Ice cone monitoring method and system based on adhesive optical fiber

Abstract

The present invention discloses an ice cone monitoring method and system based on adhesive optical fiber, which relates to the fields of optical fiber sensing technology and building safety monitoring technology. The method comprises the steps of: using adhesive optical fiber to obtain temperature signals, strain signals and vibration signals on the surface of the eaves to be detected; constructing an ice cone growth model and the ice cone growth status; determining the risk index of the current risk indicator according to the real-time growth status of the ice cone; performing multi-parameter fusion analysis based on the risk index to evaluate the ice cone falling risk; making risk decisions and early warnings based on the falling risk results in combination with a dynamic threshold method, and regularly performing calibration and dynamic baseline correction based on the falling risk results. The present invention utilizes an adhesive optical fiber sensor to monitor the status of ice cones on the eaves in real time, and performs multi-parameter fusion analysis and anti-interference design based on ice cone influencing factors, thereby realizing real-time monitoring and risk early warning of ice cones on the eaves of buildings in cold regions.

Description

Technical Field

[0001] The present invention relates to the fields of optical fiber sensing technology and building safety monitoring technology, and in particular to an ice cone monitoring method and system based on adhesive optical fiber. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In cold regions, the formation of icicles on roof eaves during winter poses a serious threat to building structural safety and pedestrian safety. Traditional monitoring methods for icicle growth rely primarily on manual inspections or single temperature sensors. These methods have numerous limitations, including low efficiency, lack of real-time performance, and limited coverage. Manual inspection cycles are constrained by both weather conditions and human resources, typically limiting inspections to a limited number of key areas per day, making it difficult to effectively capture the dynamic process of icicle growth. In extreme weather conditions (such as blizzards or strong winds), inspections are often interrupted, resulting in blind spots and further increasing safety risks. Existing electronic temperature sensors (such as thermocouples and infrared sensors) are typically deployed only in localized areas of roof eaves, indirectly inferring icing risk by monitoring ambient temperature. However, when temperatures approach freezing, these sensors cannot accurately distinguish between dry and wet surfaces, easily misinterpreting the presence of condensation as ice formation, making it difficult to provide risk warnings. Furthermore, temperature sensors only provide ambient temperature data and cannot directly assess the actual weight, stress distribution, or bond strength of icicles to the building structure. This makes setting warning thresholds extremely difficult, leading to insufficient accuracy and reliability in warning systems. Therefore, traditional monitoring methods are insufficient to address the practical needs of icicles on eaves. There is an urgent need to develop more efficient, accurate, and reliable monitoring technologies to effectively protect the safety of buildings and pedestrians in cold regions.

[0004] In recent years, fiber optic sensing technology has been widely adopted in various fields due to its significant advantages, including resistance to electromagnetic interference, distributed measurement, high sensitivity, and resistance to harsh environments. For example, fiber optic sensing technology has demonstrated significant application value in monitoring large infrastructure such as bridges, tunnels, and oil and gas pipelines. However, existing fiber optic monitoring systems primarily focus on the deformation or temperature changes of large structures (such as bridges and pipelines). Their complex installation methods make them difficult to adapt to the confined spaces and complex surfaces of eaves. The unique characteristics of eaves icicle monitoring lie in the limited space and complex environment, placing higher demands on sensor size, installation methods, and measurement accuracy. Furthermore, existing applications of fiber optic sensing technology in building structural health monitoring primarily focus on monitoring stress, strain, or temperature of the entire structure, making it difficult to directly apply to eaves icicle monitoring.

