Stress loss detection system of anchoring system

Abstract

The invention discloses a stress loss detection system of an anchoring system, which comprises a multi-source acquisition unit, an edge calculation unit, a cloud server and a data decision terminal, and is characterized in that the multi-source acquisition unit synchronously monitors strain parameters and environmental parameters of the anchoring system to realize data complementation and improve reliability; the data transmission quantity is reduced and the processing speed is increased through an edge calculation unit, and then corrosion abnormal risk prediction of stress loss is realized through strain multi-dimensional analysis; the cloud server quantifies the synergistic effect of salt mist and temperature and humidity, environment interference dynamic compensation is achieved, the measurement precision is remarkably improved, risk grading early warning is achieved through the data decision-making terminal, the missing report rate and the false report rate of monitoring are reduced, and through multi-source data fusion, side-cloud cooperative calculation and a dynamic compensation model, the monitoring accuracy is improved. The accuracy and the efficiency of stress loss detection of the anchoring system are remarkably improved, and full-link collaborative optimization is realized, so that the response speed is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of stress detection, and in particular to a stress loss detection system for an anchoring system. Background Art

[0002] Anchoring systems are widely used in bridges, tunnels, buildings and other projects. The stability of their stress state directly affects structural safety. When the anchoring system is in a complex natural environment, such as buildings or bridges in coastal areas, the anchoring structure will be affected by salt spray corrosion, which will cause the performance of the anchoring material to deteriorate, thereby affecting the accuracy of the stress loss detection results.

[0003] Among them, chloride ions in the salt spray environment will corrode the metal materials of the anchoring system, such as anchor rods and anchor cables, which will cause the metal materials to rust, reduce their effective cross-sectional area, and further reduce the strength, toughness and other mechanical properties of the materials; due to the decline in material properties and anchoring force of the anchoring system, the deformation of the anchoring structure will increase under the action of external loads; the chloride ions in the salt spray environment have strong permeability and can penetrate the protective coating and react chemically with the metal matrix, causing the coating to bubble and peel off, and lose its protective effect on the metal. Once the protective layer is destroyed, the metal material will be directly exposed to the salt spray environment, accelerating the corrosion process.

[0004] However, traditional stress detection methods mostly rely on single sensors, such as resistance strain gauges or electromagnetic sensors, and have limitations such as sensitivity to environmental interference and insufficient multi-source data fusion: environmental factors such as salt spray, temperature and humidity can easily cause sensor data drift, which affects measurement accuracy. There is also a lack of coordinated analysis of environmental factors such as corrosion and salt spray with stress loss, making it difficult to comprehensively assess risks. When choosing a multi-source data fusion model, traditional methods mainly rely on centralized cloud processing, which makes it difficult to achieve real-time analysis and early warning, and may cause data processing delays.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0006] The purpose of the present invention is to overcome the limitations of traditional methods, such as sensitivity to environmental interference and insufficient fusion of multi-source data, as well as the resulting problems of insufficient environmental interference suppression, poor real-time performance, and data silos, and to provide a high-precision and high-reliability solution for anchor system health monitoring, effectively preventing structural failures and extending the life of the project.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A stress loss detection system for an anchoring system, comprising a multi-source acquisition unit, an edge computing unit, a cloud server, and a data decision terminal; The multi-source acquisition unit includes a stress detection module and an environmental detection module, which respectively detect the stress loss of the anchoring system and the environmental status of the anchoring system to obtain multi-source sensing data; The edge computing unit is used to perform preliminary analysis of multi-source sensor data. Wavelet denoising is used to obtain the standard strain value of the anchoring system and conduct in-depth analysis to obtain the time domain characteristics, frequency domain characteristics, and environmental coupling characteristics of the standard strain value, and to assess the corrosion anomalies of the anchoring system. The cloud server is used to build an environmental interference compensation model for the anchoring system strain: by analyzing the correlation function between salt spray concentration and temperature and humidity, an environmental interference compensation function between strain and salt spray is established, and the standard strain value is compensated and corrected to generate a compensated strain value; The data decision terminal is used to assess the stress risk level of the anchoring system and generate risk warning signals for management.

