A method, device, equipment and medium for monitoring deformation of a slope surface

Abstract

The application provides a kind of slope surface deformation monitoring method, device, equipment and medium, belong to slope monitoring technical field, wherein method includes: for any one section of cable, obtain the temperature data and original displacement measurement of cable;The cable is used to connect the fixed point of the slope surface to be measured, and at least one temperature sensor is arranged on each cable;According to temperature data, construct the temperature field distribution model of cable;According to temperature field distribution model and the thermal expansion coefficient of cable material, the expansion amount of cable due to temperature change is calculated;Based on original displacement measurement and expansion amount, determine the corrected displacement value.The application constructs temperature field distribution model by distributing multiple temperature sensors on the cable, and accurately calculates the expansion amount of the cable under uneven temperature distribution based on the temperature field distribution model, thereby improving the accuracy of deformation monitoring data.

Description

Technical Field

[0001] This invention relates to the field of slope monitoring technology, and in particular to a method, device, equipment and medium for monitoring slope surface deformation. Background Technology

[0002] In the field of safety monitoring for water conservancy and geotechnical engineering, the use of draw-wire displacement sensors for long-term monitoring of surface cracks or relative displacements in structures such as high slopes and dams is a common technique. These sensors measure displacement by the change in length of a draw-wire connected between two points on the surface of the structure being measured.

[0003] However, changes in ambient temperature are a major interfering factor affecting the measurement accuracy of such sensors. The metal material constituting the tension rope expands and contracts with temperature changes. This temperature-induced length change can be misinterpreted by the sensor as displacement of the structure itself, thus introducing significant errors into the measurement data and resulting in low accuracy of deformation monitoring data. Summary of the Invention

[0004] This invention provides a method, device, equipment, and medium for monitoring slope surface deformation, in order to overcome the deficiencies in the prior art and improve the accuracy of deformation monitoring data.

[0005] This invention provides a method for monitoring slope surface deformation, comprising: For any segment of the rope, acquire the temperature data and original displacement measurement value of the rope; the rope is used to connect the fixed points on the surface of the slope to be measured, and at least one temperature sensor is arranged on each segment of the rope; Based on the temperature data, a temperature field distribution model of the pull rope is constructed; Based on the temperature field distribution model and the thermal expansion coefficient of the rope material, calculate the expansion of the rope due to temperature changes; Based on the original displacement measurement and the expansion amount, a corrected displacement value is determined.

[0006] According to the present invention, a method for monitoring slope surface deformation includes, in which, based on the temperature data, a temperature field distribution model of the tension rope is constructed, comprising: For any target point on the pull rope whose temperature is to be estimated, calculate the distance between the target point and each of the temperature sensors; Based on the distance, a predetermined number of neighboring sensors are determined from all the temperature sensors; The interpolated temperature of the target point is calculated based on the temperature values ​​measured by each of the neighboring sensors and the distance between the target point and each of the neighboring sensors. The temperature field distribution model is constructed based on the interpolated temperatures of all target points along the entire length of the rope.

[0007] According to a slope surface deformation monitoring method provided by the present invention, the step of calculating the interpolated temperature of the target point based on the temperature values ​​measured by each of the adjacent sensors and the distance between the target point and each of the adjacent sensors includes: The weighting coefficients for each neighboring sensor are determined based on the distance between the target point and each of the neighboring sensors, wherein the weighting coefficients for neighboring sensors that are closer to each other are larger. The weighted temperature sum is obtained by multiplying the temperature values ​​measured by each of the neighboring sensors by their corresponding weighting coefficients and then summing them. Sum all the weight coefficients to obtain the total weight coefficients; The interpolated temperature of the target point is obtained by dividing the sum of the weighted temperatures by the sum of the weighting coefficients.

[0008] According to a slope surface deformation monitoring method provided by the present invention, the interpolated temperature of the target point is calculated using the following formula:

[0009] In the formula, The interpolated temperature for the target point. The temperature values ​​measured by each of the adjacent sensors. These are the weighting coefficients corresponding to each of the neighboring sensors. The distance between the target point and each of the neighboring sensors is [the distance between the target point and each of the neighboring sensors]. n The number of the neighboring sensors.

[0010] According to the present invention, a method for monitoring slope surface deformation includes calculating the expansion of the tension rope due to temperature changes based on the temperature field distribution model and the thermal expansion coefficient of the tension rope material, comprising: Based on the temperature field distribution model, the first temperature of the pull rope at the initial moment and the second temperature at the preset moment are determined respectively; Obtain the initial length of the pull rope at the initial moment, and the linear expansion coefficient of the material of the pull rope. The linear expansion coefficient is used to characterize the length change of a unit length of pull rope under a unit temperature change. The expansion amount of the rope is calculated based on the linear expansion coefficient, the initial rope length, and the temperature difference between the first and second temperatures.

