Method, system and equipment for predicting service life of OPGW (Optical Fiber Composite Overhead Ground Wire) based on degradation evaluation

Abstract

The invention discloses an OPGW optical cable service life prediction method, system and equipment based on degradation evaluation, and belongs to the technical field of overhead transmission lines, the OPGW optical cable service life prediction method based on degradation evaluation comprises the following steps: selecting performance parameters which are related to OPGW optical cable service life and reliability and can be measured according to factors causing OPGW optical cable degradation failure; taking as a degradation characteristic parameter; acquiring actual operation data of the OPGW optical cable according to the degradation characteristic parameters, and extracting degradation amount data; and inputting the degradation amount data into a pre-established OPGW optical cable service life evaluation model to obtain an OPGW optical cable service life prediction value. According to the OPGW optical cable life cycle management system, an OPGW optical cable life cycle management system can be established, the service life of an old OPGW optical cable can be evaluated and predicted, the service life of a newly-built OPGW optical cable can also be evaluated and predicted, and an accurate basis is provided for safe operation, maintenance and replacement of an OPGW optical cable communication line.

Description

Technical Field

[0001] The invention belongs to the technical field of overhead power transmission lines, and in particular relates to a method, system and device for predicting the life of an OPGW optical cable based on degradation assessment. Background Art

[0002] There is no mature life assessment prediction method for OPGW (Optical Fiber Composite Overhead Ground Wire) cables at home and abroad. Optical cables are one of the main components of overhead power transmission lines and account for a large proportion of the investment in overhead power transmission line construction. They operate in the atmosphere all year round and are affected by meteorological conditions such as wind, ice, snow and temperature changes for a long time. They are subjected to changing tension and are also corroded by pollutants in the air.

[0003] OPGW must match the service life of the ground wire on the same tower, and there is currently no OPGW optical cable with such a long service life. From an economic point of view, if the service life of OPGW does not match that of other material components of the line, it will cause huge waste. For ultra-long-distance transmission lines, especially overhead communication lines, due to their own characteristics and limitations, on the premise of ensuring the reliable function of ultra-long-distance transmission lines, it is also necessary to save corridor resources, increase line transmission capacity, and reduce the overall cost of construction and operation. Since the service life of optical cables is usually related to several mechanical performance parameters of the operating environment parameters, the affected outer layer torsional toughness and comprehensive breaking force are important indicators affecting the service life. If the test data is used, the current state of the optical cable can be directly judged based on the test results. However, it is difficult to sample the optical cable in operation.

[0004] Common failure modes of OPGW optical cables include: (1) OPGW is damaged or broken due to tensile overload: The excessive tension caused by the tension elongation caused by strong winds and ice and snow causes the optical cable to sag too much or the optical fiber to break due to insufficient excess length. In recent years, extreme weather has occurred frequently, and natural disasters such as strong winds and ice have caused great damage to transmission lines. It is generally assumed that the ice at the highest point of the OPGW tower is heavier than the conductor. When heavy ice occurs, the operating tension of the OPGW is seriously overloaded, and the metal single wire undergoes irreversible plastic deformation or fracture. (2) OPGW is struck by lightning, causing the outer single wire to be damaged or broken: At present, OPGW mainly adopts the tower-by-tower grounding method. According to the theory of the lightning leader model, under this grounding method, opposite charges with opposite polarity to the leader will be induced on the OPGW, which will enhance the electric field between the two, making it easier for discharge to occur on the OPGW. OPGW is struck by instantaneous large current lightning, and multiple lightning strikes occur rapidly and concurrently, causing multiple melting and splashing of the outer single wire of the OPGW. In particular, OPGW with an aluminum alloy single wire outer layer is more susceptible to damage when struck by lightning. Summary of the invention

[0005] The purpose of the present invention is to address the problems in the above-mentioned prior art and provide a method, system and device for predicting the life of OPGW optical cables based on degradation assessment, so as to realize life assessment and prediction of old and newly built OPGW optical cables, and provide an accurate basis for the safe operation, maintenance and replacement of OPGW optical cable communication lines.

[0006] In order to achieve the above object, the present invention has the following technical solutions: In a first aspect, a method for predicting the life of an OPGW optical cable based on degradation assessment is provided, comprising: According to the factors causing degradation and failure of OPGW optical cables, performance parameters that are related to the life and reliability of OPGW optical cables and can be measured are selected as degradation characteristic parameters; Collect the actual operation data of OPGW optical cable according to the degradation characteristic parameters, and extract the degradation amount data; The degradation data is input into the pre-established OPGW optical cable life assessment model to obtain the OPGW optical cable life prediction value.

[0007] As a preferred solution, the performance parameters related to the life and reliability of the OPGW optical cable and capable of being measured include: trend parameters and fluctuation parameters with degradation trends, both of which are time-sensitive parameters.

[0008] As a preferred solution, the time-sensitive parameter is determined by calculating the range or standard deviation of the measurement data; Assume that the measured value of the parameter is ; Judgment basis 1: Calculate the range as follows , calculated as follows ; like , it means that the corresponding parameter is not sensitive to time; Judgment basis 2: The standard deviation is calculated as follows , calculated as follows ; like , it means that the corresponding parameter is not sensitive to time.

