Method for evaluating comprehensive application condition of pipeline optical fiber early warning system

Abstract

The invention discloses a method for evaluating the comprehensive application condition of a pipeline optical fiber early warning system, and relates to the technical field of pipeline optical fiber application evaluation. Comprising the following steps: S1, generating a final index set based on a pipeline optical fiber early warning public data set; s2, constructing a comprehensive application evaluation index system based on the final index set; s3, obtaining a quantitative evaluation result of the corresponding index; s4, generating a preliminary index weight set based on an analytic hierarchy process and an entropy weight method, and generating a final index weight set based on a game theory combination weighting method; generating a comprehensive application scoring criterion in combination with the quantitative evaluation result of the index and the final index weight set; and S5, evaluating the comprehensive application condition of the pipeline optical fiber early warning system by combining the obtained real-time data of the pipeline optical fiber early warning system and the fuzzy comprehensive evaluation model, and generating an analysis result. According to the method, a comprehensive and reasonable pipeline optical fiber monitoring system application condition evaluation index system is established, so that a more accurate and effective system application condition is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline optical fiber application evaluation, and particularly relates to a method for evaluating comprehensive application of a pipeline optical fiber early warning system. BACKGROUND

[0002] So far, the development of oil and gas pipeline operation mileage is still the general trend, but pipeline safety is increasingly affected by surrounding production and life, and the problems of third-party intrusion such as illegal occupation and destruction and construction excavation along the pipeline are rising. Therefore, in order to early warn the third-party intrusion events that threaten the safety of the pipeline, the optical fiber early warning system with real-time monitoring and long-distance coverage becomes the preferred method for preventing third-party intrusion. However, any complex system inevitably encounters performance degradation problems in its life cycle. With the passage of time, factors such as aging of hardware, limitations of software algorithms, and changes in external environmental conditions may cause the system to accumulate errors or slow down the response speed. Therefore, through regular application effect evaluation, not only can the health status of the optical fiber early warning system be monitored, but also potential problems can be identified and corrected in a timely manner to ensure the accuracy and reliability of the early warning system and protect the safe transportation of energy.

[0003] In the past, the research on the application evaluation method of the pipeline optical fiber early warning system established the evaluation index system from the perspective of field technical test, evaluated the application effect of the optical fiber early warning system in this test, such as alarm response time, false alarm rate, positioning accuracy and other technical indicators, and did not pay enough attention to the importance of management ability level in the application effect of the optical fiber early warning system. It is not considered that the comprehensive application effect evaluation of the pipeline optical fiber early warning system is both a technical problem and a management problem. All technical activities need to rely on perfect management system to plan, control and supervise; in the aspect of index screening, most of the existing researches construct the evaluation index system in a subjective and arbitrary way through expert experience knowledge or reference to relevant high-frequency evaluation indexes. The subjective arbitrariness is strong and the redundancy is high. Due to the numerous influencing factors of the application of the optical fiber early warning system, combined with the current situation that the oil and gas pipelines are scattered in geographical location, involve a wide area, and the optical fiber early warning systems are uneven in technology, if the evaluation indexes are selected according to the traditional principles of correlation and clear definition, the relevance and causality between the index data are lack of analysis, the human factors are too much, and the application situation of the optical fiber early warning system cannot be truly and comprehensively reflected.

[0004] In view of this, the application constructs an evaluation index system through subjective and objective comprehensive screening, fully considers the influence of the technical level and the management level on the application effect of the optical fiber early warning system, analyzes the retained indexes based on the quantitative mathematical analysis model after screening the index set in a qualitative manner, quantitatively screens the representative core indexes, reduces the redundancy between the indexes, and constructs the application effect evaluation index system of the pipeline optical fiber early warning system. The application effect of the optical fiber early warning system is evaluated through the fuzzy comprehensive evaluation method, the specific problems in the operation process of the optical fiber early warning system are clarified, the weak links are found out, the management measures of the related enterprises are targetedly guided to be further improved, the technical innovation development is promoted, the pipeline accident risk is reduced, and the energy safety transportation is ensured. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a pipeline optical fiber early warning system application condition evaluation method based on the hierarchical grey correlation analysis method and the fuzzy comprehensive evaluation method, so as to solve the problem that the existing technology is not comprehensive enough for the pipeline optical fiber application condition evaluation.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: A pipeline optical fiber early warning system comprehensive application condition evaluation method, comprising the following steps: S1. A preliminary index set is constructed based on a pipeline optical fiber early warning public data set; the preliminary index set is sequentially subjected to qualitative screening and quantitative screening to generate a key index set; the information contribution rate of each key index in the key index set is calculated, and the key index set is subjected to rationality test based on the information contribution rates of all the key indexes to generate a final index set; S2. A comprehensive application evaluation index system is constructed based on the final index set; S3. The indexes in the comprehensive application evaluation index system are subjected to index interpretation evaluation based on expert survey questionnaires, pipeline optical fiber early warning system operation history data, optical fiber early warning system manufacturer data and corresponding industry standards, and the quantified evaluation results of the corresponding indexes are obtained; S4. The indexes of the comprehensive application evaluation index system of the pipeline optical fiber early warning system are subjected to preliminary weight calculation to generate a preliminary index weight set based on the analytic hierarchy process and the entropy weight method, and the preliminary index weight set is modified to generate a final index weight set based on the game theory combination weight method; the comprehensive application scoring criteria are generated in combination with the quantified evaluation results of the indexes and the final index weight set; S5. The application condition evaluation level is determined; a fuzzy comprehensive evaluation model is constructed based on the fuzzy comprehensive evaluation method and the comprehensive application scoring criteria of the pipeline optical fiber early warning system; the comprehensive application condition of the pipeline optical fiber early warning system is evaluated in combination with the real-time data of the pipeline optical fiber early warning system and the fuzzy comprehensive evaluation model, and an analysis result is generated.

[0007] Preferably, the constructed selection index set comprises: Based on the historical operation data of the pipeline optical fiber early warning system, the influence factor data of the application effect of the pipeline optical fiber early warning system is obtained; The influence factor data is classified, and external factor data is generated based on the environmental data on which the sensing optical fiber and the regional monitoring center depend; Based on the system device reliability, technical advancement, network algorithm design and maintenance management, internal factor data is generated; Based on the management level and the technical level, the influence factor data is classified to generate the selection index set.

[0008] Preferably, the selection index set is sequentially subjected to qualitative screening and quantitative screening to generate a key index set, comprising: Based on the management and technical criterion public data set, the oil and gas pipeline management industry standard, the optical fiber early warning system management standard and the optical fiber early warning system technical standard, the corresponding comparison index data set is generated; The intersection index of the corresponding comparison index data set and the selection index set is sequentially obtained; The intersection index set is generated based on the intersection index corresponding to all comparison index data sets; The intersection index set includes multiple intersection indexes and the occurrence frequency of the corresponding intersection index; By comparing the occurrence frequency of each intersection index with the preset value, a qualitative index set is generated; Based on gray correlation analysis, the gray correlation degree of each index in the qualitative index set is calculated; Based on the analytic hierarchy process, the index weight vector of each index in the qualitative index set is obtained; The index weight vector is multiplied by the gray correlation degree obtained by the gray correlation analysis to generate the comprehensive correlation degree of each index; By comparing the comprehensive correlation degree of each index with the correlation degree preset, the index quantitative screening is completed to generate the key index set.

