulate debris flow gullies requiring monitoring and early warning, and the first, second, and third early warning times based on the simulation were obtained. The first early warning time is the time it takes for the debris flow to travel from the source area to the railway line, the second early warning time is the time it takes for the debris flow to travel from the flow area to the railway line, and the third early warning time is the time it takes for the debris flow to travel from the deposition area to the railway line.

[0009] Monitor rainfall and soil moisture data in the source area of ​​debris flow gullies, monitor mud and water level data and infrasound vibration data in the flow area of ​​debris flow gullies, and monitor video data in the deposition area of ​​debris flow gullies.

[0010] When both rainfall and soil moisture data in the source area exceed the set threshold, the first-level warning is triggered and the first warning time is output.

[0011] When the first-level warning is triggered, and the mud and water level data and infrasound vibration data in the flow area both detect the occurrence of debris flow, the second-level warning is triggered and the second warning time is output.

[0012] When the second-level warning is triggered and video data from the accumulation area detects a debris flow, the third-level warning is triggered, and the third warning time is output.

[0013] To address the problems of low coordination among sensors and the inability to achieve multi-level early warning of debris flow hazards along railway lines in existing technologies, this invention proposes a railway debris flow early warning method based on fluid characteristics and collaborative monitoring. This method first identifies which debris flow gullies along the railway line require monitoring and early warning. This identification process can be based on factors such as the probability of debris flow occurrence in different gullies, their correlation with the railway track, or the risk of track damage; the specific method is not limited here. Next, a dynamic model is established and simulated for the debris flow gullies requiring monitoring and early warning, thereby obtaining the time required for debris flow to travel from the source area to the railway line, from the flow area to the railway line, and from the deposition area to the railway line in each gully. These are defined as the first early warning time, the second early warning time, and the third early warning time, respectively. Subsequently, rainfall and soil moisture in the source area are monitored, mudwater level and infrasound vibration in the flow area are monitored, and images are acquired in the deposition area. The different monitoring results from these three areas are used to achieve tiered early warning.

[0014] This application provides primary early warning by monitoring rainfall and soil moisture data in the debris flow source area. Rainfall and soil moisture monitoring can be implemented over a large area at a reduced cost, yielding broad observation results and avoiding the representativeness issues caused by monitoring with a single or small number of sensors. The threshold values ​​for rainfall and soil moisture data can be determined based on local debris flow data and observed precipitation. This application also provides secondary early warning by monitoring mudwater level and infrasound vibration data in the flow zone. Both of these sensors require the debris flow to pass through to function effectively; placing them in the flow zone maximizes their functionality. Furthermore, monitoring images of the deposition area serves as final verification of debris flow occurrence. Without considering cost, video data can also be monitored in other areas of the debris flow channel, enabling cross-verification with other types of sensors.

[0015] As can be seen, this application, by strategically setting up different types of sensors in different areas of the debris flow gully and monitoring different signals / data, achieves coordinated linkage between different monitoring elements and effectively monitors the entire process of debris flow from occurrence to development, improving the reliability of monitoring results and thus enhancing the reliability of early warnings along railway lines. Because this application employs collaborative verification of monitoring data from multiple sensors, it can more accurately verify the authenticity of debris flow occurrences and reduce the false alarm rate caused by single-sensor monitoring. Furthermore, based on the coordinated monitoring between different monitoring areas and elements, this application achieves a three-level early warning system for debris flow hazards along railway lines. The intensity of the three levels of early warning gradually increases, which is conducive to railway management departments to more scientifically and rationally dynamically schedule and manage trains traveling on the railway line, efficiently directing trains to achieve emergency disaster reduction, and achieving precise early warning effects for trains on the line.

[0016] Furthermore, methods for identifying debris flow gullies along railway lines that require monitoring and early warning include:

[0017] Collect the hazard and vulnerability variables of each debris flow gully along the railway line and normalize them.

[0018] Calculate the hazard index and vulnerability index of each debris flow gully;

[0019] Based on the aforementioned hazard index and vulnerability index, calculate the risk index of each debris flow gully;

[0020] Risk assessments were conducted on each debris flow gully based on the risk index to identify debris flow gullies along the railway line that require monitoring and early warning.

[0021] This proposal suggests a method for identifying debris flow gullies along railway lines that require monitoring and early warning. The hazard variable refers to the variable related to the degree of harm caused by debris flows to the railway line, and the vulnerability variable refers to the variable related to the degree of damage to the railway track bed and track caused by debris flows.

