Belt conveyor state monitoring method and system based on distributed optical fibers

Abstract

The invention discloses a belt conveyor state monitoring method and system based on distributed optical fibers, and relates to the technical field of state monitoring, and the method comprises the steps: projecting the sound field intensity to an optical fiber sampling point coordinate, and calculating the sound interference field intensity; calculating a phase change rate of a channel signal, calculating a space gradient of sound interference field intensity, constructing a joint phase field, generating a coherence tomography signal, performing time integration on the coherence tomography signal, and generating a fault point position; hilbert transformation is carried out on the intensity of the sound interference field, the dominant frequency energy is calculated, and state monitoring and anomaly classification are carried out on the belt conveyor. According to the method, sound field intensity calculation is combined with optical fiber sampling point projection, the signal spatial resolution and monitoring sensitivity are improved, the phase change rate is combined with the sound interference field spatial gradient, the space-time relevance of fault propagation characteristics is enhanced, and phase field construction is combined with coherence tomography signal generation. And the accuracy and anti-noise performance of fault identification are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of state monitoring, in particular to a belt conveyor state monitoring method and system based on distributed optical fiber. BACKGROUND

[0002] With the rapid development of industrial automation and intelligent manufacturing, the belt conveyor, as a key material conveying equipment in mines, ports, power plants and production lines, its running state safety and stability is directly related to the continuity and reliability of the entire production system. The monitoring technology for the running state of the belt conveyor has been continuously developed, from the traditional point-type sensing monitoring to the distributed and multi-parameter fusion online monitoring mode.

[0003] The existing distributed optical fiber monitoring technology still has some deficiencies in the application of the belt conveyor. The signal interpretation model is relatively single, often based on scattered light intensity change or spectrum energy threshold to judge faults, which is difficult to accurately reflect the complex acousto-optic coupling effect, resulting in insufficient monitoring sensitivity and spatial positioning accuracy, and the spatio-temporal correlation characteristics are not fully utilized. Traditional methods often only analyze the signal change in a single point or a single time window, ignoring the spatial coherence of fault propagation and its influence on the acoustic interference field, which is easy to cause misjudgment. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a belt conveyor state monitoring method and system based on distributed optical fiber, which solves the problem that the signal interpretation model is relatively single, often based on scattered light intensity change or spectrum energy threshold to judge faults, which is difficult to accurately reflect the complex acousto-optic coupling effect, resulting in insufficient monitoring sensitivity and spatial positioning accuracy, and the spatio-temporal correlation characteristics are not fully utilized. Traditional methods often only analyze the signal change in a single point or a single time window, ignoring the spatial coherence of fault propagation and its influence on the acoustic interference field, which is easy to cause misjudgment.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a belt conveyor state monitoring method based on distributed optical fiber, which comprises, Collecting running data and preprocessing, generating a set of grid points, calculating the propagation time delay of the grid points, generating the sound field intensity, projecting the sound field intensity to the optical fiber sampling point coordinates, and calculating the acoustic interference field intensity; Calculate the phase change rate of the channel signal, calculate the spatial gradient of the acoustic interference field intensity, construct the joint phase field, generate the coherent tomography signal, and perform time integration on the coherent tomography signal to generate the fault point position; The Hilbert transform is performed on the acoustic interference field intensity, the main frequency energy is calculated, the state of the belt conveyor is monitored and the abnormal classification is performed, and a visual interface is constructed to display the state monitoring and abnormal classification results.

[0007] As a preferred scheme of the belt conveyor state monitoring method based on the distributed optical fiber, the grid point set is generated, the propagation time delay of the grid point is calculated, the acoustic field intensity is generated, the acoustic interference field intensity is projected to the optical fiber sampling point coordinates, and the acoustic interference field intensity is calculated. In the monitoring area, the grid point set is generated by uniformly dividing along the x-axis and y-axis, the neighborhood radius is set using the proportion method, the Euclidean distance from the grid point to the optical fiber point is calculated, the grid point corresponding to the Euclidean distance smaller than the neighborhood radius is selected, and the neighborhood grid set is generated. Based on the echo signal, the propagation time delay of each grid point is calculated, and the acoustic field intensity is generated based on the propagation time delay. The acoustic field intensity is projected to the optical fiber sampling point coordinates, and the acoustic interference field intensity is calculated.

