A foundation deformation monitoring device for traction substation support structure

Abstract

This application provides a foundation deformation monitoring device for traction substation support structures, relating to the field of power grid safety technology. A dual-axis tilt sensor for attitude measurement, a vertical distance sensor for settlement measurement, and a microprocessor for computation are integrated in the physical structure and data link. This allows the microprocessor to simultaneously acquire raw tilt data characterizing its own attitude and raw distance data characterizing settlement. Trigonometric function correction is then performed to remove geometric projection errors caused by tilting from the raw distance data. This solves the problem of distance data "contamination" due to data source separation in related technologies, ensuring that the final output monitoring data is the true vertical settlement after attitude compensation, providing a reliable original basis for subsequent structural safety assessments.

Description

Technical Field

[0001] This application relates to the field of power grid safety technology, and in particular to a foundation deformation monitoring device for traction substation support structures. Background Technology

[0002] As the backbone of the electrified railway power supply system, the traction substation support structure supports critical equipment such as the overhead contact line and feeders. These structures are exposed to the elements for extended periods, bearing not only the static loads of themselves and their equipment, but also the complex challenges of periodic vibrations from passing trains, minor geological shifts, and thermal expansion and contraction due to temperature changes. Uneven settlement or tilting can trigger stress redistribution in the superstructure, potentially leading to equipment damage or even power outages.

[0003] In related technologies, a laser ranging module is installed to measure vertical settlement in order to understand the deformation state. The laser ranging module is preset to operate along the vertical axis. It is responsible for periodically emitting laser beams to the ground and receiving the echoes. The distance between the base and the ground is calculated by the flight time or phase difference, and this value is recorded by the system as a settlement observation.

[0004] However, the relevant technology assumes that the mounting plane of the laser ranging module is always horizontal, so its output distance value can be directly equated to "vertical settlement". But in actual working conditions, when the support foundation settles unevenly, the ranging module itself, which serves as the measurement reference, will inevitably tilt as well. The "distance value" output by the laser ranging module is no longer physically a geometrically vertical height, but rather the length of the inclined side. This means that the distance data received by the acquisition system from the front-end sensor source has been "contaminated" by the tilted posture, leading to data distortion and incorrect judgments. Summary of the Invention

[0005] This application provides a foundation deformation monitoring device for traction substation support structures, which is used to separate the geometric projection error caused by tilt from the original distance data, so that the final output monitoring data is the true vertical settlement after attitude compensation, providing a reliable original basis for subsequent structural safety assessment.

[0006] In a first aspect, this application provides a foundation deformation monitoring device for a traction substation support structure, comprising: at least one measurement module, which is fixedly installed on the bottom base of the traction substation support structure to be monitored; the measurement module integrates a dual-axis tilt sensor, a vertical distance sensor, and a microprocessor; the dual-axis tilt sensor is used to measure the static tilt angle of the measurement module relative to the gravitational field in the horizontal X-axis and Y-axis directions, generating raw tilt angle data characterizing the attitude; the measurement axis of the vertical distance sensor is perpendicular to the mounting base of the measurement module, and is used to measure the vertical distance between the base of the measurement module and the ground or a preset reference surface, generating raw distance data characterizing the foundation settlement; the microprocessor is electrically connected to both the dual-axis tilt sensor and the vertical distance sensor, and is used to calculate the spatial tilt angle of the measurement module based on the real-time raw tilt angle data; based on the spatial tilt angle, the raw distance data is corrected using trigonometric functions, and foundation deformation monitoring is performed based on the corrected raw distance data.

[0007] By adopting the above technical solution, a dual-axis tilt sensor for attitude measurement, a vertical distance sensor for settlement measurement, and a microprocessor for computation are integrated in the physical structure and data link. This allows the microprocessor to simultaneously acquire raw tilt data representing its own attitude and raw distance data representing settlement. Trigonometric function correction is then performed to remove geometric projection errors caused by tilt from the raw distance data. This solves the problem of distance data being "contaminated" at the moment of measurement due to the separation of data sources in related technologies. The final output monitoring data is the true vertical settlement after attitude compensation, providing a reliable original basis for subsequent structural safety assessments.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the foundation deformation monitoring device for traction substation support structures further includes: at least one reference measurement module, at least one environmental parameter acquisition unit, and a central processing unit; the reference measurement module is used to generate raw distance data of reference points; the environmental parameter acquisition unit is used to generate real-time environmental parameter vectors; the central processing unit is electrically connected to both a dual-axis tilt sensor and a vertical distance sensor; it is also used to input the real-time environmental parameter vectors to the surface dynamic response model before trigonometric function correction of the raw distance data to obtain the model predicted distance for the reference points; compare the raw distance data of the reference points with the model predicted distances to obtain the model prediction error; optimize the internal parameters of the surface dynamic response model based on the model prediction error; and send the results to the microprocessor; the microprocessor is connected to the central processing unit, and after trigonometric function correction of the raw distance data, obtains a new real-time environmental parameter vector; provides the new real-time environmental parameter vectors to the optimized surface dynamic response model to obtain the predicted reference displacement of the measurement module; and subtracts the corrected raw distance data from the predicted reference displacement to obtain the optimized distance data.

[0009] By adopting the above technical solution, this device introduces a reference measurement module, an environmental parameter acquisition unit, and a dynamic surface response model with learning capabilities. The reference measurement module provides a reliable real-world benchmark at a known stable point, while the environmental parameters provide the physical basis for the model to interpret surface changes. The central processing unit continuously compares the model's predicted output with the actual measurements from the reference module, quantifying the model's prediction error and using this error to optimize the model's internal parameters. This allows the model to break free from fixed parameter settings and continuously adapt to real-world surface dynamic changes caused by complex environmental factors such as seasonal freeze-thaw cycles and rainfall infiltration. Ultimately, the inherent limitations of using unstable ground as a fixed measurement benchmark are overcome, enabling a precise distinction between actual structural settlement and changes in the measurement benchmark, thus obtaining more accurate optimized distance data that approximates an absolute coordinate system.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, after obtaining the optimized distance data by subtracting the corrected original distance data from the predicted reference displacement, the method further includes: determining whether the absolute value of the optimized distance data is greater than a preset first alarm threshold; if it is greater, verifying the model's predicted distance using other measurement modules; and generating and outputting a structural deformation alarm if the absolute value corresponding to the other measurement modules is not greater than the first alarm threshold.

