Optical fiber passive voltage and current fault diagnosis method, device, equipment and medium

Abstract

The invention discloses an optical fiber passive voltage and current fault diagnosis method, device, equipment and medium, and relates to the technical field of fault diagnosis, and the method comprises the following steps: generating and transmitting a test signal at a transmitting end of an optical fiber communication system, converting the test signal into an electric signal, and carrying out the time domain and frequency domain analysis of the electric signal; performing feature extraction on the electric signal based on a multi-dimensional analysis algorithm to obtain an electric signal multi-dimensional feature data set; monitoring data and fault historical data corresponding to the multi-dimensional feature data set are acquired, a fault evaluation model is constructed, and a fault evaluation coefficient is generated; and obtaining the fault evaluation coefficient of each part of the power transmission line at the same time interval within a period of time, and if the difference value of the maximum and minimum fault evaluation coefficients of each part is greater than a preset threshold value, carrying out monitoring. According to the invention, the early warning of the fault is realized, the fault can be rapidly positioned when the fault occurs, and the reliability and stability of the system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and more specifically, to a method, device, equipment and medium for diagnosing passive voltage and current faults of optical fibers. Background Art

[0002] With the rapid development of modern communication technology, optical fiber communication has become the main means of information transmission due to its advantages of high speed, large capacity, long-distance transmission and low loss. In optical fiber communication networks, passive optical networks (PON) are widely used in access networks, metropolitan area networks and other scenarios due to their simple structure, easy expansion and maintenance. However, as the scale of PON networks continues to expand, the number of access nodes has also increased dramatically, which has brought unprecedented challenges to the stability and reliability of the network.

[0003] Optical fiber passive voltage and current faults are one of the common problems in PON networks. Since the voltage and current signals in the optical fiber network may be affected by many factors during the transmission process, such as optical fiber attenuation, connector loss, changes in ambient temperature, etc., the signal quality may be reduced or even cause failures. These faults will not only affect the normal communication of the network, but may also cause damage to the equipment and increase maintenance costs.

[0004] Traditional fiber optic fault diagnosis methods mainly rely on manual inspections and offline testing. Manual inspections require maintenance personnel to regularly go to the site to check the status of the equipment. This method is not only time-consuming and labor-intensive, but also difficult to detect potential fault hazards. Offline testing is a detection method performed after a device fails. Although it can locate the fault point, it cannot monitor and warn in real time, and it is impossible to take timely measures to avoid the occurrence of failures.

[0005] For example, the invention with announcement number: CN106646114A describes a distribution network fault diagnosis method, including the following steps: obtaining equipment status information and switch action information; collecting the current on the primary side of the transmission line, including the AC current and zero-sequence current on the primary side; comparing the collected AC current value and zero-sequence current value with the preset short-circuit fault current threshold and ground fault current threshold, respectively, and judging it as the current fault information; comparing the processed equipment status information with the preset threshold information of the distribution terminal, if it exceeds the set range, it is judged as an equipment fault; obtaining switch action information, comparing the switch action information with the current power supply information of the distribution network, if the switch action information is inconsistent with the power supply information of the power grid at this time, it is judged as a switch fault. The present invention can accurately locate the cause and fault point of the fault, reduce the time of maintenance power outage, improve equipment utilization and operation reliability, and reduce maintenance costs.

[0006] For example, the invention with announcement number: CN108414896A discloses a method for diagnosing power grid faults, which belongs to the technical field of power grid fault diagnosis. The power outage area is obtained from the protection and circuit breaker information, and the lines therein are taken as suspected fault lines, and their comprehensive currents are constructed as sample sequences; the DTW algorithm is used to obtain the DTW distance between the sample sequence of each suspected fault line and the reference sequence of the reference line outside the power outage area, and the horizontal difference is constructed; at the same time, the DTW algorithm is used to obtain the DTW distance between the sample sequences before and after the fault of each suspected fault line, and the vertical difference is constructed. The horizontal and vertical differences of N suspected fault lines are formed into a 2×N difference matrix, and the matrix is ​​clustered using an artificial fish swarm clustering algorithm to be divided into a fault class and a normal class. The class with a larger cluster center value is diagnosed as a fault class, and the line in the fault class is judged as a fault line. The present invention can accurately diagnose the fault line, is not affected by the fault location, transition resistance, fault type, etc., and has good performance.

[0007] The above disclosed technical solutions have at least the following technical problems: the voltage and current signals in the optical fiber network may be affected by various factors during the transmission process, and the existing optical fiber-based voltage and current monitoring methods, although with fast processing speed, may not be able to capture some subtle optical fiber faults or losses, leading to wrong diagnosis results or delayed fault repair time, and it is difficult to meet the needs of modern communication networks for fast and accurate fault diagnosis. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a fiber optic passive voltage and current fault diagnosis method, device, equipment and medium, which combine historical data with real-time monitoring fault diagnosis to solve the problems of insufficient accuracy, slow response and inaccurate diagnostic positioning in the fiber optic-based voltage and current monitoring method.

