A kind of be applied to power transmission and transformation engineering slope deformation monitoring system

Abstract

The application discloses a kind of applied to power transmission and transformation engineering slope deformation monitoring system, it is related to slope deformation monitoring technical field, realize high-precision slope displacement calculation by fusing optical fiber voltage signal and big dipper positioning data, construct inclination prediction model in combination with historical inclination data, and introduce residual analysis to identify structure deviation trend.System dynamically calculates soil stress attenuation based on pore water pressure data, generates threshold correction factor driven by softening coefficient, and then adaptively adjusts displacement acceleration warning threshold, to adapt to different geological conditions.Through monitoring displacement acceleration and inclination prediction residual double index, closed-loop risk identification mechanism is established, with the advantages of timely response, high identification accuracy and low false alarm rate, especially suitable for complex geological conditions and large rainfall intensity variation power transmission and transformation scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of slope deformation monitoring, in particular to a slope deformation monitoring system applied to power transmission and transformation engineering. BACKGROUND

[0002] In power transmission and transformation engineering, the tower foundation is often arranged in an area with a certain slope or soft soil body. In order to ensure the operation safety, a deformation monitoring means is generally introduced in the engineering, and a real-time deformation of the slope and the tower foundation structure is monitored by using a fiber displacement meter, an inclination instrument, a Beidou positioning module and other sensing devices, and a potential instability risk is judged by the deformation amplitude and the change rate. In the current technology, the instability recognition is often based on a preset fixed threshold value, for example, a certain value monitored exceeds a certain set value to trigger an early warning, so as to guide the engineering inspection and disposal decision.

[0003] However, in a complex geological environment, especially in the background of the increase of pore water pressure induced by rainfall, the soil structure softens and the effective stress significantly decays, and the stability condition of the slope will dynamically change. Therefore, the traditional static criterion is prone to early warning errors, even false alarms or omissions, in such cases. Therefore, the existing monitoring system generally lacks dynamic adaptability to the evolution of the geological state in the setting of the instability threshold value, and it is difficult to accurately reflect the stability evolution of the slope in the softening process. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application provides a slope deformation monitoring system applied to power transmission and transformation engineering.

[0005] In order to achieve the above-mentioned purpose, the technical scheme of the present application is as follows:

[0006] A slope deformation monitoring system applied to power transmission and transformation engineering, comprising:

[0007] A data acquisition module for acquiring reference point fiber voltage signals, Beidou positioning coordinate data, pore water pressure data of the slope area, and real-time inclination data and historical inclination data of the tower;

[0008] A displacement value calculation module for calculating the slope correction displacement value based on the reference point fiber voltage signals and the Beidou positioning coordinate data;

[0009] An inclination prediction module for outputting an inclination prediction value based on the slope correction displacement value and the historical inclination data, and calculating the residual of the real-time inclination data and the inclination prediction value;

[0010] A threshold value monitoring and adjusting module for calculating the effective stress decay amount of the soil body based on the pore water pressure data, and determining a threshold correction factor according to a preset softening coefficient mapping relationship; adjusting the preset reference threshold value according to the threshold correction factor to generate a dynamic displacement acceleration threshold value;

[0011] a risk judgment and output module, configured to generate and output an instability early warning instruction when the following conditions are met simultaneously:

[0012] an acceleration of the slope correction displacement value exceeds a dynamic displacement acceleration threshold value, the acceleration of the slope correction displacement value being a second derivative of the slope correction displacement value;

[0013] the residual error continuously exceeds a preset residual error threshold value for a predetermined length of time.

[0014] Compared with the prior art, the present application has the following beneficial effects:

[0015] 1. The change of pore water pressure is used to reflect the softening of soil structure and the significant attenuation of effective stress, and a softening coefficient is constructed to calculate the stability of the soil in the current state, which is used as a basis to adjust the displacement acceleration early warning threshold, thereby solving the problem of high false alarm rate of the traditional static threshold in the rainy season and saturated working conditions.

[0016] 2. The reference point fiber voltage signal and Beidou positioning data are synchronously collected, and the two sets of signals are mutually verified to correct the drift error of Beidou positioning, and the accuracy of the fiber measurement point can be preserved to achieve centimeter-level dynamic displacement monitoring accuracy and meet the high-level safety monitoring requirements of the substation. BRIEF DESCRIPTION OF DRAWINGS

[0017] The disclosure of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0018] Figure 1 is a system module diagram of the present application;

[0019] Figure 2 is a data flow diagram of the present application. DETAILED DESCRIPTION

[0020] It is easy to understand that, according to the technical scheme of the present application, those skilled in the art can propose a plurality of structure modes and implementation modes which can be replaced with each other without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical scheme of the present application, and should not be regarded as the whole or as a limitation or restriction on the technical scheme of the present application.

[0021] SUMMARY

[0022] In traditional slope deformation monitoring systems, instability early warning mechanisms rely on preset fixed thresholds for judgment. Their core flaw lies in their inability to dynamically adapt to changes in soil mechanical parameters with environmental variations. When pore water pressure increases due to rainfall infiltration in the slope area, the effective stress within the soil undergoes nonlinear decay, significantly enhancing the softening effect. At this point, a deviation arises between the static threshold and the dynamic decay process of the slope's actual bearing capacity. This deviation directly leads to a disconnect between the system's judgment benchmark for displacement acceleration and the true stability state of the slope, causing delayed or falsely triggered early warning responses. Especially during periods of rapid change in the soil softening coefficient, the system cannot accurately capture the displacement acceleration characteristics of the critical instability state.