[0009] As a preferred solution, the trend parameter shows an increasing or decreasing trend with the test time, and the judgment basis is as follows: Assume that the measured value of the parameter is ; According to the measured data sequence, the unit time degenerate increment sequence is constructed as follows:

[0010] Calculate the mean of the degenerate increment sequence Y per unit time and standard deviation ; Based on mean and standard deviation Different parameter types are divided as follows: like , then the corresponding parameter type is fluctuation type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is a decreasing trend type; like , then the corresponding parameter type is a decreasing trend type; like , then the corresponding parameter type is a decreasing trend type; like , the corresponding parameter type is the decreasing trend type.

[0011] As a preferred solution, the fluctuation type parameter with degradation trend is judged as follows: According to whether the range changes over time, the fluctuation type parameters are divided into complete fluctuation type parameters and degenerate fluctuation type parameters; the range size of the complete fluctuation type parameters does not change over time, and the range size of the degenerate fluctuation type parameters changes over time; the discrimination steps include: Assume that the measured value of the parameter is , then the range sequence is constructed as follows:

[0012] If the range series is time-sensitive, the determination parameter has volatility degradation characteristics.

[0013] As a preferred solution, the OPGW optical cable life assessment model is constructed using a performance degradation model based on a Brownian motion wiener process.

[0014] As a preferred solution, in the OPGW optical cable life assessment model, the OPGW optical cable material degradation life T The distribution of is an inverse Gaussian distribution, and the corresponding distribution function and probability density function are:

[0015] OPGW optical cable material degradation life T The expectation and variance of are calculated by the following expressions:

[0016] In the formula, is the drift parameter, is the diffusion parameter, is the failure threshold, which is equal to the maximum upper limit of the OPGW optical cable material loss. t For the test time; Calculate drift parameters using the maximum likelihood method and diffusion parameters The estimated value of is calculated as follows: ,

[0017] In the formula, Indicates i The sample in The amount of degradation measured; , represents the measurement time interval; ,express The degradation amount at the moment The difference in degradation at each moment is equal to the degradation increment; Indicates i The number of measurements per sample; Indicates the sample number. ; represents the measurement time, ; If the performance degradation y The mean is the location parameter , standard deviation is the shape parameter The normal distribution of the corresponding life distribution and performance degradation distribution conforms to the following expression:

[0018] Using the maximum likelihood estimation method, we can calculate and The value of is calculated as follows: ,

[0019] In the formula, It is a sample i exist The amount of degradation at a moment; yes The mean of all sample degradation at the moment; Get Estimated value of the mean performance degradation at each moment , population standard deviation estimate , establish the degradation model of mean value and standard deviation of degradation, substitute it into the above formula to evaluate the average life and reliable life of OPGW optical cable materials; The expression of the life assessment model is:

[0020] Taking the logarithm of both sides of the expression of the life assessment model, we get:

[0021] In the formula, represents static fatigue parameters; represents a constant parameter; represents a constant applied stress; Different stress levels correspond to different average life values. is the horizontal axis, is the vertical axis fitting straight line, and the intercept of the straight line is The value of , the slope is Substituting the estimated value of into the above formula, we can get the estimated value of life span.

[0022] As a preferred solution, when the OPGW optical cable life assessment model calculates the predicted value of the OPGW optical cable life, an environmental coefficient is assigned to each OPGW optical cable, and the environmental coefficient takes the maximum value of the pollution coefficient and the meteorological coefficient, and the pollution coefficient is determined by salt pollution and material type; The establishment of the OPGW optical cable life assessment model takes into account the defect coefficient, which is determined by the defect level and the number of defects; each defect is classified and corresponds to each component according to the defect description; the defect level is obtained by multiplying the number of defect levels of various faults that have occurred in history by the corresponding defect level, and the defect level is accumulated; the number of defects corresponding to different defect coefficients is as follows: The defect coefficient is 1.0, and the minimum and maximum values ​​of the corresponding defect times are 0 and 0, respectively; The defect coefficient is 1.05, and the minimum value of the corresponding defect number is 0 and the maximum value is 1; The defect coefficient is 1.1, the minimum value of the corresponding defect number is 1 and the maximum value is 2; or, the minimum value of the corresponding defect number is 2 and the maximum value is 5; The defect coefficient is 1.2, and the minimum number of corresponding defects is 3 and the maximum number is 100; The state coefficient of the power optical cable corresponds to the defect coefficient. The operation and maintenance strategy under different state coefficients is formulated according to the following relationship: The status factor is 1.05, corresponding to continuous maintenance; The state coefficient is 1.1, which corresponds to broken strand repair; or, corresponds to increased fiber attenuation; The state factor is 1.2, which corresponds to excessive fiber attenuation.

[0023] In a second aspect, a system for predicting the life of an OPGW optical cable based on degradation assessment is provided, comprising: The degradation characteristic parameter selection module is used to select performance parameters related to the life and reliability of the OPGW optical cable and capable of being measured as degradation characteristic parameters according to the factors causing the degradation failure of the OPGW optical cable; An operation data acquisition module is used to collect actual operation data of the OPGW optical cable according to degradation characteristic parameters and extract degradation amount data; The life prediction module is used to input the degradation data into the pre-established OPGW optical cable life assessment model to obtain the OPGW optical cable life prediction value.

[0024] As a preferred solution, the degradation characteristic parameters selected by the degradation characteristic parameter selection module are trend type parameters and fluctuation type parameters with degradation trends, and both trend type parameters and fluctuation type parameters with degradation trends are time-sensitive parameters.