[0005] In summary, how to overcome the defects of existing fiber optic sensing technology that is difficult to apply on eaves, conduct accurate quantitative assessment of ice cone risks, and realize dynamic monitoring and early warning of the entire process of ice cones has become a technical problem that needs to be solved urgently by existing technologies. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an ice cone monitoring method and system based on adhesive optical fiber, which uses adhesive optical fiber sensors to monitor the status of icicles on the eaves in real time, and performs multi-parameter fusion analysis and anti-interference design based on the factors affecting the icicles, so as to realize real-time monitoring and risk warning of icicles on the eaves of buildings in cold areas.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0008] A first aspect of the present invention provides an ice cone monitoring method based on a bonded optical fiber, comprising the following steps:

[0009] Adhesive optical fibers are used to obtain temperature, strain, and vibration signals from the surface of the roof to be inspected, and the collected signals are preprocessed.

[0010] An ice cone growth model is constructed based on the collected signals, and the ice cone growth status is monitored based on the ice cone growth model;

[0011] Select risk indicators and determine the risk index of the current risk indicator based on the real-time growth status of the ice cone;

[0012] Conduct multi-parameter fusion analysis based on risk index to assess ice screw fall risk;

[0013] Risk decisions and early warnings are made based on the fall risk results combined with the dynamic threshold method, and calibration and dynamic baseline corrections are performed regularly based on the fall risk results.

[0014] Furthermore, the specific steps of using adhesive optical fiber to obtain the temperature signal, strain signal and vibration signal of the eaves surface to be detected are as follows:

[0015] Design the fiber layout density and fiber Bragg grating node spacing;

[0016] According to the design results, optical fibers are attached to the surface of the eaves, and signals are collected based on changes in the wavelength of the optical fibers.

[0017] Furthermore, when pasting optical fibers on the eaves surface according to the design results, ensure that the pasting area is flat and clean, then apply the base adhesive and paste the optical fibers in an array to the lower surface of the eaves.

[0018] Furthermore, the specific steps of preprocessing the collected signals are as follows:

[0019] Using discrete wavelet denoising to capture ice cone micro-vibration;

[0020] The peak value of the ice cone point is further amplified through temperature-strain decoupling.

[0021] Furthermore, the specific steps of constructing the ice cone growth model based on the collected signals are as follows:

[0022] The ice cone freezing rate is used to reflect the ice layer expansion rate;

[0023] The strain mutation is used to reflect the severity of the local deformation of the optical fiber;

[0024] Quantify the vibration energy to describe the internal cracks in ice cones.

[0025] Furthermore, the specific steps for determining the risk index of the current risk indicator based on the real-time growth status of the ice cone are as follows:

[0026] Use the classic random forest algorithm to divide the risk index according to the risk indicators;

[0027] Calculate the risk index based on the real-time growth of the ice cone and adjust the risk weight adaptively;

[0028] Dynamically adjust the warning threshold according to different ambient temperatures.

[0029] Furthermore, the specific steps for regular calibration and dynamic baseline correction based on fall risk results are as follows:

[0030] Perform heating calibration on the fiber optic temperature sensor;

[0031] Set up a sliding window to dynamically update the ice-free period data baseline.

[0032] A second aspect of the present invention provides an ice cone monitoring system based on adhesive optical fiber, comprising:

[0033] The data acquisition module is configured to acquire temperature signals, strain signals, and vibration signals of the surface of the eaves to be inspected using adhesive optical fibers, and pre-process the acquired signals;

[0034] a growth status monitoring module configured to construct an ice cone growth model according to the collected signals and monitor the growth status of the ice cone based on the ice cone growth model;

[0035] a risk index determination module configured to select a risk index and determine a risk index of a current risk index based on the real-time growth status of the ice cone;

[0036] A risk assessment module is configured to perform multi-parameter fusion analysis based on the risk index to assess the risk of ice pick falls;

[0037] The decision-making and warning module is configured to make risk decisions and warnings based on the fall risk results in combination with the dynamic threshold method, and to perform regular calibration and dynamic baseline correction based on the fall risk results.

[0038] A third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps of the ice cone monitoring method based on adhesive optical fiber as described in the first aspect of the present invention.