[0008] Furthermore, the specific processing process of the edge computing unit is as follows: The measured strain value is decomposed by discrete wavelet transform DWT to obtain the standard strain value; By calculating the changes in the corresponding data of N standard strain values ​​at adjacent time nodes, the time domain characteristics Sf of the standard strain value are obtained; Then, by setting the frequency domain interval and performing Fourier transform on the standard strain value, the frequency domain feature Pf of the standard strain value is obtained; By combining the standard strain value with the salt spray concentration value, the environmental coupling feature of the standard strain value is obtained and marked as the strain-salt spray coupling feature Dfc; A corrosion anomaly prediction model is established, and the corrosion anomaly of the anchoring system is evaluated by combining the time domain characteristics Sf, frequency domain characteristics Pf and strain-salt spray coupling characteristics Dfc of the standard strain value.

[0009] Furthermore, the specific processing process of the cloud server is as follows: The salt spray concentration, ambient temperature and activation energy of the corrosion reaction are combined through the Arrhenius equation to establish the correlation function g1 between the salt spray concentration and the ambient temperature. Through the salt spray effect experiment, the salt spray concentration, ambient humidity value and salt spray effect saturation coefficient are combined to establish the correlation function g2 between the salt spray concentration and ambient humidity value; By combining the temperature Laplace operator with the salt spray concentration, the correlation function g3 of the salt spray concentration in the temperature-induced salt deposition is established; The environmental parameters collected at the current time node are substituted into the correlation functions g1, g2, and g3 to evaluate the degree to which the salt spray concentration is affected by environmental factors, thereby compensating the standard strain value for environmental interference and obtaining the compensated strain value Fbc.

[0010] Furthermore, the process of collecting multi-source sensor data is as follows: Multi-source sensing data include strain parameters and environmental parameters; The strain parameters include the measured strain value Fcl and the reference strain value Fck; the environmental parameters include the salt spray concentration value Cyw, the ambient temperature value Whj and the ambient humidity value Hhj; The stress detection module is used to detect the stress loss of the anchoring system. The stress detection module includes an optical fiber strain sensor and an electromagnetic induction sensor. The optical fiber strain sensor collects the measured strain value Fcl, and the electromagnetic induction sensor collects the reference strain value Fck. The environmental detection module is used to detect the environmental status of the anchoring system. The environmental detection module includes an electrochemical salt spray concentration sensor and a temperature and humidity integrated sensor; wherein, the salt spray concentration value Cyw is collected by the electrochemical salt spray concentration sensor, and the ambient temperature value Whj and ambient humidity value Hhj are collected by the temperature and humidity integrated sensor.

[0011] Furthermore, the specific process of preliminary analysis of multi-source sensor data is as follows: Extract multi-source sensor data corresponding to N time nodes t; The measured strain value Fcl is decomposed by discrete wavelet transform DWT; Among them, the mark is the low-frequency scale coefficient, is the low-frequency wavelet basis function, is the high-frequency wavelet coefficient, is the high-frequency wavelet basis function, J is the number of wavelet decomposition layers, k is the sequence number of the data index and 0<k≤N; Extract wavelet basis functions The corresponding signal data and the denoised strain value Fqz are marked 0 ; By normalizing the environmental parameters and calculating the standard deviation, the fluctuation coefficients of various indicators of the environmental parameters are obtained, and then the dynamic adjustment coefficients are obtained by weighted synthesis. , and combine the number of data points N to obtain the wavelet threshold ; By wavelet thresholding Wavelet coefficients Processing is performed to reconstruct the denoised strain signal and mark it as the standard strain value Fqz 1 .

[0012] Furthermore, by normalizing the environmental parameters and calculating the standard deviation, the fluctuation coefficients of various indicators of the environmental parameters are obtained. The specific process is as follows: Mark any indicator of environmental parameters as y, normalize the indicator y to obtain the standard data of indicator y ; Then the standard data of indicator y Calculate the standard deviation and obtain the volatility coefficient of indicator y ; The standard deviation is calculated by normalizing the salt spray concentration value Cyw, the ambient temperature value Whj, and the ambient humidity value Hhj in turn, and marked as the salt spray fluctuation coefficient , temperature fluctuation coefficient , humidity fluctuation coefficient .