[0025] As a preferred solution, the OPGW optical cable life assessment model pre-established by the life prediction module is constructed using a performance degradation model based on the Brownian motion wiener process; in the OPGW optical cable life assessment model, the OPGW optical cable material degradation life T The distribution of is an inverse Gaussian distribution, and the corresponding distribution function and probability density function are:

[0026] OPGW optical cable material degradation life T The expectation and variance of are calculated by the following expressions:

[0027] In the formula, is the drift parameter, is the diffusion parameter, is the failure threshold, which is equal to the maximum upper limit of the OPGW optical cable material loss. t For the test time; Calculate drift parameters using the maximum likelihood method and diffusion parameters The estimated value of is calculated as follows: ,

[0028] In the formula, Indicatesi The sample in The amount of degradation measured; , represents the measurement time interval; ,express The degradation amount at the moment The difference in degradation at each moment is equal to the degradation increment; Indicates i The number of measurements per sample; Indicates the sample number. ; represents the measurement time, ; If the performance degradation y The mean is the location parameter , standard deviation is the shape parameter The normal distribution of the corresponding life distribution and performance degradation distribution conforms to the following expression:

[0029] Using the maximum likelihood estimation method, we can calculate and The value of is calculated as follows: ,

[0030] In the formula, It is a sample i exist The amount of degradation at a moment; yes The mean of all sample degradation at the moment; Get Estimated value of the mean performance degradation at each moment , population standard deviation estimate , establish the degradation model of mean value and standard deviation of degradation, substitute it into the above formula to evaluate the average life and reliable life of OPGW optical cable materials; The expression of the life assessment model is:

[0031] Taking the logarithm of both sides of the expression of the life assessment model, we get:

[0032] In the formula, represents static fatigue parameters; represents a constant parameter; represents a constant applied stress; Different stress levels correspond to different average life values. is the horizontal axis, is the vertical axis fitting straight line, and the intercept of the straight line is The value of , the slope is Substituting the estimated value of into the above formula, we can get the estimated value of life span.

[0033] As a preferred solution, when the life prediction module calculates the predicted value of the OPGW optical cable life through the OPGW optical cable life assessment model, an environmental coefficient is assigned to each OPGW optical cable, and the environmental coefficient takes the maximum value of the pollution coefficient and the meteorological coefficient, and the pollution coefficient is determined by salt pollution and material type; the establishment of the OPGW optical cable life assessment model takes into account the defect coefficient, and the defect coefficient is determined by the defect level and the number of defects; each defect is classified and corresponding to each component according to the defect description; the defect level is accumulated according to the number of defect levels of various faults that have occurred in history, multiplied by the corresponding defect level; The number of defects corresponding to different defect coefficients is as follows: The defect coefficient is 1.0, and the minimum and maximum values ​​of the corresponding defect times are 0 and 0, respectively; The defect coefficient is 1.05, and the minimum value of the corresponding defect number is 0 and the maximum value is 1; The defect coefficient is 1.1, the minimum value of the corresponding defect number is 1 and the maximum value is 2; or, the minimum value of the corresponding defect number is 2 and the maximum value is 5; The defect coefficient is 1.2, and the minimum number of corresponding defects is 3 and the maximum number is 100; The state coefficient of the power optical cable corresponds to the defect coefficient. The operation and maintenance strategy under different state coefficients is formulated according to the following relationship: The status factor is 1.05, corresponding to continuous maintenance; The state coefficient is 1.1, which corresponds to broken strand repair; or, corresponds to increased fiber attenuation; The state factor is 1.2, which corresponds to excessive fiber attenuation.

[0034] In a third aspect, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the OPGW optical cable life prediction method based on degradation assessment.

[0035] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the OPGW optical cable life prediction method based on degradation assessment is implemented.

[0036] Compared with the prior art, the first aspect of the present invention has at least the following beneficial effects: Strong wind, overload breakage caused by ice and lightning strikes are factors that cause OPGW optical cables to fail at one time. These factors are used as acceleration factors to evaluate the life of OPGW. The OPGW optical cable life prediction method based on degradation evaluation of the present invention focuses on the fatigue fracture of OPGW, aging damage of coating materials and electrochemical corrosion between different single-wire materials that cause OPGW performance degradation. According to the factors that cause OPGW optical cable degradation failure, performance parameters related to the life and reliability of OPGW optical cables and that can be measured are selected as degradation characteristic parameters. On the basis of the description of the evolution law of OPGW optical cable degradation, an OPGW optical cable life evaluation model is established. According to the performance requirements of OPGW optical cables, the time characteristics of OPGW optical cable performance that does not meet the requirements are estimated or predicted. The selected degradation characteristic parameter performance indicators have accurate definitions and can be measured. As the working or test time of OPGW optical cables increases, the degradation characteristic parameters have obvious trend changes, which can objectively reflect the performance status of OPGW optical cables. After determining the degradation characteristic parameters, the actual operation data of the OPGW optical cable is collected according to the degradation characteristic parameters, and the degradation data is extracted to obtain the physical quantity directly related to the degradation failure of the OPGW optical cable. Finally, the OPGW optical cable life prediction value can be obtained by using the pre-established OPGW optical cable life assessment model, which provides an accurate basis for the safe operation, maintenance and replacement of ultra-long-distance transmission lines, especially OPGW optical cable communication lines.

[0037] Furthermore, the method of the present invention integrates online monitoring technology, assigns an environmental coefficient to each OPGW optical cable, analyzes the weights of different environments in the state, and at the same time, the establishment of the OPGW optical cable life assessment model takes into account the defect coefficient, which is determined by the defect level and the number of defects. The full life cycle management concept is used to establish an OPGW optical cable full life cycle management system.