[0009] Preferably, the comprehensive correlation degree of each index is generated, comprising: (1) Determine the current index weight: Let the current index weight obtained according to the analytic hierarchy process be , wherein is the weight corresponding to the kth evaluation index,

[0010] (2) Determine the evaluation object and the reference sequence: Suppose there are m evaluation objects and n evaluation indexes, and the reference sequence is , the comparison sequence is , ; (3) Dimensionless processing: For the reference sequence Comparison of sequences Perform dimensionless processing, that is, divide each parameter of each indicator by the maximum value of the current parameter, so that the parameters of each sequence are all between 0 and 1; (4) Calculate the grey relational coefficient: Find the reference sequence Comparison sequence The grey relational coefficient on the k-th indicator is calculated using the following formula: (1); In the formula, The resolution coefficient, Under normal circumstances ; In the formula The maximum value of the difference sequence. It is the minimum value of the difference sequence; (5) Calculate the grey relational degree: Introducing the average value method to calculate grey relational degree The calculation formula is as follows: (2); (6) Calculate the overall grey relational degree: The weight vector obtained by combining the analytic hierarchy process The comprehensive grey relational degree is obtained. The calculation formula is: (3); in, For the first k Weighting coefficients for each indicator; (7) Selection of evaluation indicators: To comprehensively determine the grey relational degree Set a gray relation preset value T ; Will Sort the element values ​​in descending order and update them. In the middle, then to The elements in the array are added in descending order until the sum is greater than or equal to the sum of the elements in the array. T At the same time, the index corresponding to the elements participating in the accumulation is retained as Delete the remaining elements, that is: (4).

[0011] Preferably, the step of performing a rationality check on the key indicator set based on the information contribution rate of all key indicators to generate the final indicator set includes: The ratio of the sum of variances of all index data after quantitative screening to the sum of variances of all index data after qualitative screening is taken as a first ratio, and the first ratio is taken as information content of the index system after screening, and based on this, information contribution rate is generated by performing index system rationality test ; information contribution rate The calculation formula of the information contribution rate is: (5) In the formula, is a trace of a covariance matrix, representing a sum of main diagonal elements of the covariance matrix; is a number of indexes after quantitative screening, is a number of indexes after qualitative screening.

[0012] When , it is considered that the evaluation index system passes the rationality test; The final index set is generated by taking indexes passing the rationality test.

[0013] Preferably, a comprehensive application evaluation index system is constructed based on the final index set, and the comprehensive application evaluation index system comprises: The comprehensive application evaluation index system is constructed based on the final index set, and the comprehensive application evaluation index system comprises: a criterion layer index, a first-level index, and a second-level index; The criterion layer index comprises a management layer index and a technical layer index; The first-level index corresponding to the management layer index comprises: system management, optical fiber management, and contractor service quality; The second-level index corresponding to the system management comprises operation environment control, emergency control, alarm control, and application operation and maintenance; The second-level index corresponding to the optical fiber management comprises optical fiber laying image and optical fiber calibration; The second-level index corresponding to the contractor service quality comprises average repair time, first-time repair success rate, repair failure recurrence rate, remote support capability, and preventive maintenance; The first-level index corresponding to the technical layer index comprises: monitoring characteristic index, technical advancement index, and waterfall function; The second-level index corresponding to the monitoring characteristic index comprises monitoring distance, spatial resolution, positioning accuracy, alarm response time, false negative rate, false positive rate, and effective alarm rate; The second-level index corresponding to the technical advancement index comprises maximum monitoring distance, system interface display, device reliability, device self-checking function, autonomous controllability, data processing capability, and intrusion monitoring type; The secondary indicators corresponding to the waterfall chart function include: basic waterfall chart functions and extended waterfall chart functions.

[0014] Preferably, the determination of subjective weights for indicators based on the analytic hierarchy process includes: (1) Construct the judgment matrix: Based on Santy's scaling theory, the pairwise comparison results of the indicators are obtained, a hierarchical structure model is established, and a judgment matrix is ​​constructed. ; (2) Calculate the weight vector and the largest eigenvalue: The root mean of the normalized judgment matrix is ​​taken as the ranking weight value for indicators at the same level; the calculation formula is as follows: (6); (3) Check the consistency of the judgment matrix: Consistency index of an n-order matrix The larger the value, the greater the degree to which the matrix deviates from perfect consistency; The calculation method is as follows: (7); In the formula, The largest eigenvalue of the matrix. n The order of the matrix; Through consistency indicators The matrix consistency ratio is calculated as follows: (8); In the formula, To determine the average random consistency index, we now use the analytic hierarchy process (AHP). Value table, query The value must meet the consistency test index. If the value is less than 0.1, the consistency of the judgment matrix must be guaranteed; otherwise, the judgment evidence needs to be adjusted until the consistency requirement is met. The determination of objective weights for indicators based on the entropy weight method includes: (1) Establishing an original decision database: Based on the questionnaire, m evaluation subjects, and n evaluation indicators, the original decision data matrix X is obtained: ; (2) Matrix standardization: This includes standardization of positive indicators and standardization of negative indicators; Among them, positive indicators: (9); Negative indicators: (10) in, This is a standard value obtained after standardizing the indicator; (3) Entropy calculation: The formula for calculating entropy is: (11); (12); In the formula, For the first Under the evaluation index, the first The proportion of each evaluated object; (4) Calculate the weights : (13); in, For the first The weighting coefficients of each evaluation indicator.

[0015] Preferably, the step of revising the preliminary indicator weight set based on the game theory combinatorial weighting method to generate the final indicator weight set includes: (1) Calculate the index weights: Assume to adopt Various weighting calculation methods are used to evaluate the indicators in the system. Each evaluation indicator is weighted using a combination of factors based on game theory to obtain a corresponding weight vector. Further obtained Weight set of any linear combination of weight vectors (14); In the formula, Weighting coefficients (2) Optimized combination: By optimizing the weighting coefficients Minimize With each The deviation between them, i.e. (15); In the formula These are the weighting coefficients; This is the transpose of the basic weight vector set; Let be the basic weight vector set; where , representing the 2-norm in numerical analysis; (3) Equivalent transformation: Using the properties of matrix differentiation, equation (14) is equivalently transformed into a system of linear equations with optimal first-order derivative conditions, namely: (16); (4) Normalization process: based on The optimal linear combination coefficients are obtained and normalized using the following formula: (17); The final comprehensive weight vector of the indicators based on game theory combinatorial weighting is as follows: (18).

[0016] Preferably, the construction of the fuzzy comprehensive evaluation model based on the fuzzy comprehensive evaluation method and the comprehensive application scoring criteria of the pipeline optical fiber early warning system includes: (1) Determine the comprehensive evaluation set Based on industry standards, operational specifications, and fiber optic research literature, and with reference to the opinions and experience of on-site maintenance personnel and experts, the application status of pipeline fiber optic monitoring systems is classified into four levels: Excellent, Good, Qualified, and Substandard. The specific evaluation set is as follows: ; (2) Determine the fuzzy judgment matrix The current management and technical situations are evaluated, and finally, after sorting and summarizing the evaluation data, a fuzzy judgment matrix of secondary indicators is obtained. :

[0017] In the formula, This indicates the number of single-dimensional indicators in the comprehensive application evaluation index system for fiber optic early warning; Represents the fuzzy evaluation set The number of elements in the middle, The value is determined based on the actual situation; (3) Comprehensive evaluation of the synthesis of fuzzy relations Based on fuzzy theory, the comprehensive weights of the indicators are combined with the fuzzy evaluation matrix to perform fuzzy matrix synthesis operations. Here, a weighted average synthesis operator is selected. ; First, fuzzy judgment matrix of secondary indicators A single-level comprehensive evaluation is conducted to obtain the comprehensive application evaluation vector of the fiber optic security early warning system. Then, a higher-level fuzzy evaluation is performed, generating a multi-level comprehensive evaluation system; (19); In the formula, Assigning comprehensive weights to game theory combinatorial structures; This is a single-factor fuzzy evaluation matrix; This is a symbol for a matrix composition operation, representing a weighted average composition operator. ; According to the maximum membership principle, the comprehensive application evaluation result of the optical fiber early warning system is That is, (20).

