A remote automatic monitoring platform for slope deformation based on mountain railway engineering

Abstract

The present invention discloses a remote automatic monitoring platform for slope deformation based on mountain railway engineering, including: a water and soil vector acquisition unit, a current meteorology acquisition unit, a contribution degree construction subunit, which is used to obtain the historical meteorological contribution degree and the future meteorological contribution degree at the current time point according to the meteorological type and its meteorological parameters at the current time point; a meteorological vector construction unit, which is used to construct the historical meteorological contribution degree, the future meteorological contribution degree, the meteorological type and its meteorological parameters at the current time point into a slope meteorological vector at the current time point; a vector splicing unit and a monitoring and prediction unit, which are used to input the slope fusion vector into a pre-trained slope deformation prediction model and output predefined slope deformation parameters; the present invention can not only accurately reflect the influence of past meteorological conditions on slope deformation, but also take into account the potential influence of upcoming meteorological changes on slope stability, so as to provide more comprehensive and accurate deformation prediction.

Description

Technical Field

[0001] The invention relates to a slope deformation monitoring platform, in particular to a slope deformation remote automatic monitoring platform based on mountain railway engineering. Background Art

[0002] Slope deformation monitoring in mountain railway projects refers to the work carried out on the slopes along the railway (i.e., the earth slopes or rock slopes on both sides of the railway) during the construction and operation of mountain railways. The work mainly monitors the deformation of the slopes under different meteorological conditions, geological conditions, and human activities (such as construction, transportation, etc.) to ensure the stability and safety of the slopes. The patent document with patent publication number CN117606429A discloses a soil slope deformation monitoring and early warning method, which can not only monitor and warn the current slope conditions, but also fully analyze the subsequent trends. Then the existing slope deformation monitoring in mountain railway projects is mainly based on historical meteorological data to monitor slope deformation. It only relies on past meteorological data to evaluate the stability of the slope, lacks consideration of future meteorological conditions, and causes the system to lag in early warning of potential deformation. In addition, the existing calculation models are usually unable to effectively combine historical data with future meteorological forecast data, and often ignore the time series effect of meteorological conditions.

[0003] Therefore, the existing technology lacks the characteristics of fully combining historical data and future weather as current inputs in the prediction process of slope deformation of mountain railway projects, lacks dynamic response to current meteorological conditions, and needs to further improve the monitoring level. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a remote automated monitoring platform for slope deformation based on mountain railway projects, which combines historical actual data with future predicted data to solve the technical problems raised in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A remote automated monitoring platform for slope deformation based on mountain railway engineering, applied to a server, comprising:

[0007] A water-soil vector acquisition unit, used to acquire the water-soil vector of the slope at the current time point;

[0008] A meteorological vector construction unit, used to construct a slope meteorological vector at a current time point;

[0009] Among them, the vector features of the slope meteorological vector include the future meteorological contribution degree, which is characterized as a set of future meteorological instantaneous contribution degrees. The future meteorological instantaneous contribution degree is calculated based on the future time weight, the future meteorological weight, and the future meteorological instantaneous parameters at the corresponding future time point. The future time weight exponentially decays with an accelerating rate in a manner away from the current time point;

[0010] A vector splicing unit, configured to splice the slope water and soil vector and the slope meteorological vector to generate a slope fusion vector;

[0011] A monitoring and prediction unit, configured to input the slope fusion vector into a pre-trained slope deformation prediction model and output predefined slope deformation parameters.

[0012] In some embodiments, the steps of the water and soil vector acquisition unit for acquiring the slope water and soil vector at the current time point include:

[0013] S1-1. Acquire the slope soil type, the water content on the slope surface layer, and the slope angle at the current time point;

[0014] S1-2. Define the water permeability of the slope at the current time point according to the slope soil type;

[0015] S1-3. Construct the slope water and soil vector of the current slope according to the water content on the slope surface layer, the slope angle, and the water permeability of the slope at the current time point.

[0016] In some embodiments, the meteorological vector construction unit includes several built-in sub-units, and the several sub-units include:

[0017] A current meteorological acquisition sub-unit, configured to acquire the meteorological type and its meteorological parameters at the current time point;

[0018] A contribution degree construction sub-unit, configured to acquire the historical meteorological contribution degree and the future meteorological contribution degree at the current time point according to the meteorological type and its meteorological parameters at the current time point.

[0019] In some embodiments, the steps of the contribution degree construction sub-unit for acquiring the historical meteorological contribution degree include:

[0020] A1-1. Acquire the starting time point of the meteorological type at the current time point on the time axis;

[0021] A1-2. Calculate the historical meteorological duration between the starting time point and the current time point;

[0022] A1-3. Acquire the historical meteorological instantaneous parameters at each historical time point during the historical meteorological duration;

[0023] A1-4. Obtain the historical meteorological instantaneous contribution degree at each historical time point based on the historical meteorological instantaneous parameters at each historical time point;

[0024] A1-5. Define the total contribution degree of all historical meteorological instantaneous contribution degrees within the historical meteorological duration as the historical meteorological contribution degree at the current time point.