[0011] By adopting the above technical solution, when the optimized distance data of a certain measurement module becomes abnormal and triggers the first alarm threshold, an alarm is not immediately issued. Instead, an internal "self-check" program is initiated. The core of this program is to use other measurement modules as independent third parties to verify the accuracy of the current model's predictions. Only when all other modules perform normally, thus proving the reliability of the model and the overall working state, will the anomaly be ultimately confirmed to originate from the deformation of the structure under test itself. This collaborative verification process can distinguish between genuine structural deformation alarms and false data caused by the failure of a single sensor or interference from local extreme environments.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the microprocessor is also used to determine whether the real-time raw tilt angle data has a preset angle threshold; if it is greater than the preset threshold, the raw tilt angle data and raw distance data collected within the corresponding time period are marked as vibration interference data and discarded; the angle threshold is used to distinguish between instantaneous vibration caused by the passing of a train and tilt deformation of the electrical structure support itself.

[0013] By employing the above technical solution, the microprocessor can effectively identify mechanical vibrations characterized by "high amplitude and short duration" caused by external factors such as passing trains by judging in real time whether the drastic change in the original tilt angle data exceeds a preset threshold. Once such an event is identified, the microprocessor will actively mark all data (including tilt angle and distance) collected within that time period as invalid vibration interference and discard it. It can accurately retain and analyze the true deformation signals characterized by "small and slow" deformation, which represent the structure's own characteristics, from strong background noise, ensuring the accuracy and reliability of long-term deformation trend analysis.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the foundation deformation monitoring device for the traction substation support further includes: a graphical user interface; the graphical user interface is electrically connected to a microprocessor and is used to synthesize the corrected original distance data with a three-dimensional spatial vector to generate a real-time projection point of the current spatial position of the traction substation support; and at the same time, the real-time projection point is divided into different regions according to the monitoring results.

[0015] By employing the aforementioned technical solution, the graphical user interface synthesizes the corrected distance data representing vertical displacement with the tilt angle data representing planar attitude into a three-dimensional spatial vector, calculating a real-time projection point that uniquely characterizes the current spatial position of the support base. Subsequently, the interface projects this intuitive "point" onto a two-dimensional or three-dimensional view pre-divided into different areas such as "safe zone," "warning zone," and "danger zone." This reduces the difficulty of interpretation for maintenance personnel, enabling them to quickly and easily assess the overall safety status and risk level of the structure.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement module is provided with a non-volatile storage area; the non-volatile storage area is connected to the microprocessor; the microprocessor is also used to, after the initial installation, collect and store the initial tilt angle data and initial distance data as zero-point reference values ​​in the non-volatile storage area; subtract the initial distance data from the corrected original distance data to obtain the net subsidence; and subtract the initial tilt angle data from the original tilt angle data to obtain the tilt angle change.

[0017] By adopting the above technical solution, after the initial installation of the equipment, the microprocessor captures the initial tilt angle and distance readings at that moment and stores them as a permanent "zero-point reference value" in a non-volatile storage area. This reference value records the initial state at the time of installation. In each subsequent measurement, the microprocessor automatically subtracts this stored reference value from the real-time reading, thereby directly calculating the change in tilt angle and net subsidence. The output data itself is no longer an absolute value affected by the initial state, but rather the pure deformation increment that is of most concern to maintenance personnel.

[0018] Secondly, this application provides a method for monitoring the foundation deformation of a traction substation support structure. The method includes: using a dual-axis tilt sensor fixedly installed on the bottom base of the traction substation support structure to be monitored, measuring the static tilt angle of the measurement module relative to the gravitational field in the horizontal X-axis and Y-axis directions to generate raw tilt angle data characterizing the attitude; using a vertical distance sensor within the measurement module, measuring the vertical distance between the measurement module base surface and the ground or a preset reference surface while its measurement axis is perpendicular to the mounting base surface, to generate raw distance data characterizing foundation settlement; using a microprocessor within the measurement module, calculating the spatial tilt angle of the measurement module based on the raw tilt angle data, and performing trigonometric function correction on the raw distance data based on the calculated spatial tilt angle, and then monitoring the foundation deformation based on the corrected raw distance data.

[0019] Thirdly, this application provides a foundation deformation monitoring system for traction substation support structures. The foundation deformation monitoring system for traction substation support structures includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, which includes computer instructions. One or more processors call the computer instructions to cause the foundation deformation monitoring system for traction substation support structures to perform the method as described in any of the second aspects.

[0020] Fourthly, this application provides a computer program product containing instructions that, when the computer program product is run on a foundation deformation monitoring system for a traction substation support, causes the foundation deformation monitoring system for the traction substation support to perform the method as described in any of the second aspects.

[0021] Fifthly, this application provides a computer-readable storage medium including instructions that, when executed on a foundation deformation monitoring system for a traction substation support, cause the foundation deformation monitoring system for the traction substation support to perform the method as described in any of the second aspects.

[0022] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0023] 1. A dual-axis tilt sensor for attitude measurement, a vertical distance sensor for settlement measurement, and a microprocessor for computation are integrated in the physical structure and data link. This allows the microprocessor to simultaneously acquire raw tilt data representing its own attitude and raw distance data representing settlement. Trigonometric function correction is then performed to remove geometric projection errors caused by tilt from the raw distance data. This addresses the problem of distance data being "contaminated" at the moment of measurement due to data source separation in related technologies, ensuring that the final output monitoring data is the true vertical settlement after attitude compensation, providing a reliable original basis for subsequent structural safety assessments.