[0039] The fourth aspect of the present invention provides a device comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the ice cone monitoring method based on adhesive optical fiber as described in the first aspect of the present invention are implemented.

[0040] One or more of the above technical solutions have the following beneficial effects:

[0041] This invention discloses an ice cone monitoring method and system based on adhesive optical fibers. This method comprehensively considers the full-cycle requirements for eaves icicle monitoring, providing dynamic monitoring and early warning throughout the entire process from icicle formation and growth to detachment. By collecting temperature, strain, and vibration signals in real time through an adhesive optical fiber sensor network, and combining multi-parameter fusion analysis with machine learning algorithms, it enables precise quantitative assessment of icicle risk. Compared to traditional manual inspections or single-sensor monitoring methods, this method addresses issues such as monitoring fragmentation, delayed early warning, and high false alarm and omission rates, significantly improving monitoring efficiency and early warning accuracy, and providing reliable protection for building safety in cold regions.

[0042] This invention utilizes the convenient deployment and flexible design of adhesive-type optical fibers to adapt to the complex surfaces of various roof structures, eliminating the need for drilling or mechanical fastening, significantly reducing installation costs and construction difficulty. Furthermore, the optimized design of the waterproof adhesive layer and protective coating ensures the long-term stability of the optical fiber in low-temperature, high-humidity environments, addressing the pain points of traditional monitoring equipment, which are susceptible to environmental interference and have high maintenance costs, and has broad application prospects.

[0043] This invention utilizes a coordinated mechanism of heating calibration and baseline correction to regularly eliminate sensor zero drift, dynamically update ice-free period data baselines, and adapt to gradual environmental and seasonal changes, ensuring the long-term reliability of monitoring data. Furthermore, multi-sensor data fusion and anomaly detection mechanisms effectively enhance the system's anti-interference capabilities and fault tolerance, preventing monitoring interruptions caused by single-point failures and ensuring continuous and stable system operation.

[0044] This invention utilizes a random forest classifier and a dynamic threshold adjustment mechanism to achieve intelligent, graded early warning of ice cone risk. Adaptive adjustment of warning thresholds based on ambient temperature avoids the limitations of traditional methods that rely on fixed thresholds, significantly reducing false alarm and missed alarm rates. Furthermore, warning information is delivered in real time via audio and visual alarms and wireless communication modules, enabling remote monitoring and rapid response, providing a highly effective technical approach for building safety management.

[0045] This method improves the accuracy of risk prediction by constructing and optimizing an ice cone growth model, combining historical data with real-time monitoring feedback, and dynamically adjusting model parameters. Compared to traditional empirical threshold methods, this method is more scientific and adaptable, capable of handling complex and changing climate conditions, and provides an innovative solution for ice cone monitoring.

[0046] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0048] Figure 1 This is a flow chart of an ice cone monitoring method based on adhesive optical fiber in Example 1 of the present invention;

[0049] Figure 2 This is a cross-sectional view of the adhesive optical fiber in Example 1 of the present invention. DETAILED DESCRIPTION

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0051] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations;

[0052] Explanation of terms:

[0053] Adhesive fiber: A specialized optical fiber coated with a flexible adhesive layer, specifically designed for direct attachment to the surfaces of buildings and other structures. It is designed to enable real-time, continuous monitoring of distributed physical quantities such as temperature, strain, and vibration. The flexible adhesive layer allows the fiber to adhere tightly to the surface of the monitored object, accurately sensing changes in its physical state.

[0054] Fiber Bragg Grating (FBG): A periodic refractive index-modulated structure inscribed within the core of an optical fiber using an ultraviolet laser. Its operating principle is that the periodic arrangement of the grating reflects light of a specific wavelength, and this reflected wavelength shifts with changes in external physical quantities (such as temperature and strain). This property enables the FBG to convert changes in physical quantities into changes in light wavelength, thereby achieving high-precision sensing measurements.

