Intelligent monitoring system and method for prestressed cable structure

Abstract

The invention belongs to the technical field of building structure monitoring, and particularly relates to an intelligent monitoring system and method for a prestressed cable structure, the system comprises a cable body and an intelligent monitoring module for monitoring the stress change in the cable body, the cable body comprises a steel strand with a center steel wire removed and an optical fiber, and the optical fiber is connected with the intelligent monitoring module. The optical fiber is installed at the position of a center steel wire of the steel strand through the optical fiber protection shell, a buffer layer is arranged between the optical fiber and the inner wall of the optical fiber protection shell, the optical fiber is installed in the optical fiber protection shell through the elastic clamp, and the data collector is connected to a tail fiber of the cable body through the tail fiber leading-out component and used for receiving optical signals transmitted by the optical fiber. The optical fiber embedded in the central position in the cable body instead of a central steel wire of a steel strand is used as a sensing element, tiny stress changes can be more accurately captured, and high-precision monitoring of the stress state of the cable body is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of building structure monitoring, and in particular relates to an intelligent monitoring system and method for a prestressed cable structure. Background Art

[0002] With the continuous development of modern construction engineering technology, the forms of building structures are becoming increasingly diversified and complex. Large-scale buildings such as long-span bridges, stadiums, and exhibition halls are constantly emerging. In these building structures, prestressed cable structures have been widely used due to their unique advantages. Prestressed cable structures can effectively improve the bearing capacity of the structure. By pre-applying stress, the structure can better exert the mechanical properties of the material when subjected to external loads, thereby realizing the construction of large-span spatial structures.

[0003] However, prestressed cables face many challenges in their long-term use. Environmental factors have a significant impact on them. For example, temperature changes will cause thermal expansion and contraction of cable materials, and changes in humidity may accelerate the corrosion process of materials. Secondly, errors and material aging during construction are also issues that cannot be ignored, which will also bring hidden dangers to prestressed cable structures. Inaccurate application of pretension of the cable body during construction and deviation of the installation position of the cable body will gradually expose problems in later use. Finally, changes in load during the use of buildings are also normal. These load changes will continuously change the stress state of the cable body. The combined effect of the above factors can easily lead to the breakage or performance degradation of the cable body steel bundle, and the loss of cable prestress will also occur. The breakage of the cable body steel bundle not only directly weakens the bearing capacity of the cable structure, but the loss of cable prestress will increase the deformation of the cable structure, reduce the stiffness and stability of the structure, and affect the normal use function of the structure. In actual engineering, due to the lack of effective real-time monitoring methods, it is often difficult to discover these potential problems in the early stage. Therefore, in order to ensure the safe and reliable operation of the prestressed cable structure throughout the life cycle of the building, an intelligent monitoring system and method for prestressed cable structures is needed at this stage. Summary of the invention

[0004] In order to solve the problems of difficulty in monitoring cable prestress loss and low monitoring accuracy at this stage, the present invention provides an intelligent monitoring system and method for a prestressed cable structure.

[0005] In a first aspect, the present invention provides an intelligent monitoring system for a prestressed cable structure, which adopts the following technical solution: An intelligent monitoring system for a prestressed cable structure, comprising: A cable body and an intelligent monitoring module for monitoring the force changes inside the cable body, wherein the cable body comprises a steel strand with a central steel wire removed and an optical fiber, wherein the optical fiber is installed at the central steel wire position of the steel strand through an optical fiber protective shell, a buffer layer is provided between the optical fiber and the inner wall of the optical fiber protective shell, and the optical fiber is installed inside the optical fiber protective shell through an elastic clamp to prevent the optical fiber from being offset; The intelligent monitoring module includes a data collector and an intelligent monitoring host. The data collector is connected to the pigtail of the cable body through a pigtail lead-out component to receive optical signals transmitted by the optical fiber. The data collector transmits digital signals to the intelligent monitoring host through wireless communication.

[0006] Furthermore, the elastic clamp is made of polyoxymethylene material, one end of the elastic clamp is arc-shaped and fixedly connected to the optical fiber, and the other end of the elastic clamp is connected to the inner wall of the optical fiber protective shell.

[0007] Furthermore, one end of the steel strand is connected to an adjustable cable head, and the other end of the steel strand is connected to a fixed cable head. Pin shafts are provided at the front ends of the adjustable cable head and the fixed cable head.