[0018] Preferably, the evaluation of the comprehensive application of the pipeline optical fiber early warning system comprises: The importance of the evaluation index of the comprehensive application of the optical fiber early warning system is sorted and quantified, and the corresponding subjective weight and objective weight are generated; Finally, the relative combination weight of the first-level and second-level evaluation indexes is obtained according to the combination weighting theory of game theory; According to the evaluation index interpretation, the application situation of each second-level index is evaluated, and the fuzzy matrix, fuzzy comprehensive vector and single-level comprehensive evaluation result are obtained; Based on the single-level comprehensive evaluation result, the application situation level of the first-level index of the current optical fiber early warning system in the work station is generated; Based on the single-level comprehensive evaluation result, the fuzzy relationship matrix and fuzzy comprehensive evaluation vector of the management level, technical level and comprehensive application situation of the optical fiber early warning system are obtained, and the application situation level of the optical fiber early warning rule layer index and the overall application situation level are generated; And the comprehensive application situation of the current pipeline optical fiber early warning system is analyzed by combining the application situation level of the first-level index, the application situation level of the optical fiber early warning rule layer index and the overall application situation level, and the analysis result is obtained.

[0019] According to the above technical scheme, compared with the prior art, the evaluation method of the comprehensive application situation of the pipeline optical fiber early warning system has the following beneficial effects: 1. On the basis of the existing research which only evaluates the application effect of the optical fiber early warning system through field technical test, the influence of the management level and the advancement of the technology itself on the application effect of the optical fiber early warning system is comprehensively considered, a comprehensive and reasonable evaluation index system of the application situation of the pipeline optical fiber monitoring system is established, and more accurate and effective system application situation is obtained; 2. On the basis of the subjective screening index system, the hierarchical grey correlation analysis method is introduced to screen out representative indexes, reduce the redundancy between indexes, ensure the independence between indexes, and realize the scientificity and universality of the index system; 3. The game theory combination weighting method is used, which takes into account the advantages of subjective and objective weighting methods, maximally reduces the loss of index information, and makes the weight result as consistent as possible with the actual situation; 4. According to the fuzzy mathematical theory, the fuzzy information in the comprehensive evaluation index is quantitatively processed, the fuzzy information problem in the evaluation process is better solved, the evaluation result is more scientific and accurate, and has high practicality. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on the provided drawings.

[0021] Figure 1 A pipeline optical fiber early warning system comprehensive application situation evaluation method flowchart is described in an embodiment of the present application. Figure 2 A pipeline optical fiber early warning system comprehensive application situation index system screening framework is described in an embodiment of the present application. Figure 3 A pipeline optical fiber early warning system comprehensive application situation comprehensive application evaluation index system is described in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of the present application.

[0023] In the present application, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations, the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitation, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or equipment including the element.

[0024] The present application can be used in many general or special-purpose computing device environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices or devices, etc.

[0025] REFERENCE Figure 1The application discloses an evaluation method for comprehensive application of a pipeline optical fiber early warning system, and comprises the following steps: S1. Constructing a preliminary index set based on a pipeline optical fiber early warning public data set; performing qualitative screening and quantitative screening on the preliminary index set to generate a key index set; calculating the information contribution rate of each key index in the key index set, and performing rationality inspection on the key index set based on the information contribution rates of all the key indexes to generate a final index set; S2. Constructing a comprehensive application evaluation index system based on the final index set; S3. Performing index interpretation evaluation on indexes in the comprehensive application evaluation index system based on expert survey questionnaires, pipeline optical fiber early warning system operation historical data, optical fiber early warning system manufacturer data and corresponding industry standards to obtain quantized evaluation results of the corresponding indexes; S4. Performing preliminary weight calculation on indexes in the comprehensive application evaluation index system of the pipeline optical fiber early warning system based on the analytic hierarchy process and the entropy weight method to generate a preliminary index weight set, and modifying the preliminary index weight set based on the game theory combination weight method to generate a final index weight set; combining the quantized evaluation results of the indexes and the final index weight set to generate a comprehensive application scoring criterion; S5. Constructing a fuzzy comprehensive evaluation model based on the fuzzy comprehensive evaluation method and the comprehensive application scoring criterion of the pipeline optical fiber early warning system; and combining the real-time data of the pipeline optical fiber early warning system and the fuzzy comprehensive evaluation model to evaluate the comprehensive application situation of the pipeline optical fiber early warning system, and generating an analysis result.

[0026] Preferably, the preliminary index set is constructed, comprising: obtaining influence factor data of the application effect of the pipeline optical fiber early warning system based on historical operation data of the pipeline optical fiber early warning system; classifying the influence factor data, and generating external factor data based on environmental data on which the sensing optical fiber and the regional monitoring center depend; generating internal factor data based on system device reliability, technical advancement, network algorithm design and maintenance management; classifying the influence factor data based on the management level and the technical level to generate the preliminary index set.

[0027] Preferably, the preliminary index set is sequentially subjected to qualitative screening and quantitative screening to generate the key index set, comprising: generating corresponding comparison index data sets based on a management and technical criterion public data set, an oil and gas pipeline management industry standard, an optical fiber early warning system management standard and an optical fiber early warning system technical standard; sequentially obtaining intersection indexes of the corresponding comparison index data sets and the preliminary index set; generating an intersection index set based on the intersection indexes corresponding to all the comparison index data sets; The intersection indicator set includes a plurality of intersection indicators and the occurrence frequency of the corresponding intersection indicators; The qualitative indicator set is generated by comparing the occurrence frequency of each intersection indicator with a preset value; The gray correlation degree of each indicator in the qualitative indicator set is calculated based on gray correlation analysis; The index weight vector of each indicator in the qualitative indicator set is obtained based on the analytic hierarchy process; The comprehensive correlation degree of each indicator is generated by multiplying the index weight vector and the gray correlation degree obtained by the gray correlation analysis; The quantitative screening of the indicators is completed by comparing the comprehensive correlation degree of each indicator with the preset correlation degree, and the key indicator set is generated.

[0028] Specifically, the optical fiber early warning system is an intelligent monitoring and early warning system with complex technical principles and various component structures, so there are many factors affecting its application effect in actual application. From the optical fiber early warning system itself, it can be divided into external factors and internal factors. The external factors refer to the environmental conditions on which the sensing optical fiber and the regional monitoring center depend, such as temperature, humidity, natural disasters and emergencies, etc. The internal factors refer to the system device reliability, technical advancement, network algorithm design and maintenance management, etc. The internal factors are greatly affected by the development of optical fiber sensing technology, and human factors are also involved. Most of the external factors are caused by internal factors. Therefore, in order to comprehensively reflect the comprehensive application effect of the optical fiber early warning system, the present application selects evaluation indicators from the management level and the technical level, and the index system screening framework is as shown in Figure 2 .

[0029] Based on the current research results and the essential characteristics of the optical fiber early warning system, a series of high-frequency indicators identified by authoritative institutions and related literature at home and abroad are characterized, sorted, summarized and constructed into a preliminary selection indicator set. Since perfect management and advanced technology are the basic requirements for the efficient operation of the optical fiber early warning system, the preliminary selection indicator set is qualitatively screened based on the two criteria of management and technology. By referring to the relevant literature evaluating each criterion, as well as the oil and gas pipeline management industry standards, the optical fiber early warning system management standards and the optical fiber early warning system technology standards, the high-frequency indicators commonly mentioned in the preliminary selection indicator set are found to constitute the qualitative indicator set.