[0055] Distributed Acoustic Sensing (DAS) uses phase changes in Rayleigh backscattered light within an optical fiber to detect vibration signals distributed along the fiber. By analyzing optical signal changes within the fiber, DAS technology can monitor minute vibrations along the fiber in real time. Its core principle is to use the fiber itself as a sensor, detecting vibration events along the fiber, enabling distributed monitoring over long distances.

[0056] Ice Cone Growth Model: A mathematical model based on thermodynamics and mechanics that describes the formation, growth, and shedding of icicles on roof eaves. This model comprehensively considers external factors such as ambient temperature, humidity, wind speed, and solar radiation, as well as internal factors such as the thermal conductivity of the roof surface, the weight distribution of the icicles, and stress changes.

[0057] Example 1:

[0058] The first embodiment of the present invention provides an ice cone monitoring method based on adhesive optical fiber, such as Figure 1 As shown, the following steps are included:

[0059] Step 1: Use adhesive optical fiber to obtain the temperature signal, strain signal and vibration signal of the eaves surface to be tested, and preprocess the collected signals.

[0060] Step 1.1: Use adhesive optical fiber to obtain the temperature signal, strain signal, and vibration signal of the eaves surface to be tested.

[0061] Step 1.1.1: Design the fiber density and FBG node spacing.

[0062] In a specific embodiment, the optical fiber deployment density is calculated based on the eave length and the maximum predicted length of the ice cone:

[0063] .

[0064] in, is the fiber optic laying density, is the length of the eaves; is the maximum predicted length of the ice cone, which is calculated based on historical meteorological data (such as snow accumulation, average daily temperature) and the eaves inclination angle; 0.8 is the redundancy factor, which ensures that there is a 20% overlap in the adjacent optical fiber coverage areas to avoid monitoring blind spots. Make corrections.

[0065] Step 1.2: Design the fiber Bragg grating (FBG) node spacing, determined by the spatial resolution requirements:

[0066] .

[0067] in, is the FBG node spacing, is the sampling frequency, is the speed of light, is the effective refractive index of the optical fiber.

[0068] Step 1.1.2: Attach optical fibers to the roof surface according to the design results and collect signals based on the changes in the optical fiber wavelength.

[0069] In a specific embodiment, when attaching optical fibers to the eaves surface according to the design results, ensure that the attachment area is flat and clean, without oil or other factors that affect the adhesion of the optical fibers. Then, apply the base adhesive and attach the optical fibers to the lower surface of the eaves in an array form (such as a V-shape or an S-shape). Figure 2 shown.

[0070] In this embodiment, flexible optical fibers are attached to the roof surface in an array, forming a dense monitoring grid. By employing fiber grating (FBG) or distributed acoustic sensing (DAS) technology, the sensor network can monitor temperature, strain, and vibration signals on the roof surface in real time. FBG technology uses changes in the reflected wavelength of the grating to detect subtle changes in temperature and strain, while DAS captures vibration signals by monitoring the propagation characteristics of sound waves along the optical fiber.

[0071] Step 1.2: Preprocess the acquired signal.

[0072] Step 1.2.1: Use discrete wavelet denoising to capture the ice cone micro-vibration, avoid signal aliasing, preserve the waveform characteristics without damaging the sudden change value.

[0073] In a specific implementation, discrete wavelet denoising is used for preprocessing, the wavelet basis is Db4, the number of decomposition layers is 5, and the 1-100 Hz frequency band signal is retained:

[0074] .

[0075] in, represents the processed waveform, is the wavelet basis function; is the scale factor, which is used to determine the noise reduction range; is the translation factor, used to correct the waveform.

[0076] Step 1.2.2: Use temperature-strain decoupling to correct the strain error caused by optical fiber thermal expansion and further amplify the peak value of the ice cone point.

[0077] In a specific embodiment, the temperature-strain decoupling formula is:

[0078] ,

[0079] .