[0023] For example, under continuous heavy rainfall, the pore water pressure sensor on the slope of a 500kV transmission line tower detected a continuous rise in pressure from its initial value to saturation, and the soil compressibility coefficient increased to the critical range due to changes in water content. At this time, the Beidou positioning module monitored the eastward displacement of the slope reference point increasing at a rate of 0.2 mm / h, and the tilt sensor recorded an increase in the tower's tilt angle of 0.05 degrees per hour. Traditional systems still use a fixed displacement acceleration threshold calibrated during the dry season for judgment, failing to consider the decrease in shear strength caused by soil softening. Its preset acceleration threshold of 2.5 mm / h² is already higher than the critical value corresponding to the actual bearing capacity of the soil. Under these conditions, the system continuously outputs a safe state signal, while the slope has actually entered the asymptotic failure stage.

[0024] If the above problems are not addressed, the early warning mechanism will be unable to identify abrupt stability changes during soil softening, causing engineering inspections to miss the optimal window for intervention. When the displacement acceleration exceeds the static threshold against the backdrop of continuously decreasing soil strength, irreversible shear failure may have already occurred on the slope, causing the tower foundation displacement to exceed the structural tolerances, leading to transmission line tension imbalance or even tower collapse. Simultaneously, the system may generate false alarms during the soil parameter recovery period due to the threshold not being adjusted back in time, resulting in wasted maintenance resources. This mismatch between static criteria and dynamic geological conditions severely restricts the engineering applicability of the monitoring system in complex environments.

[0025] To address the aforementioned issues, this application first analyzes the root cause of the failure of traditional static threshold mechanisms, finding that its core lies in the failure to consider the dynamic impact of soil softening on slope bearing capacity. To resolve this, this application attempts to establish a correlation between pore water pressure data and the effective stress attenuation of the soil, correcting the displacement acceleration threshold by calculating the soil softening coefficient in real time. Furthermore, this application proposes introducing a multi-source data fusion mechanism, cross-calibrating fiber optic voltage signals with BeiDou positioning coordinates to eliminate reference point drift errors; simultaneously, it combines residual analysis of tilt angle predictions and real-time data to construct multi-dimensional early warning triggering conditions, avoiding the limitations of a single criterion. After verifying various dynamic adjustment models, a dynamic threshold is finally determined based on the nonlinear mapping relationship of the effective stress attenuation of the soil, and displacement acceleration and residual duration are coupled as a composite criterion to form an adaptive instability identification logic.

[0026] In this regard, such as Figure 1 , Figure 2 As shown, this application proposes: a slope deformation monitoring system for power transmission and transformation projects, comprising:

[0027] The data acquisition module is used to acquire the reference point fiber optic voltage signal, BeiDou positioning coordinate data, pore water pressure data, and real-time and historical tilt angle data of the tower in the slope area. The data acquisition module refers to the device used to acquire relevant monitoring data of the slope area and the tower. Specifically, it can be implemented by combining fiber optic sensors, BeiDou positioning receivers, pore water pressure gauges, and tilt sensors. The synchronous acquisition of multi-source data provides the basic input for subsequent analysis.

[0028] The displacement calculation module is used to calculate the slope correction displacement value based on the reference point fiber voltage signal and Beidou positioning coordinate data. The displacement calculation module refers to the algorithm unit that fuses the fiber voltage signal and Beidou positioning data. Specifically, it can be implemented using the differential correction method to improve the displacement measurement accuracy by eliminating the reference point drift error.

[0029] The tilt prediction module is used to output tilt prediction values ​​based on slope correction displacement values ​​and historical tilt data, and to calculate the residual between real-time tilt data and tilt prediction values. The tilt prediction module refers to a calculation model that predicts tower tilt changes based on displacement changes and historical data. Specifically, it can be implemented by combining time series analysis with residual calculation, and enhance the sensitivity of status monitoring by dynamically updating the predicted values.

[0030] The threshold monitoring and adjustment module is used to calculate the effective stress attenuation of the soil based on the pore water pressure data, and determine the threshold correction factor according to the preset softening coefficient mapping relationship; adjust the preset benchmark threshold according to the threshold correction factor to generate a dynamic displacement acceleration threshold; the threshold monitoring and adjustment module refers to an adaptive mechanism that dynamically corrects the early warning threshold according to the change of soil stress, which can be implemented by softening coefficient mapping and dispersion analysis, and adjust the threshold in real time to match the changes in geological conditions.

[0031] The risk assessment and output module is used to generate and output an instability warning command when the following conditions are met simultaneously:

[0032] If the acceleration of the slope correction displacement value exceeds the dynamic displacement acceleration threshold, the acceleration of the slope correction displacement value is the second derivative of the slope correction displacement value.

[0033] The residual continuously exceeds the preset residual threshold for a predetermined period of time. The risk judgment and output module refers to the early warning decision logic that integrates displacement acceleration and residual persistence. Specifically, it can be implemented using a dual-condition parallel triggering mechanism, which reduces the probability of misjudgment by a single indicator through joint criteria.

[0034] The core innovation of this application lies in constructing an early warning mechanism that combines dynamic displacement acceleration threshold and residual persistence criteria. By integrating fiber optic displacement, BeiDou positioning, pore water pressure and tilt angle data in real time, an adaptive threshold adjustment model based on the effective stress attenuation of the soil is established to solve the problem of insufficient early warning accuracy of traditional static thresholds under soil softening conditions.

[0035] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0036] The data acquisition module obtains fiber optic voltage signals from slope benchmarks using distributed fiber optic sensors, with a sampling frequency of 1Hz. The BeiDou positioning module collects coordinate data every 10 minutes. Pore water pressure sensors are deployed at key locations on the slope, with a sampling interval of 5 minutes. A dual-axis tilt sensor is installed at the tower foundation to record the tilt angle in real time, with a sampling frequency of 0.1Hz.

[0037] The displacement calculation module first converts the fiber optic voltage signal into fiber optic displacement, then extracts the eastward component difference between adjacent BeiDou coordinates as the reference point drift. The corrected slope displacement value is then calculated using this difference.

[0038] The dip angle prediction module outputs a predicted dip angle value based on the current corrected displacement value and historical dip angle data, using a prediction model that includes degradation condition coefficients. It calculates the residual between the predicted value and the measured dip angle in real time.