[0011] According to the present invention, a method for monitoring slope surface deformation, wherein determining a corrected displacement value based on the original displacement measurement value and the expansion amount includes: The corrected displacement value is obtained by subtracting the expansion amount from the original displacement measurement value.

[0012] The present invention also provides a slope surface deformation monitoring device, comprising: The device includes multiple sections of rope and multiple temperature sensors. The rope is used to connect to fixed points on the surface of the slope to be measured, and at least one temperature sensor is arranged on each section of the rope. The temperature sensor is fixedly connected to the rope. The displacement measurement module is connected to each section of the pull rope and is used to measure the original displacement value of each section of the pull rope. The data processing module is connected to both the temperature sensor and the displacement measurement module. It receives temperature data collected by the temperature sensor and raw displacement measurement values ​​measured by the displacement measurement module. Based on the temperature data, it constructs a temperature field distribution model for the rope. Based on the temperature field distribution model and the thermal expansion coefficient of the rope material, it calculates the expansion of the rope due to temperature changes. Based on the raw displacement measurement values ​​and the expansion, it determines a corrected displacement value.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the slope surface deformation monitoring method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the slope surface deformation monitoring method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the slope surface deformation monitoring method as described above.

[0016] In summary, the present invention has at least the following technical effects or advantages: This invention acquires the temperature data of the guy rope by placing at least one temperature sensor on it, thus directly collecting data reflecting the rope's true temperature state. This solves the problem of insufficient representativeness caused by the separation of temperature measurement points and displacement measurement subjects in traditional monitoring methods. By constructing a temperature field distribution model of the guy rope based on the collected temperature data, the temperature state of the entire rope is accurately reflected, providing a necessary foundation for accurately calculating the total thermal expansion effect under uneven temperature distribution. The expansion of the guy rope due to temperature changes is calculated based on the constructed temperature field distribution model and the thermal expansion coefficient of the rope material, thus accurately quantifying the complex, nonlinear temperature effects into a specific physical error value. Finally, by subtracting this expansion from the original displacement measurement value obtained by the displacement measurement module, the non-deformation displacement component caused by environmental temperature changes in the original data can be effectively eliminated, resulting in a corrected displacement value that truly and accurately reflects the actual deformation of the slope surface, greatly improving the accuracy of the monitoring data. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of the slope surface deformation monitoring method provided by the present invention.

[0019] Figure 2 This is a curve showing the deformation of the slope under test under temperature influence, provided by the present invention.

[0020] Figure 3 This is a curve showing the deformation of the slope under test after eliminating the influence of temperature, provided by the present invention.

[0021] Figure 4 This is a schematic diagram of the temperature field distribution model construction process provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the rope expansion calculation process provided by the present invention.

[0023] Figure 6 This is a schematic diagram of the slope surface deformation monitoring device provided by the present invention.

[0024] Figure 7 This is a schematic diagram of the monitoring system provided by the present invention.

[0025] Figure 8This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] It should be noted that in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships according to the accompanying drawings, are only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0028] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0029] The following is combined Figures 1-8 This invention describes the slope surface deformation monitoring method, apparatus, equipment, and medium provided by the present invention.

[0030] Reference Figure 1 In one embodiment of the present invention, the slope surface deformation monitoring method includes steps 100 to 400: Step 100: For any segment of the rope, obtain the temperature data and original displacement measurement value of the rope.

[0031] To accurately calculate displacement measurement errors caused by temperature changes, it is necessary to precisely obtain temperature information of the guy rope at different locations. Since slope surfaces often exhibit uneven temperature distribution due to factors such as sunlight and vegetation cover, traditional single-point temperature measurement methods cannot accurately reflect the actual temperature conditions along the guy rope, leading to insufficient accuracy in subsequent compensation calculations.

[0032] In a specific embodiment of the invention, for any segment of the pull rope, temperature data of the pull rope is first acquired. In one possible implementation, the temperature data is acquired by at least one temperature sensor arranged on the pull rope. Specifically, the pull rope may be a 304 stainless steel wire with a diameter of 2-3 mm, and its surface may be nickel-plated to enhance corrosion resistance. The temperature sensor may be a miniature platinum resistance thermometer, for example, a miniature platinum resistance thermometer with a measurement accuracy of ±0.1℃ and a tiny diameter of 0.8 mm.