[0008] Furthermore, an adjustable anchor cup is provided between the adjustable cable head and the steel strand, the adjustable cable head is connected to the adjustable anchor cup via a screw, and the rear end of the adjustable anchor cup is fixedly connected to the steel strand.

[0009] Furthermore, the pigtail lead-out component comprises a protective sleeve, one end of which is connected to the optical fiber protective shell inside the cable body, the other end of which leads out the pigtail, and the protective sleeve is connected to one end of the cable body through the optical fiber hole.

[0010] Furthermore, the data acquisition device includes a photoelectric conversion module, a signal amplification component, and an analog-to-digital conversion module. The photoelectric conversion module converts the optical signal transmitted by the optical fiber into an electrical signal, the signal amplification component amplifies the electrical signal, and the analog-to-digital conversion module converts the amplified electrical signal into a digital signal.

[0011] In a second aspect, an intelligent monitoring method for a prestressed cable structure comprises: Obtain digital signals and real-time ambient temperature, and calculate the impact of temperature changes on optical fiber sensing signals based on the relationship model between optical fiber sensing characteristics and temperature; Based on the influence amount of the optical fiber sensing signal, the change of the optical fiber sensing signal caused by the temperature change is corrected; Feature extraction is performed based on the corrected data, and the extracted feature vector dimensions are used to construct a cable body health assessment model; The cable body health assessment model is used to compare the feature vector with the preset normal state data features to calculate the assessment value of the cable body health status.

[0012] Furthermore, the calculation of the influence of temperature change on the optical fiber sensing signal includes determining the initial central wavelength and the thermo-optic coefficient according to the optical fiber characteristics, calculating the temperature change using the temperature data, and calculating the central wavelength drift caused by the temperature change using the central wavelength, the thermo-optic coefficient and the temperature change. The central wavelength drift calculation formula is: , in, is represented as the initial center wavelength, Expressed as the thermo-optical coefficient, Expressed as the change in temperature.

[0013] Furthermore, the construction of the cable body health assessment model using the extracted feature vector dimension includes constructing the cable body health assessment model using a neural network algorithm, constructing an input layer with the feature vector dimension as the number of nodes, constructing an output layer with the number of classifications of the cable body health status as the number of nodes, setting a hidden layer between the input layer and the output layer, and finally adjusting the connection weights of each layer through a back propagation algorithm to minimize the error.

[0014] Furthermore, the calculation of the evaluation value of the health status of the cable body includes standardizing the extracted feature vector and the preset normal state data feature, calculating the Euclidean distance between the feature vector and the preset normal state data feature using the Euclidean distance method, setting a distance threshold and determining the evaluation value of the health status of the cable body according to the distance threshold.

[0015] In summary, the present invention has the following beneficial technical effects: 1. The present invention adopts an optical fiber buried in the center of the cable body to replace the central steel wire of the steel strand as a sensing element. Based on the photoelastic effect of the optical fiber, it can keenly sense the stress changes inside the cable body and convert them into light signal changes. The optical fiber has a high sensitivity. Compared with traditional monitoring methods, it can more accurately capture tiny stress changes and achieve high-precision monitoring of the stress state of the cable body.

[0016] 2. The present invention obtains the real-time ambient temperature, uses the relationship model between the optical fiber sensing characteristics and temperature to calculate the impact of temperature changes on the optical fiber sensing signal, and makes corrections. This effectively eliminates the interference of temperature factors on the monitoring results, ensures that the measurement data can truly reflect the stress state of the cable body, and further improves the monitoring accuracy.

[0017] 3. The present invention removes the central steel wire in the cable body and replaces it with an optical fiber, thereby optimizing the cable body structure without affecting the overall load-bearing capacity of the cable body. At the same time, the two ends of the steel strand are respectively connected to an adjustable cable head and a fixed cable head, and the length of the cable body is precisely adjusted through an adjustable anchor cup and a screw rod, thereby ensuring that the cable body is always in the best stress state during installation and use, reducing fatigue damage caused by uneven stress on the cable body, and improving the structural strength and durability of the cable body.

[0018] 4. The high-strength, corrosion-resistant optical fiber protective shell provided on the outside of the optical fiber of the present invention provides good physical protection for the optical fiber, preventing it from being damaged by factors such as friction of the steel wire inside the cable body and corrosion from the external environment. The buffer layer inside the protective shell can effectively absorb external impact forces, avoiding damage to the optical fiber when the cable body is subjected to vibration, collision, etc., thereby ensuring the long-term stable operation of the optical fiber and indirectly improving the overall durability of the cable body structure.