[0009] To achieve the above object, the present invention provides the following technical solutions: A method for diagnosing passive voltage and current faults in optical fiber comprises the following steps: generating and sending a test signal at a transmitting end of an optical fiber communication system, converting the test signal into an electrical signal, and performing time domain and frequency domain analysis on the electrical signal; extracting features of the electrical signal based on a multidimensional analysis algorithm to obtain a multidimensional feature data set of the electrical signal; acquiring monitoring data and fault history data corresponding to the multidimensional feature data set and constructing a fault assessment model to generate a fault assessment coefficient; dividing a power transmission line of the optical fiber communication system into several parts, respectively obtaining the fault assessment coefficients of the power transmission lines of each part at the same time interval within a period of time, and requiring monitoring if the difference between the maximum and minimum fault assessment coefficients of each part is greater than a preset threshold; acquiring several fault assessment coefficients of the power transmission line during the monitoring process, constructing a first data set, and re-judging the fault according to the mean and standard deviation of the first data set.

[0010] In a preferred embodiment, the test signal is generated and sent at the transmitting end of the optical fiber communication system, the test signal is converted into an electrical signal, and the electrical signal is analyzed in the time domain and frequency domain, specifically: at the transmitting end of the optical fiber communication system, a test signal containing specific voltage and current identifiers is generated according to the test requirements; the generated test signal is modulated onto a light source through a modulator, converted into an optical signal and transmitted to a receiving end through an optical fiber; a photodetector converts the received optical signal into an electrical signal; Based on the Nyquist sampling theorem, the electrical signal is sampled and the analog-to-digital conversion is used to convert the continuous electrical signal into a discrete digital signal. The amplitude, period, and phase information of the digital signal within time t are observed by an oscilloscope to identify the instantaneous changes, periodic characteristics, and emergencies of the digital signal. The time signal is converted into the amplitude and phase information of different frequency components through fast Fourier transform. The frequency components of the time signal and the amplitude corresponding to the frequency components are obtained by calculating the FFT of the time signal, and the energy distribution of the time signal at different frequencies is obtained. Bandpass filters, low-pass filters, and high-pass filters are used to suppress the frequency components of interference. The passive voltage and current faults of the optical fiber are preliminarily identified by combining the time domain analysis and frequency domain analysis results.

[0011] In a preferred embodiment, the feature extraction of the electrical signal based on the multidimensional analysis algorithm to obtain a multidimensional feature data set of the electrical signal is specifically as follows: based on a digital filter, the noise in the electrical signal is removed, and the electrical signal is adjusted to the same scale through minimum-maximum normalization; the electrical signal is decomposed into two dimensions, time domain and frequency domain, the electrical signal is decomposed at different scales, the local frequency information of the electrical signal is extracted, and the electrical signal is subjected to time-frequency spectrum decomposition to obtain the temporal local features of the electrical signal at different times and frequencies; the electrical signal is converted from the time domain to the frequency domain to extract the frequency component of the electrical signal; the electrical signal is subjected to spatial and frequency decomposition to extract the local spatial frequency features of the electrical signal and the electrical signal is filtered in the spatial and frequency domains to obtain the spatial local features of the electrical signal; the electrical signal is converted from the time-space domain to the time-frequency domain, and the signal is subjected to spatiotemporal image decomposition at the same time, and the time and space features are extracted through a feature extraction algorithm to obtain the spatiotemporal local features; the temporal local features, the spatial local features and the spatiotemporal local features are integrated to form a multidimensional feature data set.

[0012] In a preferred embodiment, the monitoring data includes a voltage deviation index and a current fluctuation index; the fault history data includes an abnormal impact index; the specific method for obtaining the voltage deviation index is as follows: real-time acquisition of the time domain data of the voltage signal to be analyzed, and displaying the acquired voltage data in the time domain; obtaining the voltage time domain waveform, and filtering out the part with the lowest fundamental frequency in the voltage time domain waveform based on a band-stop filter; performing a fast Fourier transform on the filtered voltage time domain waveform to obtain spectrum information; extracting the amplitude and period of each harmonic from the spectrum information; obtaining the amplitude, frequency and period of each harmonic in the voltage signal through a Fourier transform analysis method; calculating the effective value of each harmonic in the voltage signal in combination with the fundamental frequency, the amplitude of each harmonic, the harmonic period and the phase, and calculating the voltage deviation index based on a preset voltage deviation index formula.