[0025] In some embodiments, obtaining the historical meteorological instantaneous contribution degree at each historical time point based on the historical meteorological instantaneous parameters at each historical time point includes:

[0026] A1-4-1. Calculate the historical time difference between each historical time point and the current time point within the historical meteorological duration;

[0027] A1-4-2. Calculate the historical time weight of each historical time point in a manner that the contribution accelerates incrementally closer to the current time point according to the historical time difference of each historical time point and the historical meteorological duration;

[0028] The calculation expression of the historical time weight is: ;

[0029] Wherein, is the historical time full assist of the i-th historical time point, is the time difference between the i-th historical time point and the current time point; is the standardized historical time difference, representing the relative proportion of this historical time point to the historical meteorological duration; is the contribution decreasing term, is the decreasing coefficient, used to control the attenuation speed of the influence of the historical time point on the current meteorology;

[0030] A1-4-3. Obtain the historical meteorological weight of each historical meteorological instantaneous parameter within the historical meteorological duration;

[0031] A1-4-4. Calculate the historical meteorological instantaneous contribution degree according to the historical time weight, the historical meteorological weight, and the historical meteorological instantaneous parameter of the corresponding historical time point;

[0032] The calculation expression of the historical meteorological instantaneous contribution degree is: ;

[0033] Wherein, is the historical meteorological instantaneous contribution degree of the i-th historical time point, is the historical meteorological weight, is the historical meteorological instantaneous parameter.

[0034] In some embodiments, obtaining the historical meteorological weight of each historical meteorological instantaneous parameter within the historical meteorological duration includes:

[0035] A1-4-3-1. Extract the historical meteorological instantaneous parameters corresponding to each historical time point within the historical meteorological duration.

[0036] A1-4-3-2. Calculate the total historical meteorological parameters within the historical meteorological duration based on the historical meteorological instantaneous parameters.

[0037] A1-4-3-3. Define the ratio of the historical meteorological instantaneous parameter corresponding to each historical time point to the total historical meteorological parameters as the historical meteorological weight of each historical time point.

[0038] In some of these embodiments, the steps for the contribution degree construction subunit to obtain the future meteorological contribution degree include:

[0039] B1-1. Obtain the end time point of the meteorological type at the current time point on the time axis.

[0040] B1-2. Calculate the future meteorological duration between the end time point and the current time point.

[0041] B1-3. Obtain the future meteorological instantaneous parameters of each future time point within the future meteorological duration.

[0042] B1-4. Obtain the future meteorological instantaneous contribution degree of each future time point based on the future meteorological instantaneous parameters of each future time point.

[0043] B1-5. Define the sum of all future meteorological instantaneous contribution degrees within the future meteorological duration as the future meteorological contribution degree of the current time point.

[0044] In some of these embodiments, obtaining the future meteorological instantaneous contribution degree of each future time point based on the future meteorological instantaneous parameters of each future time point includes:

[0045] B1-4-1. Calculate the future time difference between each future time point and the current time point within the future meteorological duration.

[0046] B1-4-2. Calculate the future time weight of each future time point in a manner that the contribution of the far time point decays rapidly based on the future time difference of each future time point and the future meteorological duration.

[0047] The calculation expression for the future time weight is: ;

[0048] Where represents the future time weight of the i-th future time point, is the time difference between the i-th future time point and the current time point; It is a standardized future time difference, representing the relative proportion of this future time point to the future meteorological duration; It indicates that as the time difference increases, the weight of the future time point gradually increases;

[0049] B1-4-3. Obtain the future meteorological weights of each future meteorological instantaneous parameter within the future meteorological duration;

[0050] B1-4-4. Calculate the future meteorological instantaneous contribution degree according to the future time weight, the future meteorological weight, and the future meteorological instantaneous parameter corresponding to the future time point.

[0051] In some of these embodiments, obtaining the future meteorological weights of each future meteorological instantaneous parameter within the future meteorological duration includes:

[0052] B1-4-3-1. Extract the future meteorological instantaneous parameters corresponding to each future time point within the future meteorological duration;

[0053] B1-4-3-2. Calculate the total future meteorological parameters within the future meteorological duration according to the future meteorological instantaneous parameters;

[0054] B1-4-3-3. Calculate the meteorological parameters of the total duration according to the total future meteorological parameters and the historical meteorological total parameters;

[0055] B1-4-3-4. Define the inverse ratio of the exponent of the future meteorological instantaneous parameter corresponding to each future time point to the meteorological parameter of the total duration as the future meteorological weight of each future time point.

[0056] In some of these embodiments, the modeling steps of the slope deformation prediction model include:

[0057] S4-1. Mark the first time point and the second time point on the time axis in the slope deformation historical database; where the first time point is the historical start time point of the meteorological type, and the second time point is the historical end time point of the meteorological type;

[0058] S4-2. Obtain the slope soil-water vector and the slope meteorological vector at the first time point, and splice them into a historical slope fusion vector;

[0059] S4-3. Obtain several slope deformation characteristics at the second time point, and construct them into a slope historical deformation vector;

[0060] S4-4. Obtain several historical slope fusion vectors and slope historical deformation vectors, and construct them into a training set;

[0061] S4-4. Receive the historical slope fusion vector in the training set as the input variable and the slope historical deformation vector as the target variable, and supervise the training of the slope deformation prediction model.

[0062] In some embodiments, the initial training model of the slope deformation prediction model is a neural network model.