[0008] As a preferred scheme of the belt conveyor state monitoring method based on the distributed optical fiber, the phase change rate of the channel signal is calculated, the spatial gradient of the acoustic interference field intensity is calculated, the joint phase field is constructed, the coherent tomographic signal is generated, and the fault point position is generated by time integration, including: The phase angle of the channel signal is calculated, the phase change rate is calculated using the time derivative based on the phase angle, the spatial gradient of the acoustic interference field intensity is calculated, and the joint phase field is constructed based on the phase change rate and the spatial gradient of the acoustic interference field. Based on the acoustic interference field intensity, the refractive index disturbance is calculated, the coherent phase shift of the optical fiber signal is calculated based on the refractive index disturbance, and the coherent tomographic signal is calculated based on the joint phase field and the coherent phase shift. The coherent tomographic signal is time integrated to generate the tomographic intensity distribution along the optical fiber, the maximum value of the tomographic intensity distribution is found to generate the fault point position.

[0009] As a preferred scheme of the belt conveyor state monitoring method based on the distributed optical fiber, the Hilbert transform is performed on the acoustic interference field intensity, the main frequency energy is calculated, including: Based on the fault point position, the Hilbert transform is performed on the acoustic interference field intensity, and the instantaneous phase is extracted. The short-time Fourier transform is performed on the instantaneous phase to generate the time-frequency spectrum, the time-frequency spectrum is classified using the empirical rule to generate the frequency interval, and the frequency energy is calculated. The energy of the frequency is normalized, and the frequency component proportion is calculated.

[0010] As a preferred embodiment of the belt conveyor condition monitoring method based on distributed optical fiber described in this invention, the step of monitoring the condition and classifying anomalies of the belt conveyor includes: Statistical analysis is used to set a detection threshold, and to filter out the tomographic intensity distributions that are greater than the detection threshold, marking them as abnormal states; otherwise, they are marked as normal states. Based on frequency ranges, a percentile method is used to set a frequency range classification threshold. The frequency percentage is compared with the frequency range classification threshold to generate fault types. If the proportion of low frequency is greater than the low frequency classification threshold, it is judged as belt tear; if the proportion of medium frequency is greater than the medium frequency classification threshold, it is judged as idler jamming; if the proportion of high frequency is greater than the high frequency classification threshold, it is judged as belt misalignment; otherwise, it is judged as normal.

[0011] As a preferred embodiment of the belt conveyor condition monitoring method based on distributed optical fiber described in this invention, the step of constructing a visual interface to display the condition monitoring and anomaly classification results includes: Use the visualization tool Matplotlib to build a visualization interface to display the status monitoring and anomaly classification results in real time; Users who have passed real-name verification are allowed to view it.

[0012] As a preferred embodiment of the belt conveyor condition monitoring method based on distributed optical fiber described in this invention, the step of collecting and preprocessing operational data includes: While the belt conveyor is running, intelligent sensors are used to collect operating data, which is then processed for noise reduction and normalization. The intelligent sensors include strain and ultrasonic sensors; The operational data includes channel signals, echo signals, and coordinate data of fiber optic sampling points.