[0013] In a preferred embodiment, the specific method for obtaining the abnormal impact index is as follows: the collected fault history data is processed in time series, and for each abnormal scenario, the instantaneous data of the fault occurrence is recorded in chronological order to obtain a number of time series data; according to the occurrence time of the fault event, the time series data is cut according to equal time differences, and the fault data in each period of time is used as a basic unit; the time series data is calibrated to distinguish between normal state, fault state and recovery state; basic events are constructed into a fault tree through logic gates and the time series data corresponding to multiple abnormal scenarios are mapped as basic events of the fault tree; abnormal events or system failures encountered by the entire system are taken as the top events of the fault tree; for each basic event, the probability of occurrence is assigned through historical data; according to the time series data, the influence coefficient of each event in the fault tree on the top event is calculated; based on the Bayesian network, the influence coefficient of each basic event is weighted and calculated to obtain the influence coefficient of the top event of the fault tree; the abnormal impact index is obtained by comprehensive calculation based on the weighted influence coefficients of each top event in multiple fault scenarios.

[0014] In a preferred embodiment, the method obtains several fault assessment coefficients of the transmission line during the monitoring process, constructs a first data set, and re-judges the fault based on the mean and standard deviation of the first data set, specifically: if the average value of the first data set is greater than the average reference threshold of the first data set, there is a fault in the monitored part and an alarm is issued; if the average value of the first data set is less than the average reference threshold of the first data set, and the standard deviation of the first data set is greater than a preset standard deviation reference threshold, continuous monitoring is performed; if the average value of the first data set is less than the average reference threshold of the first data set, and the standard deviation of the first data set is less than the preset standard deviation reference threshold, there is no fault risk.

[0015] The invention discloses a device for diagnosing a passive voltage and current fault of an optical fiber, comprising: a signal generating module, a feature extracting module, a fault assessment model building module, a fault assessment and monitoring triggering module and a fault re-judgment module; the signal generating module is used for generating and sending a test signal at a transmitting end of an optical fiber communication system, converting the test signal into an electrical signal, and performing time domain and frequency domain analysis on the electrical signal; the feature extracting module is used for performing feature extraction on the electrical signal based on a multi-dimensional analysis algorithm to obtain a multi-dimensional feature data set of the electrical signal; the fault assessment model building module is used for obtaining monitoring data and fault history data corresponding to the multi-dimensional feature data set and constructing a fault assessment model to generate a fault assessment coefficient; the fault assessment and monitoring triggering module is used for dividing a power transmission line of an optical fiber communication system into several parts, respectively obtaining the fault assessment coefficients of the power transmission lines of each part at the same time interval within a period of time, and monitoring is required if the difference between the maximum and minimum fault assessment coefficients of each part is greater than a preset threshold value; the fault re-judgment module is used for obtaining several fault assessment coefficients of the power transmission line during the monitoring process, constructing a first data set, and re-judging the fault according to the mean and standard deviation of the first data set.

[0016] An electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the optical fiber passive voltage and current fault diagnosis method.

[0017] A computer-readable storage medium stores a computer program, which implements a method for diagnosing optical fiber passive voltage and current faults when executed by a processor.

[0018] The technical effects and advantages of the optical fiber passive voltage and current fault diagnosis method, device, equipment and medium of the present invention are as follows: 1. The present invention extracts features from electrical signals through a multidimensional analysis algorithm, forming a multidimensional feature data set including temporal local features, spatial local features, and spatiotemporal local features. This multidimensional feature extraction method can comprehensively capture the features of electrical signals at different scales and in different fields, thereby more accurately identifying and locating faults in optical fiber passive voltage and current. Compared with a single-dimensional analysis method, multidimensional analysis can capture the various characteristics of electrical signals in more detail, avoid missing possible key signal features, and significantly improve the accuracy and reliability of fault diagnosis.

[0019] 2. The present invention adopts a real-time monitoring method to regularly obtain the fault assessment coefficient of the transmission line of the optical fiber communication system, and judge whether further monitoring is needed according to the fluctuation of the fault assessment coefficient. When the difference of the fault assessment coefficient of a certain part exceeds the preset threshold, the system will automatically trigger the monitoring process and continue to pay attention to potential faults. At the same time, the system will also construct a data set based on the fault assessment coefficients collected during the monitoring process, and re-judge the fault based on the mean and standard deviation of the data set. This real-time monitoring and intelligent early warning mechanism can detect and handle faults in a timely manner, avoiding losses caused by fault expansion, while also improving the work efficiency of operation and maintenance personnel and reducing operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a process flow of a method for diagnosing optical fiber passive voltage and current faults of the present invention; Figure 2 The present invention is a schematic structural diagram of an optical fiber passive voltage and current fault diagnosis device. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1, Figure 1 The present invention provides a method for diagnosing passive voltage and current faults of optical fibers, comprising the following steps: S1, generating and sending a test signal at a transmitting end of an optical fiber communication system, converting the test signal into an electrical signal, and performing time domain and frequency domain analysis on the electrical signal; The generating and sending of a test signal at the transmitting end of the optical fiber communication system, converting the test signal into an electrical signal, and performing time domain and frequency domain analysis on the electrical signal are specifically as follows: At the transmitting end of the optical fiber communication system, a test signal including specific voltage and current identifiers is generated according to the test requirements; The generated test signal is modulated onto the light source through a modulator, converted into an optical signal and transmitted to the receiving end through an optical fiber; The photodetector converts the received light signal into an electrical signal; The electrical signal is sampled based on the Nyquist sampling theorem, and the continuous electrical signal is converted into a discrete digital signal using an analog-to-digital converter (ADC); Based on the oscilloscope observation of the amplitude, period, and phase information of the digital signal within time t, the instantaneous changes, periodic characteristics, and sudden events of the digital signal can be identified; The time signal is converted into the amplitude and phase information of different frequency components through fast Fourier transform; By calculating the FFT of the time signal, the frequency components of the time signal and the amplitudes corresponding to the frequency components are obtained, and the energy distribution of the time signal at different frequencies is obtained; Use bandpass filters, lowpass filters, and highpass filters to suppress interfering frequency components; Combining the time domain analysis and frequency domain analysis results, the passive voltage and current faults of the optical fiber can be preliminarily identified.