[0063] The present invention provides a remote automatic monitoring platform for slope deformation based on mountain railway engineering, which has the following beneficial effects:

[0064] The present invention defines the ratio of the exponent of the future meteorological instantaneous parameter corresponding to each future time point to the inverse of the meteorological parameter of the total duration as the future meteorological weight of each future time point, so as to reflect the function that the future meteorological data closer to the current time point has a greater influence on the prediction of slope deformation. By means of the accelerated decay of the time weight and the accelerated attenuation of the meteorological weight, it is ensured that the future meteorological data has a stronger effect in the prediction of slope deformation. Description of the Drawings

[0065] Figure 1 It is a structural block diagram of a remote automatic monitoring platform for slope deformation based on mountain railway engineering of the present invention;

[0066] Figure 2 It is a monitoring flow chart of a remote automatic monitoring platform for slope deformation based on mountain railway engineering of the present invention;

[0067] Figure 3 It is a calculation flow chart of the future meteorological instantaneous contribution degree of the present invention;

[0068] Figure 4 It is a definition flow chart of the future meteorological full assist of the present invention. Detailed Embodiments

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0070] Embodiment 1: Please refer to Figures 1 to 4 , the present invention provides a remote automatic monitoring platform for slope deformation based on mountain railway engineering, which is applied to a server. The platform includes:

[0071] A soil and water vector acquisition unit for acquiring the slope soil and water vector at the current time point;

[0072] A meteorological vector construction unit for constructing a slope meteorological vector at the current time point;

[0073] Among them, the vector features of the slope meteorological vector include the future meteorological contribution degree, which is characterized as a set of future meteorological instantaneous contribution degrees. The future meteorological instantaneous contribution degree is calculated based on the future time weight, the future meteorological weight, and the future meteorological instantaneous parameters at the corresponding future time point. The future time weight exponentially decays with an accelerating rate in a way that is farther away from the current time point;

[0074] A vector splicing unit for splicing the slope water and soil vector and the slope meteorological vector to generate a slope fusion vector;

[0075] A monitoring and prediction unit for inputting the slope fusion vector into a pre-trained slope deformation prediction model and outputting predefined slope deformation parameters.

[0076] Among them, the slope deformation parameters are predefined parameters, which can be selected from several outputs of the slope deformation prediction model based on platform requirements. They can be:

[0077] Deformation type: such as horizontal displacement, vertical displacement, rotational deformation, etc.

[0078] Deformation direction: Predict the deformation direction of the slope according to meteorological and land factors.

[0079] Deformation area: Predict which areas may have large deformations, which may be based on real-time feedback of sensor data (such as fiber optic sensors, GPS).

[0080] Deformation amplitude: Evaluate the possible deformation amplitude in the future according to the predicted meteorological influence factors and land conditions.

[0081] Risk level: Combine the stability analysis of the slope to give the safety assessment result of the slope (such as normal, warning, pre-alarm, critical, etc.).

[0082] In this embodiment, through the soil and water vector acquisition unit, the slope soil and water vector at the current time point is acquired, including key parameters such as soil type, surface water content, and slope angle. Then, through the meteorological vector construction unit, the slope meteorological vector at the current time point is constructed. The vector characteristics of the slope meteorological vector include the future meteorological contribution degree, and the future meteorological contribution degree is the summation representation of the future meteorological instantaneous contribution degree, which reflects the potential impact of future meteorological data on the slope deformation during the prediction period at the current time point. That is to say, the platform defines the sum of all future meteorological instantaneous contribution degrees as the future meteorological contribution degree at the current time point, takes the predicted future meteorological contribution degree as the input at the current time point, and then can combine the future meteorological contribution degree (i.e., the set of future meteorological instantaneous contribution degrees) with historical data to ensure a comprehensive assessment of the impact of meteorological conditions. Specifically, the platform first predicts the potential impact of future meteorological conditions (such as precipitation, temperature, etc.) on slope deformation, then weights these meteorological data according to the future time weight and future meteorological weight, and finally calculates the future meteorological instantaneous contribution degree at each future time point based on these weighted data, ultimately ensuring a comprehensive assessment of the impact of meteorological conditions.

[0083] Furthermore, subsequently, through the vector splicing unit, a slope fusion vector is constructed by combining the slope soil and water vector and the meteorological vector. Finally, the monitoring and prediction unit inputs this fusion vector into a pre-trained slope deformation prediction model to output multiple slope deformation parameters. Among them, the multiple slope deformation parameters can be based on predefined ones including deformation type, deformation direction, deformation area, deformation amplitude, and risk level, etc.

[0084] Among them, the steps for the soil and water vector acquisition unit to acquire the slope soil and water vector at the current time point include:

[0085] S1-1. Acquire the slope soil type, slope surface water content, and slope angle (such as clay, sandy soil, rocky soil, etc.) at the current time point

[0086] Specifically, the slope soil type indicates that the stability of the slope is closely related to its soil type. Different types of soil (such as clay, sandy soil, rocky soil, etc.) have different physical properties, which determine the water penetration ability, expansibility, compressibility, etc. The slope surface water content directly affects the slope stability, especially when meteorological factors such as rainfall and drought change. When the water content is relatively high, the soil may be softer and more prone to sliding or collapse, especially for clayey soils. The slope angle directly affects its stability. Steeper slopes are more prone to landslides or collapses, especially when precipitation or temperature changes.