[0038] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 Flow chart of a method for predicting the life of an OPGW optical cable based on degradation assessment according to an embodiment of the present invention; Figure 2 A schematic diagram of a process for establishing an OPGW optical cable life assessment model according to an embodiment of the present invention; Figure 3 The reliability degradation trend curve of the OPGW optical cable according to the embodiment of the present invention over time; Figure 4 The embodiment of the present invention calculates and obtains the principle diagram of the predicted value of the OPGW optical cable life. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0042] See also Figure 1 The embodiment of the present invention provides a method for predicting the life of an OPGW optical cable based on degradation assessment, comprising: S101. According to the factors causing degradation and failure of the OPGW optical cable, performance parameters that are related to the life and reliability of the OPGW optical cable and can be measured are selected as degradation characteristic parameters; S102, collecting actual operation data of the OPGW optical cable according to the degradation characteristic parameters, and extracting degradation amount data; S103, inputting the degradation data into a pre-established OPGW optical cable life assessment model to obtain a predicted value of the OPGW optical cable life.

[0043] In the study of the life assessment of OPGW in the embodiment of the present invention, the effects of fatigue fracture, overload fracture and material aging are mainly considered. The specific fault phenomena are as follows: (1) Fatigue fracture of OPGW During long-term operation, under strong wind and vibration conditions, the OPGW fatigue resistance performance does not meet the requirements, resulting in permanent damage to the optical unit or optical fiber.

[0044] (2) Aging and damage of coating materials The relatively large heat generated by the power equipment itself, such as the large temperature rise caused by power loss and partial discharge, makes the temperature rise in the optical unit exceed the temperature that the optical fiber coating material can withstand and gradually ages.

[0045] (3) Electrochemical corrosion between different single-line materials causes OPGW performance degradation OPGW generally fills the gap between the stainless steel tube and the metal single wire with grease to prevent galvanic corrosion. However, after the optical cable has been in operation for a long time, especially in harsh environments, the performance of the anti-corrosion grease may become worse and worse, which directly affects the corrosion resistance of the OPGW optical cable and causes the mechanical and electrical properties of OPGW to continue to decline.

[0046] When evaluating the life of OPGW, the main failure causes considered are: strong wind vibration, fatigue fracture and aging caused by temperature rise. Therefore, when studying the life evaluation of OPGW, fatigue fracture of environmental wind dance, fatigue fracture of tensile bending stress and thermal aging can be considered as failure modes for studying the life evaluation of OPGW optical cables.

[0047] The embodiment of the present invention is based on the OPGW optical cable life prediction method of degradation assessment. By utilizing field tests and data, combined with the OPGW optical cable life assessment model, integrating online monitoring technology, analyzing the weights of different environments in the state, and utilizing the full life cycle management concept, an OPGW optical cable full life cycle management system is established.

[0048] The degradation-based life distribution modeling method is based on the description of the evolution law of product degradation. Assume that the product degradation amount is X and the time scale describing the product life is t. The degradation-based life distribution modeling and analysis mainly includes two parts: the modeling and analysis of the product degradation process and the modeling and analysis of the life distribution based on the degradation process and failure threshold.

[0049] The degradation process model describes the degradation amount X as a function of t, denoted as X(t). Due to the influence of many random factors such as product manufacturing, assembly, and external conditions, at any given time t, the product new energy X(t) is generally not a fixed constant, but a random variable. Therefore, the model used to describe the product degradation process is generally a random process model, denoted as . Establish the evolution law of product performance degradation over time, estimate or predict the time characteristics of product performance not meeting the requirements according to product performance requirements, and establish a probability model of this random time, which is actually the life distribution model in traditional reliability engineering.

[0050] In a possible implementation, before modeling the performance degradation in step S101, it is necessary to select measurable performance parameters that are closely related to life and reliability as degradation characteristic quantities based on the main factors causing product degradation failure, provide a method for determining the failure threshold or an interval estimation method based on test data and engineering experience, and classify the degradation characteristic quantities according to the properties of the parameters. In order to judge the degradation failure of the product, several main technical performance indicators that can reflect the health status of the product are usually selected as characteristic performance parameters. When one or more of these characteristic performance parameters exceed a certain threshold value (i.e., failure threshold), the product will fail due to degradation. The selection of characteristic performance parameters must meet two conditions: (1) The performance indicators as characteristic performance parameters must be accurately defined and measurable; (2) As the product's working or testing time increases, the characteristic performance parameters will have an obvious trend change, which can objectively reflect the performance status of the product.

[0051] See also Figure 2 ,When evaluating the reliability of a product, time sensitive parameters, trend ,parameters and fluctuation parameters with degradation trends are mainly considered. ,The following are their classification and discrimination methods.

[0052] 1) Judgment basis of time-sensitive parameters Assume that the measured value of the parameter is , parameters that are not sensitive to time do not change significantly as the test time increases, that is, each measurement value is approximately presented as a horizontal line. Therefore, by calculating the range and standard deviation of the measurement data, it is determined whether the measurement parameter is approximately a horizontal line to determine whether the parameter is a time-sensitive parameter. The following are two criteria for judgment. For time-insensitive parameters, since the parameters do not change with time, they are not considered during the evaluation.

[0053] Judgment basis 1: Calculate the range as follows , calculated as follows ; like , it means that the corresponding parameter is not sensitive to time; Judgment basis 2: The standard deviation is calculated as follows , calculated as follows ; like , it means that the corresponding parameter is not sensitive to time.