[0039] The threshold monitoring and adjustment module calculates the effective stress attenuation of the soil based on pore water pressure data. It determines the softening coefficient correction factor through a preset nonlinear mapping relationship. Combined with the current slope safety factor, it performs multi-factor coupling adjustment on the preset benchmark threshold to generate a dynamic displacement acceleration threshold.

[0040] The risk assessment and output module continuously monitors the second derivative (acceleration) of the slope correction displacement value. When the acceleration exceeds the dynamic threshold and the residual exceeds the preset value for one consecutive hour, the system generates and outputs an instability warning command.

[0041] Through the above-described scheme, this application achieves dynamic adaptation of the slope deformation monitoring system to the soil softening effect. By introducing pore water pressure data to calculate the effective stress attenuation of the soil, the system can adjust the displacement acceleration threshold in real time, avoiding the judgment deviation of the static threshold when soil parameters change. The multi-source data fusion mechanism improves the accuracy of displacement calculation and eliminates benchmark drift error. The composite early warning triggering condition overcomes the limitations of a single criterion and improves the system's ability to identify critical instability states. These improvements enable the system to more accurately capture the dynamic changes in slope stability, providing timely and reliable early warning information for engineering inspection and disposal decisions, and effectively reducing the safety risks caused by slope instability in power transmission and transformation projects.

[0042] This application further proposes a method for calculating the effective stress attenuation of soil based on the aforementioned pore water pressure data, including:

[0043] Obtain the soil type identifier of the current slope and determine the corresponding compression coefficient according to the preset soil type mapping relationship. and saturated permeability coefficient Soil type identifiers are pre-classified and stored as discrete codes based on geological exploration data, with each code corresponding to a set of compression coefficients. and saturated permeability coefficient It is used to reflect the mechanical properties of different soil types;

[0044] Determine the pore water pressure data Has the preset saturation threshold been reached? If the judgment result is yes, then it is determined to be a saturated state, and the initial effective stress is used. Stress attenuation calculation is performed using a calculation model based on the ratio of pore water pressure; otherwise, it is determined to be an unsaturated state, and stress attenuation calculation is performed after calculating the equivalent saturation based on the soil-water characteristic curve.

[0045] The preset saturation threshold is determined by the ratio of pore water pressure to saturated pore water pressure in the soil. When the ratio is greater than or equal to 1, the saturation state is determined. The equivalent saturation in the unsaturated state is calculated by the functional relationship between suction and water content in the soil-water characteristic curve.

[0046] The obtained effective stress attenuation of the soil is used for data validity verification. When the effective stress attenuation exceeds the preset statistical fluctuation range, a sensor data anomaly flag is triggered. The data validity verification adopts a statistical process control method, comparing the current effective stress attenuation with the historical data mean and standard deviation. If it exceeds three times the standard deviation, it is marked as an anomaly.

[0047] Calculating the effective stress attenuation of soil At that time, the corresponding compression coefficient is first called based on the soil type identifier. and saturated permeability coefficient This ensures that the calculated parameters match the actual geological conditions. Subsequently, pore water pressure is used... With preset saturation threshold The comparison distinguishes whether the soil is in a saturated or unsaturated state, and different calculation models are used for each.

[0048] In the saturated state, the initial effective stress is used. Stress attenuation calculations were performed using a model that calculates the ratio of pore water pressure to pore water pressure data. With preset saturation threshold The ratio directly reflects the degree of stress attenuation; the specific calculation formula is: .

[0049] In an unsaturated state, the pore water pressure needs to be converted into an equivalent degree of saturation based on the soil-water characteristic curve, and then the effective stress attenuation of the soil needs to be calculated in conjunction with the permeability coefficient; the specific process is as follows:

[0050] Calculate the equivalent saturation based on the soil-water characteristic curve model. :

[0051] ,in , , These are all prefabricated soil parameters, determined according to the soil type. This refers to pore water pressure data;

[0052] Specifically The air intake parameter is the pressure threshold when the soil begins to drain, which is related to the soil particle size and the maximum pore size of the soil. It is the pore size distribution index, used to reflect the uniformity of soil pores; These are curve shape parameters, typically set to: .

[0053] when When the value is 0, the formula simplifies to This indicates that the soil is completely saturated, meaning that the pores are filled with water.

[0054] when As the volume increases, the suction force increases, and the formula is derived from... Dominant, at this time It exhibits a non-linear decreasing state.

[0055] Then, the effective stress attenuation of the soil is calculated:

[0056] .

[0057] After the calculation is completed, the validity of the data is verified by statistically analyzing the fluctuation range. For example, when the effective stress attenuation of the soil exceeds the standard deviation of ±3 times the historical mean, it is determined to be abnormal sensor data, triggering a flag to prevent erroneous data from participating in subsequent threshold correction. This process ensures the accuracy and reliability of the calculated effective stress attenuation of the soil through dynamic adaptation to the soil condition and real-time data verification, providing effective input for dynamically adjusting the displacement acceleration threshold.

[0058] Through the above technical solution, this application can select an appropriate calculation model based on soil type and saturation state to accurately calculate the effective stress attenuation of the soil. Simultaneously, data validity verification improves the reliability of the calculation results. This calculation method, which dynamically adapts to different soil conditions and saturation states, effectively enhances the accuracy and real-time performance of slope stability assessment, providing a more reliable data foundation for subsequent early warning judgments.

[0059] This application further proposes determining the threshold correction factor based on a preset softening coefficient mapping relationship, including:

[0060] Obtain the current and historical effective stress attenuation sequence of the soil, and calculate the short-term change intensity and long-term trend intensity based on the sequence;

[0061] By comparing the ratio of short-term change intensity to long-term trend intensity, it can be determined whether the current stress decay state is a steady state or a transient state.

[0062] If it is a steady state, a softening coefficient correction factor is generated based on a linear mapping model; if it is a transient state, a softening coefficient correction factor is generated based on an exponential mapping model.

