A risk assessment method for power communication optical cable segments

Abstract

The present invention discloses a method for risk assessment of power communication optical cable segments. First, various factors affecting the risk level of power optical cables are systematically analyzed, and an evaluation index system is constructed from three perspectives: operating indicators, physical indicators, and environmental indicators of power optical cables. Secondly, the weights between indicators are determined through historical operation and maintenance data and fuzzy hierarchical analysis, and a physical-element extension model for risk assessment of power communication optical cable segments is established. Finally, the classical domain between indicators and risk levels and the node domain of indicators are analyzed, and the correlation functions of forward indicators and reverse indicators are rationally constrained. The present invention has good feasibility. While obtaining the comprehensive risk level of the power communication optical cable segment, it also reflects the specific values ​​of each risk indicator, can accurately locate the weak links of the optical cable segment, efficiently guide the operation and maintenance personnel to maintain and adjust the service routing mode, and improve the reliability of the power communication network.

Description

Technical Field

[0001] The present invention belongs to the technical field of power communication, and in particular relates to a risk assessment method for a power communication optical cable segment. Background Art

[0002] With the advancement of power grid construction, whether optical cables, as an important component of the power communication network, can safely and reliably transmit services directly affects the stability of the power grid operation. The inventors found that the laying interval of power communication optical cables is large, the operation time is long, and there are many influencing factors, which are prone to safety hazards. Therefore, it is of great practical significance to formulate a scientific and reasonable risk assessment method to ensure the low-risk operation of power communication network optical cables.

[0003] The risk assessment of the power communication optical cable segment is to study the potential risks and hidden dangers of the optical cable segment during its service, and to evaluate the dangers, vulnerabilities and the risk levels they bring. Its purpose is to scientifically judge the risk situation of the optical cable segment. When the optical cable is in a sub-healthy state or a high risk level, the operation and maintenance personnel can accurately locate the risk hazards of the optical cable segment, perform appropriate maintenance on it, and appropriately adjust the service routing method to ensure the stable and high-reliability operation of the power communication network.

[0004] At present, the risk assessment of communication optical cables is still in the exploratory stage. There are few literatures that systematically analyze the health status and risk level of power communication optical cables, and there is a lack of effective methods for fault warning and hidden danger inspection of communication optical cables. Therefore, a risk assessment method for power communication optical cable segments is proposed. Summary of the invention

[0005] In view of this, the present invention provides a risk assessment method for power communication optical cable segments from a combination of quantitative and qualitative perspectives, which effectively solves the problems of current risk assessment of power communication optical cables, such as single consideration factors, low result accuracy, and strong subjective dependence.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is:

[0007] A risk assessment method for a power communication optical cable segment comprises the following steps:

[0008] S1: Analyze the operating indicators, physical indicators and environmental indicators of power communication optical cables, and build a risk assessment indicator system for power communication optical cables.

[0009] S2: Analyze the historical operation and maintenance data of power communication optical cables and combine them with the fuzzy hierarchical analysis method to determine the weights of various indicators, and establish a matter-element extension model for risk assessment of power communication optical cable sections to obtain the risk level of the optical cable section to be evaluated.

[0010] S3: The classical domain between the indicator and the risk level, the node domain of the indicator are determined, and the correlation function of the matter-element extension model is rationally constrained.

[0011] Further: Step S1 includes the following steps:

[0012] S11: Analyze the potential risk factors of power communication optical cable operation and construct the optical cable core occupancy rate ρ(e i ), business load ratio l′(e i ) and the optical cable fault interval rate F(e i )Risk indicators.

[0013] S12: Analyze the physical factors of power communication optical cables and construct the reliability R(e i ) and optical cable transmission loss s(e i )Risk indicators.

[0014] S13: Analyze the service environment of power communication optical cables and construct risk indicators of meteorological changes around the cables, temperature changes around the cables and other external factors.