[0033] In one embodiment, multiple miniature platinum resistance thermometers are arranged at predetermined intervals of 50cm along a pull rope and fixed to the surface of the pull rope by means of laser welding, thereby forming a distributed temperature sensing chain. The weight of each sensing unit can be controlled to be less than 5g to ensure that its influence on the tension of the pull rope is negligible. The temperature data collected by each temperature sensor can then be transmitted in real time via a flexible PCB bus for subsequent processing.

[0034] By acquiring temperature data in the above manner, real-time temperature values ​​at multiple locations along the rope can be obtained. This approach effectively solves the problem of insufficient representativeness of traditional single-point temperature measurement data. For example, when there is a temperature difference of up to 8°C between vegetated and bare areas on a slope, distributed temperature sensors can accurately capture such gradient changes, thus providing a real and reliable data foundation for subsequently constructing accurate temperature field models and calculating thermal expansion.

[0035] Furthermore, in order to obtain accurate displacement values ​​that reflect the true deformation of the slope, it is necessary to obtain original measurement data that includes all influencing factors. This original data is the basis for subsequent correction calculations; without it, no effective error elimination can be performed.

[0036] Therefore, in one embodiment of the present invention, it is necessary to obtain the original displacement measurement value of the rope. In one possible implementation, the original displacement measurement value is acquired by a displacement measurement module. This original displacement measurement value is a value directly output by the sensor without any compensation processing, which simultaneously includes the actual displacement occurring on the slope surface and the change in the rope's own length caused by temperature changes. The displacement measurement module can be a magnetic grating type rope displacement sensor.

[0037] In one specific implementation, one end of the pull rope is fixed to an anchor on the slope surface, and the other end is connected to a magnetic grating reading head. When the slope deforms or the pull rope expands or contracts due to temperature changes, the pull rope drives the magnetic grating reading head to move along the magnetic grating ruler. The displacement measurement module can then output an electrical signal proportional to the displacement change, with a resolution of 0.001 mm.

[0038] Performing this step yields high-resolution, uncorrected displacement data. This data constitutes an indispensable input in the entire correction process, providing the necessary raw data for subsequent steps to obtain the final corrected displacement value by subtracting the calculated temperature expansion.

[0039] Step 200: Based on the temperature data, construct a temperature field distribution model for the rope.

[0040] After obtaining the discrete temperature data along the rope, in order to calculate the total expansion of the entire rope due to temperature changes, it is necessary to convert these discrete measurement point data into a continuous distribution that can describe the overall temperature state of the rope. Estimating using only the temperature values ​​of individual measurement points cannot accurately reflect the temperature gradient between different sections of the rope, and will introduce calculation errors.

[0041] Therefore, in one embodiment of the present invention, the next step is to construct a temperature field distribution model of the rope based on the temperature data. The temperature field distribution model is a mathematical model that can characterize the continuous temperature distribution along the entire length of the rope. This model does not simply present discrete temperature values ​​measured by each sensor, but rather generates a continuous function or dataset that can estimate the temperature at any point on the rope through specific data processing methods. In specific implementations, a spatial interpolation algorithm can be used, taking multiple discrete temperature data points as known inputs, to calculate the temperature values ​​at any location between the sensors, ultimately forming a temperature field distribution covering the entire rope.

[0042] By constructing a temperature field distribution model, the discrete information collected from a limited number of measuring points can be expanded into a complete and continuous description of the temperature state of the entire rope. This model provides an essential basis for the subsequent accurate calculation of the overall thermal expansion of the rope, ensuring that the impact of temperature changes on every minute segment of the rope is taken into account, thereby significantly improving the accuracy of the final displacement correction.

[0043] Step 300: Calculate the expansion of the rope due to temperature changes based on the temperature field distribution model and the thermal expansion coefficient of the rope material.

[0044] After constructing the temperature field distribution model of the tension rope, the error component caused by temperature changes in the original displacement measurements was still not determined. In order to separate the true slope displacement from the mixed measurement signals, this error component must first be accurately quantified.

[0045] Therefore, in one embodiment of the present invention, the next step is to calculate the expansion of the drawstring due to temperature change based on the temperature field distribution model and the thermal expansion coefficient of the drawstring material. The thermal expansion coefficient of the drawstring material is an inherent physical parameter characterizing the thermal expansion and contraction properties of a material; specifically, it represents the change in length of a unit length of drawstring under a unit temperature change. This coefficient can be pre-calibrated in a laboratory for a specific type of drawstring material (e.g., 304 stainless steel wire).