[0024] 2. This device incorporates a reference measurement module, an environmental parameter acquisition unit, and a dynamic surface response model with learning capabilities. The reference measurement module provides a reliable real-world benchmark at a known stable point, while the environmental parameters provide the physical basis for the model to interpret surface changes. The central processing unit continuously compares the model's predicted output with the actual measurements from the reference module, quantifying the model's prediction error and using this error to optimize the model's internal parameters. This allows the model to move beyond fixed parameter settings and continuously adapt to real-world surface dynamic changes caused by complex environmental factors such as seasonal freeze-thaw cycles and rainfall infiltration. Ultimately, the inherent limitations of using an unstable ground surface as a fixed measurement benchmark are overcome, allowing for a precise distinction between actual structural settlement and changes in the measurement benchmark, thus obtaining more accurate optimized distance data that approximates an absolute coordinate system.

[0025] 3. When the optimized distance data of a certain measurement module becomes abnormal and triggers the first alarm threshold, an alarm is not issued immediately. Instead, an internal "self-check" program is initiated. The core of this program is to use other measurement modules as independent third parties to verify the accuracy of the current model's predictions. Only when all other modules perform normally, thus proving the reliability of the model and the overall working state, will the anomaly be ultimately confirmed to originate from the deformation of the structure under test itself. This collaborative verification process can distinguish between genuine structural deformation alarms and false data caused by the failure of a single sensor or interference from local extreme environments. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the module of the foundation deformation monitoring device for the traction substation support structure in the embodiments of this application;

[0027] Figure 2 This is a schematic diagram showing the installation location of the measurement module in an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of the dual-axis tilt sensor in the embodiments of this application.

[0029] Figure 4 This is a schematic diagram of the installation of the dual-axis tilt sensor in an embodiment of this application.

[0030] Figure 5 This is a schematic diagram of an exemplary hardware structure of the foundation deformation monitoring system for the traction substation support structure in this application embodiment.

[0031] In the diagram: 110, Dual-axis tilt sensor (X-axis); 120, Dual-axis tilt sensor (Y-axis); 130, Vertical distance sensor (Z-axis); 140, Microprocessor; 150, Communication interface. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] Please see Figure 1 , Figure 1 This is a schematic diagram of the module of the foundation deformation monitoring device for the traction substation support structure in the embodiments of this application;

[0035] The foundation deformation monitoring device for the traction substation support includes: at least one measuring module, which is fixedly installed on the bottom base of the traction substation support to be monitored;

[0036] Please see Figure 2 , Figure 2 This is a schematic diagram showing the installation location of the measurement module in an embodiment of this application;

[0037] Among them, the measurement module refers to a physical device that integrates multiple sensors and processing units and can independently complete a specific measurement task; the traction substation support refers to a large steel structure or concrete column used in electrified railways to support high-voltage transmission equipment such as contact wires and feeders; the bottom base refers to the load-bearing foundation part of the support that is in direct contact with the ground or foundation, usually made of concrete.

[0038] Please see Figure 3 , Figure 4 , Figure 3 This is a schematic diagram of the dual-axis tilt sensor in the embodiments of this application.

[0039] Figure 4 This is a schematic diagram of the installation of the dual-axis tilt sensor in an embodiment of this application.

[0040] The measurement module integrates dual-axis tilt sensors 110 and 120, a vertical distance sensor 130, and a microprocessor 140.

[0041] Dual-axis tilt sensors 110 and 120 are used to measure the static tilt angle of the measurement module relative to the gravitational field in the horizontal X-axis and Y-axis directions, and generate raw tilt angle data characterizing the attitude.

[0042] Among them, the horizontal X-axis and Y-axis refer to two mutually orthogonal horizontal directions in a geographic coordinate system or a user-defined coordinate system, such as due east-due west and due south-due north; the gravitational field refers to the directional field formed by the Earth's gravity, which provides an absolute vertical reference for tilt angle measurement; the static tilt angle refers to the stable tilt angle of an object relative to the absolute horizontal plane under the condition of no violent vibration; the raw tilt angle data refers to the unprocessed digital or analog signals representing the tilt angles of the X-axis and Y-axis directly output by the sensor.

[0043] Specifically, it contains microelectromechanical systems (MEMS) that can sensitively sense changes in its component along the gravitational field. When the measurement module tilts along with the support base, the sensor can decompose this change in physical attitude into rotation angles around the X and Y axes in real time, and convert them into a digital raw tilt angle data stream (e.g., a pair of floating-point numbers representing the X-axis tilt angle and the Y-axis tilt angle, respectively, in degrees). This data is the basis for all subsequent attitude correction calculations.

[0044] In some embodiments, a tilt sensor based on MEMS capacitive measurement principles is employed. The sensor contains a movable mass (pendulum). When tilting occurs, the mass shifts under gravity, causing a change in the capacitance between it and a fixed electrode plate. A signal conditioning circuit detects this change in differential capacitance and resolves it into precise X-axis and Y-axis tilt angle data via a high-precision capacitance-to-digital converter (CDC), finally outputting the data through an I2C interface. No further limitations are specified here.

[0045] In actual use, when working along the railway line, it will be affected by strong and brief mechanical vibrations generated when trains pass by. These vibrations will cause the readings of the tilt angle and distance sensors to fluctuate violently, resulting in data distortion and a large number of false alarms.

[0046] Therefore, in some preferred embodiments, the microprocessor is also used to determine whether the real-time raw tilt angle data is greater than a preset angle threshold; if it is greater, the raw tilt angle data and raw distance data collected in the corresponding time period are marked as vibration interference data and discarded; the angle threshold is used to distinguish between instantaneous vibration caused by the passing of a train and tilt deformation of the electrical structure support itself.

[0047] Among them, the preset angle threshold is a critical criterion used to distinguish between normal posture changes and violent vibrations. It is usually not a static angle value, but a dynamic indicator that characterizes the severity of angle changes, such as angular velocity (degrees / second) or the amount of angle change in a very short time. Instantaneous vibration refers to structural shaking caused by external impacts (such as a train passing at high speed), which is short in duration, large in amplitude, and high in frequency. Tilt deformation refers to quasi-static posture changes that are slow in change and long in duration, caused by internal reasons such as foundation settlement or structural material fatigue. Vibration interference data refers to contaminated tilt angle and distance data collected during instantaneous vibration that cannot reflect the true static deformation of the structure.