[0019] 5. The present invention uses a neural network algorithm to construct a cable body health assessment model, and performs model training and analysis based on the extracted feature vectors. The powerful nonlinear mapping ability of the neural network can accurately identify the characteristic patterns of the cable body under different health conditions. By comparing the feature vectors of real-time monitoring data with the preset normal state data characteristics, the assessment value of the cable body health condition is calculated, thereby realizing an intelligent assessment of the cable body health condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall structure of an intelligent monitoring system for a prestressed cable structure according to Example 1 of the present invention.

[0021] Figure 2 It is a top view of an intelligent monitoring system for a prestressed cable structure according to Example 1 of the present invention.

[0022] Figure 3 It is a schematic diagram of the internal structure of a steel strand in an intelligent monitoring system for a prestressed cable structure according to embodiment 1 of the present invention.

[0023] Figure 4 This is a flow chart of an intelligent monitoring method for a prestressed cable structure according to Embodiment 2 of the present invention.

[0024] Among them, 1. optical fiber; 2. outer steel wire; 3. optical fiber protective shell; 4. data logger; 5. pigtail; 6. optical fiber hole; 7. pin shaft; 8. adjustable cable head; 9. screw; 10. fixed cable head; 11. protective sleeve; 12. adjustable anchor cup; 13. ordinary steel strand. DETAILED DESCRIPTION

[0025] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0026] Example 1 Reference Figure 1 , an intelligent monitoring system for a prestressed cable structure of this embodiment includes: A cable body and an intelligent monitoring module for monitoring the stress changes inside the cable body, wherein the cable body comprises a steel strand with a central steel wire removed and an optical fiber 1, wherein the optical fiber 1 is installed at the central steel wire position of the steel strand through an optical fiber protective shell, a buffer layer is provided between the optical fiber 1 and the inner wall of the optical fiber protective shell, and the optical fiber 1 is installed inside the optical fiber protective shell 3 through an elastic clamp to prevent the optical fiber 1 from being offset; The intelligent monitoring module includes a data collector 4 and an intelligent monitoring host. The data collector 4 is connected to the pigtail 5 of the cable body through a pigtail 5 lead-out component and is used to receive the optical signal transmitted by the optical fiber 1. The data collector 4 transmits the digital signal to the intelligent monitoring host through wireless communication.

[0027] Specifically, like Figure 3 As shown, the cable body of the present invention is formed by winding multiple bundles of steel strands. Ordinary steel strands 13 are used on the periphery of the cable body. The steel strands at the center of the cable body are processed. First, the steel strands without the central steel wire are prepared according to the design requirements. The steel strands are formed by winding or twisting a central steel wire and a peripheral steel wire 2 in a spiral twisted manner. The central steel wire is replaced with an optical fiber 1. The optical fiber protective shell 3 is processed and treated so that its inner diameter matches the outer diameter of the optical fiber 1, and a certain space is reserved for installing a buffer layer. The optical fiber protective shell 3 adopts a high-strength, corrosion-resistant material, polyetheretherketone, to ensure that it can effectively protect the optical fiber 1 in the cable body environment. An elastic clamp is prepared. The elastic clamp is made of polyoxymethylene material. One end is processed into an arc shape so as to fit tightly and fix with the optical fiber 1, and the other end is designed to be connected to the inner wall of the optical fiber protective shell 3. The buffer layer material is tubular or sheet-shaped. The buffer layer material in this embodiment is silicone, and then it is laid on the inner wall of the optical fiber protective shell 3 along the length direction of the optical fiber 1. Inside the optical fiber protective shell 3, elastic clips are installed at appropriate intervals along the axial direction of the optical fiber 1. The installation spacing of the clips is determined according to the length of the cable body and the stress conditions. A clip is installed every 0.5-2m. During installation, the connection structure of the clip is first aligned with the corresponding structure of the inner wall of the protective shell, and then the clip is fixed in the protective shell by pressing or snapping. In order to ensure that the optical fiber 1 is accurately positioned in the clip and is not damaged, a thin layer of rubber or silicone gasket is pasted on the inner wall of the clip to further prevent the optical fiber 1 from moving and to prevent the clip from scratching or indenting the surface of the optical fiber 1.