[0023] S2, extracting features of the electrical signal based on a multi-dimensional analysis algorithm to obtain a multi-dimensional feature data set of the electrical signal; Multidimensional Analysis Algorithm usually refers to the technology and methods for analyzing data in multidimensional space, which is widely used in data science, machine learning, data mining, statistics and other fields. The goal of these algorithms is to extract valuable information by analyzing data of different dimensions to help better understand the structure, pattern and trend of the data. The core idea of ​​multidimensional analysis is to project data from high-dimensional space to reveal potential relationships and patterns.

[0024] Multi-dimensional analysis algorithms include: Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that maps data from a high-dimensional space to a new, unrelated low-dimensional space (i.e., principal component space) by performing a linear transformation on the data, so that each dimension in the new space is the direction with the largest variance.

[0025] Feature dimensionality reduction: reducing the dimension of data and retaining the main information of the data. It is often used in image processing, financial data analysis, etc.

[0026] Data visualization: Mapping high-dimensional data into 2D or 3D space for human understanding.

[0027] Factor Analysis: Factor analysis is a statistical method used to explore the potential factors behind multiple observed variables. It reduces the complexity by converting multiple observed variables into a few factors through dimensionality reduction.

[0028] Cluster Analysis: Clustering algorithms divide data into several groups (clusters) so that the similarity of data within the same group is as high as possible, while the similarity between different groups is as low as possible. Common clustering algorithms include K-means, hierarchical clustering, etc.

[0029] Multidimensional Scaling (MDS): MDS is an algorithm used to analyze the similarity or distance between objects. Its purpose is to represent the relative distance between high-dimensional data points in a low-dimensional space.

[0030] The feature extraction of the electrical signal based on the multi-dimensional analysis algorithm is performed to obtain a multi-dimensional feature data set of the electrical signal, specifically: The noise in the electrical signal is removed based on a digital filter, and the electrical signal is adjusted to the same scale through minimum-maximum normalization; Decompose the electrical signal into two dimensions: time domain and frequency domain, decompose the electrical signal at different scales, extract the local frequency information of the electrical signal, and perform time-frequency spectrum decomposition on the electrical signal to obtain the temporal local characteristics of the electrical signal at different times and frequencies; Convert the electrical signal from the time domain to the frequency domain and extract the frequency component of the electrical signal; Decompose the electrical signal in space and frequency, extract the local spatial frequency characteristics of the electrical signal, and filter the electrical signal in space and frequency domains to obtain the spatial local characteristics of the electrical signal; The electrical signal is converted from the time-space domain to the time-frequency domain, and the signal is decomposed into a time-space image, and the time and space features are extracted through a feature extraction algorithm to obtain the time-space local features; The temporal local features, spatial local features and spatiotemporal local features are integrated to form a multidimensional feature data set.

[0031] The method of extracting features from electrical signals based on a multi-dimensional analysis algorithm to obtain a multi-dimensional feature data set of electrical signals has many benefits for optical fiber passive voltage and current fault diagnosis, which are specifically reflected in the following aspects: Improve signal noise suppression and signal quality: Digital filter denoising: Through digital filter-based noise removal, interference and noise in electrical signals can be effectively removed. This is particularly important for the quality of signals in optical fiber communication systems, because noise and interference often affect the accuracy of fault diagnosis. Clear signals can provide a more accurate data basis for subsequent feature extraction. Min-max normalization: After normalizing the signal, it can ensure that the amplitudes of different signals are within the same scale, so that when comparing or analyzing multiple signals, there will be no errors due to different amplitudes, ensuring the effectiveness and comparability of feature extraction.

[0032] Multi-dimensional time domain, frequency domain and time-frequency domain analysis: Time domain and frequency domain decomposition: By decomposing electrical signals into two dimensions, time domain and frequency domain, the characteristics of the signal can be analyzed from different perspectives. The time domain signal reflects the process of current or voltage changing over time, while the frequency domain signal reveals the distribution of different frequency components in the signal. Through this decomposition, the characteristics of the electrical signal can be more fully understood, which helps to find potential signs of faults.