[0080] in, is the corrected optical fiber strain; is the measured optical fiber strain; is the thermal expansion coefficient of the optical fiber, generally ; is the temperature change; is the wavelength offset; is the temperature sensitivity, generally .

[0081] Step 2: Construct an ice cone growth model based on the collected signals, and monitor the ice cone growth status based on the ice cone growth model.

[0082] Step 2.1: Construct an ice cone growth model based on the collected signals.

[0083] An ice cone growth model was constructed by combining freezing rate, sudden strain changes, and cumulative vibration energy changes to identify key characteristics of ice cone formation, growth, and shedding. The ice cone growth state was quantified using the ice cone growth model, and a vibration energy integration method was used to distinguish natural vibrations from risk signals. The ice cone state was described from three perspectives: thermodynamics, mechanics, and vibration characteristics, which together form a risk index.

[0084] Step 2.1.1: Use the ice cone freezing rate to reflect the ice expansion rate.

[0085] In a specific embodiment, the ice cone freezing rate is calculated to directly reflect the ice layer expansion rate:

[0086] ,

[0087] .

[0088] in, Indicates the ice cone freezing rate, It represents the rate of change of temperature with time; is the thermal conductivity of ice; is the latent heat of ice, generally ; It is one of the criteria for measuring the rate of ice expansion and the risk of ice cone falling off.

[0089] Step 2.1.2: Use the strain mutation to reflect the severity of the local deformation of the optical fiber.

[0090] In a specific embodiment, the strain mutation is calculated to reflect the severity of the local deformation of the optical fiber:

[0091] .

[0092] in, for The corrected strain at the moment; is the strain value at adjacent moments, It is the mutation coefficient, one of the criteria for measuring the risk of ice screw falling off.

[0093] Step 2.1.3: Quantify the vibration energy to describe the internal cracks in the ice cone.

[0094] In a specific embodiment, the vibration energy is quantified to describe the internal cracks of an ice cone:

[0095] .

[0096] in, is the Fourier transform of the moving signal, which represents the energy distribution of the signal in the frequency domain; Indicates the accumulated vibration energy and is one of the criteria for measuring the risk of ice screw falling.

[0097] Step 2.2: Monitor the ice cone growth based on the ice cone growth model. Specifically, monitor whether the ice layer expands, whether the optical fiber is locally deformed, and whether the accumulated vibration energy reaches a threshold, thereby monitoring the ice cone growth.

[0098] Step 3: Select a risk indicator and determine the risk index of the current risk indicator based on the real-time growth status of the ice cone.

[0099] This implementation prioritizes ice formation rate, taking into account mechanical and vibration characteristics. It assesses fall risk through multi-parameter fusion, combining it with a dynamic threshold method for decision-making. Furthermore, a random forest algorithm is used to classify monitoring data, enabling dynamic, graded warnings and adaptive weighting adjustments for ice cone risk.

[0100] Step 3.1: Select risk indicators.

[0101] This embodiment uses the ice layer expansion speed, the local deformation speed of the optical fiber and the accumulated vibration energy level as risk indicators.

[0102] Step 3.2: Determine the risk index of the current risk indicator based on the real-time growth status of the ice cone.

[0103] In a specific embodiment, the risk index is calculated and the risk weight is adaptively adjusted:

[0104] ,

[0105] .

[0106] in, represents the risk index, The weights corresponding to the ice layer expansion rate, mutation coefficient, and accumulated vibration energy are roughly set during the initial calculation and then adaptively adjusted. Indicates the The risk index prediction value of group data; Represents the true risk index, which is calculated by the classic random forest algorithm.

[0107] Step 3.3: Use the classic random forest algorithm to divide the risk index according to the risk indicators.

[0108] In a specific embodiment, the risk index is divided by the classic random forest algorithm, and the input features are: risk index , risk index change rate , vibration peak , the output is: the real risk index , used for weight adaptation described in step 3.2.