[0030] Gray correlation analysis is a decision analysis method that can fully mine the basic data information of indicators. It mainly analyzes the correlation degree between factors by comparing the geometric relationship of system data sequences, that is, by using gray correlation degree to judge the closeness between two indicators. If the gray correlation degree is higher, the correlation degree is greater; otherwise, it is smaller. Therefore, in the quantitative screening of indicators, indicators with higher correlation degrees can be retained, and indicators with lower correlation degrees can be deleted, thereby improving the scientificity and accuracy of the evaluation results. Hierarchical grey relational analysis involves multiplying the index weight vector obtained by the analytic hierarchy process (AHP) with the grey relational degree obtained by grey relational analysis to obtain a new comprehensive relational degree.

[0031] Preferably, (1) the current indicator weight is determined: Let the weight of the current indicator obtained according to the analytic hierarchy process be... ,in The weight corresponding to the k-th evaluation index.

[0032] (2) Determine the evaluation object and reference series: Given m evaluation objects and n evaluation indicators, the reference sequence is as follows: Compare the sequences as , ; (3) Dimensionless processing: For the reference sequence Comparison of sequences Perform dimensionless processing, that is, divide each parameter of each indicator by the maximum value of the current parameter, so that the parameters of each sequence are all between 0 and 1; (4) Calculate the grey relational coefficient: Find the reference sequence Comparison sequence The grey relational coefficient on the k-th indicator is calculated using the following formula: (1); In the formula, The resolution coefficient, Under normal circumstances ; In the formula The maximum value of the difference sequence. It is the minimum value of the difference sequence; (5) Calculate the grey relational degree: Introducing the average value method to calculate grey relational degree The calculation formula is as follows: (2); (6) Calculate the overall grey relational degree: The weight vector obtained by combining the analytic hierarchy process The comprehensive grey relational degree is obtained. The calculation formula is: (3); in, The weight coefficient for the k-th indicator; (7) Selection of evaluation indicators: To comprehensively determine the grey relational degree Set a gray relation preset value T ; Will Sort the element values ​​in descending order and update them. In the middle, then to The elements in the array are added in descending order until the sum is greater than or equal to the sum of the elements in the array. T At the same time, the index corresponding to the elements participating in the accumulation is retained as Delete the remaining elements, that is: (4).

[0033] Preferably, the step of performing a rationality check on the key indicator set based on the information contribution rate of all key indicators to generate the final indicator set includes: The first ratio is generated by comparing the sum of variances of all indicators after quantitative screening with the sum of variances of all indicators after qualitative screening. This first ratio is used as the information content of the screened indicator system, and the rationality of the indicator system is tested based on this ratio to generate the information contribution rate. ; Information contribution rate The calculation formula is: (5); In the formula, Let be the trace of the covariance matrix, and let be the sum of the elements on the main diagonal of the covariance matrix. This represents the number of indicators after quantitative screening. This represents the number of indicators after qualitative screening.

[0034] when If so, the evaluation index system is considered to have passed the rationality test; The final indicator set is generated by obtaining the indicators that pass the rationality test.

[0035] Specifically, the information contribution rate is used to reflect the information content of indicators through the variance of the indicator data, thereby verifying the rationality of the indicator system. Since the number of indicators included in each criterion layer is relatively small, calculating the information contribution rate of indicators at each level is prone to inaccuracies due to the peculiarities of individual data. Therefore, this paper uses the ratio of the sum of variances of all quantitatively screened indicator data to the sum of variances of all qualitatively screened indicator data as the information content of the screened indicator system, and uses this as the basis for verifying the rationality of the indicator system.

[0036] Preferably, a comprehensive application evaluation index system is constructed based on the final index set, including: A comprehensive application evaluation index system is constructed based on the final index set. The comprehensive application evaluation index system includes: Criterion-level indicators, primary indicators, and secondary indicators; The criterion layer indexes include: management layer indexes and technical layer indexes; The first-level indexes corresponding to the management layer indexes include: System management, fiber management, and contractor service quality; The second-level indexes corresponding to the system management include: operation environment control, emergency control, alarm control, and application operation and maintenance; The second-level indexes corresponding to the fiber management include: fiber laying image and fiber calibration; The second-level indexes corresponding to the contractor service quality include: average repair time, first-time repair success rate, repair failure recurrence rate, remote support capability, and preventive maintenance; The first-level indexes corresponding to the technical layer indexes include: Monitoring feature index, technical advancement index, and waterfall chart function; The second-level indexes corresponding to the monitoring feature index include: monitoring distance, spatial resolution, positioning accuracy, alarm response time, false negative rate, false positive rate, and effective alarm rate; The second-level indexes corresponding to the technical advancement index include: maximum monitoring distance, system interface display, device reliability, device self-checking function, autonomous controllability, data processing capability, and intrusion monitoring type; The second-level indexes corresponding to the waterfall chart function include: basic function of the waterfall chart and extended function of the waterfall chart.

[0037] Specifically, scientific and reasonable evaluation indexes are conducive to the smooth development of the comprehensive application evaluation of the fiber early warning system, and accurate understanding of the connotations of the evaluation indexes is conducive to the evaluation person grasping the evaluation range and depth, so as to give effective quantitative evaluation results, Figure 3 The connotations of the evaluation indexes are as follows: System management U1 In the actual operation process of the fiber early warning system, the operation environment of the machine room, the occurrence of unexpected events, the formation of the alarm mechanism, and even the long-term non-maintenance and update of the system will affect the use effect of the fiber early warning system. Therefore, in order to ensure the effectiveness and reliability of the fiber early warning system in actual operation, and to ensure that the system can stably operate in a complex and changeable environment, "system management" is selected as a first-level index.

[0038] Operation environment control U11 Operation environment control mainly investigates whether the management layer manages the physical and operating environment of the fiber early warning system room, including temperature, humidity, fire prevention, lightning protection, and other aspects of prevention and adjustment. Good and stable operation environment can prolong the service life of the equipment and reduce the faults caused by external factors.

[0039] Emergency control U12 Mainly investigate whether the management layer formulates the corresponding emergency strategy to deal with various hardware and software failures, network attacks, hacker intrusion, natural disasters, social incidents and other problems that may be encountered in the process of daily operation.

[0040] Alarm control U13 Mainly investigate whether the management layer effectively classifies, records, tracks and archives all alarm events, alarm event processing measures; whether the recorded alarm records are analyzed; whether a reasonable alarm threshold is set to achieve alarm analysis; whether corresponding processing measures are formulated for different alarm levels.

[0041] Application operation and maintenance U14 Mainly investigate whether the management layer formulates a test plan for the performance indicators of the optical fiber early warning system, such as spatial resolution test, positioning accuracy test, etc.; if the performance indicator error is large, whether there is a corresponding calibration plan; whether the optical fiber early warning system is backed up and updated regularly.

[0042] Optical fiber management U2 Optical fiber may gradually increase in loss due to physical damage, environmental factors and material aging, affecting signal transmission quality. As the service life of optical fiber increases, oilfield enterprises need to maintain and test related performance and loss, such as changes in the relative position of optical fiber and pipeline, changes in optical fiber attenuation, optical fiber spooling, and optical fiber fusion recording.

[0043] Optical fiber laying image U21 Mainly investigate whether the management layer records the optical fiber burial depth and the relative position of the optical fiber and the pipeline to avoid damage to the optical fiber caused by construction excavation; whether the optical fiber fusion, spooling and other conditions are recorded.

[0044] Optical fiber calibration U22 Mainly investigate whether the management layer formulates a plan to test the optical fiber attenuation; if the optical fiber attenuation is unqualified, whether the plan for calibrating the optical fiber attenuation is formulated.

[0045] Contractor service quality U3 Optical fiber early warning system is a constantly developing field, with the continuous progress of optical technology, signal processing algorithm and data analysis capability, the comprehensive application effect of optical fiber early warning system has also been affected to a certain extent. On the one hand, new technological breakthroughs make the system more sensitive and reliable. On the other hand, the upgrade of technology requires professional manufacturers to provide corresponding technical support, at the same time, with the continuous improvement of algorithm accuracy and coverage, once a fault occurs, its influence will be great, and ordinary staff will be difficult to handle technical faults professionally. Based on this, the contractor service quality is innovatively considered as one of the main influencing factors of the comprehensive application effect of the optical fiber early warning system.