[0033] Time-frequency analysis can capture the changes of signals in time and frequency at the same time, and is particularly suitable for processing signals with time-varying and frequency characteristics, such as sudden faults or short-term pulses. This feature is particularly important when diagnosing instantaneous changes in optical fiber voltage and current.

[0034] Local frequency and time-frequency features: By extracting local frequency information and time-frequency spectrum, some weak but meaningful features in the signal can be captured, such as the mutation point when the fault occurs or the abnormal frequency fluctuation. This is very important for timely identification and location of faults.

[0035] Extraction of spatial and frequency features: Spatial and frequency decomposition: By decomposing the electrical signal in the spatial and frequency domains, the local characteristics of the signal at different spatial locations and in different frequency ranges can be extracted. This analysis method is significantly helpful for signal propagation and fault location in optical fiber communication systems, especially when the signal is unevenly distributed or has multi-path propagation.

[0036] Spatial local features: Spatial domain feature extraction can reveal the changes in electrical signals at different spatial locations, and further help identify the source of faults in the circuit, such as fiber breakage, poor contact, or cable aging.

[0037] Comprehensive analysis of spatiotemporal characteristics: Space-time image decomposition: By converting the electrical signal from the space-time domain to the time-frequency domain and performing space-time image decomposition, the complex characteristics of the signal in space and time can be revealed. This is particularly suitable for fault diagnosis tasks that require considering both temporal changes and spatial distribution.

[0038] Local spatiotemporal features: The extraction of spatiotemporal features helps to identify complex spatiotemporal fault patterns, such as delay changes or frequency offsets during signal propagation. Some faults in optical fiber communication systems may only occur within a specific time and space range. The combination of spatiotemporal features can improve the accuracy and sensitivity of fault diagnosis.

[0039] Integration and comprehensive analysis of multi-dimensional feature sets: Multi-dimensional feature integration: Combining time, space and spatiotemporal features to form a multi-dimensional feature data set makes fault diagnosis more comprehensive and can analyze signals from multiple dimensions. This method can not only detect conventional faults, but also discover some difficult-to-detect potential fault modes or early fault signs.

[0040] Improve diagnostic accuracy: Multi-dimensional analysis methods can capture the various characteristics of electrical signals in more detail, avoiding signal features that may be missed by single-dimensional analysis, thereby improving the accuracy and reliability of fault diagnosis.

[0041] By extracting features from electrical signals through multi-dimensional analysis algorithms, we can fully capture the features of electrical signals at different scales and in different fields, thereby better identifying and locating faults in passive voltage and current of optical fibers. This method not only improves the accuracy of fault diagnosis, but also enhances the system's ability to adapt to various complex signal changes, which is of great significance for ensuring the stable operation of optical fiber communication systems.

[0042] S3, obtaining monitoring data and fault history data corresponding to the multi-dimensional feature data set and building a fault assessment model to generate a fault assessment coefficient; The monitoring data includes a voltage deviation index and a current fluctuation index; the fault history data includes an abnormal impact index; The voltage deviation index is used to measure the voltage fluctuation degree of the power system or equipment to determine the severity of the voltage deviation. It reflects the stability and reliability of the voltage and is essential for the normal operation of the power system. When the voltage deviation index is high, it means that there is a large difference between the actual voltage and the rated voltage, which may cause unstable operation or even damage to the power equipment. Therefore, monitoring and controlling the voltage deviation index is of great significance to ensure the stable operation of the power system. By taking timely measures to adjust the voltage, the safety and reliability of the power system can be guaranteed, and potential safety hazards and economic losses can be avoided.

[0043] The specific method for obtaining the voltage deviation index is as follows: Acquire the time domain data of the voltage signal to be analyzed in real time, and display the acquired voltage data in the time domain; Obtain a voltage time domain waveform, and filter out the part with the lowest fundamental frequency in the voltage time domain waveform using a band-stop filter; Perform fast Fourier transform on the filtered voltage time domain waveform to obtain spectrum information; Extracting the amplitude and period of each harmonic from the spectrum information; Through Fourier transform analysis method, the amplitude, frequency and period of each harmonic in the voltage signal are obtained; The effective value of each harmonic in the voltage signal is calculated by combining the fundamental frequency, each harmonic amplitude, harmonic period and phase, and the voltage deviation index is calculated based on a preset voltage deviation index formula.

[0044] The specific calculation formula of the effective value of the harmonic is:

[0045] The specific calculation formula of the voltage deviation index is:

[0046] in, is the effective value of the harmonic, is the harmonic period, is the time domain waveform of the voltage signal, is the voltage deviation index, is the fundamental frequency, is the kth harmonic amplitude, is the kth harmonic phase, and q is the total number of harmonics.