[0015] Further: Step S11 includes the following steps:

[0016] S111: Optical cable core occupancy rate. The more used optical cores in the optical cable, the more unfavorable it is for the expansion of optical paths and the replacement of faulty optical cores, and the higher the risk level. Let u(e i ) and W(e i ) are optical cables e i The number of used cores and the total number of cores, the core occupancy rate ρ(e i ) is expressed as:

[0017]

[0018] S112: Business load ratio. Optical fibers in the power communication network carry various services, and different services have different corresponding risk levels. i N services are transmitted on the network, and the importance of the jth service is δ j , then the load capacity is l(e i ), let α be the correlation coefficient of the communication network service load, and l(e i ) is normalized to obtain:

[0019]

[0020]

[0021] S113: Optical cable fault interval rate; the frequency and duration of optical cable failures during service life are important indicators for measuring stable service transmission. Indicates optical cable i Length of downtime for routine maintenance; Indicates optical cable i Service time; h represents optical cable e i The number of fiber cores; Indicates optical cable i Duration of the k-th core failure, Indicates optical cable i The number of times the kth core fault occurs, the optical cable fault interval rate F(e i ):

[0022]

[0023] Further: Step S12 includes the following steps:

[0024] S121: Optical cable segment reliability. The type of optical cable and the length of the laying are important factors affecting the physical reliability of the optical cable. The actual length of the optical cable laying can be equivalent to the cascade of optical cables of unit length. Let MTTF (mean time to failure) refer to the average working time before failure; MTTR (mean to restoration) refer to the average time to repair a fault; d(e i ) is the optical cable segment E i length, then its physical reliability R(e i )for:

[0025]

[0026] S122: Optical cable transmission loss. The losses in optical cable transmission mainly include intrinsic loss, installation loss and splicing point loss. Intrinsic loss is caused by the absorption of light energy by optical fiber materials and impurities; installation loss and splicing point loss are caused by inconsistent operations during construction and different numbers of optical fiber splicing points. Let ρ k For the cable segment e i The measured attenuation of the kth core is:

[0027]

[0028] Further: Step S13 includes the following steps:

[0029] S131: Weather changes around optical cables. Natural disasters around optical cables often cause serious damage to optical cables, mainly strong winds and lightning strikes. For overhead optical cables, the induced birefringence of optical cables caused by strong winds will affect the PMD of overhead optical cables. At the same time, the energy of strong winds will also have a significant impact on the structural parameters of overhead optical cable lines (poles, optical cables); conductive metals such as metal reinforcement cores in optical cables will experience inductive coupling under strong current lightning strikes. Let n gis the number of lightning strikes per unit area (times / km 2 ·d); D is the annual average number of lightning strike days (d / a); L is the length of the optical cable section (km); u d is the breakdown voltage of the outer sheath of the optical cable (kV); b is the lightning strike constant (I / kA), then the number of times the optical cable is struck by lightning N G for:

[0030]

[0031] S132: Temperature changes around the optical cable. Temperature changes can cause a large difference in the linear expansion coefficients of the quartz in the optical fiber and the plastic coating, affecting the stability of the optical cable transmission.

[0032] S133: Other external factors. The surrounding environment and construction conditions of the optical cable will affect the stability of business transmission, mainly including "three-span (i.e. crossing high-speed railways, highways and important power transmission channel sections) hidden dangers" and "section construction conditions". For overhead optical cables, three-span is mostly a traffic fortress, the pole tower spacing is large, the optical cable tension is strong, the perennial load is heavy, the optical cable is easily damaged by electrical corrosion, and there are risks such as disconnection; the optical cable laying section is frequently constructed, there are improper operations, and communication is not in place, resulting in digging and breaking of optical cables, vehicles hanging on optical cables, and planned optical cable relocation, which will affect the stability of business transmission.

[0033] Further: Step S2 includes the following steps:

[0034] S21: Calculate the weight w of each risk indicator based on historical operation and maintenance data and fuzzy analytic hierarchy process i .

[0035] S22: Establish a physical-element extension model for risk assessment of power communication optical cable segments.

[0036] S23: Determine the risk level of the power communication optical cable segment. Let w i (i∈1,2…n) is the risk index c of the optical cable section of the power communication network i The comprehensive relevance K of each comment level j is j (N) is:

[0037]

[0038] Comprehensive correlation K j The value of (N) describes the membership between the risk element of the optical cable section of the power communication network and each evaluation level. Let K(R) = max K j (N), and the evaluation level of the object is obtained.