[0013] Further, in-depth analysis of the standard strain value Fqz 1 The specific process is: By calculating N standard strain values ​​Fqz 1 The difference between the corresponding data at adjacent time nodes is taken, and then the absolute value is taken and the average is calculated to obtain the standard strain value Fqz 1 The time domain characteristics Sf; Then, by setting the frequency domain interval [f1, f2] and integrating the square of the Fourier transform amplitude of the standard strain value, the standard strain value Fqz is obtained. 1 Frequency domain characteristics Pf; The partial derivative of strain to salt spray concentration is obtained by multivariate regression fitting, and the rate of change of salt spray concentration over time is analyzed, and then the standard strain value Fqz is used to calculate the 1 Combined with the salt spray concentration value Cyw, the standard strain value Fqz is obtained 1 The environmental coupling feature is identified and labeled as strain-salt spray coupling feature Dfc.

[0014] Furthermore, the specific process of evaluating the corrosion anomaly of stress loss is as follows: A corrosion anomaly prediction model is established. The standard strain value Fqz is converted into 1 The time domain characteristics Sf, frequency domain characteristics Pf and strain salt spray coupling characteristics Dfc are combined to obtain the stress corrosion anomaly index Zff and evaluate the corrosion anomaly risk of stress loss.

[0015] Furthermore, the specific process of analyzing the correlation function between salt spray concentration and temperature and humidity is as follows: Under constant humidity and salt spray conditions, the temperature T was changed, the corrosion rate r was measured, and the Arrhenius equation was fitted to obtain the corrosion reaction activation energy Ea. By combining the salt spray concentration Cyw, the ambient temperature value Whj, and the corrosion reaction activation energy Ea, a correlation function g1 between the salt spray concentration Cyw and the ambient temperature value Whj was established. By changing the salt spray concentration Cyw under constant temperature and humidity experimental conditions, measuring the corrosion current density I, the salt spray effect saturation coefficient is obtained. ;Through salt spray concentration Cyw, ambient humidity value Hhj and salt spray effect saturation coefficient Combined, the correlation function g2 between the salt spray concentration Cyw and the ambient humidity value Hhj is established; Calculating the Temperature Laplace Operator by Finite Difference Method , through the temperature Laplace operator Combined with the salt spray concentration Cyw, the correlation function g3 of the salt spray concentration Cyw on the temperature-induced salt deposition is established; The degree to which salt spray concentration is affected by environmental factors is evaluated through correlation functions g1, g2 and g3.