[0047] The current fluctuation index is used to describe the degree of fluctuation or change of the current signal within a certain time range. It reflects the stability or fluctuation of the current and can be used to evaluate the change of the current in the electrical system. Calculated based on the change of the current within a certain period of time, this index can help evaluate whether there is excessive fluctuation or instability in electrical equipment or power systems.

[0048] The specific method for obtaining the current fluctuation index is as follows: Real-time current data within time t is collected and recorded in the form of time series signals; Calibrate the current data using a preset standard current value and remove abnormal current data; Integrate the calibrated current values ​​into a current set and extract a current mean value, a current standard value, and a peak current from the current set as current characteristic values; The current fluctuation index is calculated according to the current characteristic value based on a preset current fluctuation index calculation formula.

[0049] The specific calculation formula of the peak current is:

[0050] The specific calculation formula of the current fluctuation index is:

[0051] in, is the peak current, is the current fluctuation index, h is the number of current data sets collected within time t, is the i-th current data.

[0052] The abnormal impact index is a quantitative indicator used to measure the impact of abnormal conditions on the overall operation or surrounding environment of a device or system when a fault occurs. The index takes into account a variety of factors when a fault occurs, such as the duration of the fault, the severity of the fault, the scope of the fault spread, and the actual impact of the fault on production, safety, and the environment.

[0053] The specific method for obtaining the abnormal impact index is as follows: The collected fault history data is processed into time series. For each abnormal scenario, the instantaneous data of the fault occurrence is recorded in chronological order to obtain a number of time series data.

[0054] According to the occurrence time of the fault event, the time series data is cut according to the equal time difference, and the fault data in each period is taken as a basic unit; Calibrate time series data to distinguish normal state, fault state and recovery state; The basic events are constructed into a fault tree through logic gates and the time series data corresponding to multiple abnormal scenarios are mapped into the basic events of the fault tree; Take the abnormal event or system failure encountered by the entire system as the top event of the fault tree; For each basic event, the probability of occurrence is assigned through historical data; According to the time series data, the influence coefficient of each event on the top event in the fault tree is calculated.

[0055] Based on the Bayesian network, the influence coefficient of each basic event is weighted and calculated to obtain the influence coefficient of the top event of the fault tree; The abnormal impact index is obtained by comprehensively calculating the weighted impact coefficients of each top event in multiple fault scenarios.

[0056] The specific calculation formula of the abnormal impact index is:

[0057] in, is the abnormal impact index, m is the total number of fault scenarios, n is the total number of basic events in the fault tree, is the ith basic event in the fault tree, For basic events The probability of occurrence, For basic events The weight in the fault tree, is the fault duration of the basic event.

[0058] The fault assessment model is specifically:

[0059] in, is the fault assessment coefficient, is the voltage deviation index, is the current fluctuation index, is the abnormal impact index, is the preset voltage deviation exponential proportionality coefficient, is the preset current fluctuation index proportional coefficient, It is the preset abnormal impact index proportional coefficient.

[0060] S4, dividing the transmission line of the optical fiber communication system into several parts, respectively obtaining the fault assessment coefficient of each part of the transmission line at the same time interval within a period of time, and if the difference between the maximum and minimum fault assessment coefficients of each part is greater than a preset threshold, monitoring is required; Divide the transmission line into several parts according to the geographical layout, transmission distance, location of relay stations, and fiber type of the optical fiber communication system; Assign a unique identifier to each part; Determine an appropriate monitoring period, such as daily, weekly, or monthly, for regularly evaluating each part for failure.

[0061] In each monitoring cycle, the fault data of each part of the transmission line is collected, including the number of faults, duration, impact range, etc.

[0062] According to the collected fault data, the fault assessment coefficient of each section is calculated. The fault assessment coefficient can be a comprehensive indicator reflecting the fault severity and frequency of the transmission line in that section.

[0063] For each part, the difference between its maximum fault assessment coefficient and its minimum fault assessment coefficient within a monitoring period is calculated.

[0064] According to the actual situation of the system and operation and maintenance requirements, set a reasonable threshold to determine whether further monitoring of this part is needed.

[0065] The difference of each section is compared with the set threshold. If the difference is greater than the threshold, it indicates that the fault condition of the section is fluctuating greatly and further monitoring and maintenance may be required.

[0066] S5, obtaining several fault assessment coefficients of the transmission line during the monitoring process, constructing a first data set, and re-judging the fault according to the mean and standard deviation of the first data set.

[0067] The method of obtaining several fault assessment coefficients of the transmission line during the monitoring process, constructing a first data set, and re-judging the fault according to the mean and standard deviation of the first data set is specifically as follows: If the average value of the first data set is greater than the average reference threshold value of the first data set, then there is a fault in the monitoring part and an alarm is issued; If the average value of the first data set is less than the average reference threshold value of the first data set, and the standard deviation of the first data set is greater than the preset standard deviation reference threshold value, continuous monitoring is performed; If the average value of the first data set is less than the first data set average reference threshold, and the standard deviation of the first data set is less than a preset standard deviation reference threshold, there is no fault risk.