[0039] Further: Step S21 includes the following steps:

[0040] S211: Construct a fuzzy judgment matrix. Based on the historical operation and maintenance data of power communication optical cables and the experience of experts in the field, the factors at the same level are compared pairwise, and the AHP factor evaluation scale of fuzzy trigonometric function is combined to obtain a triangular fuzzy judgment matrix. Denoted as:

[0041]

[0042] S212: Risk index weight calculation. The summation and average method is used to calculate the weight of each factor in the fuzzy judgment matrix, and the factor weight matrix H is obtained, which contains three different values ​​l ij 、m 11 and u 11 . The expected values ​​of the left and right sides of H are as follows:

[0043]

[0044]

[0045]

[0046] The adjustment coefficient δ is introduced to eliminate the influence of subjective consciousness on the judgment matrix. If δ>0.5, it means that the result of the judge's evaluation is weakened by the historical operation and maintenance data of the relevant factors; if δ<0.5, it means that the result of the judge's evaluation tends to be strengthened. Then the expected value of H is E(H), which is normalized to generate the weight vector:

[0047] E(H)=δE L (H)+(1-δ)E R (H) (13)

[0048]

[0049] S213: consistency check. Judgment matrix When the diagonal element of i=j, there must be l ij = l ji 、m ij =m ji and u ij =u ji ; When i≠j, there is l ij ≤m ij ≤u ij . The weight of each factor must meet the following conditions:

[0050]

[0051] Where: w i and w i are the weights of factors i and j respectively, and w i >0,wj >0; the matrix The intermediate quantity m ij Introduce and calculate the weight ratio function G ij (w i / w j ), define the matrix Consistency check indicators:

[0052]

[0053]

[0054] Where: The value of γ satisfies 0≤γ≤1. When γ>e -1 =0.3679, it means that the weight of the factor meets the consistency check; γ = 1, it means that the evaluation matrix is ​​completely consistent. The larger the value of γ, the better the corresponding consistency.

[0055] S214: When the judgment matrix of each factor layer satisfies the consistency check, the global weight of the sub-factor is calculated. is the weight of the first-level criterion layer to the target layer; is the weight of the first-level criterion layer to its sub-criterion layer; then the global weight for:

[0056]

[0057] Further: Step S22 includes the following steps:

[0058] S221: Take object N as the risk of the optical cable segment of the power communication network. This object element has n risk indicators, and C = [c1, c2, ... c n ] indicates that its corresponding value is V = [v1, v2, …v n ], then the risk element of the optical cable section of the power communication network is defined as:

[0059]

[0060] S222: For the risk element of the optical cable section of the power communication network, it is assumed that there are multiple evaluation levels N j , each risk indicator c i (i=1,2,...n) Each comment level has a corresponding value v ji (i=1,2...n), then it is called the matter-element R of the classical domain j Each risk indicator has a corresponding value space, which is used to describe the pros and cons of the risk indicator of the evaluation object under all evaluation levels, called the matter-element R of the section domain p :

[0061]

[0062]

[0063] Where: a j1 and b j1 is the value v ji The boundary value of .

[0064] S223: The correlation function describes the fit between the object to be evaluated and the comment level through distance and position value, so as to distinguish different comment levels. Its expression is:

[0065]

[0066] In the formula, K j (v i ) represents the index value v i In the review level N j About the classic domain v ji Membership value of |v ji |=|b ji -a ji |;ρ(v i ,v ji ) represents its extension distance, that is:

[0067]

[0068] D(v i ,v ji ,v pi ) represents the index value v i In the classic domain v ji and section domain v pi The bit relationship between them is:

[0069] D(v i ,v ji ,v pi )=ρ(v i ,v pi )-ρ(v i ,v ji ) (twenty four)

[0070] Further: Step S3 includes the following steps:

[0071] S31: For each risk indicator in the risk level assessment system of the optical cable section of the power communication network, five levels of assessment expected values ​​are set, namely, no risk, slight risk, general risk, significant risk and high risk. The corresponding classical domain value ranges are as follows: p For the classic domain R j The union of .

[0072]

[0073]

[0074] Among them, the transmission loss c5 is c 51 =1550nm and c 52 =1310nm, the classical domains of different transmission wavelengths are inconsistent; meteorological changes c6 have c 61 (number of lightning strikes above level 4 per year) and c 62 (number of wind speeds above level 5 per year), the evaluation value of the meteorological change index of the overhead line is c 61 and c 62 The weighted average of the meteorological change index of other lines is c 61 ; Temperature change c7 is calculated by counting the number of days with a cable temperature <10°C and a cable temperature >40°C per year; External factors c8 are c 81 (number of “three spans” in the cable section) and c 82 (number of construction days in the optical cable section per year), the evaluation value of the external factor index of the overhead line is c 81 and c 82 The weighted average of the external factor indicators of other lines is c 82 .