[0022] This scheme calculates the hazard index and vulnerability index for the normalized hazard variable and vulnerability variable, respectively, and then obtains the risk index of each debris flow gully for risk assessment. Based on the results of the risk assessment, debris flow gullies with a high risk of debris flow can be selected as debris flow gullies that need to be monitored and warned.

[0023] Furthermore, the hazard variables include any one or more of the following variables: debris flow occurrence frequency, maximum debris flow scale, drainage area, main channel length, relative elevation difference of the drainage area, flow channel cutting density, average gradient of the main channel, loose solid material reserves, main channel bed tortuosity coefficient, and mud-sand recharge section length ratio.

[0024] The normalization method for the risk level variable is as follows:

[0025] h i，j =(H i，j -Min(H i，j ))÷(Max(H i，j )-Min(H i，j ));

[0026] Where: h i，j H is the normalized value of the j-th hazard variable for the i-th debris flow gully; i，j Let H be the j-th hazard variable of the i-th debris flow gully; Max(H i，j ) represents the maximum value of the j-th hazard variable among all debris flow gullies; Min(H i，j ) is the minimum value among the j-th hazard variables of all debris flow gullies.

[0027] Furthermore, the vulnerability variables include any one or more of the following variables: the angle between the railway line and the gully, the distance between the railway line and the gully, the railway grade, the bridge type, the debris flow scale, and the debris flow hazard type;

[0028] The normalized formulas for the angle between the railway line and the ditch, and the distance between the railway line and the ditch, are as follows:

[0029] V i，j =(Max(V) i，j )-Min(V i，j ))÷(V i，j -Min(V i，j ));

[0030] In the formula: v i，jV is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; i，j Let V be the j-th vulnerability variable of the i-th debris flow gully; Max(V i，j ) represents the maximum value of the j-th vulnerability variable among all debris flow gullies; Min(V i，j ) is the minimum value among the j-th vulnerability variables of all debris flow gullies;

[0031] The normalization formula for the debris flow scale is:

[0032] V i，j =(V i，j -Min(V i，j ))÷(Max(V i，j )-Min(V i，j ));

[0033] In the formula: v i，j V is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; i，j Let V be the j-th vulnerability variable of the i-th debris flow gully; Max(V i，j ) represents the maximum value of the j-th vulnerability variable among all debris flow gullies; Min(V i，j ) is the minimum value among the j-th vulnerability variables of all debris flow gullies;

[0034] The railway grade, bridge type, and debris flow hazard type are normalized through a graded assignment method.

[0035] This scheme specifies different normalization methods for different types of fragility variables to ensure stable calculation of the fragility index and avoid errors such as faults during the calculation process.

[0036] Furthermore, the formula for calculating the risk index is:

[0037]

[0038] In the formula: H(i) is the hazard index of the i-th debris flow gully; h i，j w is the normalized value of the j-th hazard variable for the i-th debris flow gully; j Let be the hazard weight of the j-th hazard variable;

[0039] The formula for calculating the fragility index is:

[0040]

[0041] In the formula: V(i) is the vulnerability index of the i-th debris flow gully; v i，j W is the normalized value of the j-th vulnerability variable of the i-th debris flow gully;j Let be the vulnerability weight of the j-th vulnerability variable;

[0042] The formula for calculating the risk index is:

[0043] R(i) = H(i) × V(i);

[0044] In the formula: R(i) is the risk index of the i-th debris flow gully.

[0045] The risk weight and vulnerability weight can be obtained using any existing weight acquisition method, and there are no restrictions here.

[0046] Furthermore, the method for obtaining the first warning time, the second warning time, and the third warning time includes:

[0047] Obtain the basic parameters of the debris flow gully; the basic parameters include debris flow rate.

[0048] Based on the Bingham model, a rheological model for each debris flow gully was established, and boundary conditions were set.

[0049] Simulation was used to obtain the flow velocity of the debris flow;

[0050] Calculate the average flow velocity S1 of the debris flow from the source area to the railway line, the average flow velocity S2 of the debris flow from the flow path to the railway line, and the average flow velocity S3 of the debris flow from the deposition area to the railway line.

[0051] Calculation: t1=L1 / S1; t2=L2 / S2; t3=L3 / S3;

[0052] Wherein, t1, t2, and t3 are the first warning time, the second warning time, and the third warning time, respectively; L1, L2, and L3 are the distances from the material source area to the railway line, the distance from the circulation area to the railway line, and the distance from the flow accumulation area to the railway line, respectively.

[0053] This scheme establishes a dynamic model to simulate the flow velocity of debris flows within the gully. This velocity is variable at different locations; therefore, the average velocity in different regions is calculated to obtain the required average velocities S1, S2, and S3. The corresponding warning times for the three levels of warnings can then be calculated. The boundary conditions of the debris flow gully rheological model can be set according to existing technology or, in the specific implementation of this application, based on the specific geological conditions; no limitation is imposed here.