[0063] The effective stress attenuation of soil at multiple monitoring points within the same slope is obtained, its dispersion index is calculated, and when the dispersion exceeds the preset dispersion threshold, the softening coefficient correction factor is spatially consistent to obtain the threshold correction factor.

[0064] After obtaining the threshold correction factor, a boundary range check is performed. When the threshold correction factor exceeds the preset physical reasonable range, the data is truncated to the range boundary and the abnormal data information is recorded.

[0065] During slope monitoring, the system periodically collects the effective stress attenuation of the soil at each monitoring point, forming time-series data. By calculating the absolute value of the difference in effective stress attenuation between two adjacent sampling points, the short-term variation intensity is obtained, reflecting the degree of instantaneous fluctuation. Simultaneously, a sliding window is used to linearly fit historical data to extract the long-term trend intensity, characterizing the overall attenuation trend. The ratio between the short-term variation intensity and the long-term trend intensity determines the current stress attenuation state.

[0066] If it is a steady state, then the softening coefficient correction factor ;in This is a mapping function for soil stress attenuation under steady-state conditions. Based on the softening coefficient, is a configurable steady-state model slope coefficient, representing a linear increase in the degree of softening as stress decreases;

[0067] If it is a transient state, then the softening coefficient correction factor ;in This is a mapping function for soil stress attenuation under transient conditions. It is a configurable transient model attenuation coefficient, emphasizing the rapid reduction of the system threshold during sudden stress attenuation.

[0068] Simultaneously acquire the effective stress attenuation of soil at multiple monitoring points within the slope. The ratio of its standard deviation to its mean is calculated as an index of dispersion.

[0069] ;

[0070] If the dispersion index Exceeding the preset discrete threshold This indicates the presence of local anomalies or monitoring errors; the original... There may be distortion, in which case... Perform a spatially weighted average to eliminate isolated point interference, i.e.:

[0071] ,in, Softening coefficient correction factor Threshold correction factor after spatial consistency correction. These are configurable spatial weighting coefficients.

[0072] If the dispersion index Not exceeding the preset discrete threshold Then the softening coefficient correction factor will be... Directly used as a threshold correction factor .

[0073] Finally, the threshold correction factor Compared with the theoretical range of soil shear strength, the threshold correction factor is adjusted. Excess values ​​are forcibly truncated to boundary values ​​to avoid threshold correction factors being affected by data anomalies. Distortion. For example, under rainfall conditions, a slope monitoring point may experience abnormal soil effective stress attenuation due to sensor malfunction, causing a rapid increase in the dispersion index. The system automatically reduces the weight of this point and uses the mean of the remaining normal monitoring points to correct the threshold correction factor. This ensures the reliability of dynamic adjustments.

[0074] Through the above technical solution, this application achieves dynamic threshold correction based on the effective stress attenuation state of the soil, improving the accuracy of slope deformation monitoring systems in identifying instability risks under different geological conditions. Simultaneously, by introducing spatial consistency correction and boundary range verification, the reliability and stability of the threshold correction are enhanced, avoiding erroneous judgments caused by local anomalies.

[0075] This application further proposes a proportional relationship between the intensity of short-term changes and the intensity of long-term trends, including:

[0076] Obtain the effective stress attenuation sequence of soil ,in, for The effective stress attenuation of the soil at a given time;

[0077] Calculate the intensity of short-term changes: ,in The sampling interval;

[0078] Calculate the strength of the long-term trend: , Number of sampling periods;

[0079] like If the condition is met, then the steady-state mapping model is selected; otherwise, the transient mapping model is selected. This is the preset scaling factor.

[0080] Among them, short-term change intensity By a single sampling period Calculation of the variation range of effective stress attenuation in the inner soil, and long-term trend strength. Through continuous Each sampling period Calculation of the variation trend of effective stress attenuation in soil. The frequency is set to a fixed number of cycles based on the requirements for slope geological stability monitoring. (Proportion coefficient) The correction factor is determined based on the statistical values ​​of the critical points for the transition between steady-state and transient states in historical monitoring data. For example, it can be set after cluster analysis of the short-term to long-term intensity ratio during state transitions in historical data using a machine learning model. The steady-state mapping model uses a linear relationship to generate the correction factor, while the transient mapping model uses an exponential relationship. The two models correspond to different soil stress attenuation modes.

[0081] During implementation, the effective stress attenuation of the soil is first continuously acquired from sensors to form a time series. For each sampling period, the variation amplitude of the effective stress attenuation of the soil within the current period is calculated as the short-term variation intensity. Simultaneously extract the previous The effective stress attenuation of the soil over each period is used to calculate the long-term trend strength using linear regression or moving average algorithms. .

[0082] Short-term change intensity Strength of long-term trend Multiply by a preset coefficient The results are compared, and if the result is lower than the product, it is determined that the current state is in a steady state, and a correction factor is generated using a linear mapping model.

[0083] Conversely, if the condition is not met, it is determined to be a transient state, and an exponential mapping model is used to generate a correction factor.

[0084] This judgment mechanism, by quantifying the dynamic relationship between short-term and long-term intensity, can distinguish between the gradual and abrupt states of soil stress decay, thereby matching different correction factor calculation models and improving the adaptability of threshold adjustment.

[0085] For example, based on the pore water pressure data acquired by the pore water pressure sensor in the current sampling period, the effective stress attenuation of the soil in the current sampling period is calculated to be 10 kPa, with a sampling interval of 5 minutes. If the previous sampling period's value was 9 kPa, then the short-term stress change intensity... for:

[0086] .

[0087] Assuming the effective stress attenuation of the soil in the current sampling period is 15 kPa, and the effective stress attenuation of the soil 10 sampling periods ago is 7 kPa, then the long-term trend strength... for:

[0088] .

[0089] like If the value is 1.2, then 0.2 > 1.2 * 0.16, which means the short-term change intensity is... Greater than Multiples of the long-term trend strength If the condition is met, then the transient mapping model is selected; otherwise, the steady-state mapping model is determined.