[0109] Step 3.4: Calculate the risk index based on the real-time growth status of the ice cone and adaptively adjust the risk weight.

[0110] Specifically, when Alarm the corresponding location. is the dynamic threshold; when When the data is used as feedback, the weights are corrected according to steps 3.2 and 3.3.

[0111] Step 3.5: Adjust the dynamic warning threshold according to different ambient temperatures.

[0112] In a specific embodiment, dynamic threshold adjustment is performed according to different ambient temperatures:

[0113] .

[0114] in, is the dynamic threshold, is the baseline threshold, set according to environmental conditions; It is a temperature sensitivity factor, which is set according to the sensitivity requirement of the early warning; is the current ambient temperature; The reference ambient temperature is 0°C (the ice melting limit temperature).

[0115] Step 4: Perform multi-parameter fusion analysis based on the risk index to assess the ice screw falling risk.

[0116] Step 5: Make risk decisions and early warnings based on the fall risk results in combination with the dynamic threshold method, and regularly perform calibration and dynamic baseline correction based on the fall risk results.

[0117] This embodiment eliminates sensor drift and environmental interference through regular calibration and dynamic baseline correction, ensures data reliability and warning accuracy, and ensures long-term stable operation of the monitoring system.

[0118] Step 5.1: Make risk decisions and early warnings based on the fall risk results combined with a dynamic threshold method. For example, use an audible and visual alarm to inform pedestrians on the roadside of the risk of falling icicles.

[0119] Step 5.2: Perform regular calibration and dynamic baseline correction based on fall risk results.

[0120] Step 5.2.1: Perform heating calibration on the fiber optic temperature sensor.

[0121] This embodiment eliminates zero drift and ensures temperature measurement accuracy by performing heating calibration on the optical fiber temperature sensor. Specifically, the zero drift of the temperature sensor is corrected by recording the FBG wavelength offset before and after heating.

[0122] Step 5.2.2: Set up a sliding window and dynamically update the ice-free period data baseline. The ice-free period data baseline is the data when there is no ice, which is used to compare with the data after ice formation to achieve ice cone monitoring.

[0123] In a specific embodiment, a sliding window (periodic calibration period) is set to dynamically update the ice-free period data baseline to eliminate the impact of gradual environmental changes (such as seasonal changes) on the monitoring results. The baseline calculation formula is as follows:

[0124] .

[0125] in, It is the variance baseline of the current sliding window of a certain monitoring data of the optical fiber; It is the variance baseline of a window of monitoring data of a certain optical fiber; is the variance of the current sliding window of a certain monitoring data of the optical fiber. It is the forgetting factor and needs to be adjusted according to different monitoring requirements.

[0126] Step 5.3: Calculate whether to start calibration; if Then start manual calibration; if Then the variance baseline is updated for subsequent calculations.

[0127] in, To calibrate the sensitivity coefficient, set it according to actual conditions.

[0128] This embodiment introduces an environmental noise filtering algorithm and a temperature-based strain compensation mechanism to eliminate the impact of interference factors such as wind and sunshine on the monitoring results.

[0129] In order to better illustrate the effect of the method of this embodiment, a commercial building in a northern city is used as an example for illustration:

[0130] Eaves length , install 13-channel adhesive fiber optic sensor ( ); the average daily temperature in winter is −5∘C, and the lowest temperature at night is −15∘C;

[0131] S1: Clean the eaves surface, apply the base adhesive, attach the optical fiber, and adjust the node spacing. .

[0132] S2: Temperature and strain signals are collected through fiber Bragg grating (FBG), and vibration signals are collected through distributed acoustic sensing (DAS), with a sampling frequency of 2kHz.

[0133] S3: Perform wavelet denoising and temperature-strain decoupling.

[0134] S4: Calculate the freezing rate :

[0135] Temperature change rate .

[0136] Ice contact area .

[0137] Freezing rate , indicating that the ice cone is growing slowly.