[0054] Furthermore, upon triggering the second-level warning, a first-level correction is performed on the rheological model. The correction method includes:

[0055] The estimated average flow velocity S4 of debris flow from the source area to the flow zone was calculated based on the simulation results.

[0056] Based on the monitoring results, the measured average flow velocity S6 from the debris flow source area to the flow area was calculated, where S6 = (L1 - L2) / ΔT1; and ΔT1 is the time interval between triggering the first-level warning and triggering the second-level warning.

[0057] Calculate the relative error A between S6 and S4: A = (S6 - S4) / S6:

[0058] If |A|≤M%, then the rheological model remains unchanged;

[0059] If |A|>M%, correct the debris flow rate in the basic parameters, re-simulate, obtain the debris flow velocity again, and calculate the estimated average velocity S4 until |A|≤M%.

[0060] The first warning time, second warning time, and third warning time are updated based on the first-level corrected rheological model; where M is the threshold of relative error.

[0061] This scheme modifies the rheological model established for the debris flow gully based on the actual flow velocity of the debris flow from the source area to the flow zone. When the relative error between the measured average velocity S6 and the model-predicted average velocity S4 exceeds M%, the debris flow flow parameters used in the modeling are adjusted until the relative error between S6 and S4 is within M%.

[0062] The specific methods for correcting debris flow rate during this process include:

[0063] If A > M%, then the debris flow rate is iteratively corrected with a step size k1, the simulation is re-performed, and S4 is calculated until |A| ≤ M%, where k1 > 0; if A < -M%, then the sign of A obtained in the two most recent iterations is determined:

[0064] If the signs of A obtained in the two most recent iterations are the same, then take k1 = k1 / 2 and continue iterating;

[0065] If the signs of A obtained in the last two iterations are opposite, then take k1 = -k1 / 2 and continue iterating;

[0066] If A < -M%, then the debris flow flow rate is iteratively corrected with a step size k2, the simulation is re-enhanced, and S4 is calculated until |A| ≤ M%, where k2 < 0. If A > M% occurs during the iteration process, then the sign of A obtained in the two most recent iterations is determined: if the signs of A obtained in the two most recent iterations are the same, then k2 = k2 / 2 is taken, and the iteration continues; if the signs of A obtained in the two most recent iterations are opposite, then k2 = -k2 / 2 is taken, and the iteration continues.

[0067] Furthermore, upon triggering the third-level warning, a second-level correction is performed on the rheological model. The correction method includes:

[0068] The estimated average flow velocity S5 of the debris flow from the flow zone to the deposition zone was calculated based on the simulation results.

[0069] Based on the monitoring results, the measured average flow velocity S7 of the debris flow from the flow zone to the deposition zone was calculated, where S7 = (L2 - L3) / ΔT2; and ΔT2 is the time interval between triggering the second-level warning and triggering the third-level warning.

[0070] Calculate the relative error B between S7 and S5: B = (S7 - S5) / S7:

[0071] If |B|≤M%, then the rheological model remains unchanged;

[0072] If |B|>M%, adjust the debris flow rate in the basic parameters, re-simulate, obtain the debris flow velocity again, and calculate the estimated average velocity S5 until |B|≤M%.

[0073] The first warning time, second warning time, and third warning time are updated based on the rheological model after the second-level correction; where M is the threshold of the relative error.

[0074] This scheme modifies the rheological model established for the debris flow ditch based on the actual flow velocity of the debris flow from the flow zone to the deposition zone. When the relative error between the measured average flow velocity S7 and the model-predicted average flow velocity S5 exceeds M%, the debris flow flow parameters used in the modeling are adjusted until the relative error between S7 and S5 is within M%.

[0075] The specific methods for correcting debris flow rate during this process include:

[0076] If |B|>M%, the method for correcting the debris flow rate in the basic parameters includes:

[0077] If B > M%, then the debris flow rate is iteratively corrected with a step size k3, the simulation is re-enhanced, and S5 is calculated until |B| ≤ M%, where k3 > 0; if B < -M%, then the sign of B obtained in the two most recent iterations is determined: if the signs of B obtained in the two most recent iterations are the same, then k3 = k3 / 2 is taken, and the iteration continues; if the signs of B obtained in the two most recent iterations are opposite, then k3 = -k3 / 2 is taken, and the iteration continues.