[0028] like Figure 1 , Figure 2As shown, the steel strand with the optical fiber 1 installed is connected with the adjustable cable head 8 and the fixed cable head 10, and the adjustable cable head 8 is installed at one end of the steel strand. The adjustable anchor cup 12 is first fixedly connected to the rear end of the steel strand, and the connection is ensured to be firm by extrusion or clamping. Then the screw 9 is screwed into the adjustable cable head 8, and the adjustable cable head 8 is connected to the adjustable anchor cup 12 through the screw 9. The position of the screw 9 is adjusted to make the adjustable cable head 8 in a suitable initial position. The fixed cable head 10 is installed at the other end of the steel strand to ensure that the fixed cable head 10 is tightly connected to the steel strand to prevent loosening. The pin shaft 7 is installed at the front end of the adjustable cable head 8 and the fixed cable head 10. The pin shaft 7 and the cable head are fixedly connected by bolts. Finally, the protective sleeve 11 of the lead-out component of the pigtail 5 is installed, and one end of the protective sleeve 11 is connected to the optical fiber protective shell 3 inside the cable body by a sealing connection method and applying sealant to ensure a tight connection to prevent moisture, dust, etc. from entering the cable body and affecting the performance of the optical fiber 1.

[0029] One end of the pigtail 5 is fused with the optical fiber 1 inside the cable body to ensure good connection quality and stable signal transmission. Then the pigtail 5 passes through the protective sleeve 11 and is led out from the other end of the protective sleeve 11. The lead-out length is determined according to the installation position of the actual monitoring equipment. The protective sleeve 11 is connected to one end of the cable body through the optical fiber hole 6 to ensure that the connection is firm and does not affect the structural performance of the cable body. The data collector 4 is installed at a suitable position near the lead-out end of the pigtail 5. The connector of the pigtail 5 is inserted into the optical interface of the data collector 4 for optical signal transmission. The optical signal is transmitted to the photoelectric conversion module. The photoelectric conversion module has a built-in photodiode. The photodiode works based on the internal photoelectric effect. When light is irradiated to its PN junction, an electron-air junction is generated. The photocurrent is amplified by the signal amplification component, wherein the signal amplification component includes a preamplifier and a main amplifier. The main amplifier is installed at the back end of the preamplifier. The preamplifier is used to perform preliminary amplification on the signal to improve the signal-to-noise ratio. The main amplifier further amplifies the signal to a suitable amplitude range for subsequent processing. Finally, the analog-to-digital converter converts the analog electrical signal after amplification and filtering into a digital signal, and then transmits the converted digital signal to the intelligent monitoring system host through the communication interface between the data acquisition device 4 and the intelligent monitoring system host.

[0030] The working principle of this embodiment is: When the cable body is affected by external loads or internal factors (such as steel wire breakage, aging, etc.) during use, the internal stress of the cable body changes. Since the optical fiber 1 is buried in the cable body and tightly combined with the cable body material, the stress change of the cable body will be transmitted to the optical fiber 1, causing the optical fiber 1 to generate strain. According to the photoelastic effect of the optical fiber 1, the strain of the optical fiber 1 will cause changes in its internal light propagation characteristics, such as changes in light wavelength and light intensity, thereby converting the stress change into a light signal change. The light signal is transmitted to the pigtail 5 through the optical fiber 1 and then transmitted to the data collector 4. After receiving the light signal, the data collector 4 converts the light signal into an electrical signal through the photoelectric conversion module, and then amplifies the electrical signal through the signal amplification component to improve the signal quality. Then, the analog-to-digital conversion module converts the analog electrical signal into a digital signal. The converted digital signal is transmitted to the host of the intelligent monitoring system for subsequent calculation and analysis to determine the health status of the cable body and realize fault warning and early diagnosis.

[0031] Example 2 The difference between this embodiment and embodiment 1 is that this embodiment provides an intelligent monitoring method for a prestressed cable structure, including: Obtain digital signals and real-time ambient temperature, and calculate the impact of temperature changes on optical fiber sensing signals based on the relationship model between optical fiber sensing characteristics and temperature; Based on the influence amount of the optical fiber sensing signal, the change of the optical fiber sensing signal caused by the temperature change is corrected; Feature extraction is performed based on the corrected data, and the extracted feature vector dimensions are used to construct a cable body health assessment model; The cable body health assessment model is used to compare the feature vector with the preset normal state data features to calculate the assessment value of the cable body health status.