[0013] Secondly, the present invention provides a belt conveyor condition monitoring system based on distributed optical fiber, comprising, The data collection and processing module is used to collect runtime data and perform noise reduction and normalization processing. The grid projection module is used to generate a uniformly distributed set of grid points, generate the sound field intensity distribution of each grid point, project the sound field intensity onto the coordinate system of the fiber sampling point, and calculate the acoustic interference field intensity of the fiber point. A joint construction module is used to calculate the phase change rate and the spatial gradient of the acoustic interference field, which are combined to form a joint phase field. The coherent tomography signal is calculated by refractive index perturbation and coherent phase shift, forming the tomography intensity distributed along the optical fiber. The energy detection module is used to generate time spectrum, calculate the frequency component ratio, monitor the condition of the belt conveyor, and classify and identify abnormal types based on the frequency component ratio.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the belt conveyor condition monitoring method based on distributed optical fiber as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the belt conveyor condition monitoring method based on distributed optical fiber as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: This invention improves the spatial resolution and monitoring sensitivity of the signal by combining sound field intensity calculation with optical fiber sampling point projection; enhances the spatiotemporal correlation of fault propagation characteristics by combining phase change rate with acoustic interference field spatial gradient; and enhances the accuracy of fault identification and noise resistance by combining joint phase field construction with coherent tomography signal generation. Attached Figure Description

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

[0018] Figure 1 This is a flowchart of the belt conveyor condition monitoring method based on distributed optical fiber in Example 1.

[0019] Figure 2 This is a schematic diagram of the belt conveyor condition monitoring system based on distributed optical fiber in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a belt conveyor condition monitoring method based on distributed optical fiber, including the following steps: S1. Collect and preprocess the running data, generate a grid point set, calculate the propagation delay of the grid points, generate the sound field intensity, project the sound field intensity onto the coordinates of the fiber sampling points, and calculate the sound interference field intensity. Specifically, collecting and preprocessing runtime data includes: While the belt conveyor is running, intelligent sensors are used to collect operating data, which is then processed for noise reduction and normalization. The intelligent sensors include strain and ultrasonic sensors; The operational data includes channel signals, echo signals, and coordinate data of fiber optic sampling points.

[0024] Strain sensors can reflect changes in the mechanical stress of the belt in real time, while ultrasonic sensors can detect high-frequency sound signals generated by roller abnormalities, belt tears, or connection defects. Through normalization processing, signal deviations caused by differences in sensitivity, gain, or installation position between different sensing channels can be eliminated, making subsequent sound field calculations comparable and physically consistent.

[0025] Furthermore, a grid point set is generated, and the propagation delay of the grid points is calculated to generate the sound field intensity. The sound field intensity is then projected onto the coordinates of the fiber sampling points to calculate the acoustic interference field intensity, including: Within the monitoring area, the area is uniformly divided along the x-axis and y-axis to generate a set of grid points. The fiber spatial resolution is calculated by dividing by 2 using the proportional method, the neighborhood radius is set, the Euclidean distance from the grid point to the fiber point is calculated, and the grid point corresponding to the Euclidean distance smaller than the neighborhood radius is selected to generate a neighborhood grid set. Based on the echo signal, the propagation delay at each grid point is calculated using the following formula: , in From the transmitting unit to the grid point Then there's the sound wave propagation delay in the receiving unit. and These are the transmitting and receiving units, respectively. Speed ​​of sound; Based on the propagation delay, the echo time offset is corrected and then accumulated to generate the sound field intensity, as shown in the formula: , in Let X be the sound field intensity at grid point X at time t, and p and q be the indices of the transmitting and receiving units, respectively. The acoustic field intensity is projected onto the coordinates of the fiber optic sampling point to calculate the acoustic interference field intensity, using the following formula: , in Let be the intensity of the acoustic interference field at the fiber optic point at time t. Let k be the coordinates of the fiber sampling point. For the set of grids near the fiber optic point, This represents the area of ​​a grid cell.