[0078] If B < -M%, then the debris flow flow rate is iteratively corrected with a step size k4, the simulation is re-enhanced, and S5 is calculated until |B| ≤ M%, where k4 < 0. If B > M% occurs during the iteration process, then the sign of B obtained in the two most recent iterations is determined: if the signs of B obtained in the two most recent iterations are the same, then k4 = k4 / 2 is taken, and the iteration continues; if the signs of B obtained in the two most recent iterations are opposite, then k4 = -k4 / 2 is taken, and the iteration continues.

[0079] It can be seen that this application realizes the dynamic correction of the debris flow early warning model along the railway. After the debris flow occurs, the first warning time, the second warning time, and the third warning time can be updated according to the actual working conditions on site through dynamic correction of the model. This enables dynamic adjustment of the debris flow early warning time along the railway, making the provided warning time more accurate and improving the applicability of debris flow monitoring and early warning along the railway.

[0080] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0081] 1. This invention provides a railway debris flow early warning method based on fluid characteristics and collaborative monitoring. Different types of sensors are strategically set up in different areas of the debris flow gully to monitor different signals / data. This achieves collaborative linkage between different monitoring elements and enables effective monitoring of the entire process of debris flow from occurrence to development, thereby improving the reliability of monitoring results and thus improving the reliability of early warnings along the railway line.

[0082] 2. The present invention provides a railway debris flow early warning method based on fluid characteristics and collaborative monitoring. It adopts collaborative verification of monitoring data from multiple sensors, thus enabling more accurate verification of the authenticity of debris flow occurrence and reducing the false alarm rate caused by single sensor monitoring.

[0083] 3. This invention provides a railway debris flow early warning method based on fluid characteristics and collaborative monitoring. Based on collaborative monitoring among different monitoring areas and monitoring elements, it realizes a three-level early warning for debris flow hazards along the railway line. The intensity of the three-level early warning gradually increases, which is conducive to railway management departments to more scientifically and rationally conduct dynamic scheduling and management of trains running on the railway line, and to efficiently direct trains on the line to achieve emergency disaster reduction, thereby achieving a precise early warning effect for trains on the line.

[0084] 4. The present invention provides a railway debris flow early warning method based on fluid characteristics and collaborative monitoring, and proposes a method for determining debris flow gullies along railway lines that require monitoring and early warning.

[0085] 5. The present invention provides a railway debris flow early warning method based on fluid characteristics and collaborative monitoring, which realizes dynamic correction of the debris flow early warning model along the railway line, and thus realizes dynamic adjustment of the debris flow early warning time along the railway line, making the provided early warning time more accurate and improving the applicability of debris flow monitoring and early warning along the railway line. Attached Figure Description

[0086] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0087] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention;

[0088] Figure 2 This is a schematic diagram of the dynamic correction process in a specific embodiment of the present invention. Detailed Implementation

[0089] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explaining the invention only and are not intended to limit the invention. In the description of this application, it should be understood that terms such as "front," "rear," "left," "right," "upper," "lower," "vertical," "horizontal," "high," "low," "inner," and "outer," indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of this application.

[0090] Example 1:

[0091] like Figure 1 The method for early warning of railway debris flows based on fluid characteristics and collaborative monitoring, as shown, includes the following steps:

[0092] S1. Locate all debris flow gullies along the railway line and identify those that require monitoring and early warning.

[0093] S2. A dynamic model is used to simulate the debris flow gully that requires monitoring and early warning, and the first, second, and third early warning times based on the simulation are obtained. The first early warning time is the time it takes for the debris flow to travel from the source area to the railway line, the second early warning time is the time it takes for the debris flow to travel from the flow area to the railway line, and the third early warning time is the time it takes for the debris flow to travel from the deposition area to the railway line.

[0094] S3. Install rain gauges and soil temperature and humidity sensors in the source area of ​​debris flow gullies that require monitoring and early warning; install mud level gauges and ground acoustic detectors in the flow area; and install video monitoring equipment in the accumulation area.

[0095] S4. Real-time monitoring of 10-minute precipitation and soil moisture in the source area, real-time monitoring of mud and water level data and infrasound vibration data in the circulation area, and real-time monitoring of video data in the accumulation area.

[0096] When the 10-minute precipitation and soil moisture in the source area both exceed their respective set thresholds, the first-level warning is triggered and the first warning time is output.

[0097] When the first-level warning is triggered, and the mud and water level data and infrasound vibration data in the flow area both detect the occurrence of debris flow, the second-level warning is triggered and the second warning time is output.

[0098] When the second-level warning is triggered and video data from the accumulation area detects a debris flow, the third-level warning is triggered, and the third warning time is output.