[0138] S5: Calculate the strain mutation coefficient , assuming , indicating that the deformation of the eaves has intensified.

[0139] S6: Calculate the accumulated vibration energy , assuming , indicating that the crack energy inside the ice cone is low.

[0140] S7: Calculate risk factor , assuming the weight factor They are 0.6, 0.3, 0.1 respectively. .

[0141] S8: Input random forest classification, assuming the output is low risk.

[0142] S9: Adjust the dynamic threshold, assuming ,but .

[0143] S10: Early warning execution, , no warning is triggered.

[0144] S11: Heating calibration, baseline correction.

[0145] Example 2:

[0146] A second embodiment of the present invention provides an ice cone monitoring system based on adhesive optical fibers, comprising:

[0147] The data acquisition module is configured to acquire temperature signals, strain signals, and vibration signals of the surface of the eaves to be inspected using adhesive optical fibers, and pre-process the acquired signals;

[0148] a growth status monitoring module configured to construct an ice cone growth model according to the collected signals and monitor the growth status of the ice cone based on the ice cone growth model;

[0149] a risk index determination module configured to select a risk index and determine a risk index of a current risk index based on the real-time growth status of the ice cone;

[0150] A risk assessment module is configured to perform multi-parameter fusion analysis based on the risk index to assess the risk of ice pick falls;

[0151] The decision-making and warning module is configured to make risk decisions and warnings based on the fall risk results in combination with the dynamic threshold method, and to perform regular calibration and dynamic baseline correction based on the fall risk results.

[0152] Example 3:

[0153] A third embodiment of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps of the ice cone monitoring method based on adhesive optical fiber as described in the first embodiment of the present invention are implemented.

[0154] Example 4:

[0155] Embodiment 4 of the present invention provides a device comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the ice cone monitoring method based on adhesive optical fiber as described in Embodiment 1 of the present invention are implemented.

[0156] The steps involved in the above embodiments 2, 3 and 4 correspond to those in the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of the embodiment 1.

[0157] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0158] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for monitoring ice cones based on adhesive optical fibers, characterized in that: The following steps are involved: Adhesive optical fibers are used to obtain temperature, strain, and vibration signals from the surface of the roof to be inspected, and the collected signals are preprocessed. The adhesive optical fibers are attached to the surface of the eaves in an array; An ice cone growth model is constructed based on the collected signals, and the ice cone growth status is monitored based on the ice cone growth model; The specific steps for constructing an ice cone growth model based on the collected signals are as follows: The ice cone freezing rate is used to reflect the ice layer expansion rate; The strain mutation is used to reflect the severity of the local deformation of the optical fiber; Quantify the vibration energy to describe the internal cracks of ice cones; The method of using the ice cone freezing rate to reflect the ice layer expansion rate includes: ， ； in, represents the ice contact area, It represents the rate of change of temperature with time; is the thermal conductivity of ice; is the latent heat of ice; is the FBG node spacing; The density of optical fiber layout; It is one of the criteria for measuring the rate of ice expansion and the risk of ice cone falling off. The use of sudden strain to reflect the severity of local deformation of optical fibers includes: ； in, for The corrected strain at the moment; is the strain value at adjacent moments, The mutation coefficient is one of the criteria for measuring the risk of ice screw falling; Quantification of vibrational energy to describe internal cracks in ice cones includes: ； in, is the Fourier transform of the moving signal, which represents the energy distribution of the signal in the frequency domain; Indicates the cumulative vibration energy, one of the criteria for measuring the risk of ice screw falling off; Select risk indicators and determine the risk index of the current risk indicator based on the real-time growth status of the ice cone; Conduct multi-parameter fusion analysis based on risk index to assess ice screw fall risk; Risk decisions and early warnings are made based on the fall risk results combined with the dynamic threshold method, and calibration and dynamic baseline corrections are performed regularly based on the fall risk results.