[0026] By discretizing the monitoring area into a regular grid, uniform sampling of the conveyor belt's operating space is achieved, making the sound field distribution calculable and visualizeable. Setting a neighborhood radius makes the spatial mapping between the fiber optic sampling points and the sound wave propagation area closer, thereby reducing discretization errors and improving positioning accuracy. Calculating the Euclidean distance from the grid points to the fiber optic sampling points and filtering the neighborhood set allows for capturing the local propagation characteristics of sound wave energy around the fiber optic without increasing computational load, reflecting the spatial coupling characteristics of the acousto-optic response. Compared to traditional algorithms that assume a uniform medium or simplify the propagation distance, the propagation delay formula accurately reflects the geometric characteristics and time response differences of sound wave propagation. Time offset correction based on the propagation delay effectively compensates for phase shifts caused by multipath propagation of sound waves. By correcting and accumulating the phase of signals from multiple transmitting and receiving units, phase distortion can enhance the common vibration response, suppress uncorrelated noise, and improve the signal-to-noise ratio of the reconstructed sound field. The generated sound field intensity can reflect the sound energy distribution pattern of different parts during the operation of the belt conveyor in real time. By combining propagation delay correction and sound field superposition, the fuzzy superposition problem in traditional acoustic inversion is avoided, and spatial focusing of sound source information is achieved, laying the foundation for subsequent fiber response projection. This projection operation realizes the coupling between the sound field distribution and the fiber sampling coordinate system, giving the fiber's response to sound wave disturbances a spatial mapping meaning. By using the energy weighted summation of the neighborhood grid set, the interference enhancement effect of sound waves near the fiber can be simulated, improving the sensitivity to local stress fluctuations.

[0027] S2. Calculate the phase change rate of the channel signal, calculate the spatial gradient of the acoustic interference field intensity, construct the joint phase field, generate the coherent tomography signal, perform time integration on the coherent tomography signal, and generate the fault location. Specifically, the phase change rate of the channel signal is calculated, the spatial gradient of the acoustic interference field intensity is calculated, a joint phase field is constructed, a coherent tomography signal is generated, and the coherent tomography signal is integrated over time to generate the fault location, including: The formula for calculating the phase angle of the channel signal is: , in The phase angle represents the directional change of the polarization signal. and These are the horizontal and vertical polarization channel signals, respectively, with t representing time. Based on the phase angle, the rate of phase change is calculated using the time derivative, with the following formula: , in The fiber phase change rate reflects the dynamic phase disturbance caused by vibration. The spatial gradient of the acoustic interference field intensity is calculated using the following formula: , in The spatial gradient of the acoustic interference field intensity. For fiber optic points in time The intensity of the acoustic interference field, The fiber optic sampling spacing; Based on the phase change rate and the spatial gradient of the acoustic interference field, a joint phase field is constructed to represent the synergistic enhancement effect of the acousto-optic signals at the fault point, as shown in the formula: , in For joint phase field; Based on the acoustic interference field intensity, the refractive index perturbation is calculated using the following formula: , in The refractive index perturbation of the fiber point at time t is denoted by α, and the acousto-optic refractive index modulation coefficient is denoted by experimental calibration. Based on the refractive index perturbation, the coherent phase shift of the fiber optic signal is calculated using the following formula: , in For coherent phase shift, Where λ is the laser wavelength and O is the length of the fiber optic sampling unit; Based on the joint phase field and coherent phase shift, the coherent tomography signal is calculated using the following formula: , in This is a coherent tomography signal; Time integration of the coherent tomography signal generates the tomographic intensity distribution along the optical fiber, as shown in the formula: , in For chromatography intensity, The starting time of the integration. The length of the integration time window for the chromatographic intensity distribution is set using a sliding window. Find the fiber coordinates corresponding to the maximum value of the tomographic intensity distribution to generate the fault location.