[0099] Example 2:

[0100] A railway debris flow early warning method based on fluid characteristics and collaborative monitoring, building upon Example 1, includes the following method for identifying debris flow gullies along the railway line that require monitoring and early warning:

[0101] S101. Collect the hazard and vulnerability variables of each debris flow gully along the railway line and normalize them.

[0102] Among them, the risk variables include: debris flow occurrence frequency, maximum scale of a single debris flow, watershed area, main channel length, relative elevation difference of the watershed, flow channel cutting density, average gradient of the main channel, loose solid material reserves, main channel bed tortuosity coefficient, and mud-sand recharge section length ratio.

[0103] Vulnerability variables include: the angle between the railway line and the gully, the distance between the railway line and the gully, the railway grade, the bridge type, the debris flow scale, and the debris flow hazard type.

[0104] For the hazard variable, this embodiment normalizes it using the following method:

[0105] h i，j =(H i，j -Min(H i，j ))÷(Max(H i，j )-Min(H i，j ));

[0106] Where: h i，j H is the normalized value of the j-th hazard variable for the i-th debris flow gully; i，jLet H be the j-th hazard variable of the i-th debris flow gully; Max(H i，j ) represents the maximum value of the j-th hazard variable among all debris flow gullies; Min(H i，j ) is the minimum value among the j-th hazard variables of all debris flow gullies.

[0107] For the vulnerability variables, the angle between the railway line and the trench, and the distance between the railway line and the trench, this embodiment normalizes them using the following method:

[0108] V i，j =(Max(V) i，j )-Min(V i，j ))÷(V i，j -Min(V i，j ));

[0109] In the formula: v i，j V is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; i，j Let V be the j-th vulnerability variable of the i-th debris flow gully; Max(V i，j ) represents the maximum value of the j-th vulnerability variable among all debris flow gullies; Min(V i，j ) is the minimum value among the j-th vulnerability variables of all debris flow gullies.

[0110] For the debris flow scale in the vulnerability variable, this embodiment normalizes it using the following method:

[0111] V i，j =(V i，j -Min(V i，j ))÷(Max(V i，j )-Min(V i，j ));

[0112] In the formula: v i，j V is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; i，j Let V be the j-th vulnerability variable of the i-th debris flow gully; Max(V i，j ) represents the maximum value of the j-th vulnerability variable among all debris flow gullies; Min(V i，j ) is the minimum value among the j-th vulnerability variables of all debris flow gullies;

[0113] For the railway grade, bridge type, and debris flow hazard type among the vulnerability variables, this embodiment uses a graded assignment method for normalization. The specific assignment method is shown in the table below:

[0114]

[0115] S102. Calculate the hazard index and vulnerability index of each debris flow gully.

[0116]

[0117] In the formula: H(i) is the hazard index of the i-th debris flow gully; h i，j w is the normalized value of the j-th hazard variable for the i-th debris flow gully; j V is the hazard weight of the j-th hazard variable; V(i) is the vulnerability index of the i-th debris flow gully; v i，j W is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; j Let be the vulnerability weight of the j-th vulnerability variable. The risk weight and vulnerability weight are both determined by the analytic hierarchy process (AHP).

[0118] S103. Based on the aforementioned hazard index and vulnerability index, calculate the risk index R(i) for each debris flow gully.

[0119] R(i) = H(i) × V(i).

[0120] S104. Conduct risk assessments on each debris flow gully based on the risk index to identify debris flow gullies along the railway line that require monitoring and early warning.

[0121] Specifically, this embodiment can classify the risk index of debris flow gullies according to the quantile classification method or the natural discontinuity method, realize risk assessment, and identify debris flow gullies along the railway line that need to be monitored and warned.

[0122] Example 3:

[0123] A railway debris flow early warning method based on fluid characteristics and collaborative monitoring, in addition to Example 1 or 2, includes the following specific steps in step S2:

[0124] S201. Obtain the basic parameters of the debris flow gully; the basic parameters include debris flow unit weight, debris flow flow rate, inlet velocity, etc.

[0125] S202. Based on the Bingham model, establish the rheological model of each debris flow gully and set the boundary conditions (inlet, outlet, wall, internal unit partition and internal boundary).

[0126] S203. The debris flow velocity was obtained by simulation using Fluent software.

[0127] S204. Calculate the average flow velocity S1 of the debris flow from the source area to the railway line, the average flow velocity S2 of the debris flow from the flow zone to the railway line, and the average flow velocity S3 of the debris flow from the deposition area to the railway line.