[0016] Furthermore, the specific process of establishing the environmental interference compensation function between strain and salt spray is as follows: Substitute the environmental parameters collected at the current time node into the correlation functions g1, g2, and g3, assign corresponding sensitivity coefficients to the correlation functions in turn, and compare the salt spray concentration Cyw with the temperature-induced salt deposition effect and the salt spray dynamic effect coefficient. Combined with the standard strain value Fqz 1 Perform compensation correction for environmental interference and obtain the compensation strain value Fbc; The sensitivity coefficients corresponding to the correlation functions g1, g2, and g3 are integrated into the environmental sensitivity coefficient matrix R. The environmental sensitivity coefficient matrix R and the salt spray dynamic effect coefficient are obtained by minimizing the sum of the squares of the difference between the reference strain value Fck and the compensation strain value Fbc. ; By setting the risk interval of the compensation strain value Fbc and performing interval comparison, the stress risk level of the anchoring system can be evaluated and a corresponding risk warning signal can be generated.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This invention significantly improves the accuracy and efficiency of stress loss detection in anchoring systems through multi-source data fusion, edge-cloud collaborative computing, and a dynamic compensation model for environmental interference. The edge computing unit is responsible for real-time preprocessing, the cloud server focuses on complex model optimization, and the data decision terminal quickly generates management signals, achieving full-link collaborative optimization and thus improving response speed. Among them, the strain parameters and environmental parameters of the anchoring system are monitored synchronously through a multi-source acquisition unit, and compared and verified by combining optical fiber strain sensors and electromagnetic induction sensors to achieve data complementarity and improve reliability; The edge computing unit performs real-time wavelet denoising and combines it with environmental parameter analysis to reduce data transmission volume and improve processing speed. Multi-dimensional analysis is then performed using the strain's time-domain characteristics, frequency-domain characteristics, and strain-salt spray coupling characteristics to predict the risk of corrosion anomalies caused by stress loss. The synergistic effect of salt spray and temperature and humidity is quantified by obtaining correlation functions through cloud servers, and dynamic compensation for environmental interference is achieved by combining dynamic adjustment coefficients, which significantly improves measurement accuracy. Risk classification warning is then achieved through the corrosion anomaly prediction model, reducing the missed reporting rate and false alarm rate of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 shows a schematic diagram of the connection of the system modules of the present invention; Figure 2 A schematic diagram showing the steps of the workflow of the present invention is shown. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1: like Figure 1-Figure 2 As shown, a stress loss detection system for an anchoring system includes a multi-source acquisition unit, an edge computing unit, a cloud server, and a data decision terminal, wherein the multi-source acquisition unit, the edge computing unit, the cloud server, and the data decision terminal are communicatively connected; The specific working steps are as follows: S1: The multi-source acquisition unit includes a stress detection module and an environmental detection module, which respectively detect the stress loss of the anchoring system and the environmental status of the anchoring system to obtain multi-source sensing data; Multi-source sensing data include strain parameters and environmental parameters; The strain parameters include the measured strain value Fcl and the reference strain value Fck; the environmental parameters include the salt spray concentration value Cyw, the ambient temperature value Whj and the ambient humidity value Hhj; The stress detection module is used to detect the stress loss of the anchoring system. The stress detection module includes an optical fiber strain sensor and an electromagnetic induction sensor. The optical fiber strain sensor collects the measured strain value Fcl, and the electromagnetic induction sensor collects the reference strain value Fck. The environmental detection module is used to detect the environmental status of the anchoring system. The environmental detection module includes an electrochemical salt spray concentration sensor and a temperature and humidity integrated sensor. The electrochemical salt spray concentration sensor collects the salt spray concentration value Cyw, and the temperature and humidity integrated sensor collects the ambient temperature value Whj and the ambient humidity value Hhj. Through multi-source data acquisition and fusion, combined with optical fiber strain sensors (high precision) and electromagnetic induction sensors (anti-interference), strain parameters (measured value Fcl, reference value Fck) and environmental parameters (salt spray Cyw, temperature Whj, humidity Hhj) are synchronously collected to achieve data complementarity and improve reliability.

[0021] S2, the edge computing unit preliminarily analyzes the multi-source sensor data: it obtains the standard strain value of the anchor system through wavelet de-noising and performs in-depth analysis to obtain the time domain characteristics, frequency domain characteristics and environmental coupling characteristics of the standard strain value, and evaluates the corrosion anomaly of the anchor system; S2-1, the specific process of preliminary analysis of multi-source sensor data is as follows: Extract multi-source sensor data corresponding to N time nodes t; The measured strain value Fcl is decomposed by discrete wavelet transform DWT: ; in, is the low-frequency scale coefficient, is the low-frequency wavelet basis function, is the high-frequency wavelet coefficient, is the high-frequency wavelet basis function, J is the number of wavelet decomposition layers, k is the sequence number of the data index and 0<k≤N; Extract wavelet basis functions The corresponding signal data and the denoised strain value Fqz are marked 0 : ; By normalizing the environmental parameters and calculating the standard deviation, the fluctuation coefficient of each indicator of the environmental parameters is obtained; Mark any indicator of environmental parameters as y, normalize the indicator y to obtain the standard data of indicator y : ;in, and are the maximum and minimum values ​​of indicator y respectively; Then the standard data of indicator y Calculate the standard deviation and obtain the volatility coefficient of indicator y : ,in, N standard data for indicator y The average value of ; The standard deviation is calculated by normalizing the salt spray concentration value Cyw, the ambient temperature value Whj, and the ambient humidity value Hhj in turn, and marked as the salt spray fluctuation coefficient , temperature fluctuation coefficient , humidity fluctuation coefficient ,but ; Then weighted comprehensive acquisition of dynamic adjustment coefficient , and combine the number of data points N to obtain the wavelet threshold : ; Among them, m is the index number of any environmental parameter, M is the number of environmental parameter indicators, is the fluctuation coefficient of any indicator of environmental parameters, is the coefficient of fluctuation The weight factor is preset after a large amount of data verification; By wavelet thresholding Wavelet coefficients Processing is performed to reconstruct the denoised strain signal and mark it as the standard strain value Fqz 1 : .