[0028] The phase change rate is calculated by time derivative, which can accurately reflect the minute optical phase disturbances caused by mechanical vibration, impact or friction during the operation of the belt conveyor, and eliminate static stress interference. By calculating the sound field intensity gradient, concentrated areas or abrupt change points of sound energy can be identified. Gradient calculation is equivalent to high-pass filtering, which can amplify local changes, thereby achieving millimeter-level spatial anomaly detection accuracy. The gradient direction is consistent with the sound wave propagation direction, which can help determine the propagation path of the fault source and realize multi-point collaborative positioning. At the fault point, the sound energy is concentrated and the phase disturbance is significant. The multiplication of the two causes the response signal to have a local peak, forming obvious identification features. Compared with single-channel signal analysis, the introduction of spatial gradient components in the combined phase field realizes the fusion of dual features in the time domain and spatial domain. The coherent tomography signal comprehensively reflects the synergistic effect of refractive index disturbance and phase change rate caused by sound waves, and can realize quantitative mapping from sound energy to optical signal response. Through coherent superposition in time and space, a distribution map along the fiber length direction can be generated. Only coherent components are retained in the coherent tomography signal, which can effectively filter out pseudo signals caused by random scattering and light source fluctuations, and improve the reliability of monitoring.

[0029] Furthermore, a Hilbert transform is performed on the acoustic interference field intensity to calculate the dominant frequency energy, including: Based on the location of the fault point, a Hilbert transform is performed on the acoustic interference field intensity to extract the instantaneous phase, as shown in the formula: , in The instantaneous phase of the acoustic interference field intensity. Location of the fault. , respectively, are the Hilbert transform results of the acoustic interference field intensity, where j is the imaginary unit; Perform a short-time Fourier transform on the instantaneous phase to generate the time spectrum. Use empirical rules to classify the time spectrum to generate frequency ranges, including low-frequency, mid-frequency, and high-frequency ranges. Calculate the frequency energy using the following formula: , in The fault point is at frequency f, where the energy is... The integral time window length of the main frequency energy is given by f, where f is the frequency. The time-spectral intensity of the acoustic interference field; The energy of the frequency is normalized, and the proportion of the frequency component is calculated using the following formula: , in The fault point is at the frequency. The proportion of energy, The fault point is at the frequency. The main frequency energy, and These are the lowest and highest frequencies, based on the lower and upper limits of belt conveyor vibration settings.

[0030] Different types of faults have significant differences in frequency distribution. By analyzing the frequency proportion, the transformation from fixed-point detection to state classification can be realized, providing quantifiable indicators for intelligent monitoring. The Hilbert transform combined with STFT can handle non-stationary signals with constantly changing vibration frequencies during operation, ensuring the stability of spectrum analysis.

[0031] S3. Perform Hilbert transform on the acoustic interference field intensity, calculate the dominant frequency energy, perform condition monitoring and anomaly classification on the belt conveyor, and build a visual interface to display the condition monitoring and anomaly classification results; Specifically, condition monitoring and anomaly classification of belt conveyors include: The mean of the chromatographic intensity distribution is calculated by adding twice the standard deviation using statistical analysis. A detection threshold is set, and chromatographic intensity distributions that exceed the detection threshold are screened out and marked as abnormal; otherwise, they are marked as normal. Based on the frequency range, the percentage of frequency components is calculated using the percentile method to set the frequency range classification threshold. The frequency percentage is then compared with the frequency range classification threshold to generate the fault type. If the proportion of low frequency is greater than the low frequency classification threshold, it is judged as belt tear; if the proportion of medium frequency is greater than the medium frequency classification threshold, it is judged as idler jamming; if the proportion of high frequency is greater than the high frequency classification threshold, it is judged as belt misalignment; otherwise, it is judged as normal.

[0032] Hilbert transform can effectively extract the instantaneous phase change of a signal and is suitable for non-stationary characteristic signals whose frequency changes with load during belt conveyor operation. Different types of faults have different characteristic energy distributions in the frequency domain. The calculation of the main frequency energy can quantitatively distinguish different fault types such as belt tearing, idler jamming, and belt misalignment. Different fault types have obvious distribution characteristics in the spectrum. This method can automatically identify the fault category through energy proportion, avoiding reliance on human experience. The percentile method can dynamically adjust the threshold based on historical data. Through energy proportion analysis, the impact of signal amplitude fluctuations on the classification results is reduced, ensuring the robust performance of the system in noisy environments.