[0075] S32: Rationalization constraints of correlation functions. The risk index of the power communication optical cable segment includes positive index and negative index, so the constraint on equation (22) is as follows: v i ∈v ji And v i ≠a ji ,b ji The necessary and sufficient condition for K j (v i )∈(0,1); for positive indicators, when v i =a ji or v i =b ji The necessary and sufficient condition for K j (v i )=0orK j (v i )=1; for the reverse indicator, when v i =a ji or v i =b ji The necessary and sufficient condition for K j (v i )=1orK j (v i )=0; when And v i ∈v pi The necessary and sufficient condition for K j (v i)<0. The correlation function can effectively indicate v i Belongs to that comment set, and the pros and cons are on the interval boundary.

[0076] The beneficial effects of the present invention are as follows: the risk level of power communication optical cables in service will be affected by various factors, and a risk assessment system for power optical cables is constructed from three aspects: operating indicators, physical indicators and environmental indicators; the FAHP analysis method is used to weaken the influence of subjective consciousness on the indicator weights, and the risk level of the power communication optical cable segment is qualitatively and quantitatively analyzed through the physical element extension model; the characteristics of the indicators are further analyzed, effective quantitative standards are given, and the correlation function is rationally constrained. It effectively solves the problems of single consideration factors, low result accuracy, and strong subjective dependence in the current risk assessment of power communication optical cables. The risk assessment method established by the present invention improves the accuracy of optical cable risk assessment, shortens the assessment time, and has good feasibility. While obtaining the comprehensive risk level of the power communication optical cable segment, it also reflects the specific values ​​of each risk indicator, can accurately locate the weak links of the optical cable segment, and efficiently guide the operation and maintenance personnel to maintain and adjust the service routing mode, and improve the reliability of the power communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of the process of Embodiment 1 of the present invention.

[0078] Figure 2 Schematic diagram of the risk assessment indicator system of Example 1 of the present invention.

[0079] Figure 3 The figure is a schematic diagram of specific implementation steps of the first embodiment of the present invention. DETAILED DESCRIPTION

[0080] The specific implementation modes of the present invention are described below with reference to examples.

[0081] like Figure 1 As shown, the power communication optical cable segment risk assessment method provided by the first embodiment of the present invention includes the following steps:

[0082] S1: Analyze the operation index, physical index and environmental index of power communication optical cable, and build the risk assessment index system of power communication optical cable, such as Figure 2 shown.

[0083] Step S1 includes the following steps:

[0084] S11: Analyze the potential risk factors of power communication optical cable operation and construct the optical cable core occupancy rate ρ(e i ), business load ratio l′(e i ) and the optical cable fault interval rate F(e i )Risk indicators.

[0085] Step S11 includes the following steps:

[0086] S111: Optical cable core occupancy rate. The more used optical cores in the optical cable, the more unfavorable it is for the expansion of optical paths and the replacement of faulty optical cores, and the higher the risk level. Let u(e i ) and W(e i ) are optical cables e i The number of used cores and the total number of cores, the core occupancy rate ρ(e i ) is expressed as:

[0087]

[0088] S112: Business load ratio. Optical fibers in the power communication network carry various services, and different services have different corresponding risk levels. i N services are transmitted on the network, and the importance of the jth service is δ j , then the load capacity is l(e i ), let α be the correlation coefficient of the communication network service load, and l(e i ) is normalized to obtain:

[0089]

[0090]

[0091] S113: Optical cable fault interval rate; the frequency and duration of optical cable failures during service life are important indicators for measuring stable service transmission. Indicates optical cable i Length of downtime for routine maintenance; Indicates optical cable i Service time; h represents optical cable e i The number of fiber cores; Indicates optical cable i Duration of the k-th core failure, Indicates optical cable i The number of times the kth core fault occurs, the optical cable fault interval rate F(e i ):

[0092]

[0093] S12: Analyze the physical factors of power communication optical cables and construct the reliability R(e i ) and optical cable transmission loss s(e i )Risk indicators.