[0090] Through the above technical solution, this application achieves accurate judgment of the effective stress attenuation state of soil. This allows for the selection of a suitable mapping model based on the actual attenuation state, improving the calculation accuracy of the softening coefficient correction factor. Furthermore, by comparing the intensity of short-term changes with the intensity of long-term trends, sudden attenuation events can be effectively identified, providing a reliable basis for subsequent early warning.

[0091] This application further adjusts the preset benchmark threshold according to the correction factor to generate the dynamic displacement acceleration threshold, including:

[0092] When the threshold correction factor exceeds the preset correction threshold, the duration is recorded, and the timeliness weight is calculated based on the duration. The current threshold correction factor and the historical average are weighted according to the timeliness weight to obtain the weighted correction factor.

[0093] Determine whether the weighted adjustment factor exceeds the preset risk threshold;

[0094] If so, then based on the ratio of the current slope safety factor to the reference safety factor, and the weighted correction factor, a multi-factor coupling adjustment of the acceleration threshold is performed;

[0095] Otherwise, set the dynamic displacement acceleration threshold to the minimum predefined value.

[0096] During slope monitoring, when changes in pore water pressure cause the effective stress attenuation of the soil to remain at a consistently high level, the system quantifies the persistence of this state by recording the duration. The weighting process eliminates interference from abnormal fluctuations at a single point in time by fusing current and historical data. When the weighted result exceeds the risk threshold, a safety factor ratio is introduced for secondary correction. The multi-factor coupling mechanism can simultaneously reflect the degree of soil softening and the structural safety margin, allowing the acceleration threshold to adaptively adjust with the actual safety state, avoiding false alarms or missed alarms caused by fixed thresholds in traditional methods.

[0097] Through the above technical solution, this application achieves adaptive adjustment of the dynamic displacement acceleration threshold. Therefore, the system can dynamically adjust the early warning threshold according to changes in the degree of slope softening, improving the accuracy and timeliness of slope instability early warning. Furthermore, by introducing time-dependent weighting and multi-factor coupled adjustment, this solution can more comprehensively consider the dynamic changes in slope condition, avoiding false alarms or missed alarms that may be caused by a single fixed threshold. Specifically, this solution calculates a weighted correction factor, comprehensively considering the current threshold correction factor and historical average, reflecting both real-time changes in slope condition and maintaining a certain degree of stability. Simultaneously, by incorporating the slope safety factor into the calculation, the rationality and reliability of the threshold adjustment are further improved. Therefore, the technical solution of this application can more accurately reflect the stability evolution of slopes during the softening process, providing a more reliable early warning basis for slope deformation monitoring in power transmission and transformation projects.

[0098] This application further proposes the following calculation process for the dynamic displacement acceleration threshold:

[0099] The duration for which the threshold correction factor exceeds the preset correction threshold. ;

[0100] Calculate the time-sensitive weight : ;in, The preset time constant;

[0101] Calculate the weighted correction factor : ,in, This is the current threshold correction factor. This is the average threshold correction factor of the previous period;

[0102] when hour, ;in To preset risk thresholds, For a predefined minimum acceleration threshold, The dynamic displacement acceleration threshold;

[0103] when hour, ;in, The current safety factor of the slope, As the baseline value for the safety factor, For safety sensitivity coefficient, This is a preset baseline threshold.

[0104] The calculation of the time-related weights adopts an exponential decay model. This function can reflect the degree of influence of duration on weights. By controlling the decay rate of historical data through a time constant, it ensures that recent correction factors have a higher weight in the weighting process.

[0105] Weighted correction factor The calculation incorporates the mean of the threshold correction factor from the previous period. Through time-sensitive weighting To balance the correlation between the current state and historical trends, the current threshold correction factor is implemented. Mean of threshold correction factor compared to the previous period The dynamic equilibrium.

[0106] During the multi-factor coupled adjustment process, the current safety factor of the slope Compared with the safety factor benchmark value The ratio is included in the calculation, through the security sensitivity coefficient. The impact of quantified safety status on acceleration threshold.

[0107] Specifically, when the threshold correction factor continuously exceeds the preset correction threshold, the duration is... Quantified as timeliness weight This weight varies with duration. The increase is non-linear, until it approaches a saturation value.

[0108] Weighted correction factor Time-sensitivity weight Dynamically allocate the current threshold correction factor Mean of threshold correction factor compared to the previous period The contribution ratio is adjusted to suppress the interference of short-term abnormal fluctuations on threshold adjustment.

[0109] When weighted correction factor Exceeding the risk threshold At that time, dynamic displacement acceleration threshold The calculation not only considers the correction factor itself, but also incorporates the current safety factor of the slope. Compared with the safety factor benchmark value The relative change, through the safety sensitivity coefficient Adjust the coupling strength between the two.

[0110] For example, when the current safety factor of the slope Below the safety factor benchmark value At that time, the acceleration threshold will be determined based on the safety sensitivity coefficient. The deviation is reduced accordingly, thereby triggering an early warning during the soil softening stage. This process uses mathematical formulas to achieve linear or nonlinear relationships between parameters, ensuring that the dynamic threshold can simultaneously reflect the combined effects of soil effective stress attenuation and structural safety status.

[0111] Through the above technical solution, this application achieves adaptive adjustment of the dynamic displacement acceleration threshold. By introducing time-dependent weighting and multi-factor coupling, the threshold calculation process fully considers the dynamic evolution characteristics of the slope state, improving the rationality and sensitivity of the threshold setting. Simultaneously, by setting a minimum predefined value, the system's basic early warning capability is ensured. This dynamic threshold mechanism can more accurately reflect the stability changes of the slope during the softening process, effectively reducing the risk of false alarms and missed alarms.