[0032] Specifically, like Figure 4 As shown, first, according to the specifications and characteristics of the optical fiber, its initial central wavelength is determined and thermo-optic coefficient , using the temperature sensor installed near the cable body to obtain the ambient temperature data in real time , where t represents time, calculate the temperature change , according to the calculation formula of the center wavelength drift: , in, is represented as the initial center wavelength, Expressed as the thermo-optical coefficient, It is expressed as the change in temperature. The center wavelength drift caused by temperature change is calculated. The center wavelength of the optical fiber will be affected by temperature change. According to the relationship model between the optical fiber sensing characteristics and temperature, the influence of temperature change on the center wavelength of the optical fiber can be calculated through known optical fiber characteristic parameters and real-time temperature data. This influence reflects the contribution of temperature change to the optical fiber sensing signal. In subsequent processing, it needs to be removed from the total signal to obtain a signal that more accurately reflects the change in cable body stress. The center wavelength drift caused by the calculated temperature change is Subtracting it from the original optical fiber sensing signal, we get the corrected optical fiber sensing signal, which mainly reflects the information of cable body stress change. Since the optical fiber sensing signal is disturbed by temperature change, the influence of temperature change on the central wavelength is subtracted to eliminate the influence of temperature factor on the signal.

[0033] The corrected optical fiber sensing signal data is subjected to feature extraction to obtain a feature vector. This embodiment extracts the mean, variance, maximum, minimum and change rate of the strain change, and these features are combined into a feature vector F: , in, is the mean of the feature, is the variance, and are the maximum and minimum values ​​of strain change, is the rate of change, and the input layer is constructed with the dimension of the feature vector as the number of nodes. If n features are extracted, the input layer has n nodes. The number of output layer nodes is determined according to the number of classifications of the health status of the cable body. In this embodiment, the health status of the cable body is divided into three categories: normal, slightly damaged, and seriously damaged. Then, the output layer has 3 nodes. A hidden layer is set between the input layer and the output layer. The number of hidden layers and the number of nodes can be selected by experimentally comparing the performance of neural networks with different structures. Start with one hidden layer, gradually increase the number of hidden layers and adjust the number of nodes to find the best network structure.

[0034] The historical monitoring data used include the data of the cable body in normal state and different damage degrees to train the neural network. The feature vector of a training sample is input into the neural network, from the input layer through the hidden layer, and finally reaches the output layer to obtain the predicted output of the model, and calculate the error between the predicted output and the actual cable body health value (label). The error function uses the mean square error function: , Where m is the number of samples, is the actual cable health value, It is the health value of the cable predicted by the model. The error of the output layer is back-propagated to the hidden layer and the input layer, and the contribution of each connection weight to the error is calculated. The connection weights between the layers are adjusted according to the contribution. The weight update formula uses the gradient descent method. For a connection weight, its update formula is: , in, is the learning rate, which controls the step size of weight update. After multiple rounds of iterative training, the connection weights are continuously adjusted so that the prediction error of the neural network for the training data is gradually reduced.

[0035] The extracted feature vectors and the preset normal state data features are normalized, and the feature values ​​are mapped to a specific interval through normalization operations. , if normalized to the interval [0, 1] is used, the calculation formula is: , in, and are the minimum and maximum values ​​of the feature in all training data respectively. Standardization converts the feature value into a distribution with a mean of 0 and a standard deviation of 1. The calculation formula is: , in, is the mean value of this feature, is the standard deviation, and the Euclidean distance calculation formula is used: , in, is the i-th eigenvalue in the eigenvector, is the i-th eigenvalue in the preset normal state data feature, n is the dimension of the feature vector, the Euclidean distance between the feature vector and the preset normal state data feature is calculated by the Euclidean distance calculation formula, and the distance threshold is set , the health status assessment value of the cable body is determined according to the distance value. When , the cable body is considered to be in good health, and the evaluation value can be set to a higher value (such as 0.8-1); When the distance increases, the evaluation value decreases, indicating that the health of the cable body is gradually getting worse. The specific mapping relationship between the evaluation value and the distance can be determined by experimental data according to the actual situation. Through standardization, different features can be made comparable to avoid the influence of factors such as different feature dimensions on the comparison results. The Euclidean distance is used to calculate the similarity between the feature vector and the preset normal state data feature. The smaller the distance, the more similar it is, and the health of the cable body is closer to normal. By setting the distance threshold, the distance can be converted into an evaluation value of the health of the cable body. The evaluation value can intuitively reflect the health of the cable body and provide a basis for judging whether the cable body is abnormal.