[0094] Step S12 includes the following steps:

[0095] S121: Optical cable segment reliability. The type of optical cable and the length of the laying are important factors affecting the physical reliability of the optical cable. The actual length of the optical cable laying can be equivalent to the cascade of optical cables of unit length. Let MTTF (mean time to failure) refer to the average working time before failure; MTTR (mean to restoration) refer to the average time to repair a fault; d(e i ) is the optical cable segment E i length, then its physical reliability R(e i )for:

[0096]

[0097] S122: Optical cable transmission loss. The losses in optical cable transmission mainly include intrinsic loss, installation loss and splicing point loss. Intrinsic loss is caused by the absorption of light energy by optical fiber materials and impurities; installation loss and splicing point loss are caused by inconsistent operations during construction and different numbers of optical fiber splicing points. Let ρ k For the cable segment e i The measured attenuation of the kth core is:

[0098]

[0099] S13: Analyze the service environment of power communication optical cables and construct risk indicators of meteorological changes around the cables, temperature changes around the cables and other external factors.

[0100] Step S13 includes the following steps:

[0101] S131: Weather changes around optical cables. Natural disasters around optical cables often cause serious damage to optical cables, mainly strong winds and lightning strikes. For overhead optical cables, the induced birefringence of optical cables caused by strong winds will affect the PMD of overhead optical cables. At the same time, the energy of strong winds will also have a significant impact on the structural parameters of overhead optical cable lines (poles, optical cables); conductive metals such as metal reinforcement cores in optical cables will experience inductive coupling under strong current lightning strikes. Let n g is the number of lightning strikes per unit area (times / km 2 ·d); D is the annual average number of lightning strike days (d / a); L is the length of the optical cable section (km); u d is the breakdown voltage of the outer sheath of the optical cable (kV); b is the lightning strike constant (I / kA), then the number of times the optical cable is struck by lightning N G for:

[0102]

[0103] S132: Temperature changes around the optical cable. Temperature changes can cause a large difference in the linear expansion coefficients of the quartz in the optical fiber and the plastic coating, affecting the stability of the optical cable transmission.

[0104] S133: Other external factors. The surrounding environment and construction conditions of the optical cable will affect the stability of business transmission, mainly including "three-span hidden dangers" and "section construction conditions". For overhead optical cables, the three spans are mostly traffic fortresses, the pole tower spacing is large, the optical cable tension is strong, the perennial load is heavy, the optical cable is easily damaged by electrical corrosion, and there are risks such as disconnection; the optical cable laying section is frequently constructed, there are improper operations, and communication is not in place, resulting in digging and breaking of optical cables, vehicles hanging on optical cables, and planned optical cable relocation, which will affect the stability of business transmission.

[0105] S2: Analyze the historical operation and maintenance data of power communication optical cables and determine the weights of various indicators by combining fuzzy analytic hierarchy process, and establish a matter-element extension model for risk assessment of power communication optical cable segments to obtain the risk level of the optical cable segment to be assessed. The specific implementation process is as follows: Figure 3 shown.

[0106] Step S2 includes the following steps:

[0107] S21: Calculate the weight w of each risk indicator based on historical operation and maintenance data and fuzzy analytic hierarchy process i .

[0108] Step S21 includes the following steps:

[0109] S211: Construct a fuzzy judgment matrix. Based on the historical operation and maintenance data of power communication optical cables and the experience of experts in the field, the factors at the same level are compared pairwise, and the AHP factor evaluation scale of fuzzy trigonometric function is combined to obtain a triangular fuzzy judgment matrix. Denoted as:

[0110]

[0111] S212: Risk index weight calculation. The summation and average method is used to calculate the weight of each factor in the fuzzy judgment matrix, and the factor weight matrix H is obtained, which contains three different values ​​l ij 、m 11 and u 11 . The expected values ​​of the left and right sides of H are as follows:

[0112]

[0113]

[0114]

[0115] The adjustment coefficient δ is introduced to eliminate the influence of subjective consciousness on the judgment matrix. If δ>0.5, it means that the result of the judge's evaluation is weakened by the historical operation and maintenance data of the relevant factors; if δ<0.5, it means that the result of the judge's evaluation tends to be strengthened. Then the expected value of H is E(H), which is normalized to generate the weight vector:

[0116] E(H)=δE L (H)+(1-δ)E R (H) (13)