[0087] S1-2. Define the slope water permeability at the current time point according to the slope soil type;

[0088] Water permeability refers to the ability of soil or rock layers to allow water to penetrate, which determines the rate of water movement in slopes. Different soil types have different permeability characteristics. For example, sandy soil has high permeability and water flows quickly, while clay has poor permeability and water may stay for a longer time.

[0089] S1-3. Construct the slope soil-water vector of the current slope according to the water content on the surface layer of the slope, the slope angle, and the water permeability of the slope at the current time point.

[0090] The slope soil-water vector is a multi-dimensional vector that comprehensively affects slope stability, especially the related characteristics of soil and water. Specifically, the slope soil-water vector represents the comprehensive situation of the soil characteristics, water state, and water infiltration process of the slope at the current time point.

[0091] In this embodiment, by obtaining information such as the soil type, water content on the surface layer, and slope angle of the slope at the current time point, the stability of the slope can be evaluated in real time. Different soil types affect the water permeability and soil expansibility, thereby affecting the slope stability. When the water content on the surface layer of the slope is high, the soil is prone to being soft, especially under meteorological conditions such as rainfall or drought, the deformation risk increases. The slope angle directly affects the landslide or collapse risk of the slope. By combining the above factors, the slope soil-water vector comprehensively describes the soil-water state of the slope at the current time point.

[0092] Furthermore, the meteorological vector construction unit is built-in with several sub-units, and the several sub-units include:

[0093] The current meteorological acquisition sub-unit is used to acquire the meteorological type and its meteorological parameters at the current time point; the meteorological type covers different meteorological conditions such as heavy rain, drought, frost, and storm. For example, heavy rain and its instantaneous precipitation, drought and its instantaneous humidity, frost and its instantaneous temperature, storm and its instantaneous wind speed and direction, to ensure that the platform can accurately evaluate the potential impact of meteorology on slope deformation.

[0094] The contribution degree construction sub-unit is used to obtain the historical meteorological contribution degree and the future meteorological contribution degree according to the meteorological type and its meteorological parameters at the current time point.

[0095] Specifically, therefore, the vector characteristics of the slope meteorological vector include not only the future meteorological contribution degree, but also the meteorological type and its meteorological parameters at the previous time point, and the historical meteorological contribution degree.

[0096] Furthermore, in this embodiment, the steps for the contribution degree construction sub-unit to obtain the historical meteorological contribution degree include:

[0097] A1-1. Obtain the starting time point of the meteorological type at the current time point on the time axis;

[0098] A1-2. Calculate the historical meteorological duration between the starting time point and the current time point;

[0099] A1-3. Obtain the historical meteorological instantaneous parameters at each historical time point within the historical meteorological duration;

[0100] A1-4. Obtain the historical meteorological instantaneous contribution degree at each historical time point based on the historical meteorological instantaneous parameters at each historical time point;

[0101] A1-5. Define the total contribution degree of all historical meteorological instantaneous contribution degrees within the historical meteorological duration as the historical meteorological contribution degree at the current time point.

[0102] In this embodiment, by defining the historical meteorological contribution degree, first, the starting time point of the meteorological type at the current time point is obtained to determine the starting moment of the historical meteorological influence. Then, the historical meteorological duration between the starting time point and the current time point is calculated to provide a time range for the subsequent calculation of the meteorological contribution degree. Next, the historical meteorological instantaneous parameters at each historical time point within this duration are obtained, and the historical meteorological instantaneous contribution degree at each historical time point is calculated based on the meteorological instantaneous parameters. Finally, by defining the sum of all historical meteorological instantaneous contribution degrees as the historical meteorological contribution degree at the current time point, comprehensive historical meteorological influence data is provided for slope deformation prediction.

[0103] Further, the specific steps of step A1-4 include:

[0104] A1-4-1. Calculate the historical time difference between each historical time point within the historical meteorological duration and the current time point;

[0105] A1-4-2. Calculate the historical time weight of each historical time point in a way that the contribution accelerates and increases towards the current time point based on the historical time difference of each historical time point and the historical meteorological duration;

[0106] The calculation expression of the historical time weight is: ;

[0107] Where, is the historical time full assist of the i-th historical time point, representing the relative time difference of the historical time point. The contribution of a farther time point to the current time point is smaller. is the time difference between the i-th historical time point and the current time point; as increases, that is, the historical time point is farther from the current time point, the historical time weight will gradually decrease. is the normalized historical time difference, representing the relative ratio of this historical time point to the historical meteorological duration. As increases, the weight will decrease; It is a contribution decreasing term, indicating that as the time difference increases, the value of the contribution decreasing term will decrease, thus reflecting that the influence of historical meteorological data from the current time point is decreasing. is the decreasing coefficient, which is used to control the attenuation speed of the influence of historical time points on the current meteorology. If = 1, the attenuation speed is linearly decreasing; if > 1, the attenuation speed will accelerate, and the influence of historical meteorology farther from the current time point on the contribution degree is smaller; if < 1, the attenuation is slower.

[0108] The historical time weight reflects the influence of time in a decreasing manner, ensuring that historical data farther from the current time has a smaller influence on the prediction contribution, which conforms to the law that the influence of historical data on the current meteorology gradually weakens.

[0109] A1-4-3. Obtain the historical meteorological weight of each historical meteorological instantaneous parameter within the historical meteorological duration;

[0110] A1-4-4. Calculate the historical meteorological instantaneous contribution degree according to the historical time weight, historical meteorological weight, and historical meteorological instantaneous parameter at the corresponding historical time point.