[0046] In practical implementation, the established continuous temperature field distribution model is used as input, and combined with the pre-determined thermal expansion coefficient of the material, the total length change of the entire rope caused by temperature change, i.e., the expansion amount, can be obtained through physical calculations. This calculation process comprehensively considers the influence of the uneven temperature distribution along the rope on the length change of each segment, and accumulates them to obtain an accurate value that can represent the overall effect.

[0047] By performing this step, the abstract temperature field distribution model can be transformed into a specific, quantifiable physical quantity: the total thermal expansion of the rope. This expansion accurately reflects the actual interference of the non-uniform temperature field on the displacement measurement, providing accurate error data for subsequent high-precision displacement correction.

[0048] Step 400: Determine the corrected displacement value based on the original displacement measurement and expansion amount.

[0049] After obtaining the original displacement measurements that include temperature interference and independently calculating the expansion caused by temperature changes, the two must be effectively integrated to separate the net displacement component that can truly reflect the slope deformation, thereby achieving the ultimate goal of high-precision monitoring.

[0050] In one possible implementation, the specific method for determining the corrected displacement value based on the original displacement measurement value and the expansion amount is as follows: subtract the expansion amount from the original displacement measurement value to obtain the corrected displacement value.

[0051] The corrected displacement value represents the true deformation amount resulting solely from the slope's own deformation after eliminating the influence of temperature changes. Specifically, it receives the raw displacement measurement value from the displacement measurement module and the expansion amount calculated in the previous steps. Then, it performs a subtraction operation in the internal processor and outputs or transmits the result as the corrected displacement value, thus obtaining the true deformation amount of the slope. Specific deformation changes can be found in [reference needed]. Figure 2 and Figure 3,in, Figure 2 This is a curve showing the deformation of the slope under test under temperature influence, provided by the present invention. Figure 3 This is a curve showing the deformation of the slope under test after eliminating the influence of temperature, provided by the present invention.

[0052] Without temperature compensation, the original displacement measurements exhibit significant periodic fluctuations, which closely match the periodicity of diurnal temperature variations. For example, in Figure 2 In the data, the daily displacement curves all exhibit a regular pattern of first increasing and then decreasing, with peak values ​​typically occurring during the hottest part of the day and troughs corresponding to the coldest part of the night. This periodic fluctuation can reach several millimeters in amplitude, severely masking the true deformation trend of the slope itself.

[0053] In contrast, after the temperature compensation process of this invention, such as Figure 3 As shown, the previously significant periodic fluctuations were effectively eliminated, and the curve exhibited a relatively stable trend. The displacement data at this point clearly reflects the gradual deformation process of the slope, such as the slow accumulation of displacement caused by rainfall infiltration or internal stress adjustment. Comparing the two figures also reveals that the data fluctuation range before temperature compensation was approximately ±1.2 mm, while the fluctuation range after compensation was reduced to within ±0.3 mm, significantly improving the signal-to-noise ratio of the displacement measurement.

[0054] By performing this final correction step, accurate displacement information reflecting the true deformation trend of the slope can be extracted from the original measurement data, which contains significant noise and interference. Compared with the uncompensated original data, the corrected displacement value greatly eliminates the fluctuations in measurement data caused by environmental factors such as diurnal temperature differences and changes in sunlight, making the subtle and gradual slope deformation trends clearly visible, thereby significantly improving the reliability of monitoring data and the accuracy of early warning decisions.

[0055] In a preferred embodiment, the process of constructing the temperature field distribution model of the guy rope is defined in detail. Since the temperature sensors arranged along the guy rope are discrete in physical space, the temperature data they collect can only represent the state of a limited number of measuring points. However, in actual working conditions, influenced by complex environmental factors such as the angle of sunlight and vegetation shading, the temperature distribution along the guy rope often exhibits a non-linear, continuous change. In order to accurately calculate the total expansion of the entire guy rope due to this non-uniform temperature field, an effective spatial interpolation method must be used to transform the discrete measuring point data into a continuous temperature distribution that can accurately describe the entire guy rope. Therefore, referring to... Figure 4 , Figure 4 This is a schematic diagram of the temperature field distribution model construction process provided by the present invention. Step 200 specifically includes the following steps: Step 201: For any target point on the rope whose temperature is to be estimated, calculate the distance between the target point and each temperature sensor.

[0056] Step 202: Determine a preset number of neighboring sensors from all temperature sensors based on the distance.

[0057] Step 203: Calculate the interpolated temperature of the target point based on the temperature values ​​measured by each neighboring sensor and the distance between the target point and each neighboring sensor.

[0058] Step 204: Construct a temperature field distribution model based on the interpolated temperatures of all target points along the entire length of the rope.