[0048] Specifically, after receiving the raw data stream from the dual-axis tilt sensors 110 and 120, the microprocessor 140 does not immediately use it for calculations. Instead, it first performs a dynamic validity check. It continuously calculates the rate of change of the tilt angle data per unit time. When no train is passing, the tilt deformation of the support is extremely slow, with this rate of change approaching zero. However, when a train passes, the support shakes violently, and the tilt angle reading changes drastically and repeatedly, causing its rate of change to momentarily exceed the preset angle threshold. Once the microprocessor detects this situation, it determines that a vibration disturbance state has been entered. Subsequently, it marks all data collected within a short period of time from this moment (e.g., a few seconds before and after the train passes), including distance data from the dual-axis tilt sensors 110 and 120 and from the vertical distance sensor 130, as invalid vibration disturbance data. This marked data is directly discarded and does not participate in subsequent trigonometric function correction and deformation analysis, thereby ensuring that only high-quality data collected under static or quasi-static conditions is used for the final structural health assessment.

[0049] The measuring axis of the vertical distance sensor 130 is perpendicular to the mounting base of the measuring module, and is used to measure the vertical distance between the base of the measuring module and the ground or a preset reference surface, generating raw distance data characterizing foundation settlement.

[0050] Among them, the measurement axis refers to the core path direction of the distance sensor's transmission and reception of signals, such as the center line of a laser beam; the mounting base refers to the bottom plane of the measurement module, that is, the surface that contacts the bracket base; the preset reference surface refers to a stable and wear-resistant reference point artificially set on the ground, such as a fixed metal plate or concrete block, to avoid interference from ground vegetation, water accumulation, and other factors; the raw distance data refers to the straight-line distance value directly measured by the distance sensor without any attitude correction.

[0051] Specifically, the sensor's emission direction (measuring axis) is strictly designed to be perpendicular to the bottom surface of the module (mounting base). This means that when the module is horizontally mounted, the sensor measures the vertical distance directly below. The sensor periodically emits a detection signal (such as laser or ultrasonic) towards the ground or reference surface below and receives its echo. The distance between the two is calculated by determining the signal's time of flight or phase difference. This measured distance value is the unprocessed raw distance data, which directly reflects the spatial relationship between the module and the ground.

[0052] In some embodiments, a phase-based laser rangefinder is employed. This sensor emits a continuous laser beam modulated at a specific frequency, and the distance is indirectly calculated by measuring the phase difference between the emitted laser and the received reflected laser; however, this method is not limited to a single approach.

[0053] The microprocessor 140 is electrically connected to the dual-axis tilt sensors 110 and 120 and the vertical distance sensor 130. It is used to calculate the spatial tilt angle of the measurement module based on the real-time raw tilt angle data; based on the spatial tilt angle, the raw distance data is corrected by trigonometric functions; and the foundation deformation is monitored based on the corrected raw distance data.

[0054] Among them, the spatial tilt angle refers to the vector angle synthesized from the two original tilt angle data of the X-axis and Y-axis, which can completely describe the tilt direction and magnitude of the measurement module in three-dimensional space; the trigonometric function correction represents a mathematical operation process that uses the spatial tilt angle to convert the oblique distance (original distance data) measured in the tilted state into the true vertical distance; the corrected original distance data is the result obtained after trigonometric function correction, which represents the true vertical settlement distance after eliminating the influence of attitude.

[0055] Specifically, tilt angle and distance data are captured simultaneously (or within a very small time window). The X-axis tilt angle (α) and Y-axis tilt angle (β) are vector-synthesized to calculate the total spatial tilt angle (θ), for example, θ = arccos(cos(α)cos(β)). Then, the actual vertical distance H = the measured oblique distance Lcos(θ). Essentially, this process uses the tilt angle data to project the measured hypotenuse L back onto its intended vertical side H. Finally, this high-precision H value sequence is used for long-term trend analysis, rate of change calculation, etc., to achieve accurate monitoring of foundation deformation.

[0056] As can be seen, the dual-axis tilt sensor for attitude measurement, the vertical distance sensor for settlement measurement, and the microprocessor for computation are integrated in the physical structure and data link. This allows the microprocessor to simultaneously acquire raw tilt data representing its own attitude and raw distance data representing settlement. It then performs trigonometric function correction to remove geometric projection errors caused by tilt from the raw distance data. This solves the problem of distance data being "contaminated" at the moment of measurement due to the separation of data sources in related technologies, ensuring that the final output monitoring data is the true vertical settlement after attitude compensation, providing a reliable original basis for subsequent structural safety assessments.

[0057] In other embodiments, the foundation deformation monitoring device for the traction substation support structure further includes: a graphical user interface; the graphical user interface is electrically connected to a microprocessor and is used to synthesize the corrected original distance data with a three-dimensional spatial vector to generate a real-time projection point of the current spatial position of the traction substation support structure; and at the same time, the real-time projection point is divided into different regions according to the monitoring results.

[0058] Among them, the graphical user interface refers to the monitoring software running on the central processing unit (such as an industrial control computer or server), which displays monitoring data and status to maintenance personnel through visual elements such as graphics, charts, and icons; three-dimensional spatial vector synthesis refers to a mathematical calculation process that merges the three independent deformation components of X-direction tilt, Y-direction tilt, and Z-direction settlement, which are measured separately, into a vector that can completely describe the magnitude and direction of the total displacement of the support foundation in three-dimensional space; real-time projection point is the visual representation of the three-dimensional spatial displacement vector on a two-dimensional plane (usually a horizontal plane), usually displayed as a point; different areas refer to the safety level areas with different colors and meanings that are pre-divided on the graphical user interface, such as safety areas, warning areas, and alarm areas.