[0046] Mean time to repair U31 Mainly investigate the average repair time of the contractor from system failure to normal operation.

[0047] First fix success rate U32 Mainly investigate the proportion of successful problem solving in the first repair or maintenance activity of the contractor. If the system can be repaired in the first attempt, the downtime caused by failure will be greatly shortened.

[0048] Repair failure recurrence rate U33 Mainly investigate the frequency of the same failure or related problems occurring again after a repair.

[0049] Remote support capability U34 Mainly investigate whether the contractor can diagnose, configure, update and troubleshoot the system through network connection Preventive maintenance U35 Mainly investigate whether the contractor provides timely preventive maintenance services for the system.

[0050] Monitoring characteristic index U4 In the actual application of the optical fiber early warning system, the "monitoring characteristic index" is the key to measure the effectiveness of the system. Through comprehensive evaluation of this index, the monitoring performance of an optical fiber early warning system can be fully understood, and corresponding adjustments and optimizations can be made to ensure the reliability and practicality of the system.

[0051] Monitoring distance U41 Mainly investigate the monitoring distance of the optical fiber early warning system.

[0052] Spatial resolution U42 Mainly investigate the spatial resolution during testing, the spatial resolution during the historical application period, and whether the spatial resolution during testing is consistent.

[0053] Positioning accuracy U43 Mainly investigate the positioning accuracy during testing, the positioning accuracy during the historical application period, and whether the positioning accuracy during testing is consistent.

[0054] Alarm response time U44 Mainly investigate the alarm response time during testing, the alarm response time during the historical application period, and whether the alarm response during testing is consistent.

[0055] False negative rate U45 False negative rate refers to the proportion of the number of intrusions without alarm to the total number of intrusions. Mainly investigate the false negative rate during testing and the false negative rate during the historical application period.

[0056] False Alarm Rate U46 False alarm rate refers to the proportion of the number of alarms generated by the system without intrusion behavior to the total number of alarms. It mainly investigates the false alarm rate of the optical fiber early warning system during testing and the false alarm rate during the historical application period.

[0057] Effective Alarm Rate U47 Effective alarm rate refers to the proportion of the third party damage event that truly threatens the safe operation of the pipeline to the system alarm event. It mainly investigates the effective alarm rate of the optical fiber early warning system during testing and the effective alarm rate during the historical application period.

[0058] Technical Advancement Index U5 In the current market, there are many technology providers of optical fiber early warning systems, and the products of each manufacturer differ in technical level, performance index, and function. Therefore, in order to reflect this differentiation phenomenon and promote the progress and development of optical fiber early warning system technology, the "technical advancement index" is selected as one of the main influencing factors of the comprehensive application effect of the optical fiber early warning system.

[0059] Maximum Monitoring Distance U51 It mainly investigates the difference between the maximum monitoring distance of the optical fiber early warning system and the current most advanced system. Longer monitoring distance often means more advanced sensing technology and signal processing algorithm behind it.

[0060] System Interface Display U52 It mainly investigates whether the optical fiber early warning system can transmit real-time waterfall chart and display it in real time on the system interface, and whether it can display the alarm hot area on the system interface.

[0061] Device Reliability U53 It mainly investigates the temperature resistance, anti-interference, anti-vibration performance, and mean time between failures of the optical fiber early warning system.

[0062] Device Self-checking Function U54 It mainly investigates whether the optical fiber early warning system has a fiber break self-checking function, whether it has a system fault self-checking function, and whether it can identify alarm type errors and issue alarms when the intrusion behavior and alarm type do not match.

[0063] Self-controllable Ability U55 It mainly investigates whether the operating system, database, and core components of the optical fiber early warning system are localized.

[0064] Data Processing Capability U56 It mainly investigates the data processing and alarm data statistical capability of the optical fiber early warning system, and whether the signal processing algorithm can be optimized through training data and automatically adjust the alarm threshold according to environmental changes.

[0065] Intrusion monitoring type U57 Mainly investigate whether the optical fiber early warning system can accurately identify the vehicle passing, manual excavation, mechanical construction and other intrusion behaviors.

[0066] Waterfall chart function U6 The waterfall chart provides an intuitive way to visualize the changes in vibration signals. In the optical fiber early warning system, the vibration along the pipeline can be monitored in real time by distributed optical fiber sensing technology. These vibration data can be displayed in the form of a waterfall chart, which converts complex vibration data into easily understood visual information, greatly improving monitoring efficiency and response speed, and directly related to the overall effectiveness of the early warning system.

[0067] Waterfall chart basic function U61 Mainly investigate whether the waterfall chart supports real-time and historical viewing; whether it can generate alarm data; and whether it can reflect the severity of intrusion events.

[0068] Waterfall chart extended function U62 Mainly investigate whether the distance error between the excitation signal in the waterfall chart raw data and the actual distance conforms to the standard; and whether the display of mechanical construction, manual excavation and other intrusion behaviors in the waterfall chart conforms to the standard in the simulation test scenario.

[0069] Preferably, the preliminary weight calculation of the indicators of the comprehensive application evaluation index system of the pipeline optical fiber early warning system is generated based on the analytic hierarchy process and entropy weight method to generate a preliminary index weight set, including: The subjective weight determination of the indicators based on the analytic hierarchy process includes: (1) Construct a judgment matrix: According to Santy's 1~9 scale theory, the comparison results of the indicators are obtained, a hierarchical structure model is established, and a judgment matrix is constructed. The scale meaning is shown in Table 1.

[0070] Table 1 1~9 scale meaning

[0071] According to the 1~9 scale meaning table, the relative importance between the same level indicators is compared, and the higher the relative importance; the judgment matrix obtained is as follows: ; (2) Calculate the weight vector and the maximum eigenvalue: Take the square root average of the normalized processing result of the judgment matrix as the sorting weight value of the same level indicators; the calculation formula is as follows: (6); (3) Consistency check of the judgment matrix: Consistency index of n order matrix The greater the value, the greater the degree of deviation of the matrix from complete consistency; The calculation method is as follows: (7); In the formula, is the maximum eigenvalue of the matrix, and n is the order of the matrix; The consistency index of the matrix is calculated by The value, and the calculation method is as follows: (8); In the formula, is the average random consistency index, which is obtained by the analytic hierarchy process The value is shown in Table 2. The consistency check index <0.1, which ensures the consistency of the judgment matrix; otherwise, the judgment matrix needs to be adjusted until the consistency requirement is met. <0.1, which ensures the consistency of the judgment matrix; otherwise, the judgment matrix needs to be adjusted until the consistency requirement is met.

[0072] Table 2 Value of average random consistency index

[0073] The determination of the objective weight of the index based on the entropy weight method includes: The entropy weight method is an objective weighting method that starts from the index itself and comprehensively organizes the data of each index item. For a certain index, the entropy value can be used to describe its dispersion degree. The greater the dispersion degree of a certain evaluation index, the smaller the information entropy of the index, and the greater the weight. The specific calculation steps are as follows.

[0074] (1) Establish the original decision database: According to the questionnaire, m evaluation objects and n evaluation indexes, the original decision data matrix X is obtained: ; (2) Matrix standardization: In a multi-index evaluation system, due to the different nature, unit and magnitude of each index, it cannot be directly compared and calculated. For example, the unit of "monitoring distance" is kilometers, and the larger the value, the better; while the "false alarm rate" is a percentage, and the smaller the value, the better. In order to eliminate these differences, it is necessary to standardize the original data of all indexes and unify the direction of all indexes. After processing, whether the original index is positive or negative, the standardized value has the same meaning, that is, the larger the value, the better the performance.