[0112] This application further proposes that, after the dynamic displacement acceleration threshold is generated, it also includes:

[0113] Determine whether the change in the current dynamic displacement acceleration threshold compared to the previous cycle exceeds the preset change limit. If it does, output a smooth correction value according to the amplitude limiting adjustment strategy and record the overload alarm status.

[0114] Among them, the magnitude of change The detection is achieved by calculating the absolute difference between thresholds of adjacent periods, and the preset change limit is set based on the standard deviation of historical threshold fluctuations. The calculation formula for the amplitude limiting adjustment strategy is:

[0115] ;in, This is a smoothed correction value output according to the amplitude limiting adjustment strategy. To preset the variation limit, This is the acceleration threshold used in the actual application of the previous cycle.

[0116] Specifically, after calculating the dynamic displacement acceleration threshold, the system automatically compares the difference between the current threshold and the previous period threshold. When the difference value Exceeding the variation limit set according to the creep characteristics of slope soil and rock When necessary, adjustments are made to output a smooth correction value.

[0117] During this process, the system synchronously writes the occurrence time, correction value, and associated geological parameters of the exceeding event into the anomaly log database for subsequent slope stability assessment. This mechanism avoids excessive threshold response to single-point interference by suppressing monitoring data jumps caused by abrupt threshold changes, prevents false alarms, and ensures a controllable rate of change, facilitating manual review by experts.

[0118] Through the above technical solution, this application can effectively avoid abrupt changes in the dynamic displacement acceleration threshold, improving the smoothness and continuity of threshold adjustment. Simultaneously, by recording overload alarm status, it provides important reference information for subsequent monitoring and early warning, enhancing the system's reliability and practicality. Furthermore, this solution can promptly detect threshold adjustment anomalies, preventing false alarms or missed alarms caused by sudden threshold changes, thus improving the accuracy and reliability of slope deformation monitoring.

[0119] This application further proposes a method for calculating slope correction displacement values ​​based on reference point fiber optic voltage signals and BeiDou positioning coordinate data, including:

[0120] The reference point fiber voltage signal is converted into fiber displacement by pre-calibration coefficients, and the difference between the eastward components of the BeiDou positioning coordinates at adjacent times is extracted as the reference point drift. The pre-calibration coefficients are determined by laboratory calibration or historical data fitting, for example, the linear relationship coefficient between fiber voltage and displacement is 0.85. The reference point drift is obtained by continuously collecting the eastward components of the coordinates at adjacent times by the BeiDou positioning system and calculating their difference, for example, the sampling interval is 5 minutes.

[0121] Based on the difference between the fiber displacement and the reference point drift, a slope correction displacement value is generated. The slope correction displacement value is obtained by subtracting the reference point drift from the fiber displacement, thus eliminating the error introduced by the reference point's own displacement. During fiber voltage signal conversion, a moving average filter is used to suppress high-frequency noise; the eastward component difference of the BeiDou positioning coordinates is extracted using a differential algorithm, for example, a cubic polynomial fitting is used to eliminate random errors.

[0122] Specifically, when the pre-calibration coefficient converts the fiber optic voltage signal into a displacement, for example, a displacement of 12.5 mm corresponds to an input voltage signal of 3.2V. The difference in the eastward component of the BeiDou positioning coordinates is calculated using the coordinate difference between adjacent moments. For example, if the eastward coordinate at the previous moment was E1=102.35° and the current moment is E2=102.351°, the difference is 0.001°, which is converted to a reference point drift of 1.2 mm. The slope correction displacement value is obtained by subtracting the reference point drift (e.g., 1.2 mm) from the fiber optic displacement (e.g., 12.5 mm), resulting in an actual slope displacement value of 11.3 mm. This process is implemented through hardware circuitry or software algorithms, such as using a floating-point arithmetic unit to perform the difference calculation in an embedded system. By eliminating the reference point drift error, the accuracy of the corrected displacement value is improved to ±0.5 mm, significantly reducing the input error of subsequent tilt angle prediction and risk assessment modules, and avoiding false alarms caused by reference point displacement.

[0123] Through the above technical solution, this application effectively solves the problem of measurement error accumulation caused by benchmark drift in traditional displacement monitoring. By dynamically compensating for the spatial offset of the Beidou positioning benchmark, it ensures the spatial benchmark consistency of slope displacement data, provides high-precision input for subsequent displacement acceleration calculation and instability early warning, and avoids the risk of misjudgment caused by the displacement of the benchmark itself.

[0124] This application further proposes to output predicted dip angle values ​​based on slope correction displacement values ​​and historical dip angle data, including:

[0125] Obtain the standard deviation of the displacement change rate and the dip angle change rate in the historical time period, and determine whether the slope is in a state of degradation based on the comparison between the two and the corresponding historical reference values.

[0126] If the condition is determined to be in a degraded state, the predicted dip angle value is output using a prediction model that includes the degradation condition coefficient and the weight of the historical deviation, based on the current corrected displacement value, the historical dip angle mean and its deviation.

[0127] If the condition is determined to be normal, the tilt angle prediction value is generated based on the linear relationship between the current corrected displacement value and the normal operating condition coefficient.

[0128] The displacement change rate is calculated as the ratio of the change in slope correction displacement value to the sampling time interval, and the standard deviation of the historical dip angle change rate is obtained by statistically analyzing the fluctuation range of historical dip angle data.

[0129] The criteria for determining the degradation state are the dual conditions that the rate of change of displacement exceeds the preset displacement threshold and the standard deviation of the inclination angle exceeds the preset inclination angle threshold. That is, by calculating the rate of change of displacement and the standard deviation of the historical inclination angle in real time, it is possible to dynamically identify whether the slope has entered the degradation stage of the soil structure.

[0130] The degradation condition coefficient is used to characterize the accelerating effect of soil softening on dip angle changes, while the historical deviation weight is used to introduce the correction effect of historical dip angle fluctuations on the current prediction.