[0059] Specifically, firstly, for any unknown point on the rope whose temperature value needs to be determined, i.e., the target point, it is necessary to calculate the spatial distance between the target point and each of the already deployed temperature sensors. This distance is the basis for subsequent weighted calculations and directly determines the degree of influence of each known temperature point on the temperature value of the target point.

[0060] After calculating all distances, instead of using data from all sensors for interpolation, a predetermined number of neighboring sensors are selected from all temperature sensors based on the distance. This step aims to improve the accuracy and efficiency of the calculation. Specifically, a threshold number can be set, for example, selecting 3 to 10 sensors closest to the target point as the neighboring sensor set. This ensures that only sensor data with the strongest correlation to the target point's temperature participates in subsequent calculations, while effectively eliminating noise and computational redundancy that may be introduced by data from distant, irrelevant sensors, thereby optimizing the model.

[0061] Once the set of neighboring sensors is determined, the interpolated temperature of the target point can be calculated based on the temperature values ​​measured by each neighboring sensor and the distance between the target point and each neighboring sensor.

[0062] In one possible implementation, the interpolated temperature of the target point is calculated based on the temperature values ​​measured by each neighboring sensor and the distance between the target point and each neighboring sensor. This is achieved through the following steps: The weighting coefficients for each neighboring sensor are determined based on the distance between the target point and each neighboring sensor, with the weighting coefficients being larger for the closer neighboring sensors. The weighted temperature sum is obtained by multiplying the temperature values ​​measured by each neighboring sensor by their corresponding weighting coefficients and then summing the results. Sum all the weight coefficients to get the total weight coefficients; Divide the weighted sum of temperatures by the sum of weighting coefficients to obtain the interpolated temperature of the target point.

[0063] Specifically, since temperature is spatially continuous during the calculation of the interpolated temperature at the target point, the temperature values ​​measured by sensors closer to the target point have a greater impact on the actual temperature of the target point, while the impact of sensors farther away is relatively smaller. Therefore, a distance-related weighting mechanism needs to be established.

[0064] Specifically, firstly, based on the distance between the target point and each neighboring sensor, the weighting coefficient corresponding to each neighboring sensor is determined, with closer neighboring sensors receiving larger weighting coefficients. In one implementation, the weighting coefficient can be set as the inverse square of the distance, that is, for the i-th neighboring sensor, its weighting coefficient is... ,in This represents the distance between the target point and the nearest sensor. This setting ensures that the temperature interpolation results are more influenced by nearby sensors.

[0065] Next, the temperature values ​​measured by each neighboring sensor are multiplied by their corresponding weighting coefficients and then summed to obtain the weighted temperature sum. For example, if there are n neighboring sensors, and the temperature value measured by the i-th sensor is... The corresponding weighting coefficient is The weighted total temperature is then... At the same time, summing all the weight coefficients yields the total weight coefficient, i.e. .

[0066] Finally, the weighted sum of temperatures is divided by the sum of the weighting coefficients to obtain the interpolated temperature of the target point. This calculation process ensures that the interpolation result is a weighted average of the temperature values ​​of each neighboring sensor, and that the weighting distribution reasonably reflects the physical law of the influence of spatial distance on temperature.

[0067] The interpolated temperature obtained using the above method can accurately estimate the temperature value at any location on the rope, even if a temperature sensor is not directly placed at that location. This distance-weighted interpolation method ensures the physical validity of the calculation results and achieves an effective conversion from discrete measuring points to a continuous temperature field.

[0068] Specifically, the above steps can be represented by the following steps:

[0069] In the formula, The interpolated temperature for the target point. The temperature values ​​measured by each neighboring sensor. These are the weighting coefficients corresponding to each neighboring sensor. The distance between the target point and each of its neighboring sensors. n This represents the number of neighboring sensors.

[0070] The core idea embodied in this formula is that the contribution of each neighboring sensor to the temperature of the target point is inversely proportional to the square of its distance from the target point; that is, the closer the distance, the larger the weighting coefficient. Finally, by traversing the above interpolation calculation process over the entire length of the rope and calculating for a sufficient number of target points, a high-density, continuous temperature field distribution model can be constructed based on the interpolated temperatures of all target points within the entire length of the rope.

[0071] Through the detailed implementation steps described above, limited and discrete sensor readings can be transformed into a continuous digital model that accurately reflects the true temperature at any location along the rope. This model precisely characterizes the temperature gradient changes caused by environmental factors, providing a complete and reliable data foundation for the subsequent accurate calculation of the total thermal expansion of the entire rope, thereby fundamentally ensuring the accuracy and reliability of the final displacement correction results.