[0068] Embodiment 2, Figure 2 The present invention provides an optical fiber passive voltage and current fault diagnosis device, comprising: a signal generation module, a feature extraction module, a fault assessment model construction module, a fault assessment and monitoring trigger module and a fault re-judgment module; Signal generation module 201: used to generate and send a test signal at the transmitting end of the optical fiber communication system, convert the test signal into an electrical signal, and perform time domain and frequency domain analysis on the electrical signal; Feature extraction module 202: used to extract features of the electrical signal based on a multi-dimensional analysis algorithm to obtain a multi-dimensional feature data set of the electrical signal; Fault assessment model building module 203: used to obtain monitoring data and fault history data corresponding to the multi-dimensional feature data set and build a fault assessment model to generate a fault assessment coefficient; Fault assessment and monitoring trigger module 204: used to divide the transmission line of the optical fiber communication system into several parts, respectively obtain the fault assessment coefficient of each part of the transmission line at the same time interval within a period of time, and if the difference between the maximum and minimum fault assessment coefficients of each part is greater than a preset threshold, monitoring is required; Fault re-judgment module 205: used to obtain several fault assessment coefficients of the transmission line during the monitoring process, construct a first data set, and re-judge the fault according to the mean and standard deviation of the first data set.

[0069] The present invention also includes an electronic device, the electronic device comprising: At least one processor; and a memory in communication with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform a fiber optic passive voltage and current fault diagnosis method.

[0070] The present invention comprises a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, a method for diagnosing optical fiber passive voltage and current faults is implemented.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0072] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0073] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0074] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0075] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0076] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for diagnosing passive voltage and current faults in optical fiber, characterized in that: The following steps are involved: Generate and send a test signal at a transmitting end of an optical fiber communication system, convert the test signal into an electrical signal, and perform time domain and frequency domain analysis on the electrical signal; Extract features of electrical signals based on a multi-dimensional analysis algorithm to obtain a multi-dimensional feature data set of electrical signals; Obtain monitoring data and fault history data corresponding to the multidimensional feature data set and build a fault assessment model to generate a fault assessment coefficient; The transmission line of the optical fiber communication system is divided into several parts, and the fault assessment coefficients of each part of the transmission line at the same time interval within a period of time are obtained respectively. If the difference between the maximum and minimum fault assessment coefficients of each part is greater than a preset threshold, monitoring is required; A plurality of fault assessment coefficients of the transmission line during the monitoring process are obtained, a first data set is constructed, and the fault is re-judged according to the mean and standard deviation of the first data set.

2. A fiber optic passive voltage and current fault diagnosis method according to claim 1, characterized in that: The generating and sending of a test signal at the transmitting end of the optical fiber communication system, converting the test signal into an electrical signal, and performing time domain and frequency domain analysis on the electrical signal are specifically as follows: At the transmitting end of the optical fiber communication system, a test signal including specific voltage and current identifiers is generated according to the test requirements; The generated test signal is modulated onto the light source through a modulator, converted into an optical signal and transmitted to the receiving end through an optical fiber; The photodetector converts the received light signal into an electrical signal; The electrical signal is sampled based on the Nyquist sampling theorem, and the continuous electrical signal is converted into a discrete digital signal using analog-to-digital conversion; Based on the oscilloscope observation of the amplitude, period, and phase information of the digital signal within time t, the instantaneous changes, periodic characteristics, and sudden events of the digital signal can be identified; The time signal is converted into the amplitude and phase information of different frequency components through fast Fourier transform; By calculating the FFT of the time signal, the frequency components of the time signal and the amplitudes corresponding to the frequency components are obtained, and the energy distribution of the time signal at different frequencies is obtained; Use bandpass filters, lowpass filters, and highpass filters to suppress interfering frequency components; Combining the time domain analysis and frequency domain analysis results, the passive voltage and current faults of the optical fiber can be preliminarily identified.

3. A fiber optic passive voltage and current fault diagnosis method according to claim 2, characterized in that: The feature extraction of the electrical signal based on the multi-dimensional analysis algorithm is performed to obtain a multi-dimensional feature data set of the electrical signal, specifically: The noise in the electrical signal is removed based on a digital filter, and the electrical signal is adjusted to the same scale through minimum-maximum normalization; Decompose the electrical signal into two dimensions: time domain and frequency domain, decompose the electrical signal at different scales, extract the local frequency information of the electrical signal, and perform time-frequency spectrum decomposition on the electrical signal to obtain the temporal local characteristics of the electrical signal at different times and frequencies; Convert the electrical signal from the time domain to the frequency domain and extract the frequency component of the electrical signal; Decompose the electrical signal in space and frequency, extract the local spatial frequency characteristics of the electrical signal, and filter the electrical signal in the space and frequency domains to obtain the spatial local characteristics of the electrical signal; The electrical signal is converted from the time-space domain to the time-frequency domain, and the signal is decomposed into a time-space image, and the time and space features are extracted through a feature extraction algorithm to obtain the time-space local features; The temporal local features, spatial local features and spatiotemporal local features are integrated to form a multidimensional feature data set.