[0059] Specifically, the graphical user interface (GUI) receives the final, purified deformation data from the various measurement modules, namely the net settlement (Δz) and the change in tilt angle (Δα, Δβ). The GUI first approximates the change in tilt angle into horizontal displacement based on a preset structural height H: Δx = Htan(Δα) and Δy = Htan(Δβ). The GUI then obtains a three-dimensional displacement vector (Δx, Δy, Δz). For intuitive display on a two-dimensional screen, it primarily uses the horizontal components (Δx, Δy) to determine the position of a real-time projection point in a planar coordinate system. The origin (0,0) of this coordinate system represents the initial healthy position of the support structure. As the support tilts, this projection point deviates from the origin. Simultaneously, the GUI draws several concentric circles on this coordinate system: the innermost circle is a red alarm zone, the middle circle is a yellow warning zone, and the outermost circle is a green safety zone. Maintenance personnel do not need to look at complicated numbers; they can instantly determine the tilt risk level of the support by simply observing which color area the real-time projection point falls on. The color or size of the point can also be used to indicate the severity of settlement.

[0060] In some specific embodiments, the main interface of the GUI can be an electronic map containing the geographical locations of all monitored supports. Each support is displayed as an icon on the map. Under normal circumstances, the icon is green. When the real-time projection point of a support enters the warning zone, its icon on the map turns yellow and flashes slowly; when it enters the alarm zone, it turns red and flashes rapidly. When the user clicks the icon, a detailed window pops up, which displays an independent real-time trajectory map of the projection point of that support, with safety zone divisions; this is not limited here.

[0061] As can be seen, the graphical user interface synthesizes the corrected distance data representing vertical displacement with the tilt angle data representing planar attitude into a three-dimensional spatial vector, calculating a real-time projection point that uniquely characterizes the current spatial position of the support base. Subsequently, the interface projects this intuitive "point" onto a two-dimensional or three-dimensional view pre-divided into different areas such as "safe zone," "warning zone," and "danger zone." This reduces the difficulty of interpretation for maintenance personnel, enabling them to quickly and easily assess the overall safety status and risk level of the structure.

[0062] In some preferred embodiments, the measurement module is provided with a non-volatile storage area; the non-volatile storage area is connected to the microprocessor. The microprocessor is also used to collect and store initial tilt angle data and initial distance data as zero-point reference values ​​in the non-volatile storage area after initial installation.

[0063] Non-volatile memory refers to an electronic storage medium that can retain data for a long time even after power failure, such as Flash memory or EEPROM; the zero-point reference value represents a set of original tilt angle and distance readings collected and stored by the measurement module after the equipment is installed, debugged and confirmed to be in a stable and healthy initial state. It defines the relative reference origin for all subsequent measurements.

[0064] Specifically, after physical installation is completed on-site, technicians trigger the "zero-point setting" procedure through a specific operation (such as pressing a button or sending a remote command). Upon receiving the command, the microprocessor of the measurement module performs a high-precision measurement, acquiring initial tilt data (e.g., 0.15 degrees X-axis, -0.08 degrees Y-axis) from dual-axis tilt sensors 110 and 120, and initial distance data (e.g., 891.4 mm) from vertical distance sensor 130. This set of data represents the "zero state" of the support at the start of its monitoring lifecycle. The microprocessor then writes this unique zero-point reference value into its internal, power-independent, non-volatile memory. This operation is one-time and crucial because it provides an unshakeable, localized reference for calculating all subsequent deformations.

[0065] The net subsidence is obtained by subtracting the initial distance data from the corrected original distance data; the change in dip angle is obtained by subtracting the initial dip angle data from the original dip angle data.

[0066] Among them, net settlement refers to the pure, relative displacement change of the support foundation in the vertical direction relative to its initial position when it was first installed; tilt change refers to the pure, relative angular change of the support foundation in the two horizontal axes relative to its initial posture when it was first installed.

[0067] Specifically, this involves the conversion from absolute measurement to relative change. At any given measurement moment, the microprocessor first reads the previously stored zero-point reference values ​​(initial tilt angle and initial distance) from the non-volatile memory. Then, it performs two subtraction operations: 1. Subtract the stored initial distance from the current vertical distance after trigonometric function attitude correction; the difference is the net subsidence during this period. 2. Subtract the stored initial tilt angle data (X-axis and Y-axis) from the current raw tilt angle data (X-axis and Y-axis); the two differences are the changes in tilt angle in the two directions. For example, if the initial distance is 891.4 mm and the current corrected distance is 893.6 mm, the net subsidence is +2.2 mm. If the initial X-tilt angle is 0.15 degrees and the current angle is 0.45 degrees, the change in tilt angle in the X direction is +0.30 degrees. What the microprocessor ultimately sends out through the communication interface 150 are these "changes" that contain clear physical meaning, rather than raw, uninterpretable absolute values.

[0068] In actual use, there are dynamic instabilities in the ground (seasonal frost heave and thaw settlement, vegetation growth, and debris accumulation). The vertical distance sensor treats a dynamic and unreliable ground as a static and reliable measurement benchmark, resulting in the measurement results reflecting the "relative change between the module and the ground" rather than the "absolute settlement of the foundation".

[0069] Therefore, in some embodiments, the foundation deformation monitoring device for traction substation support structures further includes: at least one reference measurement module and at least one environmental parameter acquisition unit and a central processing unit;

[0070] The reference measurement module is a device that is structurally and functionally identical or similar to the measurement module, but is installed at a recognized geologically stable point to provide a benchmark. The environmental parameter acquisition unit is a device that integrates multiple sensors to measure external environmental factors that affect the physical state of the Earth's surface, such as surface temperature, air humidity, soil moisture content, atmospheric pressure, and recent rainfall. The central processing unit is a computing core with stronger computing power and larger storage space than the microprocessor inside the measurement module. It is usually deployed in a background monitoring center or edge computing gateway to perform complex model calculations and multi-source data fusion.

[0071] Specifically, the introduction of the reference measurement module aims to establish a dynamic benchmark. Its displacement changes primarily reflect the non-structural deformation of the measurement reference surface, i.e., the ground, caused by environmental factors. The environmental parameter acquisition unit provides a quantitative physical explanation for this non-structural deformation.

[0072] In practical applications, improper selection of reference points can render them unstable, thus contaminating the entire measurement baseline. For example, a reference point may appear stable, but there may be unexplored soft soil interlayers or slow-moving landslides beneath it, and its displacement may not be purely caused by environmental factors.