[0075] Among them, the positive index: the larger the original value of the index, the better the performance.

[0076] The specific standardization formula is shown in equation (9).

[0077] (9); Negative indicator: the smaller the original value of the indicator, the better its performance.

[0078] The specific standardization formula is shown in equation (10).

[0079] (10) wherein, is a standard value obtained after standardization of the indicator; (3) Entropy value calculation: After completing the standardization of the original data of the indicators, entropy values of each indicator are calculated according to the standardization results. The entropy value is a measure of the uncertainty of information contained in an indicator, and is the core basis for determining the objective weight.

[0080] The entropy value calculation formula is: (11); (12); wherein, is the proportion of the jth evaluated object under the ith evaluation indicator; (4) Calculate the weight : According to the above entropy value, the contribution of each indicator to the overall evaluation, i.e. the indicator weight, is determined. The weight calculation formula is:

[0081] (13); wherein, is the weight coefficient of the ith evaluation indicator. Preferably, the combination weighting method based on game theory modifies the preliminary indicator weight set to generate a final indicator weight set, comprising: (1) Calculate the indicator weight:

[0082] Suppose that a weight calculation method is adopted, and the combination weighting based on the game theory is performed on the evaluation indicators in the indicator evaluation system to obtain the corresponding weight vector , and further obtain an arbitrary linear combination weight set of the weight vectors. (14); wherein, is the weight coefficient​ (2) Optimization combination: By optimizing the weight coefficient , the deviation between each is minimized , that is (15); In the formula is the weight coefficient; is the transpose matrix of the basic weight vector set; is the basic weight vector set; in the formula , indicates the 2-norm in numerical analysis; (3) Equivalent conversion: Using the matrix differential property, formula (14) is equivalent to the linear equation set of the optimal first-order derivative condition, that is: (16); (4) Normalization processing: Based on , the optimized linear combination coefficient is obtained, and the normalization processing is carried out, and the formula is: (17); The final index comprehensive weight vector based on game theory combination weighting is: (18).

[0083] Preferably, the comprehensive application scoring criterion based on the fuzzy comprehensive evaluation method and the pipeline optical fiber early warning system constructs a fuzzy comprehensive evaluation model, comprising: (1) Determine the comprehensive evaluation set Combined with industry standards, operation specifications and optical fiber related research literature, combined with the opinions and experience of field operation personnel and experts, the application situation of the pipeline optical fiber monitoring system is divided into excellent, good, qualified and unqualified four identification levels, and the specific evaluation set is: ; The evaluation level interpretation is shown in Table 3.

[0084] Table 3 Evaluation level table of optical fiber monitoring system application

[0085] (2) Determine the fuzzy judgment matrix The current management and technical conditions are evaluated, and finally the evaluation data is arranged and summarized, and the secondary index fuzzy judgment matrix is obtained:

[0086] In the formula, represents the number of single-dimensional indexes in the optical fiber early warning comprehensive application evaluation index system; represents the number of elements in the fuzzy evaluation set The value is determined according to the actual situation; (3) Synthesis of comprehensive evaluation fuzzy relation According to the Fuzzy theory, the comprehensive weight of the index and the fuzzy evaluation matrix are subjected to fuzzy matrix synthesis operation, and the weighted average type synthesis operator is selected here ; First, the two-level index fuzzy judgment matrix is subjected to single-level comprehensive evaluation, and the optical fiber safety early warning system comprehensive application evaluation vector is obtained, and then the fuzzy evaluation of the primary index is performed to generate a multi-level comprehensive evaluation system; (19) In the formula, is the comprehensive weight of the game theory combination weighting; is the single-factor fuzzy evaluation matrix; is a symbol of matrix synthesis operation, which represents the weighted average synthesis method here; According to the maximum membership degree principle, the optical fiber early warning system comprehensive application evaluation result is , that is: (20).

[0087] As new input data, the fuzzy relation matrix of the criterion layer is constructed. Then, combined with the corresponding weight of each primary index under the criterion layer, the weighted average synthesis operator (formula 19) is used again to perform fuzzy matrix synthesis operation, so as to obtain the comprehensive evaluation vector of the criterion layer. This process is iterated layer by layer upwards until the evaluation vector of the "comprehensive application effect" is calculated. Finally, according to the maximum membership degree principle (formula 20), the final comprehensive evaluation vector is analyzed, and the evaluation grade corresponding to the maximum membership degree is taken as the final evaluation grade of the whole system, such as "excellent", "good" or "qualified".

[0088] Preferably, the evaluation of the comprehensive application of the pipeline optical fiber early warning system comprises: The importance of the optical fiber early warning system comprehensive application evaluation index is sorted and scored by importance quantization, and the corresponding subjective weight and objective weight are generated; Finally, the relative combination weight of the primary and secondary evaluation indexes is obtained according to the game theory combination weighting theory; According to the evaluation index interpretation, the application situation of each secondary index is evaluated to obtain the fuzzy matrix, fuzzy comprehensive vector and single-level comprehensive evaluation result; ​generate the application level of the first-level index of the current optical fiber early warning system in the working station based on the single-stage comprehensive evaluation result; obtain the fuzzy relation matrix and the fuzzy comprehensive evaluation vector of the management level, the technical level and the comprehensive application of the optical fiber early warning system based on the single-stage comprehensive evaluation result, generate the application level of the optical fiber early warning criterion layer index and the overall application level, and analyze the comprehensive application of the current pipeline optical fiber early warning system in combination with the application level of the first-level index, the application level of the optical fiber early warning criterion layer index and the overall application level, and obtain the analysis result.

[0089] Specifically, comprehensive evaluation refers to making a general evaluation of multiple factors affecting the results of things. Fuzzy mathematics is based on fuzzy mathematical theory to quantitatively analyze fuzzy phenomena existing in the objective world, effectively expanding the application range of mathematics from precise phenomena to fuzzy phenomena, and fuzzy comprehensive evaluation is a method of making a general evaluation of things influenced by multiple factors by means of fuzzy mathematical theory. Fuzzy comprehensive evaluation is an evaluation method based on fuzzy mathematical theory to construct a membership function to quantitatively describe fuzzy boundaries, which can effectively quantize and compare decision factors with unclear boundaries and high fuzziness, and has the advantages of simple operation and easy comparison. The basic principle is to determine the index set in each level from low to high according to the analytic hierarchy model, then determine the weight and membership vector of each index layer by layer, construct a fuzzy evaluation matrix, perform fuzzy operation on the fuzzy evaluation matrix and the index weight and normalize to obtain a fuzzy evaluation vector, and finally multiply the fuzzy evaluation vector by the scores corresponding to the evaluation set to obtain the final comprehensive evaluation number.

[0090] The various application evaluation index data of the pipeline optical fiber early warning system have certain dispersion and fuzziness, and the influence degree of the application situation also has uncertainty, which is called fuzzy uncertainty. The essential purpose of fuzzy comprehensive evaluation is to use the membership function to transform the qualitative evaluation of the research object into quantitative evaluation, and to obtain an application evaluation result that can scientifically and reasonably comprehensively evaluate the index by comprehensively analyzing the related evaluation index of the research object.

[0091] Example 2 In order to verify the feasibility and accuracy of the pipeline optical fiber early warning system comprehensive application evaluation method based on the hierarchical grey correlation analysis method and the fuzzy comprehensive evaluation method established by the present application, the DVS early warning system in a certain area of Suining, Sichuan is taken as the research object, and the index system (as shown in Figure 3 ) constructed by the present application and the comprehensive application evaluation method proposed by the present application are used to evaluate it. The specific implementation is as follows: 1. Index weight calculation ​The importance of the evaluation index of the optical fiber early warning system is sorted and quantified by the index weight calculation process, and the corresponding subjective and objective weights are obtained. Finally, the relative combination weights of the first and second level evaluation indexes are obtained according to the game theory combination weighting theory, and the combination weights of the second level indexes are normalized for easy calculation. The combination weights of the first level evaluation indexes based on game theory are shown in Table 4, and the normalized combination weights of the second level evaluation indexes based on game theory are shown in Table 5.