[0117]

[0118] S213: consistency check. Judgment matrix When the diagonal element of i=j, there must be l ij = l ji 、m ij =m ji and u ij =u ji ; When i≠j, there is l ij ≤m ij ≤u ij . The weight of each factor must meet the following conditions:

[0119]

[0120] Where: w i and w j are the weights of factors i and j respectively, and w i >0,w j >0; the matrix The intermediate quantity m ij Introduce, calculate the ratio function of weight Defining the Matrix Consistency check indicators:

[0121]

[0122]

[0123] Where: The value of γ satisfies 0≤γ≤1. When γ>e -1 =0.3679, it means that the weight of the factor meets the consistency check; γ = 1, it means that the evaluation matrix is ​​completely consistent. The larger the value of γ, the better the corresponding consistency.

[0124] S214: When the judgment matrix of each factor layer satisfies the consistency check, the global weight of the sub-factor is calculated. is the weight of the first-level criterion layer to the target layer; is the weight of the first-level criterion layer to its sub-criterion layer; then the global weight for:

[0125]

[0126] S22: Establish a physical-element extension model for risk assessment of power communication optical cable segments.

[0127] Step S22 includes the following steps:

[0128] S221: Take object N as the risk of the optical cable segment of the power communication network. This object element has n risk indicators, and C = [c1, c2, ... c n ] indicates that its corresponding value is V = [v1, v2, …v n ], then the risk element of the optical cable section of the power communication network is defined as:

[0129]

[0130] S222: For the risk element of the optical cable section of the power communication network, it is assumed that there are multiple evaluation levels N j , each risk indicator c i (i=1,2,...n) Each comment level has a corresponding value v ji (i=1,2...n), then it is called the matter-element R of the classical domain j Each risk indicator has a corresponding value space, which is used to describe the pros and cons of the risk indicator of the evaluation object under all evaluation levels, called the matter-element R of the section domain p :

[0131]

[0132]

[0133] Where: a j1 and b j1 is the value v ji The boundary value of .

[0134] S223: The correlation function describes the fit between the object to be evaluated and the comment level through distance and position value, so as to distinguish different comment levels. Its expression is:

[0135]

[0136] In the formula, K j (v i ) represents the index value v i In the review level N j About the classic domain v ji Membership value of |v ji |=|b ji -a ji|;ρ(v i ,v ji ) represents its extension distance, that is:

[0137]

[0138] D(v i ,v ji ,v pi ) represents the index value v i In the classic domain v ji and section domain v pi The bit relationship between them is:

[0139] D(v i ,v ji ,v pi )=ρ(v i ,v pi )-ρ(v i ,v ji ) (twenty four)

[0140] S23: Determine the risk level of the power communication optical cable segment. Let w i (i∈1,2…n) is the risk index c of the optical cable section of the power communication network i The comprehensive relevance K of each comment level j is j (N) is:

[0141]

[0142] Comprehensive correlation K j The value of (N) describes the membership between the risk element of the optical cable section of the power communication network and each evaluation level. Let K(R) = max K j (N), and the evaluation level of the object is obtained.

[0143] S3: The classical domain between the indicator and the risk level, the node domain of the indicator are determined, and the correlation function of the matter-element extension model is rationally constrained.

[0144] Step S3 includes the following steps:

[0145] S31: For each risk indicator in the risk level assessment system of the optical cable section of the power communication network, five levels of assessment expected values ​​are set, namely, no risk, slight risk, general risk, significant risk and high risk. The corresponding classical domain value ranges are as follows: p For the classic domain R j The union of .

[0146]

[0147]

[0148] Among them, the transmission loss c5 is c 51 =1550nm and c 52 =1310nm, the classical domains of different transmission wavelengths are inconsistent; meteorological changes c6 have c 61 (number of lightning strikes above level 4 per year) and c 62 (number of wind speeds above level 5 per year), the evaluation value of the meteorological change index of the overhead line is c 61 and c 62 The weighted average of the meteorological change index of other lines is c 61 ; Temperature change c7 is calculated by counting the number of days with a cable temperature <10°C and a cable temperature >40°C per year; External factors c8 are c 81 (number of “three spans” in the cable section) and c 82 (number of construction days in the optical cable section per year), the evaluation value of the external factor index of the overhead line is c 81 and c 82 The weighted average of the external factor indicators of other lines is c 82 .