[0073] Therefore, in some embodiments, a multi-reference point cross-validation and weighted fusion strategy is employed. In the initial deployment phase, not just one reference point, but at least three reference points (A, B, C) arranged in a triangular pattern are established. During the initial operation, the central processing unit continuously monitors the relative stability among these three reference points. By calculating changes in their distances or elevation differences, it can be determined whether any point has experienced an abnormal independent displacement. If a systematic shift in point C relative to points A and B is detected, the weight of point C's data is reduced, or it may even be temporarily removed from the model training. Ultimately, the reference benchmark used is the fusion result of multiple reference point data after stability testing and weighted averaging.

[0074] The reference measurement module is used to generate raw distance data for reference points;

[0075] Among them, the original distance data of the reference point refers to the straight-line distance value between the base surface of the reference measurement module and the ground or reference surface below it, measured by the vertical distance sensor inside the reference measurement module installed on the stable reference point, without any correction.

[0076] The environmental parameter acquisition unit is used to generate real-time environmental parameter vectors;

[0077] Among them, the real-time environmental parameter vector refers to the set of multiple environmental parameter values ​​measured simultaneously by multiple sensors (such as temperature, humidity, rain gauge, etc.) in the environmental parameter acquisition unit at a specific moment. These values ​​are organized into a mathematical vector form, such as [temperature, humidity, soil moisture content, ...].

[0078] Specifically, this unit does not simply measure individual parameters independently, but rather ensures that it captures a snapshot of all relevant environmental parameters simultaneously and combines them into a structured data packet, namely, an environmental parameter vector. This vector is the input variable for the surface dynamic response model, and each dimension corresponds to a physical factor that may affect surface displacement. For example, the vector [25.5, 60.2, 35.8, 0.0] might represent the current surface temperature of 25.5 degrees Celsius, air humidity of 60.2%, soil moisture content of 35.8%, and rainfall of 0.0 millimeters in the past hour. The central processing unit periodically retrieves this vector from this unit.

[0079] The central processing unit is electrically connected to both the dual-axis tilt sensor and the vertical distance sensor; it is also used to input the real-time environmental parameter vector into the surface dynamic response model before the raw distance data is corrected by trigonometric functions, so as to obtain the model predicted distance for the reference point.

[0080] Among them, the surface dynamic response model refers to a pre-built mathematical or machine learning model that can describe the nonlinear relationship between changes in environmental parameters and vertical displacement of the surface; the model predicted distance represents the theoretical distance between the reference point module base surface and the ground below it, calculated by the model based on the current input real-time environmental parameter vector.

[0081] Specifically, at a certain time t, the central processing unit (CPU) acquires a real-time environmental parameter vector V(t) from the environmental parameter acquisition unit. It uses this vector V(t) as input and feeds it into the internally stored surface dynamic response model M. Based on its internal parameters and structure, model M performs a series of complex calculations on this input vector, ultimately outputting a scalar value P(t). This P(t) is the model's predicted distance to the reference point under the current environmental conditions. For example, if the model receives a vector containing high temperature and low humidity, it might predict that the ground will subside due to drying and shrinkage, thus outputting a model-predicted distance slightly larger than the average.

[0082] The original distance data of the reference point is compared with the model's predicted distance to obtain the model's prediction error;

[0083] The model prediction error refers to the arithmetic difference between the theoretical predicted distance value calculated by the surface dynamic response model and the original distance value actually measured by the reference measurement module at the same time.

[0084] Specifically, this step is the core of model self-evaluation and optimization, namely, calculating the loss function. The central processing unit (CPU) obtains the model's predicted distance P(t) at time t. Simultaneously, it also obtains the actual measured distance R(t) at the same time t from the reference measurement module. The CPU then performs a simple subtraction operation: Error(t) = R(t) - P(t). This calculated Error(t), i.e., the model's prediction error, directly quantifies the model's performance. If the error is close to zero, it indicates that the model's understanding of the current environment is very accurate; if the absolute value of the error is large, it indicates that the model's prediction deviates from reality.

[0085] The internal parameters of the surface dynamic response model are optimized based on the model prediction error and then sent to the microprocessor.

[0086] Internal parameters refer to the specific values ​​that constitute the dynamic response model of the land surface, such as the regression coefficients in a linear regression model, or the connection weights and bias terms between neurons in a neural network model.

[0087] Specifically, these are the execution steps of model learning and evolution. After receiving the model's prediction error, the central processing unit (CPU) immediately initiates an optimization algorithm. The essence of this algorithm is to fine-tune the model's internal parameters based on the magnitude and direction of the error, aiming to reduce the model's prediction error under similar environmental inputs in the future. This is a continuous iterative process. For example, if the model's prediction error is positive, it means the predicted distance is too small (the actual ground subsidence is greater than predicted). The optimization algorithm might adjust the weights of parameters related to rainfall to give them a greater impact on subsidence. After completing one or a batch of parameter optimizations, the CPU sends this updated, better-performing set of model parameters to the microprocessors of each measurement module via communication interface 150, or stores it in a shared location for the microprocessors to access at any time.

[0088] The microprocessor is connected to the central processing unit and obtains a new real-time environmental parameter vector after the original distance data is corrected by trigonometric functions.

[0089] The new real-time environmental parameter vector refers to the set of real-time environmental parameters at the location of the measurement module to be monitored at the same moment it is performing deformation calculation.

[0090] Specifically, at the measurement module end, its internal microprocessor has already completed its function: using data from the dual-axis tilt sensors 110 and 120, it performs attitude correction on the raw distance data measured by the vertical distance sensor 130, obtaining a more accurate vertical distance that eliminates the influence of the module's own tilt. After this, in order to perform further, more advanced corrections, the microprocessor needs to know the current environmental conditions. Therefore, it actively requests or passively receives the latest real-time environmental parameter vector provided by the central processing unit (or environmental parameter acquisition unit) through a communication connection with the central processing unit (e.g., via communication interface 150).

[0091] The new real-time environmental parameter vector is provided to the optimized surface dynamic response model to obtain the predicted reference displacement of the measurement module.

[0092] Among them, the optimized surface dynamic response model refers to a model whose internal parameters have been continuously updated and can more accurately reflect the characteristics of the current geological environment; the predicted reference displacement represents the vertical displacement that the optimized model predicts should occur at the location of the structure under test due to environmental factors (rather than structural loads).