[0022] S2-2, depth analysis standard strain value Fqz 1 The specific process is: By calculating N standard strain values ​​Fqz 1 The difference between the corresponding data at adjacent time nodes is taken, and then the absolute value is taken and the average is calculated to obtain the standard strain value Fqz 1 The time domain characteristics Sf: ; Among them, the higher the time domain feature Sf is, the more intense the strain change in the time domain is, which reflects the average intensity of strain change in a short period of time. The early stage of corrosion often manifests as local strain mutation, such as micro-area stress concentration caused by pitting corrosion. Then, by setting the frequency domain interval [f1, f2] and integrating the square of the Fourier transform amplitude of the standard strain value, the standard strain value Fqz is obtained. 1 Frequency domain characteristics Pf: ; Among them, FT is Fourier transform, which converts the standard strain value Fqz 1 The signal is converted from the time domain to the frequency domain, The amplitude is obtained after Fourier transformation. When the frequency domain feature Pf is higher, it means that the energy distribution of strain in the frequency domain interval [f1, f2] is higher, thereby quantifying the high-frequency vibration energy caused by corrosion, such as crack friction, corrosion product shedding, etc. By standard strain value Fqz 1 Combined with the salt spray concentration value Cyw, the standard strain value Fqz is obtained 1 The environmental coupling feature is labeled as strain-salt spray coupling feature Dfc: ; Among them, the partial derivative of strain to salt spray concentration is obtained by multivariate regression fitting ,pass Analyze the rate of change of salt spray concentration over time; characterize the synergistic effect between the environment and strain through the strain-salt spray coupling characteristic Dfc, such as the additional stress caused by salt crystallization. The higher the strain-salt spray coupling characteristic Dfc, the more the strain is affected by the salt spray concentration.

[0023] S2-3, the specific process for evaluating stress loss corrosion anomalies is as follows: Establish a corrosion anomaly prediction model and use the standard strain value Fqz 1 The stress corrosion anomaly index Zff is obtained by combining the time domain characteristics Sf, frequency domain characteristics Pf and strain salt spray coupling characteristics Dfc: ; in, 、 、 are the weight coefficients of the time domain feature Sf, frequency domain feature Pf and strain salt spray coupling feature Dfc, respectively. The weight coefficients are preset and obtained through salt spray test measurement. When the stress corrosion anomaly index Zff is higher, the corrosion anomaly risk of the stress loss assessment is more serious.

[0024] High-frequency noise is dynamically removed through wavelet denoising (DWT), and standard strain values ​​are extracted by combining environmental parameter normalization and fluctuation coefficient analysis, reducing data transmission volume and improving processing speed. Multi-dimensional analysis of time domain characteristics, frequency domain characteristics, and strain-salt spray coupling characteristics enables risk prediction of initial corrosion anomalies associated with stress loss, such as pitting corrosion and crack friction. S3: The cloud server builds an environmental interference compensation model for the anchoring system strain. By analyzing the correlation function between salt spray concentration and temperature and humidity, an environmental interference compensation function between strain and salt spray is established. The standard strain value is compensated and corrected to generate a compensated strain value. S3-1, the specific process of analyzing the correlation function between salt spray concentration and temperature and humidity is as follows: S3-101, under constant humidity and constant salt spray conditions, the temperature T is changed, the corrosion rate r is measured, and the Arrhenius equation is fitted: , obtain the corrosion reaction activation energy Ea: Where R0 is the universal gas constant and R0 is 8.314, A is the frequency factor, As the horizontal axis, As the ordinate, draw the straight line L by least squares fitting, then the intercept of the straight line L is The slope of the straight line L is , through the intercept Get the frequency factor A through the slope Obtain the activation energy Ea of the corrosion reaction; By combining the salt spray concentration Cyw, the ambient temperature value Whj and the corrosion reaction activation energy Ea, the correlation function g1 between the salt spray concentration Cyw and the ambient temperature value Whj is established: ; Among them, e is the natural constant, and its value is 2.7; T0 is the thermodynamic temperature, and T0 is 273.16; S3-102, by changing the salt spray concentration Cyw under constant temperature and humidity experimental conditions, measuring the corrosion current density I, and obtaining the salt spray effect saturation coefficient : ; Through the salt spray concentration Cyw, ambient humidity value Hhj and salt spray effect saturation coefficient Combined with the above, the correlation function g2 between the salt spray concentration Cyw and the ambient humidity value Hhj is established: ; in, ; S3-103, by temperature Laplace operator Combined with the salt spray concentration Cyw, the correlation function g3 of the salt spray concentration Cyw in temperature-induced salt deposition is established: ; Among them, the temperature Laplace operator is calculated by the finite difference method : ; Represented in spatial grid <x-y>The temperature value at the coordinate (p,q) is Refers to the spatial grid spacing in the x-direction, and the spatial grid spacing in the x-direction is the same as that in the y-direction.