[0072] In a preferred embodiment, the process for calculating the expansion of the rope due to temperature changes is defined in detail. After constructing a temperature field distribution model that reflects the continuous temperature distribution along the rope, the next core task is to use this model, combined with the physical properties of the material, to accurately quantify the change in the physical length of the rope caused by temperature changes. Therefore, referring to... Figure 5 , Figure 5 This is a schematic diagram of the rope expansion calculation process provided by the present invention. Step 300 specifically includes the following steps: Step 301: Based on the temperature field distribution model, determine the first temperature of the rope at the initial moment and the second temperature at the preset moment.

[0073] Step 302: Obtain the initial length of the pull rope at the initial moment, and the linear expansion coefficient of the pull rope material. The linear expansion coefficient is used to characterize the length change of a unit length of pull rope under a unit temperature change.

[0074] Step 303: Calculate the expansion of the rope based on the coefficient of linear expansion, the initial rope length, and the temperature difference between the first and second temperatures.

[0075] Specifically, firstly, based on the temperature field distribution model, the first and second temperatures of the pull rope at the initial and preset times are determined. The initial time typically refers to the moment when the sensor is installed or the system is initialized, and its corresponding first temperature... This serves as the benchmark for all subsequent temperature difference calculations. The preset time is the current measurement time, and its corresponding second temperature. The temperature changes in real time. Since the temperature field distribution model provides continuous temperature information along the entire length of the rope, the first and second temperatures are not the temperatures of a single measuring point, but rather effective temperature values ​​that can represent the overall average temperature state of the rope, calculated by integrating or weighting the entire model.

[0076] Next, obtain the initial length of the pull rope at the initial moment. And the coefficient of linear expansion of the material of the rope. The initial draw rope length is the baseline length recorded after sensor installation and tensioning. The coefficient of linear expansion is used to characterize the change in length of a unit length of draw rope under a unit temperature change. This parameter is an inherent physical property of the material and can be obtained in the laboratory before implementation by precisely calibrating the draw rope material (e.g., 304 stainless steel wire) of the same batch and type as that used in the field.

[0077] After obtaining all the above parameters, the expansion amount of the rope can be calculated based on the coefficient of linear expansion, the initial rope length, the temperature difference between the first and second temperatures, and the preset process noise. In a specific embodiment, the expansion amount can be predicted according to the following formula:

[0078] in, It is a theoretical expansion amount calculated based on physical laws, while the preset process noise This is a key parameter in the Kalman filter model, used to characterize the model's inherent uncertainty and dynamic error over time. By fusing the time series of historical temperature data with the spatial series of the current temperature distribution, the Kalman filter can adaptively adjust its internal weights and covariance matrix, thereby optimally estimating the instantaneous expansion of each segment of the rope. The resulting expansion... It is a value that has been dynamically predicted and corrected, and is closer to the actual physical process.

[0079] Through the detailed implementation steps described above, a dynamic and highly accurate estimate of the rope expansion can be obtained. This method not only considers the spatial influence of the non-uniform temperature field but also solves the compensation lag problem caused by the rapid change of the temperature field over time by introducing a dynamic filtering algorithm. Compared with traditional static formula compensation, this method can significantly reduce the final displacement error, especially under conditions of drastic diurnal temperature differences, thus providing a solid guarantee for obtaining highly reliable real deformation data of the slope.

[0080] The present invention also provides a slope surface deformation monitoring device, with reference to Figure 6 ,include: Multiple sections of rope and multiple temperature sensors are used to connect fixed points on the surface of the slope to be measured. Each section of rope is equipped with at least one temperature sensor, which is fixedly connected to the rope. The displacement measurement module is connected to each section of the rope and is used to measure the original displacement value of each section of the rope. The data processing module is connected to both the temperature sensor and the displacement measurement module. It receives temperature data collected by the temperature sensor and raw displacement measurement values ​​measured by the displacement measurement module. Based on the temperature data, it constructs a temperature field distribution model for the rope. Based on the temperature field distribution model and the thermal expansion coefficient of the rope material, it calculates the expansion of the rope due to temperature changes. Based on the raw displacement measurement values ​​and the expansion, it determines the corrected displacement value.

[0081] In one possible implementation, the temperature sensor is laser-bonded to the surface of the pull rope.