[0111] The calculation expression of the historical meteorological instantaneous contribution degree is: ;

[0112] Among them, is the historical meteorological instantaneous contribution degree at the i-th historical time point, is the historical meteorological weight, indicating the proportion of the meteorological parameter at the i-th historical time point in the entire historical meteorological duration. By the meteorological instantaneous parameter at this time point and the sum of all historical meteorological instantaneous parameters, the proportion of the influence of this meteorological data on the overall historical meteorology is calculated. is the historical meteorological instantaneous parameter; it represents the meteorological data at the i-th historical time point, such as precipitation, temperature, etc.

[0113] This embodiment defines the detailed steps of the historical meteorological instantaneous contribution degree. First, by calculating the historical time difference of each historical time point within the historical meteorological duration, the platform can determine the time difference between each historical time point and the current time point, and then evaluate the influence degree of historical meteorological data. Subsequently, according to the historical time difference and historical meteorological duration of each historical time point, the historical time weight is calculated in an accelerated decreasing manner, ensuring that as the time difference increases, the contribution of historical data to the current time point gradually decreases, thus reflecting the law of the attenuation of the influence of historical meteorological data on the current prediction.

[0114] Next, the platform obtains the historical meteorological weights of each historical meteorological instantaneous parameter within the historical meteorological duration. This weight represents the proportion of the meteorological parameter at each historical time point within the entire historical meteorological duration, thereby reflecting the proportion of the impact of each historical meteorological data on the overall historical meteorology. Through the calculation of the historical meteorological instantaneous contribution degree, the historical time weight, the historical meteorological weight, and the historical meteorological instantaneous parameter are combined, and finally the historical meteorological instantaneous contribution degree of each historical time point is calculated.

[0115] Furthermore, step A1-4-3 specifically includes:

[0116] A1-4-3-1: Extract the historical meteorological instantaneous parameter corresponding to each historical time point within the historical meteorological duration;

[0117] A1-4-3-2: Calculate the total historical meteorological parameter within the historical meteorological duration according to the historical meteorological instantaneous parameter;

[0118] A1-4-3-3: Define the ratio of the historical meteorological instantaneous parameter corresponding to each historical time point to the total historical meteorological parameter as the historical meteorological weight of each historical time point.

[0119] This embodiment discloses a method for calculating historical meteorological weights. By extracting the historical meteorological instantaneous parameter corresponding to each historical time point within the historical meteorological duration, accurate collection of meteorological data for each historical time point is ensured. Next, according to the historical meteorological instantaneous parameter, the total historical meteorological parameter within the historical meteorological duration is calculated to obtain the sum of meteorological data during the entire historical period. Finally, by defining the ratio of the historical meteorological instantaneous parameter corresponding to each historical time point to the total historical meteorological parameter as the historical meteorological weight of each historical time point, it reflects the relative contribution of the meteorological data at each time point within the entire historical meteorological period. This calculation method provides accurate historical meteorological impact data for the slope deformation prediction model by reasonably weighing the relationship between the meteorological data at each historical time point and the total historical meteorological data.

[0120] In this embodiment, the steps for obtaining the future meteorological contribution degree include:

[0121] B1-1: Obtain the end time point of the meteorological type at the current time point on the time axis;

[0122] Specifically, the end time point of the meteorological type at the current time point on the time axis represents the possible end time of the same meteorological type in the future at the current moment, which can be obtained through weather forecasts or future meteorological prediction models.

[0123] B1-2: Calculate the future meteorological duration between the end time point and the current time point;

[0124] B1-3. Obtain the future meteorological instantaneous parameters at each future time point within the future meteorological duration;

[0125] B1-4. Obtain the future meteorological instantaneous contribution degree at each future time point according to the future meteorological instantaneous parameters at each future time point;

[0126] B1-5. Define the total contribution degree of all future meteorological instantaneous contribution degrees within the future meteorological duration as the future meteorological contribution degree at the current time point.

[0127] This embodiment discloses a calculation method for future meteorological contribution degree, which further ensures the accuracy of slope deformation prediction. First, by obtaining the end time point of the meteorological type at the current time point on the time axis, the end moment of future meteorological influence is determined. The end time point can be obtained through weather forecasts or future meteorological prediction models, ensuring accurate prediction of future meteorological conditions. Next, calculate the future meteorological duration between the end time point and the current time point, providing a time range for future meteorological data. Then, the platform obtains the future meteorological instantaneous parameters at each future time point within the future meteorological duration. The parameters include meteorological data such as future precipitation, temperature, and wind speed. The platform further calculates the future meteorological instantaneous contribution degree at each future time point, which reflects the potential impact of this meteorological data on slope deformation during the prediction period. Finally, the platform defines the sum of all future meteorological instantaneous contribution degrees as the future meteorological contribution degree at the current time point, ensuring a comprehensive assessment of the impact of meteorological conditions.

[0128] It should be noted that since the calculation of future meteorological contribution degree depends on data such as future time points and future meteorological instantaneous parameters, this data is not collected in real time but is based on the prediction data provided by meteorological prediction models. For example, based on meteorological forecast data, numerical meteorological models, etc., to ensure the accuracy of future meteorological contribution degree calculation. Generally, accurate meteorological prediction data can be obtained through the weather forecast system of the meteorological department, numerical weather prediction (NWP) models, etc. The prediction data is usually provided by the meteorological department and covers meteorological parameters such as temperature, precipitation, and wind speed within a future period of time.