[0092] Table 4 Combination weights of first level evaluation indexes based on game theory

[0093] Table 5 Normalized combination weights of second level evaluation indexes based on game theory

[0094] 2. Fuzzy comprehensive evaluation (1) Single level comprehensive evaluation Ten experts with more than 5 years of experience in oil and gas pipeline optical fiber early warning system projects (including pipeline management personnel, optical fiber engineers, and pipeline designers) were invited to evaluate the application of each index according to the index interpretation. The fuzzy matrix, fuzzy comprehensive vector, and single level comprehensive evaluation results were obtained by formula (19), formula (20), and Table 4, Table 5 index weights, as shown in Table 6.

[0095] Table 6 Fuzzy matrix, fuzzy comprehensive evaluation vector

[0096] ; ; ; ; ; ; According to the above calculation results, the optical fiber early warning system in the work station is excellent in system management, monitoring characteristic index, technical advancement, and waterfall function, and good in contractor service quality, and qualified in optical fiber management.

[0097] (2) High level comprehensive evaluation Based on the single level comprehensive evaluation, the fuzzy relationship matrix and fuzzy comprehensive evaluation vector of the management level, technical level, and comprehensive application of the optical fiber early warning system were calculated according to formula (19), formula (20), and Table 6, as shown in Table 7. The management level indexes are U1-U3, and the technical level indexes are U4-U6.

[0098] Table 7: Criteria layer / target layer fuzzy relationship matrix and fuzzy comprehensive evaluation vector

[0099] From Table 7, it can be obtained that: From the above comprehensive evaluation results, it can be obtained that the application of the optical fiber early warning system in the management level is good, the application of the optical fiber early warning system in the technical level is excellent, and the overall application of the optical fiber early warning system is excellent, and the optical fiber early warning system has reached a relatively ideal running state at present. From the single-level evaluation result analysis, it can be obtained that the optical fiber management evaluation grade is only pass, although the weight of this index is small, but it is also an important management factor that cannot be ignored. The personnel of the project still need to strengthen the daily management of the optical fiber, prevent problems from occurring, and continue to maintain the efficient operation of the optical fiber early warning system. After verifying the evaluation results and the actual comprehensive application of the optical fiber early warning system in the project, it is found that the two are basically consistent, which shows that the model has high reliability and feasibility.

[0100] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other. Especially, for the system or system embodiment, since it is basically similar to the method embodiment, it is described more simply, and the related parts can be referred to the part of the method embodiment. The system and system embodiment described above are only illustrative, and the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, they can be located in one place or distributed on multiple network units. According to the actual needs, some or all of the modules can be selected to achieve the purpose of the embodiment. Those skilled in the art can understand and implement without creative labor.

[0101] The professional person can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present text can be realized by electronic hardware, computer software or combination of both.

[0102] In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been described in the above description in general terms according to function. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical scheme. The professional person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.​​​

[0103] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Numerous modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of the inventive faculty. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the application situation of a pipeline optical fiber early warning system, characterized in that, The method comprises the following steps: S1. Constructing a set of selected indicators based on the pipeline optical fiber early warning public data set; qualitative screening and quantitative screening of the set of selected indicators to generate a set of key indicators; calculating the information contribution rate of each key indicator in the set of key indicators, and performing a reasonableness test on the set of key indicators based on the information contribution rates of all key indicators to generate a final indicator set; S2. Constructing a comprehensive application evaluation index system based on the final indicator set; S3. Based on expert survey questionnaires, pipeline optical fiber early warning system operation history data, optical fiber early warning system manufacturer data, and corresponding industry standards, evaluating the indicators in the comprehensive application evaluation index system to obtain quantitative evaluation results for the corresponding indicators; S4. Based on the analytic hierarchy process and entropy weight method, performing preliminary weight calculation on the indicators of the comprehensive application evaluation index system of the pipeline optical fiber early warning system to generate a preliminary indicator weight set, and based on the game theory combination weighting method, modifying the preliminary indicator weight set to generate a final indicator weight set; combining the quantitative evaluation results of the indicators and the final indicator weight set to generate a comprehensive application scoring criterion; S5. Determine the application level of the pipeline optical fiber monitoring system, construct a fuzzy comprehensive evaluation model based on the fuzzy comprehensive evaluation method and the comprehensive application scoring criterion of the pipeline optical fiber early warning system; and evaluate the comprehensive application of the pipeline optical fiber early warning system based on the real-time data of the pipeline optical fiber early warning system and the fuzzy comprehensive evaluation model to generate an analysis result.

2. The method of claim 1, wherein the method further comprises: The construction of the set of selected indicators comprises: obtaining influence factor data of the application effect of the pipeline optical fiber early warning system based on historical operation data of the pipeline optical fiber early warning system; classifying the influence factor data, and generating external factor data based on the environment data on which the sensing optical fiber and the regional monitoring center depend; generating internal factor data based on system device reliability, technical advancement, network algorithm design, and maintenance management; classifying the influence factor data based on the management level and the technical level to generate the set of selected indicators.

3. The method of claim 2, wherein the method further comprises: The qualitative screening and quantitative screening of the set of selected indicators to generate a set of key indicators comprises: generating corresponding comparison indicator data sets based on the management and technical criterion public data set, the oil and gas pipeline management industry standard, the optical fiber early warning system management standard, and the optical fiber early warning system technical standard; sequentially obtaining the intersection indicators of the corresponding comparison indicator data sets and the set of selected indicators; generating an intersection indicator set based on the intersection indicators corresponding to all comparison indicator data sets; the intersection indicator set includes a plurality of intersection indicators and the occurrence frequencies of the corresponding intersection indicators; generating a qualitative indicator set by comparing the occurrence frequency of each intersection indicator with a preset value; calculating the gray correlation degree of each indicator in the qualitative indicator set based on gray correlation analysis; obtaining the indicator weight vector of each indicator in the qualitative indicator set based on the analytic hierarchy process; multiplying the indicator weight vector and the gray correlation degree obtained by the gray correlation analysis to generate the comprehensive correlation degree of each indicator; completing the quantitative screening of the indicators to generate a set of key indicators by comparing and screening the comprehensive correlation degree of each indicator with a preset correlation degree.

4. The method of claim 2, wherein the method further comprises: The generation of the comprehensive correlation degree of each indicator comprises: (1) determining the current indicator weight: Let the current index weight obtained according to the analytic hierarchy process be wherein is the weight corresponding to the kth evaluation index, ; (2) determining the evaluation object and the reference sequence: There are m evaluation objects and n evaluation indexes, the reference series is , the comparison series is , ; (3) Dimensionless processing: Reference series and comparison series Non-dimensionalization is performed, i.e. each parameter of each index is divided by the maximum value of the current parameter, so that each parameter of each series is between 0 and 1; (4) Calculate the grey correlation coefficient: Reference sequence Correlation sequence Grey correlation coefficient on the kth index, the calculation formula is: （1）； In the formula, is a resolution coefficient, , in general ; wherein is the maximum value of the difference series, is the minimum value of the difference series; (5) Calculate the grey correlation degree: The average value method is introduced to calculate the grey correlation degree The calculation formula is: （2）； (6) Calculate the comprehensive grey correlation degree: The weight vector obtained by combining the analytic hierarchy process The comprehensive grey correlation degree is obtained The calculation formula is: （3）； wherein, is the weight coefficient of the kth index; (7) Evaluation index screening: For comprehensive grey correlation degree Setting a grey correlation degree preset value T ; Will Sort the element values ​​in descending order and update them. In the middle, then to The elements in the array are added in descending order until the sum is greater than or equal to the sum of the elements in the array. T At the same time, the index corresponding to the elements participating in the accumulation is retained as Delete the remaining elements, that is: （4）。 5. The method of claim 4, wherein the method further comprises: The information contribution rate based on all key indicators is used to reasonably test the key indicator set to generate a final indicator set, including: The ratio of the sum of the variances of all the index data after quantitative screening to the sum of the variances of all the index data after qualitative screening is generated as a first ratio, and the first ratio is taken as the information content of the index system after screening, and based on this, the information contribution rate is generated by performing index system rationality test ; Information contribution rate The calculation formula is: （5）； In the formula, is the trace of the covariance matrix, representing the sum of the main diagonal elements of the covariance matrix; is the number of indicators after quantitative screening, is the number of indicators after qualitative screening; When , the evaluation index system is considered to pass the rationality test; Obtaining the final indicator set through rationality test.