[0082] Specifically, the device includes multiple sections of guy ropes and multiple temperature sensors. The guy ropes, used to connect two or more fixed points on the surface of the slope to be measured, are made of 304 stainless steel wire with a diameter of 2-3 mm and may be nickel-plated to enhance corrosion resistance. At least one temperature sensor is arranged on each section of the guy rope, and the temperature sensor is laser-welded to the surface of the guy rope. In a preferred embodiment, a miniature platinum resistance thermometer with high accuracy (e.g., ±0.1℃) and small size (e.g., 0.8 mm in diameter) can be used as the temperature sensor.

[0083] In one possible implementation, the temperature sensor is laser-welded to the surface of the pull rope. This robust connection ensures good thermal conductivity between the sensor and the pull rope, enabling the sensor to accurately measure the temperature of the pull rope itself. By arranging multiple such temperature sensors along the pull rope at predetermined intervals (e.g., 50 cm), a distributed temperature sensing chain can be formed to collect temperature data at different locations along the pull rope.

[0084] In one specific implementation, the displacement measurement module can be a high-resolution magnetic grating displacement sensor with a resolution of 0.001 mm. One end of the pull rope is connected to the reading head of the displacement measurement module. When the length of the pull rope changes due to slope deformation or its own thermal expansion and contraction, the displacement measurement module can output the corresponding original displacement measurement value.

[0085] Specifically, the data processing module receives temperature data collected by multiple temperature sensors via a flexible PCB bus or similar means, and simultaneously receives the original displacement measurement value obtained by the displacement measurement module. This data processing module aggregates the two types of data for subsequent temperature compensation calculations, thereby obtaining a corrected displacement value that accurately reflects the deformation of the slope surface. The corrected displacement value is obtained through the following method: a temperature field distribution model of the tension rope is constructed based on the temperature data; the expansion of the tension rope due to temperature changes is calculated based on the temperature field distribution model and the thermal expansion coefficient of the tension rope material; and the corrected displacement value is determined based on the original displacement measurement value and the expansion amount. The specific method for calculating the corrected displacement value has been described in detail in the foregoing embodiments, and therefore will not be repeated here.

[0086] Reference Figure 7 The present invention also provides a slope surface deformation monitoring system, comprising: The acquisition module is used to acquire the temperature data and original displacement measurement values ​​of any section of the rope. The processing module is used to construct a temperature field distribution model of the rope based on the temperature data; The processing module is also used to calculate the expansion of the rope due to temperature changes based on the temperature field distribution model and the thermal expansion coefficient of the rope material. The processing module is also used to determine the corrected displacement value based on the original displacement measurement value and the expansion amount.

[0087] In one possible implementation, the processing module is further configured to: For any target point on the rope whose temperature is to be estimated, calculate the distance between the target point and each temperature sensor; Based on the distance, determine a preset number of neighboring sensors from all temperature sensors; The interpolated temperature of the target point is calculated based on the temperature values ​​measured by each neighboring sensor and the distance between the target point and each neighboring sensor. A temperature field distribution model is constructed based on the interpolated temperatures of all target points along the entire length of the rope.

[0088] In one possible implementation, the processing module is further configured to: The weighting coefficients for each neighboring sensor are determined based on the distance between the target point and each neighboring sensor, with the weighting coefficients being larger for the closer neighboring sensors. The weighted temperature sum is obtained by multiplying the temperature values ​​measured by each neighboring sensor by their corresponding weighting coefficients and then summing the results. Sum all the weight coefficients to get the total weight coefficients; Divide the weighted sum of temperatures by the sum of weighting coefficients to obtain the interpolated temperature of the target point.

[0089] In one possible implementation, the processing module is further configured to: The interpolated temperature at the target point is calculated using the following formula:

[0090] In the formula, The interpolated temperature for the target point. The temperature values ​​measured by each neighboring sensor. These are the weighting coefficients corresponding to each neighboring sensor. The distance between the target point and each of its neighboring sensors. n This represents the number of neighboring sensors.

[0091] In one possible implementation, the processing module is further configured to: Based on the temperature field distribution model, the first temperature of the rope at the initial moment and the second temperature at the preset moment are determined. Obtain the initial length of the pull rope at the initial moment, as well as the linear expansion coefficient of the rope material. The linear expansion coefficient is used to characterize the length change of a unit length of pull rope under a unit temperature change. The expansion of the rope is calculated based on the coefficient of linear expansion, the initial rope length, and the temperature difference between the first and second temperatures.

[0092] It should be noted that the slope surface deformation monitoring system provided by the present invention can execute the slope surface deformation monitoring method of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0093] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a slope surface deformation monitoring method. This method includes: for any segment of the tension rope, acquiring the temperature data and original displacement measurement values ​​of the rope; constructing a temperature field distribution model of the rope based on the temperature data; calculating the expansion of the rope due to temperature changes based on the temperature field distribution model and the thermal expansion coefficient of the rope material; and determining a corrected displacement value based on the original displacement measurement values ​​and the expansion amount.