[0128] Calculation: t1=L1 / S1; t2=L2 / S2; t3=L3 / S3;

[0129] Wherein, t1, t2, and t3 are the first warning time, the second warning time, and the third warning time, respectively; L1, L2, and L3 are the distances from the material source area to the railway line, the distance from the circulation area to the railway line, and the distance from the flow accumulation area to the railway line, respectively.

[0130] Example 4:

[0131] A railway debris flow early warning method based on fluid characteristics and collaborative monitoring, building upon Example 3, such as... Figure 2 As shown, it also includes dynamic correction of the rheological model and the warning times at each level, and the correction methods include:

[0132] In step S204, the estimated average flow velocity S4 of the debris flow from the source area to the flow area and the estimated average flow velocity S5 of the debris flow from the flow area to the deposition area are also calculated.

[0133] When a Level 2 warning is triggered, a Level 1 correction will be performed:

[0134] S401. Calculate the measured average flow velocity S6 from the debris flow source area to the flow area based on the monitoring results, S6=(L1-L2) / ΔT1; where ΔT1 is the time interval between triggering the first-level warning and triggering the second-level warning;

[0135] S402. Calculate the relative error A between S6 and S4: A = (S6 - S4) / S6:

[0136] S403, Determine the relative error A:

[0137] If |A|≤5%, then the rheological model remains unchanged;

[0138] If |A|>5%, adjust the debris flow rate in the basic parameters, re-simulate, obtain the debris flow velocity again, and calculate the estimated average velocity S4 until |A|≤5%.

[0139] The first, second, and third warning times are updated based on the first-level corrected rheological model, and the updated second warning time is released to the public. Specifically:

[0140] When |A| > 5%, the methods for correcting the debris flow rate in the basic parameters include:

[0141] If A > 5%, the debris flow rate is corrected to (1 + k1 × n) times the original value. Iterative correction is performed, the simulation is repeated, and S4 is calculated until |A| ≤ 5%, where k1 > 0 and n is the number of iterations. If A < -5% occurs during the iteration process, the sign of A obtained from the two most recent iterations is determined: if the signs of A obtained from the two most recent iterations are the same, k1 = k1 / 2 is taken, and the iteration continues; if the signs of A obtained from the two most recent iterations are opposite, k1 = -k1 / 2 is taken, and the iteration continues.

[0142] If A < -5%, the debris flow rate is corrected to (1 + k2 × n) times the original value. Iterative correction is performed, the simulation is repeated, and S4 is calculated until |A| ≤ 5%, where k2 < 0 and n is the number of iterations. If A > 5% occurs during the iteration process, the sign of A obtained in the two most recent iterations is determined: if the signs of A obtained in the two most recent iterations are the same, k2 = k2 / 2 is taken, and the iteration continues; if the signs of A obtained in the two most recent iterations are opposite, k2 = -k2 / 2 is taken, and the iteration continues.

[0143] A secondary correction will be made when a Level 3 warning is triggered.

[0144] S404. Calculate the measured average flow velocity S7 of the debris flow from the flow zone to the deposition zone based on the monitoring results, S7 = (L2 - L3) / ΔT2; where ΔT2 is the time interval between triggering the second-level warning and triggering the third-level warning;

[0145] S405. Calculate the relative error B between S7 and S5: B = (S7 - S5) / S7:

[0146] S406. Determine the relative error B:

[0147] If |B|≤5%, then the rheological model remains unchanged;

[0148] If |B|>5%, adjust the debris flow rate in the basic parameters, re-simulate, obtain the debris flow velocity again, and calculate the estimated average velocity S5 until |B|≤5%.

[0149] The first, second, and third warning times are updated based on the second-level corrected rheological model, and the updated third warning time is released to the public. Specifically:

[0150] When |B| > 5%, the method for correcting the debris flow rate in the basic parameters includes:

[0151] If B > 5%, the debris flow rate is corrected to (1 + k3 × n) times the original value. Iterative correction is performed, the simulation is repeated, and S5 is calculated until |B| ≤ 5%, where k3 > 0 and n is the number of iterations. If B < -5% occurs during the iteration process, the sign of B obtained from the two most recent iterations is determined: if the signs of B obtained from the two most recent iterations are the same, k3 = k3 / 2 is taken, and the iteration continues; if the signs of B obtained from the two most recent iterations are opposite, k3 = -k3 / 2 is taken, and the iteration continues.

[0152] If B < -5%, the debris flow rate is corrected to (1 + k4 × n) times the original value. Iterative correction is performed, the simulation is repeated, and S5 is calculated until |B| ≤ 5%, where k4 < 0 and n is the number of iterations. If B > 5% occurs during the iteration process, the sign of B obtained in the two most recent iterations is determined: if the signs of B obtained in the two most recent iterations are the same, k4 = k4 / 2 is taken, and the iteration continues; if the signs of B obtained in the two most recent iterations are opposite, k4 = -k4 / 2 is taken, and the iteration continues.