[0054] 2) Classification basis of trend parameters and fluctuation parameters Assume that the measured value of the parameter is , according to whether the measured value of the parameter has an obvious change trend (increasing or decreasing), the time-sensitive parameters are further divided into trend parameters and fluctuation parameters. Trend parameters show an increasing or decreasing trend with the test time, and fluctuation parameters show a fluctuating change with the test time, without an obvious increasing or decreasing trend.

[0055] According to the measured data sequence, the unit time degenerate increment sequence is constructed as follows:

[0056] Calculate the mean of the degenerate increment sequence Y per unit time and standard deviation ; Based on mean and standard deviation The different parameter types are shown in the following table:

[0057] 3) Degradation trend determination of fluctuation parameters For fluctuation type parameters, to determine whether the parameter has a degradation trend, the range is usually used as the main basis for analyzing the variation law of the fluctuation type parameters. According to whether the range changes over time, it is divided into complete fluctuation type parameters and degraded fluctuation type parameters. The range size of the complete fluctuation type parameter does not change over time, while the range size of the degraded fluctuation type parameter changes over time. The discrimination steps include: Step 1: Construct the range sequence. Suppose the measured value of the parameter is , then the range sequence can be constructed:

[0058] Step 2 If the range series is time-sensitive, the parameter is considered to have volatility-degenerate characteristics.

[0059] See also Figure 3 , Figure 3 The reliability degradation trend curve of OPGW optical cable over time is shown.

[0060] After determining the characteristic parameters, the data must be preprocessed and feature extracted. Due to the limitations of test methods and measurement equipment, the measured data needs to be preprocessed in order to remove anomalies, pollution, noise, etc., to facilitate the subsequent modeling process. In addition, due to the microscopic nature of the product degradation process, it is not always possible to obtain physical quantities directly related to product degradation failure during the degradation test process. Various data processing methods are needed to extract degradation data from the measured test data.

[0061] According to the statistical analysis and distribution test of the average fiber core loss of each line, it is used as the degradation characteristic parameter to describe the degradation of OPGW optical cable. The performance degradation model based on Wiener process is used to predict the life of OPGW optical cable.

[0062] The Wiener process, also known as the Brownian motion process, is a continuous-time random process.

[0063] A Gamma process is a continuous-time random process whose increments follow independent Gamma distributions.

[0064] In a possible implementation manner, in the OPGW optical cable life assessment model described in step S103, the OPGW optical cable material degradation life T The distribution of is an inverse Gaussian distribution, and the corresponding distribution function and probability density function are:

[0065] OPGW optical cable material degradation life T The expectation and variance of are calculated by the following expressions:

[0066] In the formula, is the drift parameter, is the diffusion parameter, is the failure threshold, which is equal to the maximum upper limit of the OPGW optical cable material loss. t For the test time; Calculate drift parameters using the maximum likelihood method and diffusion parameters The estimated value of is calculated as follows: ,

[0067] In the formula, Indicates i The sample in The amount of degradation measured; , represents the time interval of measurement; ,express The degradation amount at the moment The difference in degradation at each moment is equal to the degradation increment; Indicates i The number of measurements per sample; Indicates the sample number. ; represents the measurement time, ; If the performance degradation y The mean is the location parameter , standard deviation is the shape parameter The normal distribution of the corresponding life distribution and performance degradation distribution conforms to the following expression:

[0068] Using the maximum likelihood estimation method, we can calculate and The value of is calculated as follows: ,

[0069] In the formula, It is a sample i exist The amount of degradation at a moment; yes The mean of all sample degradation at the moment; Get Estimated value of the mean performance degradation at each moment , population standard deviation estimate , establish the degradation model of mean value and standard deviation of degradation, substitute it into the above formula to evaluate the average life and reliable life of OPGW optical cable materials; The expression of the life assessment model is:

[0070] Taking the logarithm of both sides of the expression of the life assessment model, we get:

[0071] In the formula, represents static fatigue parameters; represents a constant parameter; represents a constant applied stress; Different stress levels correspond to different average life values. is the horizontal axis, is the vertical axis fitting straight line, and the intercept of the straight line is The value of , the slope is Substituting the estimated value of into the above formula, we can get the estimated value of life span.

[0072] See also Figure 4 ,In a possible implementation method, when calculating the OPGW optical cable life prediction value using the OPGW optical cable life assessment model, an ,environmental coefficient is assigned to each OPGW optical cable, and the environmental coefficient consists of two parts: the first is the environmental pollution migration level, and the second is the local meteorological conditions.,The pollution migration coefficient and the meteorological coefficient are determined by the fuzzy comprehensive evaluation method, and the environmental coefficient is selected based on the worst environmental conditions, and the environmental coefficient takes the maximum value of the pollution coefficient and the meteorological coefficient.