[0149] S32: Rationalization constraints of correlation functions. The risk index of the power communication optical cable segment includes positive index and negative index, so the constraint on equation (22) is as follows: v i ∈v ji And v i ≠a ji ,b ji The necessary and sufficient condition for K j (v i )∈(0,1); for positive indicators, when v i =a ji or v i =b ji The necessary and sufficient condition for K j (v i )=0orK j (v i )=1; for the reverse indicator, when v i =a ji or v i =b ji The necessary and sufficient condition for K j (v i )=1orK j (v i )=0; when And v i ∈v pi The necessary and sufficient condition for K j (v i )<0. The correlation function can effectively indicate v iBelongs to that comment set, and the pros and cons are on the interval boundary.

[0150] In order to verify the effect of the present invention in the risk assessment of power communication optical cables, a power communication optical cable of a power supply company is selected as an evaluation example. Two optical cables (one overhead optical cable and one direct buried optical cable) in the power communication optical cable are randomly selected as evaluation samples, and the labels are e 01 and e 02 .

[0151] Analysis of power communication cable segment e 01 and e 02 The basic data obtained through investigation are shown in Table 2. Among them, the data of meteorological changes, temperature changes and external factors in the past three years are counted and the weighted average is taken.

[0152] Table 2. Basic data of the object element to be evaluated for the power communication optical cable segment

[0153]

[0154] The historical operation and maintenance data of power communication optical cables were analyzed, and a number of experts in the field evaluated the relative importance of each indicator in the risk assessment index system of power communication optical cables. After taking the weighted average, the following were obtained: the fuzzy judgment matrix of the first-level indicator layer to the target layer, and the fuzzy judgment matrix of the second-level indicator layer to the first-level indicator layer. The weight coefficients of each indicator were obtained according to formula (18) as shown in Table 5.

[0155] Table 3. Fuzzy judgment matrix of the first-level indicator layer to the target layer

[0156]

[0157] Table 4. Judgment matrix of the second-level indicator layer on the first-level indicator layer

[0158]

[0159] Table 5. Weights of risk assessment indicators for power communication optical cable segments

[0160]

[0161] Among them, the consistency check value of the first-level indicator is γ=0.8167; the consistency check values ​​of the second-level indicators are: γ=0.7819, γ=0.9365, γ=0.4443; so γ>0.3679, and the obtained weight meets the consistency check.

[0162] The matter element e is calculated using equations (22)-(24): 01 and e 02 The correlation between the risk indicators and risk levels is shown in Table 6.

[0163] Table 6. Risk rating correlation table of power communication network

[0164]

[0165] Substituting the weight of the index and the data in Table 6 into formula (8), the risk level of the optical cable segment is obtained as shown in Table 7. 01 For a slightly risky 02 For no risk.

[0166] Table 7. Comprehensive risk level of power communication optical cable segments

[0167]

[0168] Analysis Table 6, Table 7, Power Communication Optical Cable e 01 The risk assessment is "slightly risky". Among them, the evaluation results of the indicators of optical cable reliability and temperature change are "no risk", and the evaluation result of the debt ratio is "significant risk". Therefore, the optical cable e 01 The risk level of power communication optical cable is mainly considered to improve the business ratio of its transmission and reduce the load rate of optical path transmission. 02 The risk assessment is "no risk". Among them, the external factors are assessed as "slightly risky" and the transmission loss assessment result is "average risk". We should focus on the transmission loss of the optical cable, check the connection points and lines of the optical cable, and eliminate hidden dangers.

[0169] The present invention combines FAHP and matter-element extension model to construct a risk assessment method for power communication optical cable segments. Through systematic analysis of the risk factors affecting power communication optical cables, relevant indicators are selected to establish an assessment system, and the indicator weights are obtained by combining historical operation and maintenance data and fuzzy hierarchical analysis method; a matter-element model of the optical cable segment is established, and the correlation function is constrained according to the characteristics of the indicator; this model not only reflects the fuzziness of the risk factors of power optical cables, but also ensures the objectivity and accuracy of risk assessment. The simulation results of the example show that the present invention has good feasibility. While obtaining the comprehensive risk level of the power optical cable segment, it also reflects the specific values ​​of each risk indicator, can accurately locate the weak links of the optical cable segment, and efficiently guide the operation and maintenance personnel to maintain it.