[0025] S3-2, by substituting the environmental parameters collected at the current time node into the correlation functions g1, g2 and g3, evaluate the degree to which the salt spray concentration is affected by environmental factors, and combine the salt spray concentration Cyw with the temperature-induced salt deposition effect. , for the standard strain value Fqz 1 Perform compensation correction for environmental interference and obtain the compensation strain value Fbc: ; in, is the salt spray dynamic effect coefficient, is the sensitivity coefficient of any correlation function gr, and the sensitivity coefficients corresponding to the three correlation functions are integrated into the environmental sensitivity coefficient matrix R; The formula is expanded to: ; S3-3, by minimizing the sum of squares of the difference between the reference strain value Fck and the compensation strain value Fbc, obtain the environmental sensitivity coefficient matrix R and the salt spray dynamic effect coefficient : .

[0026] S4, the data decision terminal evaluates the stress risk level of the anchoring system and generates risk warning signals for management; By setting the risk interval of the compensation strain value Fbc and performing interval comparison, the stress risk level of the anchoring system can be evaluated and a corresponding risk warning signal can be generated.

[0027] In summary, the present invention significantly improves the accuracy and efficiency of stress loss detection in anchor systems through multi-source data fusion, edge-cloud collaborative computing, and a dynamic compensation model. The edge computing unit is responsible for real-time preprocessing, the cloud server focuses on complex model optimization, and the data decision terminal quickly generates management signals, achieving full-link collaborative optimization and thus improving response speed. Among them, the strain parameters and environmental parameters of the anchoring system are monitored synchronously through a multi-source acquisition unit, and compared and verified by combining optical fiber strain sensors and electromagnetic induction sensors to achieve data complementarity and improve reliability; Through real-time edge computing processing, wavelet denoising is performed and analyzed in combination with environmental parameters to reduce data transmission volume and improve processing speed. Multi-dimensional analysis is then performed using the time domain characteristics, frequency domain characteristics, and strain-salt spray coupling characteristics to predict the risk of corrosion anomalies caused by stress loss. The synergistic effect of salt spray and temperature and humidity is quantified by obtaining correlation functions through cloud servers, and dynamic compensation for environmental interference is achieved by combining dynamic adjustment coefficients, which significantly improves measurement accuracy. Risk classification warning is then achieved through the corrosion anomaly prediction model, reducing the missed reporting rate and false alarm rate of monitoring.

[0028] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0029] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by those skilled in the art according to actual conditions. The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A stress loss detection system for an anchoring system, characterized by: Includes multi-source acquisition unit, edge computing unit, cloud server and data decision terminal; The multi-source acquisition unit includes a stress detection module and an environmental detection module, which respectively detect the stress loss of the anchoring system and the environmental status of the anchoring system to obtain multi-source sensing data; The edge computing unit is used to perform preliminary analysis of multi-source sensor data. Wavelet denoising is used to obtain the standard strain value of the anchoring system and conduct in-depth analysis to obtain the time domain characteristics, frequency domain characteristics, and environmental coupling characteristics of the standard strain value, and to assess the corrosion anomalies of the anchoring system. The cloud server is used to build an environmental interference compensation model for the anchoring system strain: by analyzing the correlation function between salt spray concentration and temperature and humidity, an environmental interference compensation function between strain and salt spray is established, and the standard strain value is compensated and corrected to generate a compensated strain value; The data decision terminal is used to assess the stress risk level of the anchoring system and generate risk warning signals for management.