[0033] Furthermore, a visual interface is constructed to display the status monitoring and anomaly classification results, including: Use the visualization tool Matplotlib to build a visualization interface to display the status monitoring and anomaly classification results in real time; Users who have passed real-name verification are allowed to view it.

[0034] Real-time visualization allows maintenance personnel to intuitively understand abnormal situations and the status of the belt conveyor on the interface.

[0035] This embodiment also provides a belt conveyor condition monitoring system based on distributed optical fiber, including: The data collection and processing module is used to collect runtime data and perform noise reduction and normalization processing. The grid projection module is used to generate a uniformly distributed set of grid points, generate the sound field intensity distribution of each grid point, project the sound field intensity onto the coordinate system of the fiber sampling point, and calculate the acoustic interference field intensity of the fiber point. A joint construction module is used to calculate the phase change rate and the spatial gradient of the acoustic interference field, which are combined to form a joint phase field. The coherent tomography signal is calculated by refractive index perturbation and coherent phase shift, forming the tomography intensity distributed along the optical fiber. The energy detection module is used to generate time spectrum, calculate the frequency component ratio, monitor the condition of the belt conveyor, and classify and identify abnormal types based on the frequency component ratio.

[0036] This embodiment also provides a computer device applicable to the condition monitoring method of belt conveyor based on distributed optical fiber, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the condition monitoring method of belt conveyor based on distributed optical fiber proposed in the above embodiment.

[0037] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0038] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the distributed optical fiber-based belt conveyor condition monitoring method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0039] In summary, this invention improves signal spatial resolution and monitoring sensitivity by combining sound field intensity calculation with fiber optic sampling point projection, enhances the spatiotemporal correlation of fault propagation characteristics by combining phase change rate with acoustic interference field spatial gradient, and enhances fault identification accuracy and noise resistance by combining joint phase field construction with coherent tomography signal generation.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the condition of a belt conveyor based on distributed optical fiber, characterized in that: include, Collect and preprocess the running data to generate a grid point set, calculate the propagation delay of the grid points, generate the sound field intensity, project the sound field intensity onto the coordinates of the fiber sampling points, and calculate the sound interference field intensity. Calculate the phase change rate of the channel signal, calculate the spatial gradient of the acoustic interference field intensity, construct the joint phase field, generate the coherent tomography signal, perform time integration on the coherent tomography signal, and generate the fault location. The intensity of the acoustic interference field is subjected to Hilbert transformation to calculate the dominant frequency energy. The condition of the belt conveyor is monitored and anomalies are classified. A visual interface is constructed to display the results of condition monitoring and anomaly classification.

2. The belt conveyor condition monitoring method based on distributed optical fiber as described in claim 1, characterized in that: The process of generating a set of grid points, calculating the propagation delay of the grid points, generating the sound field intensity, projecting the sound field intensity onto the coordinates of the fiber optic sampling points, and calculating the acoustic interference field intensity includes: Within the monitoring area, the area is uniformly divided along the x-axis and y-axis to generate a set of grid points. The neighborhood radius is set using a proportional method, the Euclidean distance from the grid point to the fiber optic point is calculated, and the grid points corresponding to the Euclidean distances smaller than the neighborhood radius are selected to generate a neighborhood grid set. Based on the echo signal, the propagation delay of each grid point is calculated, and the sound field intensity is generated based on the propagation delay. The acoustic field intensity is projected onto the coordinates of the optical fiber sampling point to calculate the acoustic interference field intensity.