[0073] The pollution factor is determined by salt contamination and material type, as shown in the following table:

[0074] The establishment of the OPGW optical cable life assessment model in the embodiment of the present invention takes into account the defect coefficient, which is determined by the defect level and the number of defects; the defect incidence rate can indicate the status of the equipment, and can also indicate the possibility of future defects or failures in the equipment. Classify each defect and correspond to each component according to the defect description. If there is no information in the defect description column, the defect will be ignored. According to the number of defect levels of various types of faults that have occurred in the past, multiply by the corresponding defect level, and accumulate to obtain the defect level; the calculation method is as follows: defect level = number of general defects × general defect base + number of severe defects × severe defect base + number of emergency defects × emergency defect base; the number of defects corresponding to different defect coefficients is as follows:

[0075] The OPGW optical cable status is shown in the following table:

[0076] According to the above-mentioned embodiment of the present invention, the OPGW optical cable life prediction method based on degradation assessment is actually applied, and the life prediction process of Tianmu 5484 line A line is shown in the following table, which shows the parameter estimation value and the life prediction value:

[0077] According to the average core loss data of Tianmu 5484 line A, the parameters of the performance degradation model based on the Wiener process are estimated, and the lifespans at reliability levels of 0.9, 0.8, and 0.7 are calculated. Therefore, the remaining lifespans of Tianmu 5484 line A at reliability levels of 0.9, 0.8, and 0.7 are 13, 15, and 16 years, respectively, with an average lifespan of 11 years.

[0078] Another embodiment of the present invention further provides an OPGW optical cable life prediction system based on degradation assessment, comprising: The degradation characteristic parameter selection module is used to select performance parameters related to the life and reliability of the OPGW optical cable and capable of being measured as degradation characteristic parameters according to the factors causing the degradation failure of the OPGW optical cable; An operation data acquisition module is used to collect actual operation data of the OPGW optical cable according to degradation characteristic parameters and extract degradation amount data; The life prediction module is used to input the degradation data into the pre-established OPGW optical cable life assessment model to obtain the OPGW optical cable life prediction value.

[0079] Furthermore, the degradation characteristic parameters selected by the degradation characteristic parameter selection module are trend type parameters and fluctuation type parameters with degradation trends, and both trend type parameters and fluctuation type parameters with degradation trends are time-sensitive parameters.

[0080] In a possible implementation, the OPGW optical cable life assessment model pre-established by the life prediction module is constructed using a performance degradation model based on the Brownian motion wiener process; in the OPGW optical cable life assessment model, the OPGW optical cable material degradation life T The distribution of is an inverse Gaussian distribution, and the corresponding distribution function and probability density function are:

[0081] OPGW optical cable material degradation life T The expectation and variance of are calculated by the following expressions:

[0082] In the formula, is the drift parameter, is the diffusion parameter, is the failure threshold, which is equal to the maximum upper limit of the OPGW optical cable material loss. t For the test time; Calculate drift parameters using the maximum likelihood method and diffusion parameters The estimated value of is calculated as follows: ,

[0083] In the formula, Indicates i The sample in The amount of degradation measured; , represents the measurement time interval; ,express The degradation amount at the moment The difference in degradation at each moment is equal to the degradation increment; Indicates i The number of measurements per sample; Indicates the sample number. ; represents the measurement time, ; If the performance degradation y The mean is the location parameter , standard deviation is the shape parameter The normal distribution of the corresponding life distribution and performance degradation distribution conforms to the following expression:

[0084] Using the maximum likelihood estimation method, we can calculate and The value of is calculated as follows: ,

[0085] In the formula, It is a sample i exist The amount of degradation at a moment; yes The mean of all sample degradation at the moment; Get Estimated value of the mean performance degradation at each moment , population standard deviation estimate , establish the degradation model of mean value and standard deviation of degradation, substitute it into the above formula to evaluate the average life and reliable life of OPGW optical cable materials; The expression of the life assessment model is:

[0086] Taking the logarithm of both sides of the expression of the life assessment model, we get:

[0087] In the formula, represents static fatigue parameters; represents a constant parameter; represents a constant applied stress; Different stress levels correspond to different average life values. is the horizontal axis, is the vertical axis fitting straight line, and the intercept of the straight line is The value of , the slope is Substituting the estimated value of into the above formula, we can get the estimated value of life span.

[0088] In a possible implementation, when the life prediction module calculates the predicted value of the OPGW optical cable life through the OPGW optical cable life assessment model, an environmental coefficient is assigned to each OPGW optical cable, and the environmental coefficient takes the maximum value of the pollution coefficient and the meteorological coefficient, and the pollution coefficient is determined by salt pollution and material type; the establishment of the OPGW optical cable life assessment model takes into account the defect coefficient, and the defect coefficient is determined by the defect level and the number of defects; each defect is classified and corresponds to each component according to the defect description; the defect level is accumulated according to the number of defect levels of various types of faults that have occurred in history, multiplied by the corresponding defect level; The number of defects corresponding to different defect coefficients is as follows: The defect coefficient is 1.0, and the minimum and maximum values ​​of the corresponding defect times are 0 and 0, respectively; The defect coefficient is 1.05, and the minimum value of the corresponding defect number is 0 and the maximum value is 1; The defect coefficient is 1.1, the minimum value of the corresponding defect number is 1 and the maximum value is 2; or, the minimum value of the corresponding defect number is 2 and the maximum value is 5; The defect coefficient is 1.2, and the minimum number of corresponding defects is 3 and the maximum number is 100; The state coefficient of the power optical cable corresponds to the defect coefficient. The operation and maintenance strategy under different state coefficients is formulated according to the following relationship: The status factor is 1.05, corresponding to continuous maintenance; The state coefficient is 1.1, which corresponds to broken strand repair; or, corresponds to increased fiber attenuation; The state factor is 1.2, which corresponds to excessive fiber attenuation.

[0089] Another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the OPGW optical cable life prediction method based on degradation assessment.

[0090] Another embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the OPGW optical cable life prediction method based on degradation assessment is implemented.

[0091] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. For ease of explanation, the above content only shows the part related to the embodiment of the present invention. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium is non-temporary and can be stored in a storage device formed by various electronic devices, which can realize the execution process recorded in the method of the embodiment of the present invention.