[0093] Specifically, the microprocessor of the measurement module takes the newly acquired real-time environmental parameter vector, representing its own environment, as input to the optimized surface dynamic response model, either stored locally or retrieved from the central processing unit. The model then performs a forward inference calculation and outputs a result. The physical meaning of this result is: under the current environmental conditions, if there were no support structure at this location, the ground itself would rise or sink by how many millimeters relative to its initial stable state due to thermal expansion and contraction, and shrinkage due to moisture. This predicted displacement is the reference surface noise that is being attempted to be separated from the total measurement value.

[0094] Optimized distance data is obtained by subtracting the corrected original distance data from the predicted baseline displacement.

[0095] Among them, the optimized distance data is the core output of this solution. It represents the pure and absolute vertical settlement or uplift of the support foundation relative to its initial state after eliminating the tilt error of the measurement module itself and the ground reference displacement error.

[0096] Specifically, the microprocessor has two key data points at this point: one is the vertical distance H_corrected after attitude correction (which reflects the total displacement of the structure and the ground), and the other is the ground baseline displacement H_baseline_shift predicted by the model (which only reflects the ground displacement). The microprocessor performs the final subtraction operation (note the sign definition; here, settlement is assumed to be negative): Optimized distance data = (H_corrected - H_initial) - H_baseline_shift. For example: the initial distance is 850mm. The current attitude-corrected distance is 853mm, indicating a total subsidence of 3mm. The model predicts that the ground itself has risen by 0.5mm due to environmental factors. Therefore, the optimized distance data = -3mm - (+0.5mm) = -3.5mm. This means that the actual net subsidence of the structure is 3.5mm.

[0097] As can be seen, this device incorporates a reference measurement module, an environmental parameter acquisition unit, and a dynamic surface response model with learning capabilities. The reference measurement module provides a reliable real-world benchmark at a known stable point, while the environmental parameters provide the physical basis for the model to interpret surface changes. The central processing unit continuously compares the model's predicted output with the actual measurements from the reference module, quantifying the model's prediction error and using this error to optimize the model's internal parameters. This allows the model to move beyond fixed parameter settings and continuously adapt to real-world surface dynamic changes caused by complex environmental factors such as seasonal freeze-thaw cycles and rainfall infiltration. Ultimately, the inherent limitations of using an unstable ground as a fixed measurement benchmark are overcome, allowing for a precise distinction between actual structural settlement and changes in the measurement benchmark, thus obtaining more accurate optimized distance data that approximates an absolute coordinate system.

[0098] After obtaining the optimized distance data by subtracting the corrected original distance data from the predicted baseline displacement, the process also includes:

[0099] Determine whether the absolute value of the optimized distance data is greater than the preset first alarm threshold;

[0100] The first alarm threshold is a critical value scientifically set by a structural safety engineer based on factors such as the design specifications, geological conditions, material properties, and safety margins of the traction substation support structure, representing a state where structural deformation may have entered a warning state.

[0101] Specifically, after calculating each new optimized distance data point, a comparison operation is immediately performed. It takes the absolute value of the data (because both settlement and uplift exceeding a certain amount can pose a risk) and compares it to a preset first alarm threshold. For example, if the first alarm threshold is set to 10 mm, and the currently calculated optimized distance data is -11.2 mm, its absolute value of 11.2 mm is greater than 10 mm, then this judgment condition is met, triggering the next response step. If the calculated value is -8.5 mm, its absolute value is less than 10 mm, then the structural deformation is considered to be within the allowable range, the current monitoring cycle ends, and the system awaits the next data collection.

[0102] If the distance is greater than the given distance, other measurement modules are used to verify the model's predicted distance.

[0103] Other measurement modules refer to all other measurement modules in the same monitoring network except for the one that has triggered the first alarm threshold, including the reference measurement module used as a benchmark.

[0104] Specifically, when the optimized distance data of module A exceeds the limit, an alarm is not immediately issued. Instead, the process is paused and an internal verification procedure is initiated. The core suspicion is: could the surface dynamic response model's prediction at the current moment, specifically at the local location of module A, have a significant deviation, leading to spurious optimized distance data? To verify this, other "circumstantial evidence" within the network—modules B, C, and the reference module—is used. The system examines the performance of these modules at the same time point to see if their behavior matches the model's predictions, thereby inferring the health of the model itself.

[0105] If the absolute value corresponding to other measurement modules is not greater than the first alarm threshold, a structural deformation alarm is generated and output.

[0106] Specifically, after cross-validation, the conclusion was reached that the optimized distance data of all other measurement modules within the network, including the most reliable reference module, fluctuated within a normal range, well below the first alarm threshold. This fact strongly proves that the surface dynamic response model is accurate at the current moment, and the entire measurement benchmark is reliable. Based on the principle of elimination, since the "ruler" (measurement and model) is accurate, the out-of-limit reading exhibited by the measured object (the support foundation where module A is located) is highly unlikely to be caused by measurement error. Therefore, the final decision was made to confirm that this was a valid alarm triggered by the actual deformation of the structural foundation itself, and a high-priority "structural deformation alarm" was immediately generated, notifying maintenance personnel through a graphical interface, audible and visual alarms, SMS, or App push notifications.

[0107] As can be seen, when the optimized distance data of a certain measurement module becomes abnormal and triggers the first alarm threshold, an alarm is not issued immediately. Instead, an internal "self-check" program is initiated. The core of this program is to use other measurement modules as independent third parties to verify the accuracy of the current model's predictions. Only when all other modules perform normally, thus proving the reliability of the model and the overall working state, will the anomaly be ultimately confirmed to originate from the deformation of the structure under test itself. This collaborative verification process can distinguish between genuine structural deformation alarms and false data caused by the failure of a single sensor or interference from local extreme environments.

[0108] The following describes an exemplary traction substation support foundation deformation monitoring system 500 provided in an embodiment of this application. Figure 5 This is an exemplary hardware structure diagram of the foundation deformation monitoring system 500 for the traction substation support provided in this application embodiment.