4. A fiber optic passive voltage and current fault diagnosis method according to claim 3, characterized in that: The monitoring data includes a voltage deviation index and a current fluctuation index; the fault history data includes an abnormal impact index; The specific method for obtaining the voltage deviation index is as follows: Acquire the time domain data of the voltage signal to be analyzed in real time, and display the acquired voltage data in the time domain; Obtain a voltage time domain waveform, and filter out the part with the lowest fundamental frequency in the voltage time domain waveform using a band-stop filter; Perform fast Fourier transform on the filtered voltage time domain waveform to obtain spectrum information; Extracting the amplitude and period of each harmonic from the spectrum information; Through Fourier transform analysis method, the amplitude, frequency and period of each harmonic in the voltage signal are obtained; The effective value of each harmonic in the voltage signal is calculated by combining the fundamental frequency, each harmonic amplitude, harmonic period and phase, and the voltage deviation index is calculated based on a preset voltage deviation index formula.

5. The optical fiber passive voltage and current fault diagnosis method according to claim 4, characterized in that: The specific method for obtaining the abnormal impact index is as follows: The collected fault history data is processed into time series. For each abnormal scenario, the instantaneous data of the fault occurrence is recorded in chronological order to obtain a number of time series data. According to the occurrence time of the fault event, the time series data is cut according to the equal time difference, and the fault data in each period is taken as a basic unit; Calibrate time series data to distinguish normal state, fault state and recovery state; The basic events are constructed into a fault tree through logic gates and the time series data corresponding to multiple abnormal scenarios are mapped into the basic events of the fault tree; Take the abnormal event or system failure encountered by the entire system as the top event of the fault tree; For each basic event, the probability of occurrence is assigned through historical data; According to the time series data, calculate the influence coefficient of each event on the top event in the fault tree; Based on the Bayesian network, the influence coefficient of each basic event is weighted and calculated to obtain the influence coefficient of the top event of the fault tree; The abnormal impact index is obtained by comprehensively calculating the weighted impact coefficients of each top event in multiple fault scenarios.

6. A fiber optic passive voltage and current fault diagnosis method according to claim 5, characterized in that: The method of obtaining several fault assessment coefficients of the transmission line during the monitoring process, constructing a first data set, and re-judging the fault according to the mean and standard deviation of the first data set is specifically as follows: If the average value of the first data set is greater than the average reference threshold value of the first data set, then there is a fault in the monitoring part and an alarm is issued; If the average value of the first data set is less than the average reference threshold value of the first data set, and the standard deviation of the first data set is greater than the preset standard deviation reference threshold value, continuous monitoring is performed; If the average value of the first data set is less than the first data set average reference threshold, and the standard deviation of the first data set is less than a preset standard deviation reference threshold, there is no fault risk.

7. A fiber optic passive voltage and current fault diagnosis method according to claim 6, characterized in that: The specific calculation formula of the abnormal impact index is: in, is the abnormal impact index, m is the total number of fault scenarios, n is the total number of basic events in the fault tree, is the ith basic event in the fault tree, For basic events The probability of occurrence, For basic events The weight in the fault tree, is the fault duration of the basic event.

8. A device using the optical fiber passive voltage and current fault diagnosis method according to any one of claims 1 to 7, characterized in that: include: Signal generation module, feature extraction module, fault assessment model building module, fault assessment and monitoring trigger module and fault re-judgment module; Signal generation module: used to generate and send a test signal at the transmitting end of the optical fiber communication system, convert the test signal into an electrical signal, and perform time domain and frequency domain analysis on the electrical signal; Feature extraction module: used to extract features of electrical signals based on a multi-dimensional analysis algorithm to obtain a multi-dimensional feature data set of electrical signals; Fault assessment model building module: used to obtain monitoring data and fault history data corresponding to the multi-dimensional feature data set and build a fault assessment model to generate fault assessment coefficients; Fault assessment and monitoring trigger module: used to divide the transmission line of the optical fiber communication system into several parts, and obtain the fault assessment coefficient of each part of the transmission line at the same time interval within a period of time. If the difference between the maximum and minimum fault assessment coefficients of each part is greater than the preset threshold, monitoring is required; Fault re-judgment module: used to obtain several fault assessment coefficients of the transmission line during the monitoring process, construct a first data set, and re-judge the fault according to the mean and standard deviation of the first data set.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the optical fiber passive voltage and current fault diagnosis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the optical fiber passive voltage and current fault diagnosis method according to any one of claims 1 to 7 is implemented.

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