[0153] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0154] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A railway debris flow early warning method based on fluid characteristics and collaborative monitoring, characterized in that, include: Identify debris flow gullies along the railway line that require monitoring and early warning; A dynamic model was used to simulate debris flow gullies requiring monitoring and early warning, and the first, second, and third early warning times based on the simulation were obtained. The first warning time is the time it takes for the debris flow to travel from the source area to the railway line; the second warning time is the time it takes for the debris flow to travel from the circulation area to the railway line; and the third warning time is the time it takes for the debris flow to travel from the deposition area to the railway line. Monitor rainfall and soil moisture data in the source area of ​​debris flow gullies, monitor mud and water level data and infrasound vibration data in the flow area of ​​debris flow gullies, and monitor video data in the deposition area of ​​debris flow gullies. When both rainfall and soil moisture data in the source area exceed the set threshold, the first-level warning is triggered and the first warning time is output. When the first-level warning is triggered, and the mud and water level data and infrasound vibration data in the flow area both detect the occurrence of debris flow, the second-level warning is triggered and the second warning time is output. When the second-level warning is triggered and video data of the accumulation area detects a debris flow, the third-level warning is triggered, and the third warning time is output. The method for obtaining the first warning time, the second warning time, and the third warning time includes: Obtain the basic parameters of the debris flow gully; the basic parameters include debris flow rate. Based on the Bingham model, a rheological model for each debris flow gully was established, and boundary conditions were set. Simulation was used to obtain the flow velocity of the debris flow; Calculate the average flow velocity S1 of the debris flow from the source area to the railway line, the average flow velocity S2 of the debris flow from the flow path to the railway line, and the average flow velocity S3 of the debris flow from the deposition area to the railway line. Calculation: t1=L1 / S1; t2=L2 / S2; t3=L3 / S3; Where t1, t2, and t3 are the first warning time, the second warning time, and the third warning time, respectively; L1, L2, and L3 are the distances from the material source area to the railway line, the distance from the circulation area to the railway line, and the distance from the flow accumulation area to the railway line, respectively. When the second-level warning is triggered, a first-level correction is also performed on the rheological model. The correction method includes: The estimated average flow velocity S4 of debris flow from the source area to the flow zone was calculated based on the simulation results. Based on the monitoring results, the measured average flow velocity S6 from the debris flow source area to the flow area was calculated, S6=(L1-L2) / ΔT1; where ΔT1 is the time interval between triggering the first-level warning and triggering the second-level warning; Calculate the relative error A between S6 and S4: A = (S6 - S4) / S6: If |A|≤M%, then the rheological model remains unchanged; If |A|>M%, correct the debris flow rate in the basic parameters, re-simulate, obtain the debris flow velocity again, and calculate the estimated average velocity S4 until |A|≤M%; The first warning time, second warning time, and third warning time are updated based on the first-level corrected rheological model; where M is the threshold of relative error.

2. The railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 1, characterized in that, Methods for identifying debris flow gullies along railway lines that require monitoring and early warning include: Collect the hazard and vulnerability variables of each debris flow gully along the railway line and normalize them. Calculate the hazard index and vulnerability index of each debris flow gully; Based on the aforementioned hazard index and vulnerability index, calculate the risk index of each debris flow gully; Risk assessments were conducted on each debris flow gully based on the risk index to identify debris flow gullies along the railway line that require monitoring and early warning.

3. The railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 2, characterized in that, The hazard variables include any one or more of the following variables: debris flow occurrence frequency, maximum debris flow scale, drainage area, main channel length, relative elevation difference of the drainage area, flow channel cutting density, average gradient of the main channel, loose solid material reserves, main channel bed tortuosity coefficient, and mud-sand recharge section length ratio. The normalization method for the risk level variable is as follows: ； Where: h i,j H is the normalized value of the j-th hazard variable for the i-th debris flow gully; i,j Let H be the j-th hazard variable of the i-th debris flow gully; Max(H i,j ) represents the maximum value of the j-th hazard variable among all debris flow gullies; Min(H i,j ) is the minimum value among the j-th hazard variables of all debris flow gullies.