[0129] Furthermore, the step B1-4 specifically includes:

[0130] B1-4-1. Calculate the future time difference between each future time point and the current time point within the future meteorological duration;

[0131] B1-4-2. Calculate the future time weight of each future time point in a way that the contribution of the far-away time point decays rapidly according to the future time difference and the future meteorological duration of each future time point;

[0132] The calculation expression of the future time weight is: ;

[0133] Among them, represents the future time weight at the i-th future time point, is the time difference between the i-th future time point and the current time point. As increases, that is, the farther the future time point is from the current time point, the weight will gradually increase. is the standardized future time difference, representing the relative proportion of this future time point to the future meteorological duration. As increases, the weight will gradually increase. It means that as the time difference increases, the weight of the future time point gradually increases. If α = 1, the increasing speed is linear; if α > 1, the increasing speed is faster; if α < 1, the increasing speed is slower.

[0134] To accurately evaluate the impact of future meteorological conditions on slope deformation, the platform adopts an exponential decay method for future time weights. The core of this method is that as time goes by, the impact of meteorological data farther from the current moment on slope deformation prediction will gradually decrease. This exponential decay ensures that the impact of future meteorological data in prediction decreases according to the distance from the current time, thus improving the prediction accuracy and real-time performance at the current moment. That is to say, the future meteorological conditions will decay faster as the distance from the current moment increases, ensuring that the system relies more on the impact of current meteorological conditions on slope deformation and reducing the potential errors of over-relying on long-term meteorological predictions.

[0135] The future time weight reflects that the closer the future meteorological data is to the current time point, the greater the impact on slope deformation prediction. Through the way of accelerating the decay of time weight and accelerating the decay of meteorological weight, it is ensured that future meteorological data has a stronger role in slope deformation prediction. Especially in the adjacent time periods, the prediction accuracy and response speed will be enhanced.

[0136] B1-4-3. Obtain the future meteorological weights of each future meteorological instantaneous parameter within the future meteorological duration;

[0137] B1-4-4. Calculate the future meteorological instantaneous contribution degree according to the future time weight, the future meteorological weight and the future meteorological instantaneous parameter at the corresponding future time point.

[0138] This embodiment discloses a deformation prediction method based on future meteorological conditions. First, calculate the time difference between each future time point and the current time point, and calculate the future time weight of the future time point in an increasing manner according to the time difference and the future meteorological duration. As the time difference increases, the impact of future meteorological data gradually increases, ensuring that the meteorological data at adjacent time points has a stronger impact on slope deformation prediction, thereby improving the prediction accuracy.

[0139] Next, the platform calculates the future meteorological weight based on the future meteorological instantaneous parameters, and combines the future time weight with the historical meteorological data to calculate the future meteorological instantaneous contribution degree at each future time point. This method can accurately evaluate the impact of future meteorological conditions on slope deformation and provide timely deformation prediction and monitoring.

[0140] Furthermore, step B1-4-3 specifically includes:

[0141] B1-4-3-1: Extract the future meteorological instantaneous parameters corresponding to each future time point within the future meteorological duration;

[0142] B1-4-3-2: Calculate the total future meteorological parameters within the future meteorological duration based on the future meteorological instantaneous parameters;

[0143] B1-4-3-3: Calculate the meteorological parameters for the total duration based on the total future meteorological parameters and the historical total meteorological parameters;

[0144] B1-4-3-4: Define the ratio of the exponent of the future meteorological instantaneous parameter corresponding to each future time point to the inverse of the meteorological parameter for the total duration as the future meteorological weight for each future time point.

[0145] This embodiment discloses a method for calculating the future meteorological weight. First, the meteorological instantaneous parameters at each future time point within the future meteorological duration are extracted, and then the total future meteorological parameters within the future meteorological duration are calculated, that is, the future meteorological data is accumulated, covering the meteorological conditions of the entire prediction period. At the same time, the platform also takes into account the total historical meteorological parameters, that is, the impact of the past actual meteorology on the current situation.

[0146] Through this method, the platform can more precisely incorporate future meteorological changes into slope deformation prediction, considering the potential impact of upcoming meteorological changes on slope stability. At the same time, through the exponential decay mechanism, the response ability to the current meteorological conditions is enhanced, avoiding the excessive influence of long-term prediction.

[0147] Next, the platform evaluates the comprehensive impact of meteorological changes on slope deformation by calculating the ratio of the total future meteorological parameters to the total historical meteorological parameters and combining the relative impacts of historical and future meteorological conditions. Finally, the platform takes the ratio of the exponential value of the future meteorological instantaneous parameter at each future time point to the inverse of the meteorological parameter for the total duration as the basis for the future meteorological weight. The inverse ratio reflects the accelerating increasing trend of the impact of future meteorology on slope deformation over the entire duration. As time goes by, the future time weight decays rapidly, while the future meteorological weight increases rapidly, and the impact of future meteorological conditions will increase rapidly, thus ensuring a comprehensive assessment of the impact of future meteorology. Through the future meteorological weight, past and future meteorological data are effectively integrated to provide accurate slope deformation prediction.