[0094] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by the computer, the computer is able to execute the slope surface deformation monitoring method provided in the above embodiments.

[0096] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the slope surface deformation monitoring method provided in the above embodiments.

[0097] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring slope surface deformation, characterized in that, include: For any segment of the pull rope, acquire the temperature data and the original displacement measurement value of the pull rope; The pull rope is used to connect fixed points on the surface of the slope to be measured, and at least one temperature sensor is arranged on each section of the pull rope. Based on the temperature data, a temperature field distribution model of the pull rope is constructed; Based on the temperature field distribution model and the thermal expansion coefficient of the rope material, calculate the expansion of the rope due to temperature changes; Based on the original displacement measurement and the expansion amount, a corrected displacement value is determined.

2. The slope surface deformation monitoring method according to claim 1, characterized in that, The step of constructing a temperature field distribution model for the draw rope based on the temperature data includes: For any target point on the pull rope whose temperature is to be estimated, calculate the distance between the target point and each of the temperature sensors; Based on the distance, a predetermined number of neighboring sensors are determined from all the temperature sensors; The interpolated temperature of the target point is calculated based on the temperature values ​​measured by each of the neighboring sensors and the distance between the target point and each of the neighboring sensors. The temperature field distribution model is constructed based on the interpolated temperatures of all target points along the entire length of the rope.

3. The slope surface deformation monitoring method according to claim 2, characterized in that, The step of calculating the interpolated temperature of the target point based on the temperature values ​​measured by each of the neighboring sensors and the distance between the target point and each of the neighboring sensors includes: The weighting coefficients for each neighboring sensor are determined based on the distance between the target point and each of the neighboring sensors, wherein the weighting coefficients for neighboring sensors that are closer to each other are larger. The weighted temperature sum is obtained by multiplying the temperature values ​​measured by each of the neighboring sensors by their corresponding weighting coefficients and then summing them. Sum all the weight coefficients to obtain the total weight coefficients; The interpolated temperature of the target point is obtained by dividing the sum of the weighted temperatures by the sum of the weighting coefficients.

4. The slope surface deformation monitoring method according to claim 3, characterized in that, The interpolated temperature at the target point is calculated using the following formula: In the formula, The interpolated temperature for the target point. The temperature values ​​measured by each of the adjacent sensors. These are the weighting coefficients corresponding to each of the neighboring sensors. The distance between the target point and each of the neighboring sensors is [the distance between the target point and each of the neighboring sensors]. n The number of the neighboring sensors.

5. The slope surface deformation monitoring method according to claim 1, characterized in that, The step of calculating the expansion of the rope due to temperature changes based on the temperature field distribution model and the thermal expansion coefficient of the rope material includes: Based on the temperature field distribution model, the first temperature of the pull rope at the initial moment and the second temperature at the preset moment are determined respectively; Obtain the initial length of the pull rope at the initial moment, and the linear expansion coefficient of the material of the pull rope. The linear expansion coefficient is used to characterize the length change of a unit length of pull rope under a unit temperature change. The expansion amount of the rope is calculated based on the linear expansion coefficient, the initial rope length, and the temperature difference between the first and second temperatures.

6. The method for monitoring slope surface deformation according to claim 1, characterized in that, The step of determining the corrected displacement value based on the original displacement measurement value and the expansion amount includes: The corrected displacement value is obtained by subtracting the expansion amount from the original displacement measurement value.

7. A slope surface deformation monitoring device, characterized in that, include: The device includes multiple sections of rope and multiple temperature sensors. The rope is used to connect to fixed points on the surface of the slope to be measured, and at least one temperature sensor is arranged on each section of the rope. The temperature sensor is fixedly connected to the rope. The displacement measurement module is connected to each section of the pull rope and is used to measure the original displacement value of each section of the pull rope. The data processing module is connected to both the temperature sensor and the displacement measurement module. It receives temperature data collected by the temperature sensor and raw displacement measurement values ​​measured by the displacement measurement module. Based on the temperature data, it constructs a temperature field distribution model for the rope. Based on the temperature field distribution model and the thermal expansion coefficient of the rope material, it calculates the expansion of the rope due to temperature changes. Based on the raw displacement measurement values ​​and the expansion, it determines a corrected displacement value.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the slope surface deformation monitoring method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the slope surface deformation monitoring method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the slope surface deformation monitoring method as described in any one of claims 1 to 6.

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