[0092] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the life of an OPGW optical cable based on degradation assessment, characterized in that: include: According to the factors causing degradation and failure of OPGW optical cables, performance parameters that are related to the life and reliability of OPGW optical cables and can be measured are selected as degradation characteristic parameters; Collect the actual operation data of OPGW optical cable according to the degradation characteristic parameters, and extract the degradation amount data; The degradation data is input into the pre-established OPGW optical cable life assessment model to obtain the OPGW optical cable life prediction value.

2. The OPGW optical cable life prediction method based on degradation assessment according to claim 1 is characterized in that: The performance parameters that are related to the life and reliability of the OPGW optical cable and can be measured include: trend type parameters and fluctuation type parameters with degradation trends, and both trend type parameters and fluctuation type parameters with degradation trends are time sensitive parameters.

3. The OPGW optical cable life prediction method based on degradation assessment according to claim 2 is characterized in that: The time-sensitive parameter is determined by calculating the range or standard deviation of the measurement data; Assume that the measured value of the parameter is ; Judgment basis 1: Calculate the range as follows , calculated as follows ; like , it means that the corresponding parameter is not sensitive to time; Judgment basis 2: The standard deviation is calculated as follows , calculated as follows ; like , it means that the corresponding parameter is not sensitive to time.

4. The OPGW optical cable life prediction method based on degradation assessment according to claim 2 is characterized in that: The trend type parameters show an increasing or decreasing trend with the test time, and the judgment basis is as follows: Assume that the measured value of the parameter is ; According to the measured data sequence, the unit time degenerate increment sequence is constructed as follows: Calculate the mean of the degenerate increment sequence Y per unit time and standard deviation ; Based on mean and standard deviation Different parameter types are divided as follows: like , then the corresponding parameter type is fluctuation type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is increasing trend type; like , then the corresponding parameter type is a decreasing trend type; like , then the corresponding parameter type is a decreasing trend type; like , then the corresponding parameter type is a decreasing trend type; like , the corresponding parameter type is the decreasing trend type.

5. The OPGW optical cable life prediction method based on degradation assessment according to claim 2 is characterized in that: The fluctuation type parameter with a degenerative trend is judged as follows: According to whether the range changes over time, the fluctuation parameters are divided into complete fluctuation parameters and degenerate fluctuation parameters; The range of the fully fluctuating parameter does not change with time, while the range of the degenerate fluctuating parameter changes with time. The identification steps include: Assume that the measured value of the parameter is , then the range sequence is constructed as follows: If the range series is time-sensitive, the determination parameter has volatility degradation characteristics.

6. The OPGW optical cable life prediction method based on degradation assessment according to claim 1 is characterized in that: The OPGW optical cable life assessment model is constructed using a performance degradation model based on a Brownian motion wiener process.

7. The OPGW optical cable life prediction method based on degradation assessment according to claim 6 is characterized in that: In the OPGW optical cable life assessment model, the OPGW optical cable material degradation life T The distribution of is an inverse Gaussian distribution, and the corresponding distribution function and probability density function are: OPGW optical cable material degradation life T The expectation and variance of are calculated by the following expressions: In the formula, is the drift parameter, is the diffusion parameter, is the failure threshold, which is equal to the maximum upper limit of the OPGW optical cable material loss. t For the test time; Calculate drift parameters using the maximum likelihood method and diffusion parameters The estimated value of is calculated as follows: ， In the formula, Indicates i The sample in The amount of degradation measured; , represents the measurement time interval; ,express The degradation amount at the moment The difference in degradation at each moment is equal to the degradation increment; Indicates i The number of measurements per sample; Indicates the sample number. ; represents the measurement time, ; If the performance degradation y The mean is the location parameter , standard deviation is the shape parameter The normal distribution of the corresponding life distribution and performance degradation distribution conforms to the following expression: Using the maximum likelihood estimation method, we calculated and The value of is calculated as follows: ， In the formula, It is a sample i exist The amount of degradation at a moment; yes The mean of the degradation of all samples at the moment; Get Estimated value of the mean performance degradation at each moment , population standard deviation estimate , establish the degradation model of mean value and standard deviation of degradation, substitute it into the above formula to evaluate the average life and reliable life of OPGW optical cable materials; The expression of the life assessment model is: Taking the logarithm of both sides of the expression of the life assessment model, we get: In the formula, represents static fatigue parameters; represents a constant parameter; represents a constant applied stress; Different stress levels correspond to different average life values. is the horizontal axis, is the vertical axis fitting straight line, and the intercept of the straight line is The value of , the slope is Substituting the estimated value of into the above formula, we can get the estimated value of life span.

8. The OPGW optical cable life prediction method based on degradation assessment according to claim 1 is characterized in that: When the OPGW optical cable life assessment model calculates the predicted value of the OPGW optical cable life, an environmental coefficient is assigned to each OPGW optical cable. The environmental coefficient takes the maximum value of the pollution coefficient and the meteorological coefficient. The pollution coefficient is determined by salt pollution and material type. The establishment of the OPGW optical cable life assessment model takes into account the defect coefficient, which is determined by the defect level and the number of defects; According to the defect description, each defect is classified and matched to each component; according to the number of defect levels of various faults that have occurred in history, multiply it by the corresponding defect level and accumulate the defect level; the number of defects corresponding to different defect coefficients is as follows: The defect coefficient is 1.0, and the minimum and maximum values ​​of the corresponding defect times are 0 and 0, respectively; The defect coefficient is 1.05, and the minimum value of the corresponding defect number is 0 and the maximum value is 1; The defect coefficient is 1.1, the minimum value of the corresponding defect number is 1 and the maximum value is 2; or, the minimum value of the corresponding defect number is 2 and the maximum value is 5; The defect coefficient is 1.2, and the minimum number of corresponding defects is 3 and the maximum number is 100; The state coefficient of the power optical cable corresponds to the defect coefficient. The operation and maintenance strategy under different state coefficients is formulated according to the following relationship: The status factor is 1.05, corresponding to continuous maintenance; The state coefficient is 1.1, which corresponds to broken strand repair; or, corresponds to increased fiber attenuation; The state factor is 1.2, which corresponds to excessive fiber attenuation.