[0109] In some embodiments, the foundation deformation monitoring system 500 for the traction substation support is a computer device or includes a computer device in the foundation deformation monitoring system 500 for the traction substation support. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0110] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application 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. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0112] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0113] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A foundation deformation monitoring device for a traction substation architecture support, characterized by, include: At least one measurement module, at least one reference measurement module, and at least one environmental parameter acquisition unit and a central processing unit; The measurement module is fixedly installed on the bottom base of the traction substation structure support to be monitored; The measurement module integrates a dual-axis tilt sensor, a vertical distance sensor, and a microprocessor. The dual-axis tilt sensor is used to measure the static tilt angle of the measurement module relative to the gravitational field in the horizontal X-axis and Y-axis directions, and generate raw tilt angle data characterizing the attitude. The measurement axis of the vertical distance sensor is perpendicular to the mounting base of the measurement module, and is used to measure the vertical distance between the base of the measurement module and the ground or a preset reference surface, generating raw distance data characterizing foundation settlement. The microprocessor is electrically connected to both the dual-axis tilt sensor and the vertical distance sensor, and is used to calculate the spatial tilt angle of the measurement module based on real-time raw tilt data; based on the spatial tilt angle, the raw distance data is corrected using trigonometric functions, and the foundation deformation is monitored based on the corrected raw distance data; The reference measurement module is used to generate raw distance data for reference points; The environmental parameter acquisition unit is used to generate a real-time environmental parameter vector; The central processing unit is electrically connected to both the dual-axis tilt sensor and the vertical distance sensor; it is also used to input the real-time environmental parameter vector to the surface dynamic response model before the raw distance data is corrected by trigonometric functions, so as to obtain the model predicted distance for the reference point. The original distance data of the reference point is compared with the model's predicted distance to obtain the model prediction error; The internal parameters of the surface dynamic response model are optimized based on the model prediction error, and then sent to the microprocessor. The microprocessor is connected to the central processing unit and obtains a new real-time environmental parameter vector after the original distance data is corrected by trigonometric functions. The new real-time environmental parameter vector is provided to the optimized surface dynamic response model to obtain the predicted reference displacement of the measurement module; The optimized distance data is obtained by subtracting the corrected original distance data from the predicted baseline displacement.

2. The apparatus according to claim 1, characterized in that, After obtaining the optimized distance data by subtracting the corrected original distance data from the predicted reference displacement, the process further includes: Determine whether the absolute value of the optimized distance data is greater than a preset first alarm threshold; If the distance is greater than the predicted distance, other measurement modules are used to verify the distance predicted by the model. If the absolute value corresponding to other measurement modules is not greater than the first alarm threshold, a structural deformation alarm is generated and output.

3. The apparatus according to claim 1, characterized in that, The microprocessor is also used to determine whether the real-time raw tilt angle data has a preset angle threshold. If the value is greater than the threshold, the original tilt angle data and the original distance data collected within the corresponding time period are marked as vibration interference data and discarded; the angle threshold is used to distinguish between instantaneous vibration caused by the passing of a train and the tilt deformation of the electrical structure support itself.

4. The apparatus according to claim 1, characterized in that, The foundation deformation monitoring device for the traction substation support structure also includes: a graphical user interface; The graphical user interface is electrically connected to the microprocessor and is used to synthesize the corrected original distance data with the three-dimensional spatial vector to generate a real-time projection point of the current spatial position of the traction substation support; at the same time, the real-time projection point is divided into different regions according to the monitoring results.

5. The apparatus according to claim 1, characterized in that, The measurement module is equipped with a non-volatile storage area; The non-volatile memory area is connected to the microprocessor; The microprocessor is also used for, After the initial installation, the microprocessor will collect and store the initial tilt angle data and initial distance data as zero-point reference values ​​in the non-volatile storage area; The net subsidence is obtained by subtracting the initial distance data from the corrected original distance data. The change in inclination angle is obtained by subtracting the initial inclination angle data from the original inclination angle data.

6. A method for monitoring the foundation deformation of a traction substation support structure, characterized in that, include: Using a dual-axis tilt sensor fixedly installed on the base of the traction substation support frame to be monitored, the static tilt angle of the measurement module relative to the gravitational field in the horizontal X-axis and Y-axis directions is measured to generate raw tilt angle data characterizing the attitude. Using the vertical distance sensor in the measurement module, the vertical distance between the base surface of the measurement module and the ground or a preset reference surface is measured when the measurement axis is perpendicular to the mounting base surface, so as to generate raw distance data characterizing the foundation settlement. Using the microprocessor within the measurement module, the spatial tilt angle of the measurement module is calculated based on the original tilt angle data. The original distance data is then corrected using trigonometric functions based on the calculated spatial tilt angle. Finally, foundation deformation monitoring is performed based on the corrected original distance data. The original distance data of the reference point is generated using the reference measurement module; Real-time environmental parameter vectors are generated using the environmental parameter acquisition unit; Using a central processing unit, before performing trigonometric function correction on the original distance data, the real-time environmental parameter vector is used as input to the surface dynamic response model to obtain the model predicted distance for the reference point. The original distance data of the reference point is compared with the model prediction distance to obtain the model prediction error; and the internal parameters of the surface dynamic response model are optimized based on the model prediction error and sent to the microprocessor. Using the microprocessor, after performing trigonometric function correction on the original distance data, a new real-time environmental parameter vector is obtained; The new real-time environmental parameter vector is provided to the optimized surface dynamic response model to obtain the predicted reference displacement of the measurement module; The optimized distance data is obtained by subtracting the corrected original distance data from the predicted baseline displacement.

7. A foundation deformation monitoring system for traction substation support structures, characterized in that, The foundation deformation monitoring system of the traction substation support includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the foundation deformation monitoring system of the traction substation support to perform the method as described in claim 6.

8. A computer program product containing instructions, characterized in that, When the computer program product is run on the foundation deformation monitoring system of the traction substation support, the foundation deformation monitoring system of the traction substation support performs the method as described in claim 6.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the foundation deformation monitoring system of the traction substation support, the foundation deformation monitoring system of the traction substation support performs the method as described in claim 6.

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