2. The ice cone monitoring method based on adhesive optical fiber according to claim 1, characterized in that: The specific steps for using adhesive optical fiber to obtain the temperature signal, strain signal, and vibration signal of the eaves surface to be tested are as follows: Design the fiber layout density and fiber Bragg grating node spacing; According to the design results, optical fibers are attached to the surface of the eaves, and signals are collected based on changes in the wavelength of the optical fibers.

3. The ice cone monitoring method based on adhesive optical fiber according to claim 2, characterized in that: When sticking optical fibers on the eaves surface according to the design results, ensure that the sticking area is flat and clean, then apply the base adhesive and stick the optical fibers in an array to the lower surface of the eaves.

4. The ice cone monitoring method based on adhesive optical fiber according to claim 1, characterized in that: The specific steps for preprocessing the collected signals are: Using discrete wavelet denoising to capture ice cone micro-vibration; The peak value of the ice cone point is further amplified through temperature-strain decoupling.

5. The ice cone monitoring method based on adhesive optical fiber according to claim 1, characterized in that: The specific steps for determining the risk index of the current risk indicator based on the real-time growth status of the ice cone are as follows: Use the classic random forest algorithm to divide the risk index according to the risk indicators; Calculate the risk index based on the real-time growth of the ice cone and adjust the risk weight adaptively; Dynamically adjust the warning threshold according to different ambient temperatures.

6. The ice cone monitoring method based on adhesive optical fiber according to claim 1, characterized in that: The specific steps for regular calibration and dynamic baseline correction based on fall risk results are as follows: Perform heating calibration on the fiber optic temperature sensor; Set up a sliding window to dynamically update the ice-free period data baseline.

7. An ice cone monitoring system based on adhesive optical fiber, characterized in that: include: The data acquisition module is configured to acquire temperature signals, strain signals, and vibration signals of the surface of the eaves to be inspected using adhesive optical fibers, and pre-process the acquired signals; The adhesive optical fibers are attached to the surface of the eaves in an array; a growth status monitoring module configured to construct an ice cone growth model according to the collected signals and monitor the growth status of the ice cone based on the ice cone growth model; The specific steps for constructing an ice cone growth model based on the collected signals are as follows: The ice cone freezing rate is used to reflect the ice layer expansion rate; The strain mutation is used to reflect the severity of the local deformation of the optical fiber; Quantify the vibration energy to describe the internal cracks of ice cones; The method of using the ice cone freezing rate to reflect the ice layer expansion rate includes: ， ； in, represents the ice contact area, It represents the rate of change of temperature with time; is the thermal conductivity of ice; is the latent heat of ice; is the FBG node spacing; The density of optical fiber layout; It is one of the criteria for measuring the rate of ice expansion and the risk of ice cone falling off. The use of sudden strain to reflect the severity of local deformation of optical fibers includes: ； in, for The corrected strain at the moment; is the strain value at adjacent moments, The mutation coefficient is one of the criteria for measuring the risk of ice screw falling; Quantification of vibrational energy to describe internal cracks in ice cones includes: ； in, is the Fourier transform of the moving signal, which represents the energy distribution of the signal in the frequency domain; Indicates the cumulative vibration energy, one of the criteria for measuring the risk of ice screw falling off; a risk index determination module configured to select a risk index and determine a risk index of a current risk index based on the real-time growth status of the ice cone; A risk assessment module is configured to perform multi-parameter fusion analysis based on the risk index to assess the risk of ice pick falls; The decision-making and warning module is configured to make risk decisions and warnings based on the fall risk results in combination with the dynamic threshold method, and to perform regular calibration and dynamic baseline correction based on the fall risk results.

8. A computer-readable storage medium, characterized in that A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing the ice cone monitoring method based on adhesive optical fiber according to any one of claims 1 to 6.

9. A terminal device, characterized in that: The invention comprises a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executed by the ice cone monitoring method based on adhesive optical fiber according to any one of claims 1 to 6.

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