Claims

1. A method for risk assessment of power communication optical cable segments, characterized in that: Includes steps: S1: Analyze the operation index, physical index and environmental index of power communication optical cable, and build the risk assessment index system of power communication optical cable; The step S1 comprises the following steps: S11: Analyze the potential risk factors of power communication optical cable operation and construct the optical cable core occupancy rate ρ(e i ), business load ratio l′(e i ) and the optical cable fault interval rate F(e i ) Risk indicators; S12: Analyze the physical factors of power communication optical cables and construct the reliability R(e i ) and optical cable transmission loss s(e i ) Risk indicators; S13: Analyze the service environment of power communication optical cables and construct risk indicators of meteorological changes around the cables, temperature changes around the cables and other external factors; S2: Analyze the historical operation and maintenance data of power communication optical cables and determine the weights between various indicators by combining the fuzzy analytic hierarchy process, and establish a matter-element extension model for risk assessment of power communication optical cable segments to obtain the risk level of the optical cable segment to be assessed; S3: The classic domain between the indicator and the risk level, the node domain of the indicator are determined, and the correlation function of the matter-element extension model is rationally constrained; The step S11 comprises the following steps: S111: Optical cable core occupancy rate; the more used optical cores in the optical cable, the more unfavorable it is for the expansion of the optical path and the replacement of the faulty optical cores, and the higher the risk level; let u(e i ) and W(e i ) are optical cables e i The number of used cores and the total number of cores, the core occupancy rate ρ(e i ) is expressed as: S112: Business load ratio; Optical fibers in the power communication network carry various services, and the risk levels corresponding to different services are also different; if the optical cable e i N services are transmitted on the network, and the importance of the jth service is δ j , then the load capacity is l(e i ), let α be the correlation coefficient of the communication network service load, and l(e i ) is normalized to obtain: S113: Optical cable fault interval rate; the frequency and duration of optical cable failures during service life are important indicators for measuring stable service transmission. Indicates optical cable i Length of downtime for routine maintenance; Indicates optical cable i Service time; h represents optical cable e i The number of fiber cores; Indicates optical cable i Duration of the k-th core failure, Indicates optical cable i The number of times the kth core fault occurs, the optical cable fault interval rate F(e i ): The step S12 comprises the following steps: S121: Reliability of optical cable segment; The type of optical cable and the length of the laying are important factors affecting the physical reliability of the optical cable. The actual length of the optical cable laying can be equivalent to the cascade of optical cables of unit length; Let MTTF refer to the mean working time before failure; MTTR refer to the mean time to repair a fault; d(e i ) is the optical cable segment E i length, then its physical reliability R(e i )for: S122: Optical cable transmission loss. The loss in optical cable transmission includes intrinsic loss, installation loss and splicing point loss. Intrinsic loss is caused by the absorption of light energy by optical fiber materials and impurities. Installation loss and splicing point loss are caused by inconsistent operations during construction and different numbers of optical fiber splicing points. Let ρ k For the cable segment e i The measured attenuation of the kth core is: The step S13 comprises the following steps: S131: Meteorological changes around the optical cable: including the number of lightning strikes above level 4 per year and the number of wind speeds above level 5 per year; Let n g is the number of lightning strikes per unit area; D is the average number of lightning strikes per year; L is the length of the optical cable segment; u d is the breakdown voltage of the outer sheath of the optical cable; b is the constant of the number of lightning strikes, then the number of lightning strikes on the optical cable NG is: S132: Cable ambient temperature: including the number of days in a year when the cable temperature is <10°C and the number of days when the cable temperature is >40°C; S133: Other external factors: including the number of three spans in the optical cable section and the number of construction days in the optical cable section per year.

2. A method for risk assessment of a power communication optical cable segment according to claim 1, characterized in that: The step S2 comprises the following steps: S21: Calculate the weight w of each risk indicator based on historical operation and maintenance data and fuzzy analytic hierarchy process i ; S22: Establish a matter-element extension model for risk assessment of power communication optical cable segments; S23: Determine the risk level of the power communication cable segment; Let w i , i∈1,2...n is the risk index c of the optical cable section of the power communication network i The comprehensive relevance K of each comment level j is j (N) is: K j (v i ) represents the index value v i In the review level N j About the classic domain v ji The membership value of .

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