[0148] Example 2: The technical solution of this Example 2 is different from that of Example 1 in that the modeling steps of the slope deformation prediction model are disclosed, including:

[0149] S4-1. Mark the first time point and the second time point on the time axis in the slope deformation history database; wherein, the first time point is the historical start time point of the meteorological type, and the second time point is the historical end time point of the meteorological type;

[0150] S4-2. Obtain the slope soil-water vector and the slope meteorological vector at the first time point, and splice them into a historical slope fusion vector;

[0151] S4-3. Obtain several slope deformation characteristics at the second time point, and construct them into a slope historical deformation vector;

[0152] S4-4. Obtain several historical slope fusion vectors and slope historical deformation vectors, and construct them into a training set;

[0153] S4-4. Receive the historical slope fusion vector in the training set as the input variable and the slope historical deformation vector as the target variable, and train the slope deformation prediction model through supervised training.

[0154] This example discloses the modeling steps of the slope deformation prediction model. First, by marking the first time point and the second time point on the time axis in the slope deformation history database, it helps to determine the historical influence range of meteorological conditions on slope deformation.

[0155] Next, the platform obtains the slope soil-water vector and the slope meteorological vector at the first time point, and splices them into a historical slope fusion vector. This fusion vector integrates factors such as the soil, water, and meteorology of the slope, reflecting the overall stability of the slope at that moment. Then, the platform obtains several slope deformation characteristics at the second time point and constructs them into a slope historical deformation vector to represent the slope deformation state at that time point.

[0156] By collecting several historical slope fusion vectors and slope historical deformation vectors, a training set is constructed. Then, using the historical data of the training set, through supervised training, the slope deformation prediction model is trained so that the model can predict the future slope deformation situation according to the input historical meteorological and soil-water data.

[0157] In this example, the initial training model of the slope deformation prediction model is a neural network model.

[0158] Specifically, using a neural network model as the initial training model for the slope deformation prediction model means that the platform utilizes the powerful non - linear fitting ability of the neural network to handle complex slope deformation prediction problems. The neural network can automatically learn complex rules and relationships from the input data. Especially when there are complex and non - linear relationships between slope stability and meteorological, soil and water factors, the neural network can provide relatively high prediction accuracy.

[0159] As the initial training model, the neural network model can gradually optimize the model's parameters through the backpropagation algorithm and a large amount of training data, enabling it to more accurately predict the deformation trend of the slope. By training on historical data, the neural network can identify key factors related to slope deformation and learn to make effective predictions when facing new meteorological conditions and soil and water changes. Therefore, using a neural network as the initial training model can improve the adaptability and prediction accuracy of the slope deformation prediction model in complex environments, especially in the case of dynamic meteorological and soil condition changes.

[0160] The above - mentioned embodiments can be implemented in whole or in part by software, hardware, firmware, or any arbitrary combination thereof. When implemented using software, the above - mentioned embodiments can 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general - purpose computer, a special - purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer - readable storage medium or transmitted from one computer - readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means.

[0161] The computer - readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVD ) or semiconductor media. The semiconductor media can be a solid - state drive.

[0162] In 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 merely illustrative. For example, the division of the units is only a logical function division of a system and method for analyzing the underwater terrain changes of a waterway. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0163] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A remote automatic monitoring platform for slope deformation based on mountain railway engineering, which is applied to a server, and is characterized in that, The platform includes: A soil and water vector acquisition unit for acquiring the slope soil and water vector at the current time point; A meteorological vector construction unit for constructing the slope meteorological vector at the current time point; Among them, the vector features of the slope meteorological vector include the future meteorological contribution degree, and the future meteorological contribution degree is characterized as a set of future meteorological instantaneous contribution degrees. The future meteorological instantaneous contribution degree is calculated based on the future time weight, the future meteorological weight, and the future meteorological instantaneous parameters at the corresponding future time point. The future time weight exponentially decays with an accelerating rate away from the current time point; A vector splicing unit for splicing the slope soil and water vector and the slope meteorological vector to generate a slope fusion vector; A monitoring and prediction unit for inputting the slope fusion vector into a pre-trained slope deformation prediction model and outputting predefined slope deformation parameters; The meteorological vector construction unit has several sub-units, and several of the sub-units include: A current meteorological acquisition sub-unit for acquiring the meteorological type and its meteorological parameters at the current time point; A contribution degree construction sub-unit for obtaining the historical meteorological contribution degree and the future meteorological contribution degree at the current time point according to the meteorological type and its meteorological parameters at the current time point; The steps for the contribution degree construction sub-unit to obtain the future meteorological contribution degree include: Obtaining the end time point of the meteorological type at the current time point on the time axis; Calculating the future meteorological duration between the end time point and the current time point; Obtaining the future meteorological instantaneous parameters at each future time point within the future meteorological duration; According to the future meteorological instantaneous parameters at each future time point, obtaining the future meteorological instantaneous contribution degree at each future time point; Defining the sum of all future meteorological instantaneous contribution degrees within the future meteorological duration as the future meteorological contribution degree at the current time point; The obtaining the future meteorological instantaneous contribution degree at each future time point according to the future meteorological instantaneous parameters at each future time point includes: Calculating the future time difference between each future time point within the future meteorological duration and the current time point; According to the future time difference and the future meteorological duration at each future time point, calculating the future time weight at each future time point in a way that the contribution to the time point decays with an accelerating rate; The calculation expression of the future time weight is as follows: ; Among them, represents the future time weight of the i-th future time point, is the time difference between the i-th future time point and the current time point; is the normalized future time difference, representing the relative proportion of this future time point to the future meteorological duration; indicates that as the time difference increases, the weight of the future time point gradually increases; Obtaining the future meteorological weight of each future meteorological instantaneous parameter within the future meteorological duration; Calculating the future meteorological instantaneous contribution degree according to the future time weight, the future meteorological weight, and the future meteorological instantaneous parameters at the corresponding future time point; The obtaining the future meteorological weight of each future meteorological instantaneous parameter within the future meteorological duration includes: Extracting the future meteorological instantaneous parameters corresponding to each future time point within the future meteorological duration; Calculating the total future meteorological parameters within the future meteorological duration according to the future meteorological instantaneous parameters; Calculating the meteorological parameters for the total duration according to the total future meteorological parameters and the historical meteorological total parameters; Defining the ratio of the exponent of the future meteorological instantaneous parameter corresponding to each future time point to the inverse of the meteorological parameter for the total duration as the future meteorological weight at each future time point.