[0036] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring system for prestressed cable structures, characterized in that: include: A cable body and an intelligent monitoring module for monitoring the force changes inside the cable body, wherein the cable body comprises a steel strand with a central steel wire removed and an optical fiber, wherein the optical fiber is installed at the central steel wire position of the steel strand through an optical fiber protective shell, a buffer layer is provided between the optical fiber and the inner wall of the optical fiber protective shell, and the optical fiber is installed inside the optical fiber protective shell through an elastic clamp to prevent the optical fiber from being offset; The intelligent monitoring module includes a data collector and an intelligent monitoring host. The data collector is connected to the pigtail of the cable body through a pigtail lead-out component to receive optical signals transmitted by the optical fiber. The data collector transmits digital signals to the intelligent monitoring host through wireless communication.

2. The intelligent monitoring system for prestressed cable structure according to claim 1, characterized in that: The elastic clamp is made of polyoxymethylene material, one end of the elastic clamp is arc-shaped and fixedly connected to the optical fiber, and the other end of the elastic clamp is connected to the inner wall of the optical fiber protective shell.

3. The intelligent monitoring system for prestressed cable structure according to claim 1, characterized in that: One end of the steel strand is connected to an adjustable cable head, and the other end of the steel strand is connected to a fixed cable head. The front ends of the adjustable cable head and the fixed cable head are both provided with pin shafts.

4. The intelligent monitoring system for prestressed cable structure according to claim 3, characterized in that: An adjustable anchor cup is arranged between the adjustable cable head and the steel strand. The adjustable cable head is connected to the adjustable anchor cup through a screw rod. The rear end of the adjustable anchor cup is fixedly connected to the steel strand.

5. The intelligent monitoring system for prestressed cable structure according to claim 4, characterized in that: The pigtail lead-out component comprises a protective sleeve, one end of which is connected to the optical fiber protective shell inside the cable body, the other end of which leads out the pigtail, and the protective sleeve is connected to one end of the cable body through the optical fiber hole.

6. The intelligent monitoring system for prestressed cable structure according to claim 1, characterized in that: The data collector includes a photoelectric conversion module, a signal amplification component, and an analog-to-digital conversion module. The photoelectric conversion module converts the optical signal transmitted by the optical fiber into an electrical signal, the signal amplification component amplifies the electrical signal, and the analog-to-digital conversion module converts the amplified electrical signal into a digital signal.

7. An intelligent monitoring method for prestressed cable structures, characterized in that: include: Obtain digital signals and real-time ambient temperature, and calculate the impact of temperature changes on optical fiber sensing signals based on the relationship model between optical fiber sensing characteristics and temperature; Based on the influence amount of the optical fiber sensing signal, the change of the optical fiber sensing signal caused by the temperature change is corrected; Feature extraction is performed based on the corrected data, and the extracted feature vector dimensions are used to construct a cable body health assessment model; The cable body health assessment model is used to compare the feature vector with the preset normal state data features to calculate the assessment value of the cable body health status.

8. The intelligent monitoring method for prestressed cable structure according to claim 7, characterized in that: The calculation of the influence of temperature change on the optical fiber sensing signal includes determining the initial central wavelength and the thermo-optic coefficient according to the optical fiber characteristics, calculating the temperature change using the temperature data, and calculating the central wavelength drift caused by the temperature change using the central wavelength, the thermo-optic coefficient and the temperature change. The central wavelength drift calculation formula is: ， in, is represented as the initial center wavelength, Expressed as the thermo-optical coefficient, Expressed as a change in temperature.

9. The intelligent monitoring method for prestressed cable structure according to claim 8, characterized in that: The method of constructing a cable body health assessment model using the extracted feature vector dimension includes constructing the cable body health assessment model using a neural network algorithm, constructing an input layer with the feature vector dimension as the number of nodes, constructing an output layer with the number of classifications of the cable body health status as the number of nodes, setting a hidden layer between the input layer and the output layer, and finally adjusting the connection weights of each layer through a back propagation algorithm to minimize the error.

10. An intelligent monitoring method for prestressed cable structure according to claim 9, characterized in that: The calculation of the evaluation value of the health status of the cable body includes standardizing the extracted feature vector and the preset normal state data feature, calculating the Euclidean distance between the feature vector and the preset normal state data feature using the Euclidean distance method, setting a distance threshold and determining the evaluation value of the health status of the cable body according to the distance threshold.

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