9. An OPGW optical cable life prediction system based on degradation assessment, characterized in that: include: The degradation characteristic parameter selection module is used to select performance parameters related to the life and reliability of the OPGW optical cable and capable of being measured as degradation characteristic parameters according to the factors causing the degradation failure of the OPGW optical cable; An operation data acquisition module is used to collect actual operation data of the OPGW optical cable according to degradation characteristic parameters and extract degradation amount data; The life prediction module is used to input the degradation data into the pre-established OPGW optical cable life assessment model to obtain the OPGW optical cable life prediction value.

10. The OPGW optical cable life prediction system based on degradation assessment according to claim 9, characterized in that: The degradation characteristic parameters selected by the degradation characteristic parameter selection module are trend type parameters and fluctuation type parameters with degradation trends. Both trend type parameters and fluctuation type parameters with degradation trends are time-sensitive parameters.

11. The OPGW optical cable life prediction system based on degradation assessment according to claim 9, characterized in that: The OPGW optical cable life assessment model pre-established by the life prediction module is constructed using a performance degradation model based on the Brownian motion wiener process; in the OPGW optical cable life assessment model, the OPGW optical cable material degradation life T The distribution of is an inverse Gaussian distribution, and the corresponding distribution function and probability density function are: OPGW optical cable material degradation life T The expectation and variance of are calculated by the following expressions: In the formula, is the drift parameter, is the diffusion parameter, is the failure threshold, which is equal to the maximum upper limit of the OPGW optical cable material loss. t For the test time; Calculate drift parameters using the maximum likelihood method and diffusion parameters The estimated value of is calculated as follows: ， In the formula, Indicates i The sample in The amount of degradation measured; , represents the measurement time interval; ,express The degradation amount at the moment The difference in degradation at each moment is equal to the degradation increment; Indicates i The number of measurements per sample; Indicates the sample number. ; represents the measurement time, ; If the performance degradation y The mean is the location parameter , standard deviation is the shape parameter The normal distribution of the corresponding life distribution and performance degradation distribution conforms to the following expression: Using the maximum likelihood estimation method, we calculated and The value of is calculated as follows: ， In the formula, It is a sample i exist The amount of degradation at a moment; yes The mean of the degradation of all samples at the moment; Get Estimated value of the mean performance degradation at each moment , population standard deviation estimate , establish the degradation model of mean value and standard deviation of degradation, substitute it into the above formula to evaluate the average life and reliable life of OPGW optical cable materials; The expression of the life assessment model is: Taking the logarithm of both sides of the expression of the life assessment model, we get: In the formula, represents static fatigue parameters; represents a constant parameter; represents a constant applied stress; Different stress levels correspond to different average life values. is the horizontal axis, is the vertical axis fitting straight line, and the intercept of the straight line is The value of , the slope is Substituting the estimated value of into the above formula, we can get the estimated value of life span.

12. The OPGW optical cable life prediction system based on degradation assessment according to claim 9, characterized in that: When the life prediction module calculates the predicted value of the OPGW optical cable life through the OPGW optical cable life assessment model, an environmental coefficient is assigned to each OPGW optical cable, and the environmental coefficient takes the maximum value of the pollution coefficient and the meteorological coefficient, and the pollution coefficient is determined by salt pollution and material type; the establishment of the OPGW optical cable life assessment model takes into account the defect coefficient, and the defect coefficient is determined by the defect level and the number of defects; According to the defect description, each defect is classified and matched to each component; according to the number of defect levels of various faults that have occurred in the past, multiply it by the corresponding defect level and accumulate the defect level; The number of defects corresponding to different defect coefficients is as follows: The defect coefficient is 1.0, and the minimum and maximum values ​​of the corresponding defect times are 0 and 0, respectively; The defect coefficient is 1.05, and the minimum value of the corresponding defect number is 0 and the maximum value is 1; The defect coefficient is 1.1, the minimum value of the corresponding defect number is 1 and the maximum value is 2; or, the minimum value of the corresponding defect number is 2 and the maximum value is 5; The defect coefficient is 1.2, and the minimum number of corresponding defects is 3 and the maximum number is 100; The state coefficient of the power optical cable corresponds to the defect coefficient. The operation and maintenance strategy under different state coefficients is formulated according to the following relationship: The status factor is 1.05, corresponding to continuous maintenance; The state coefficient is 1.1, which corresponds to broken strand repair; or, corresponds to increased fiber attenuation; The state factor is 1.2, which corresponds to excessive fiber attenuation.

13. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the OPGW optical cable life prediction method based on degradation assessment as claimed in any one of claims 1 to 8.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method for predicting the life of an OPGW optical cable based on degradation assessment according to any one of claims 1 to 8 is implemented.

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