[0131] The degradation condition coefficient, calibrated through laboratory soil softening tests, is used to amplify the impact of displacement increments on dip angle prediction. The historical deviation weight is determined by statistically analyzing the magnitude of historical dip angle deviations from the mean, and is used to suppress abrupt changes in the predicted value. For example, when the displacement change rate reaches 0.5 mm / h and the dip angle standard deviation exceeds 0.3 degrees, the degradation state prediction mode is triggered. At this time, the predicted value not only includes the product of the current displacement increment and the degradation condition coefficient, but also a correction term for the historical dip angle deviation. This mechanism allows the predicted value to reflect both the accelerated deformation trend caused by soil softening and to suppress prediction anomalies caused by sensor noise, thereby improving the accuracy of residual calculation and providing reliable input for dynamic threshold adjustment.

[0132] The normal operating condition coefficient is calibrated by the linear regression relationship between displacement and tilt angle under steady-state conditions.

[0133] Through the above technical solution, this application effectively solves the problem of insufficient early warning accuracy of traditional static thresholds when soil softening or pore water pressure changes. By dynamically judging the slope degradation state and adaptively switching the prediction model, it can more accurately reflect the impact of effective stress attenuation in the soil on the structural tilt angle, improving the consistency between the predicted tilt angle and the actual deformation trend. In the case of rainfall-induced slope instability, this solution can significantly reduce the risk of misjudgment caused by dynamic changes in geological conditions, providing a more reliable decision-making basis for slope safety monitoring in power transmission and transformation projects.

[0134] This application further proposes the following calculation process for the predicted tilt angle:

[0135] Calculate the current rate of change of displacement Standard deviation of historical dip rate :

[0136] ;in, This represents the change in the slope correction displacement value. The sampling time interval;

[0137] rate of change of displacement Through unit time Change in internal correction displacement value Calculate the standard deviation of the historical rate of change of dip angle. This is obtained by statistically analyzing the fluctuations in tilt angle data over historical time periods.

[0138] If both conditions are met , If it does not, it is determined to be in a degenerate state; otherwise, it is determined to be in a normal state. To preset the displacement threshold, The preset tilt angle threshold;

[0139] Predicted dip angle under degraded conditions The calculation formula is:

[0140] ,in, The historical mean dip angle For degraded working conditions, Historical deviation weighting;

[0141] The change in slope correction displacement under degradation conditions As the primary driver of forecasting Reflects the long-term structural attitude, the deviation between the dip angle at the previous moment and the historical mean. This reflects the dynamic offset trend of the system; degraded condition coefficient Historical deviation weighting reflects the sensitivity of displacement to changes in dip angle. This is used to correct for the impact of historical data fluctuations on predictions; its value can be set through regression training or experience. and The synergistic effect enhances the predictive robustness of the degradation stage. Furthermore... This indicates that the structural response is more sensitive to displacement in the degraded state.

[0142] Predicted tilt angle under normal conditions The calculation formula is:

[0143] ,in, The steady-state coefficient represents the proportion of tilt response caused by a unit displacement change, and can be obtained through historical data regression or field calibration.

[0144] Since the dip angle value is from the previous moment, to ensure model continuity and boundary handling capabilities, the system determines whether it is the model initialization moment before each sampling period; that is, before performing dip angle prediction. Before calculation, time-time determination is performed;

[0145] Determine the current time If the result is yes, then there is no tilt angle value from the previous moment. At this point, using an empirical value as the initial value, the historical average dip angle can be selected. ;

[0146] If the result is negative, it indicates that it is not the initial moment, and the tilt angle value from the previous moment can be used directly. Used for subsequent calculations.

[0147] This scheme addresses the problem of insufficient adaptability of a single model during the soil softening stage by dynamically switching prediction models. At the same time, it reduces prediction errors caused by data fluctuations by using historical dip angle mean deviation correction.

[0148] Through the above technical solution, this application effectively solves the technical problem of insufficient adaptability of traditional static threshold early warning in the process of soil softening. By dynamically identifying the slope degradation state and introducing a historical deviation correction mechanism, the accuracy of the slope angle prediction model under the condition of effective soil stress attenuation is significantly improved, avoiding the risk of misjudgment caused by dynamic changes in soil parameters. This solution can more reliably capture the precursory characteristics of slope instability, providing accurate early warning basis for the safety monitoring of power transmission and transformation projects.

[0149] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A slope deformation monitoring system for power transmission and transformation projects, characterized in that: include: The data acquisition module is used to acquire the reference point fiber voltage signal, Beidou positioning coordinate data, pore water pressure data, and real-time and historical tilt angle data of the iron tower in the slope area. The displacement calculation module is used to calculate the slope correction displacement value based on the reference point fiber voltage signal and Beidou positioning coordinate data. The tilt angle prediction module is used to output tilt angle prediction values ​​based on the slope correction displacement value and historical tilt angle data, and to calculate the residual between the real-time tilt angle data and the tilt angle prediction value. The threshold monitoring and adjustment module is used to calculate the effective stress attenuation of the soil based on the pore water pressure data, and to determine the threshold correction factor according to the preset softening coefficient mapping relationship. The preset baseline threshold is adjusted according to the threshold correction factor to generate a dynamic displacement acceleration threshold. The risk assessment and output module is used to generate and output an instability warning command when the following conditions are met simultaneously: If the acceleration of the slope correction displacement value exceeds the dynamic displacement acceleration threshold, the acceleration of the slope correction displacement value is the second derivative of the slope correction displacement value. The residual continuously exceeds the preset residual threshold for a predetermined period of time.

2. The slope deformation monitoring system for power transmission and transformation projects according to claim 1, characterized in that: The calculation of the effective stress attenuation of the soil based on the pore water pressure data includes: Obtain the soil type identifier of the current slope, and determine the corresponding compression coefficient and saturated permeability coefficient according to the preset soil type mapping relationship; Determine whether the pore water pressure data has reached a preset saturation threshold. If the determination result is yes, it is determined to be in a saturated state, and stress attenuation calculation is performed using a calculation model based on the ratio of initial effective stress to pore water pressure. Otherwise, it is determined to be in an unsaturated state, and stress attenuation calculation is performed after calculating the equivalent saturation based on the soil-water characteristic curve. The obtained effective stress attenuation of the soil is used to verify the validity of the data. When the effective stress attenuation of the soil exceeds the preset statistical fluctuation range, the sensor data abnormality flag is triggered.