2. The remote automatic monitoring platform for slope deformation based on mountain railway engineering according to claim 1, characterized in that The steps for the soil and water vector acquisition unit to acquire the slope soil and water vector at the current time point include: S1-1. Obtain the slope soil type, surface water content of the slope, and slope angle at the current time point; S1-2. Define the slope water permeability at the current time point according to the slope soil type; S1-3. Construct the slope water and soil vector of the current slope according to the surface water content of the slope, slope angle, and slope water permeability at the current time point.

3. The remote automatic monitoring platform for slope deformation based on mountain railway engineering according to claim 2, characterized in that, The steps for the contribution degree construction subunit to obtain the historical meteorological contribution degree include: A1-1. Obtain the starting time point of the meteorological type on the time axis at the current time point; A1-2. Calculate the historical meteorological duration between the starting time point and the current time point; A1-3. Obtain the historical meteorological instantaneous parameters at each historical time point during the historical meteorological duration; A1-4. Obtain the historical meteorological instantaneous contribution degree at each historical time point according to the historical meteorological instantaneous parameters at each historical time point; A1-5. Define the sum contribution degree of all historical meteorological instantaneous contribution degrees during the historical meteorological duration as the historical meteorological contribution degree at the current time point.

4. The remote automatic monitoring platform for slope deformation based on mountain railway engineering according to claim 3, characterized in that, Obtaining the historical meteorological instantaneous contribution degree at each historical time point according to the historical meteorological instantaneous parameters at each historical time point includes: A1-4-1. Calculate the historical time difference between each historical time point and the current time point during the historical meteorological duration; A1-4-2. Calculate the historical time weight of each historical time point in a way that the contribution accelerates and increases towards the current time point according to the historical time difference and historical meteorological duration of each historical time point; The calculation expression of the historical time weight is as follows: ; Among them, is the total historical assist at the i-th historical time point, is the time difference between the i-th historical time point and the current time point; is the standardized historical time difference, representing the relative proportion of this historical time point to the historical meteorological duration; is the contribution decreasing term, is the decreasing coefficient, used to control the attenuation speed of the influence of the historical time point on the current meteorology; A1-4-3. Obtain the historical meteorological weight of each historical meteorological instantaneous parameter during the historical meteorological duration; A1-4-4. Calculate the historical meteorological instantaneous contribution degree according to the historical time weight, historical meteorological weight, and historical meteorological instantaneous parameters at the corresponding historical time point; The calculation expression of the historical meteorological instantaneous contribution degree is as follows: ; Among them, is the historical meteorological instantaneous contribution degree at the i-th historical time point, is the historical meteorological weight, is the historical meteorological instantaneous parameter.

5. The remote automatic monitoring platform for slope deformation based on mountain railway engineering according to claim 4, characterized in that, Obtaining the historical meteorological weight of each historical meteorological instantaneous parameter during the historical meteorological duration includes: A1-4-3-1. Extract the historical meteorological instantaneous parameters corresponding to each historical time point during the historical meteorological duration; A1-4-3-2. Calculate the total historical meteorological parameter during the historical meteorological duration according to the historical meteorological instantaneous parameters; A1-4-3-3. Define the ratio of the historical meteorological instantaneous parameter corresponding to each historical time point to the total historical meteorological parameter as the historical meteorological weight of each historical time point.

6. The remote automatic monitoring platform for slope deformation based on mountain railway engineering according to claim 1, characterized in that, The modeling steps of the slope deformation prediction model include: S4-1. Mark the first time point and the second time point on the time axis in the slope deformation historical database; where the first time point is the historical start time point of the meteorological type, and the second time point is the historical end time point of the meteorological type; S4-2. Obtain the slope water and soil vector and slope meteorological vector at the first time point, and splice them into a historical slope fusion vector; S4-3. Obtain several slope deformation characteristics at the second time point, and construct them into a slope historical deformation vector; S4-4. Obtain several historical slope fusion vectors and slope historical deformation vectors, and construct them into a training set; S4-4. Receive the historical slope fusion vectors in the training set as input variables and the historical slope deformation vectors as target variables, and supervise the training of the slope deformation prediction model.

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