2. The stress loss detection system for an anchoring system according to claim 1, characterized in that: The specific processing process of the edge computing unit is as follows: The measured strain value is decomposed by discrete wavelet transform DWT to obtain the standard strain value; By calculating the changes in the corresponding data of N standard strain values ​​at adjacent time nodes, the time domain characteristics Sf of the standard strain value are obtained; Then, by setting the frequency domain interval and performing Fourier transform on the standard strain value, the frequency domain feature Pf of the standard strain value is obtained; By combining the standard strain value with the salt spray concentration value, the environmental coupling feature of the standard strain value is obtained and marked as the strain-salt spray coupling feature Dfc; A corrosion anomaly prediction model is established, and the corrosion anomaly of the anchoring system is evaluated by combining the time domain characteristics Sf, frequency domain characteristics Pf and strain-salt spray coupling characteristics Dfc of the standard strain value.

3. The stress loss detection system for an anchoring system according to claim 1, characterized in that: The specific processing process of the cloud server is as follows: The salt spray concentration, ambient temperature and activation energy of the corrosion reaction are combined through the Arrhenius equation to establish the correlation function g1 between the salt spray concentration and the ambient temperature. Through the salt spray effect experiment, the salt spray concentration, ambient humidity value and salt spray effect saturation coefficient are combined to establish the correlation function g2 between the salt spray concentration and ambient humidity value; By combining the temperature Laplace operator with the salt spray concentration, the correlation function g3 of the salt spray concentration in the temperature-induced salt deposition is established; The environmental parameters collected at the current time node are substituted into the correlation functions g1, g2, and g3 to evaluate the degree to which the salt spray concentration is affected by environmental factors, thereby compensating the standard strain value for environmental interference and obtaining the compensated strain value Fbc.

4. The stress loss detection system for an anchoring system according to claim 1, characterized in that: The process of collecting multi-source sensor data is as follows: Multi-source sensing data include strain parameters and environmental parameters; The strain parameters include the measured strain value Fcl and the reference strain value Fck; the environmental parameters include the salt spray concentration value Cyw, the ambient temperature value Whj and the ambient humidity value Hhj; The stress detection module is used to detect the stress loss of the anchoring system. The stress detection module includes an optical fiber strain sensor and an electromagnetic induction sensor. The optical fiber strain sensor collects the measured strain value Fcl, and the electromagnetic induction sensor collects the reference strain value Fck. The environmental detection module is used to detect the environmental status of the anchoring system. The environmental detection module includes an electrochemical salt spray concentration sensor and a temperature and humidity integrated sensor; wherein, the salt spray concentration value Cyw is collected by the electrochemical salt spray concentration sensor, and the ambient temperature value Whj and ambient humidity value Hhj are collected by the temperature and humidity integrated sensor.

5. The stress loss detection system for an anchoring system according to claim 2, characterized in that: The specific process of preliminary analysis of multi-source sensor data is as follows: Extract multi-source sensor data corresponding to N time nodes t; The measured strain value Fcl is decomposed by discrete wavelet transform DWT; Among them, the mark is the low-frequency scale coefficient, is the low-frequency wavelet basis function, is the high-frequency wavelet coefficient, is the high-frequency wavelet basis function, J is the number of wavelet decomposition layers, k is the sequence number of the data index and 0<k≤N; Extract wavelet basis functions The corresponding signal data and the denoised strain value Fqz are marked 0 ; By normalizing the environmental parameters and calculating the standard deviation, the fluctuation coefficients of various indicators of the environmental parameters are obtained, and then the dynamic adjustment coefficients are obtained by weighted synthesis. , and combine the number of data points N to obtain the wavelet threshold ; By wavelet thresholding Wavelet coefficients Processing is performed to reconstruct the denoised strain signal and mark it as the standard strain value Fqz 1 .