3. The slope deformation monitoring system for power transmission and transformation projects according to claim 1, characterized in that: The threshold correction factor is determined based on the preset softening coefficient mapping relationship, including: Obtain the current and historical effective stress attenuation sequence of the soil, and calculate the short-term change intensity and long-term trend intensity based on the sequence; By comparing the ratio of short-term change intensity to long-term trend intensity, it can be determined whether the current stress decay state is a steady state or a transient state. If it is a steady state, a softening coefficient correction factor is generated based on a linear mapping model; if it is a transient state, a softening coefficient correction factor is generated based on an exponential mapping model. The effective stress attenuation of soil at multiple monitoring points within the same slope is obtained, its dispersion index is calculated, and when the dispersion exceeds the preset dispersion threshold, the softening coefficient correction factor is spatially consistent to obtain the threshold correction factor. After obtaining the threshold correction factor, a boundary range check is performed. When the threshold correction factor exceeds the preset physical reasonable range, the data is truncated to the range boundary and the abnormal data information is recorded.

4. The slope deformation monitoring system for power transmission and transformation projects according to claim 3, characterized in that: The proportional relationship between the intensity of short-term changes and the intensity of long-term trends includes: Obtain the effective stress attenuation sequence of soil ,in, for The effective stress attenuation of the soil at a given time; Calculate the intensity of short-term changes: ,in The sampling interval; Calculate the strength of the long-term trend: ; like If the condition is met, then the steady-state mapping model is selected; otherwise, the transient mapping model is selected. This is the preset scaling factor.

5. The slope deformation monitoring system for power transmission and transformation projects according to claim 1, characterized in that: The preset baseline threshold is adjusted based on the threshold correction factor to generate the dynamic displacement acceleration threshold, including: When the threshold correction factor exceeds the preset correction threshold, the duration is recorded, and the timeliness weight is calculated based on the duration. The current threshold correction factor and the historical average are weighted according to the timeliness weight to obtain the weighted correction factor. Determine whether the weighted adjustment factor exceeds the preset risk threshold; If so, then based on the ratio of the current slope safety factor to the reference safety factor, and the weighted correction factor, a multi-factor coupling adjustment of the acceleration threshold is performed; Otherwise, set the dynamic displacement acceleration threshold to the minimum predefined value.

6. The slope deformation monitoring system for power transmission and transformation projects according to claim 5, characterized in that: The calculation process for the dynamic displacement acceleration threshold is as follows: The duration for which the threshold correction factor exceeds the preset correction threshold. ; Calculate the timeliness weight : ;in, The preset time constant; Calculate the weighted correction factor : ,in, This is the current threshold correction factor. This is the average threshold correction factor of the previous period; when hour, ;in To preset risk thresholds, For a predefined minimum acceleration threshold, The dynamic displacement acceleration threshold; when hour, ;in, The current safety factor of the slope, As the baseline value for the safety factor, For safety sensitivity coefficient, This is a preset baseline threshold.

7. A slope deformation monitoring system for power transmission and transformation projects according to claim 5, characterized in that: After the dynamic displacement acceleration threshold is generated, the method further includes: determining whether the change amplitude of the current dynamic displacement acceleration threshold and the dynamic displacement acceleration threshold of the previous period exceeds the preset change limit. If it exceeds the limit, a smooth correction value is output according to the amplitude limit adjustment strategy, and the overload alarm status is recorded.

8. A slope deformation monitoring system for power transmission and transformation projects according to claim 1, characterized in that: The calculation of slope correction displacement based on reference point fiber optic voltage signal and BeiDou positioning coordinate data includes: The reference point fiber voltage signal is converted into fiber displacement by pre-calibration coefficients, and the difference of the eastward component of the BeiDou positioning coordinates at adjacent times is extracted as the reference point drift. Based on the difference between the fiber displacement and the reference point drift, the slope correction displacement value is generated.

9. A slope deformation monitoring system for power transmission and transformation projects according to claim 1, characterized in that: Based on the corrected slope displacement value and historical dip angle data, the output dip angle prediction value includes: Obtain the standard deviation of the displacement change rate and the dip angle change rate in the historical time period, and determine whether the slope is in a state of degradation based on the comparison between the two and the corresponding historical reference values. If the condition is determined to be in a degraded state, the predicted dip angle value is output using a prediction model that includes the degradation condition coefficient and the weight of the historical deviation, based on the current corrected displacement value, the historical dip angle mean and its deviation. If the condition is determined to be normal, the tilt angle prediction value is generated based on the linear relationship between the current corrected displacement value and the normal operating condition coefficient.

10. A slope deformation monitoring system for power transmission and transformation projects according to claim 9, characterized in that: The calculation process for the predicted tilt angle is as follows: Calculate the current rate of change of displacement Standard deviation of historical dip rate : ;in, This represents the change in the slope correction displacement value. The sampling time interval; If both conditions are met , If it does not, it is determined to be in a degenerate state; otherwise, it is determined to be in a normal state. To preset the displacement threshold, The preset tilt angle threshold; Predicted dip angle under degraded conditions The calculation formula is: ,in, The historical mean dip angle For degraded working conditions, Historical deviation weighting; Predicted tilt angle under normal conditions The calculation formula is: ,in, The steady-state coefficient; Given the dip angle value at the previous moment, the dip angle prediction value is then calculated. Before calculation, time-time determination is performed: Determine the current time Is it the initial moment? If the result is yes, then the historical empirical value is the tilt angle relative to the previous moment. Configure the settings; if the result is negative, retrieve the tilt angle value from the previous moment. Used for subsequent calculations.

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