3. The belt conveyor condition monitoring method based on distributed optical fiber as described in claim 2, characterized in that: The calculation includes the phase change rate of the channel signal, the spatial gradient of the acoustic interference field intensity, the construction of a joint phase field, the generation of a coherent tomography signal, and the time integration of the coherent tomography signal to generate the fault location, including: Calculate the phase angle of the channel signal, calculate the phase change rate based on the phase angle using the time derivative, calculate the spatial gradient of the acoustic interference field intensity, and construct a joint phase field based on the phase change rate and the spatial gradient of the acoustic interference field. Based on the acoustic interference field intensity, the refractive index perturbation is calculated; based on the refractive index perturbation, the coherent phase shift of the fiber signal is calculated; based on the joint phase field and the coherent phase shift, the coherent tomography signal is calculated. The coherent tomography signal is integrated over time to generate the tomography intensity distribution along the optical fiber. The optical fiber coordinates corresponding to the maximum value of the tomography intensity distribution are then found to generate the location of the fault point.

4. The belt conveyor condition monitoring method based on distributed optical fiber as described in claim 3, characterized in that: The calculation of the dominant frequency energy by performing a Hilbert transform on the acoustic interference field intensity includes: Based on the location of the fault point, a Hilbert transform is performed on the intensity of the acoustic interference field to extract the instantaneous phase. Perform a short-time Fourier transform on the instantaneous phase to generate a time spectrum. Use empirical rules to classify the time spectrum, generate frequency intervals, and calculate the frequency energy. The energy of the frequency is normalized, and the proportion of the frequency component is calculated.

5. The belt conveyor condition monitoring method based on distributed optical fiber as described in claim 4, characterized in that: The condition monitoring and anomaly classification of the belt conveyor includes: Statistical analysis is used to set a detection threshold, and to filter out the tomographic intensity distributions that are greater than the detection threshold, marking them as abnormal states; otherwise, they are marked as normal states. Based on frequency ranges, a percentile method is used to set a frequency range classification threshold. The frequency percentage is compared with the frequency range classification threshold to generate fault types. If the proportion of low frequency is greater than the low frequency classification threshold, it is judged as belt tear; if the proportion of medium frequency is greater than the medium frequency classification threshold, it is judged as idler jamming; if the proportion of high frequency is greater than the high frequency classification threshold, it is judged as belt misalignment; otherwise, it is judged as normal.

6. The belt conveyor condition monitoring method based on distributed optical fiber as described in claim 5, characterized in that: The construction of a visual interface to display the status monitoring and anomaly classification results includes: Use the visualization tool Matplotlib to build a visualization interface to display the status monitoring and anomaly classification results in real time; Users who have passed real-name verification are allowed to view it.

7. The belt conveyor condition monitoring method based on distributed optical fiber as described in claim 6, characterized in that: The collection and preprocessing of operational data includes: While the belt conveyor is running, intelligent sensors are used to collect operating data, which is then processed for noise reduction and normalization. The intelligent sensors include strain and ultrasonic sensors; The operational data includes channel signals, echo signals, and coordinate data of fiber optic sampling points.

8. A belt conveyor condition monitoring system based on distributed optical fiber, based on the belt conveyor condition monitoring method based on distributed optical fiber according to any one of claims 1 to 7, characterized in that: include, The data collection and processing module is used to collect runtime data and perform noise reduction and normalization processing. The grid projection module is used to generate a uniformly distributed set of grid points, generate the sound field intensity distribution of each grid point, project the sound field intensity onto the coordinate system of the fiber sampling point, and calculate the acoustic interference field intensity of the fiber point. A joint construction module is used to calculate the phase change rate and the spatial gradient of the acoustic interference field, which are combined to form a joint phase field. The coherent tomography signal is calculated by refractive index perturbation and coherent phase shift, forming the tomography intensity distributed along the optical fiber. The energy detection module is used to generate time spectrum, calculate the frequency component ratio, monitor the condition of the belt conveyor, and classify and identify abnormal types based on the frequency component ratio.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the belt conveyor condition monitoring method based on distributed optical fiber as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the belt conveyor condition monitoring method based on distributed optical fiber as described in any one of claims 1 to 7.

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