6. The stress loss detection system for an anchoring system according to claim 5, characterized in that: By normalizing the environmental parameters and calculating the standard deviation, the fluctuation coefficients of various indicators of the environmental parameters are obtained. The specific process is as follows: Mark any indicator of environmental parameters as y, normalize the indicator y to obtain the standard data of indicator y ; Then the standard data of indicator y Calculate the standard deviation and obtain the volatility coefficient of indicator y ; The standard deviation is calculated by normalizing the salt spray concentration value Cyw, the ambient temperature value Whj, and the ambient humidity value Hhj in turn, and marked as the salt spray fluctuation coefficient , temperature fluctuation coefficient , humidity fluctuation coefficient .

7. The stress loss detection system for an anchoring system according to claim 6, characterized in that: In-depth analysis of standard strain value Fqz 1 The specific process is: By calculating N standard strain values ​​Fqz 1 The difference between the corresponding data at adjacent time nodes is taken, and then the absolute value is taken and the average is calculated to obtain the standard strain value Fqz 1 The time domain characteristics Sf; Then, by setting the frequency domain interval [f1, f2] and integrating the square of the Fourier transform amplitude of the standard strain value, the standard strain value Fqz is obtained. 1 Frequency domain characteristics Pf; The partial derivative of strain to salt spray concentration is obtained by multivariate regression fitting, and the rate of change of salt spray concentration over time is analyzed, and then the standard strain value Fqz is used to calculate the 1 Combined with the salt spray concentration value Cyw, the standard strain value Fqz is obtained 1 The environmental coupling feature is identified and labeled as strain-salt spray coupling feature Dfc.

8. The stress loss detection system for an anchoring system according to claim 7, characterized in that: The specific process for evaluating stress loss corrosion anomalies is: A corrosion anomaly prediction model is established. The standard strain value Fqz is converted into 1 The time domain characteristics Sf, frequency domain characteristics Pf and strain salt spray coupling characteristics Dfc are combined to obtain the stress corrosion anomaly index Zff and evaluate the corrosion anomaly risk of stress loss.

9. The stress loss detection system for an anchoring system according to claim 3, characterized in that: The specific process of analyzing the correlation function between salt spray concentration and temperature and humidity is as follows: Under constant humidity and salt spray conditions, the temperature T was changed, the corrosion rate r was measured, and the Arrhenius equation was fitted to obtain the corrosion reaction activation energy Ea. By combining the salt spray concentration Cyw, the ambient temperature value Whj, and the corrosion reaction activation energy Ea, a correlation function g1 between the salt spray concentration Cyw and the ambient temperature value Whj was established. By changing the salt spray concentration Cyw under constant temperature and humidity experimental conditions, measuring the corrosion current density I, the salt spray effect saturation coefficient is obtained. ;Through salt spray concentration Cyw, ambient humidity value Hhj and salt spray effect saturation coefficient Combined, the correlation function g2 between the salt spray concentration Cyw and the ambient humidity value Hhj is established; Calculating the Temperature Laplace Operator by Finite Difference Method , through the temperature Laplace operator Combined with the salt spray concentration Cyw, the correlation function g3 of the salt spray concentration Cyw on the temperature-induced salt deposition is established; The degree to which salt spray concentration is affected by environmental factors is evaluated through correlation functions g1, g2 and g3.

10. The stress loss detection system for an anchoring system according to claim 9, characterized in that: The specific process of establishing the environmental interference compensation function between strain and salt spray is as follows: Substitute the environmental parameters collected at the current time node into the correlation functions g1, g2, and g3, assign corresponding sensitivity coefficients to the correlation functions in turn, and compare the salt spray concentration Cyw with the temperature-induced salt deposition effect and the salt spray dynamic effect coefficient. Combined with the standard strain value Fqz 1 Perform compensation correction for environmental interference and obtain the compensation strain value Fbc; The sensitivity coefficients corresponding to the correlation functions g1, g2, and g3 are integrated into the environmental sensitivity coefficient matrix R. The environmental sensitivity coefficient matrix R and the salt spray dynamic effect coefficient are obtained by minimizing the sum of the squares of the difference between the reference strain value Fck and the compensation strain value Fbc. ; By setting the risk interval of the compensation strain value Fbc and performing interval comparison, the stress risk level of the anchoring system can be evaluated and a corresponding risk warning signal can be generated.

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