4. The railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 2, characterized in that, The vulnerability variables include any one or more of the following variables: the angle between the railway line and the gully, the distance between the railway line and the gully, the railway grade, the bridge type, the debris flow scale, and the debris flow hazard type; The normalized formulas for the angle between the railway line and the ditch, and the distance between the railway line and the ditch, are as follows: ； In the formula: v i,j V is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; i,j Let V be the j-th vulnerability variable of the i-th debris flow gully; Max(V i,j ) represents the maximum value among the j-th vulnerability variables of all debris flow gullies; Min(V i,j () is the minimum value among the j-th vulnerability variables of all debris flow gullies; The normalization formula for the debris flow scale is: ； In the formula: v i,j V is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; i,j Let V be the j-th vulnerability variable of the i-th debris flow gully; Max(V i,j ) represents the maximum value among the j-th vulnerability variables of all debris flow gullies; Min(V i,j () is the minimum value among the j-th vulnerability variables of all debris flow gullies; The railway grade, bridge type, and debris flow hazard type are normalized through a graded assignment method.

5. A railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 2, characterized in that, The formula for calculating the risk index is: ； In the formula: H(i) is the hazard index of the i-th debris flow gully; h i,j w is the normalized value of the j-th hazard variable for the i-th debris flow gully; j Let be the hazard weight of the j-th hazard variable; The formula for calculating the fragility index is: ； In the formula: V(i) is the vulnerability index of the i-th debris flow gully; v i,j W is the normalized value of the j-th vulnerability variable of the i-th debris flow gully; j Let be the vulnerability weight of the j-th vulnerability variable; The formula for calculating the risk index is: ； In the formula: R(i) is the risk index of the i-th debris flow gully.

6. The railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 1, characterized in that, If |A|>M%, methods for correcting the debris flow rate in the basic parameters include: If A > M%, then the debris flow rate is iteratively corrected with a step size k1, the simulation is re-enhanced, and S4 is calculated until |A| ≤ M%, where k1 > 0; if A < -M%, then the sign of A obtained in the last two iterations is determined: if the signs of A obtained in the last two iterations are the same, then k1 = k1 / 2 is taken, and the iteration continues; if the signs of A obtained in the last two iterations are opposite, then k1 = -k1 / 2 is taken, and the iteration continues. If A < -M%, then the debris flow flow rate is iteratively corrected with a step size k2, the simulation is re-enhanced, and S4 is calculated until |A| ≤ M%, where k2 < 0. If A > M% occurs during the iteration process, then the sign of A obtained in the two most recent iterations is determined: if the signs of A obtained in the two most recent iterations are the same, then k2 = k2 / 2 is taken, and the iteration continues; if the signs of A obtained in the two most recent iterations are opposite, then k2 = -k2 / 2 is taken, and the iteration continues.

7. A railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 1, characterized in that, When the third-level warning is triggered, a second-level correction is also performed on the rheological model. The correction method includes: The estimated average flow velocity S5 of the debris flow from the flow zone to the deposition zone was calculated based on the simulation results. Based on the monitoring results, the measured average flow velocity S7 of the debris flow from the flow zone to the deposition zone was calculated, S7=(L2-L3) / ΔT2; where ΔT2 is the time interval between triggering the second-level warning and triggering the third-level warning; Calculate the relative error B between S7 and S5: B = (S7 - S5) / S7: If |B|≤M%, then the rheological model remains unchanged; If |B|>M%, adjust the debris flow rate in the basic parameters, re-simulate, obtain the debris flow velocity again, and calculate the estimated average velocity S5 until |B|≤M%; The first warning time, second warning time, and third warning time are updated based on the rheological model after the second-level correction; where M is the threshold of the relative error.

8. A railway debris flow early warning method based on fluid characteristics and collaborative monitoring according to claim 7, characterized in that, If |B|>M%, the methods for correcting the debris flow rate in the basic parameters include: If B > M%, then the debris flow rate is iteratively corrected with a step size k3, the simulation is re-enhanced, and S5 is calculated until |B| ≤ M%, where k3 > 0; if B < -M%, then the sign of B obtained in the two most recent iterations is determined: if the signs of B obtained in the two most recent iterations are the same, then k3 = k3 / 2 is taken, and the iteration continues; if the signs of B obtained in the two most recent iterations are opposite, then k3 = -k3 / 2 is taken, and the iteration continues. If B < -M%, then the debris flow flow rate is iteratively corrected with a step size k4, the simulation is re-enhanced, and S5 is calculated until |B| ≤ M%, where k4 < 0. If B > M% occurs during the iteration process, then the sign of B obtained in the two most recent iterations is determined: if the signs of B obtained in the two most recent iterations are the same, then k4 = k4 / 2 is taken, and the iteration continues; if the signs of B obtained in the two most recent iterations are opposite, then k4 = -k4 / 2 is taken, and the iteration continues.

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