6. The method of claim 5, wherein the method further comprises: Including: Based on the final indicator set, a comprehensive application evaluation index system is constructed, including: Based on the final indicator set, a comprehensive application evaluation index system is constructed, and the comprehensive application evaluation index system includes: Criteria layer indicators, first-level indicators and second-level indicators; The criteria layer indicators include management level indicators and technical level indicators; Among them, the first-level indicators corresponding to the management level indicators include: System management, optical fiber management and contractor service quality; The second-level indicators corresponding to system management include: operation environment control, emergency control, alarm control, application operation and maintenance; The second-level indicators corresponding to optical fiber management include: optical fiber laying image, optical fiber calibration; The second-level indicators corresponding to contractor service quality include: average repair time, first-time repair success rate, repair failure recurrence rate, remote support capability, preventive maintenance; The first-level indicators corresponding to the technical level indicators include: Monitoring characteristic indicators, technical advancement indicators, waterfall chart functions; The second-level indicators corresponding to the monitoring characteristic indicators include: monitoring distance, spatial resolution, positioning accuracy, alarm response time, false negative rate, false positive rate, effective alarm rate; The second-level indicators corresponding to the technical advancement indicators include: maximum monitoring distance, system interface display, device reliability, device self-checking function, autonomous controllability, data processing capability, intrusion monitoring type; The second-level indicators corresponding to the waterfall chart function include: basic function of waterfall chart, extension function of waterfall chart.

7. The method of claim 5, wherein the method further comprises: determining a number of the integrated applications of the pipeline optical fiber warning system; and determining a number of the integrated applications of the pipeline optical fiber warning system per unit length of the pipeline optical fiber. Based on the analytic hierarchy process and entropy weight method, the preliminary weight calculation of the indicators of the comprehensive application evaluation index system of the pipeline optical fiber early warning system is generated to generate a preliminary indicator weight set, including: The subjective weight determination of the indicators based on the analytic hierarchy process includes: (1) Constructing a judgment matrix: According to Santy's proportional scale theory, the comparison results of the indicators are obtained, a hierarchical structure model is established, and a judgment matrix is constructed; ； (2) Calculate the weight vector and the maximum eigenvalue: Take the square root average value of the normalized processing result of the judgment matrix as the same level indicator sorting weight value; The calculation formula is as follows: （6）； (3) Consistency test of judgment matrix: Consistency index of an n x n matrix The larger the value, the greater the degree to which the matrix deviates from perfect consistency. The calculation method is as follows: （7）； wherein is the largest eigenvalue of the matrix, and n is the order of the matrix. By the consistency index The value of the matrix consistency ratio is calculated as follows: （8）； In the formula, is the average random consistency index, and the analytic hierarchy process is used to determine the consistency index Value table, query Value, which needs to meet the consistency check index <0.1, ensure that the judgment matrix is consistent; otherwise, the judgment matrix needs to be adjusted until the consistency requirement is met. The objective weight determination of the indicators based on the entropy weight method includes: (1) Establishing an original decision database: According to the questionnaire, m evaluation objects and n evaluation indicators, the original decision data matrix X is obtained: ； (2) Matrix standardization: including positive index standardization and negative index standardization; Among them, the positive index: （9）； The negative index: （10） wherein, is a standard value obtained after the index is normalized; (3) Entropy value calculation: The calculation formula of the entropy value is: （11）； （12）； In the formula, is the first evaluation index under the first evaluation index under the first (4) Calculate weight : （13）； wherein is the weight coefficient of the evaluation index.

8. The method of claim 7, wherein the method further comprises: The preliminary indicator weight set is modified based on the game theory combination weighting method to generate a final indicator weight set, including: (1) Calculate the indicator weight: Assume to adopt Various weighting calculation methods are used to evaluate the indicators in the system. Each evaluation indicator is weighted using a combination of factors based on game theory to obtain a corresponding weight vector. Further obtained Weight set of any linear combination of weight vectors （14）； In the formula, is a weight coefficient (2) Optimization combination: By optimizing the weight coefficients , the deviation between each and each is minimized, that is （15）； wherein are weight coefficients; is the transpose matrix of the set of basic weight vectors; is the set of basic weight vectors; wherein denotes the 2-norm in numerical analysis; (3) Equivalent conversion: Using the matrix differential property, formula (14) is equivalent to the linear equation set of the optimal first-order derivative condition, that is: （16）； (4) Normalization processing: Based on The optimized linear combination coefficients are obtained and normalized, and the formula is: （17）； The index comprehensive weight vector based on the combination weighting of game theory is finally obtained as: （18）。 9. The method of claim 8, wherein the method further comprises: determining a number of the integrated applications of the pipeline optical fiber warning system; and determining a number of the integrated applications of the pipeline optical fiber warning system per unit length of the pipeline optical fiber. The application score criterion based on the fuzzy comprehensive evaluation method and the pipeline optical fiber early warning system constructs a fuzzy comprehensive evaluation model, including: (1) Determine the comprehensive evaluation set Combined with industry standards, operation specifications, and optical fiber related research literature, and referring to the opinions and experiences of field operation personnel and experts, the application level of the pipeline optical fiber monitoring system is divided into four levels: excellent, good, qualified, and substandard. The specific evaluation set is: ； (2) Determine the fuzzy judgment matrix The current management and technology are evaluated, and finally, the fuzzy judgment matrix of the secondary index is obtained by sorting and summarizing the evaluation data : In the formula, represents the number of single-dimensional indexes in the optical fiber early warning comprehensive application evaluation index system; represents the number of elements in the fuzzy evaluation set The value is determined according to the actual situation.​ (3) Synthesis of fuzzy relationship of comprehensive evaluation According to the Fuzzy theory, the comprehensive weight of the index and the fuzzy evaluation matrix are subjected to fuzzy matrix synthesis operation, and a weighted average type synthesis operator is selected ; First, the secondary index fuzzy judgment matrix The single-level comprehensive evaluation is carried out to obtain the comprehensive application evaluation vector of the optical fiber safety early warning system The multi-level comprehensive evaluation system is generated by high-level fuzzy evaluation. （19）； In the formula, is a game theory combination weighting comprehensive weight; is a single factor fuzzy evaluation matrix; is a symbol of a matrix synthesis operation, indicating a weighted average type synthesis operator ; According to the maximum membership principle, the comprehensive application evaluation result of the optical fiber early warning system is that is: （20）。 10. The method of claim 9, wherein the method further comprises: determining a number of the integrated applications of the pipeline optical fiber warning system; and determining a number of the integrated applications of the pipeline optical fiber warning system per unit length of the pipeline optical fiber. The evaluation of the comprehensive application of the pipeline optical fiber early warning system includes: The importance of the evaluation index of the optical fiber early warning system is ranked and quantified to score the importance, and the corresponding subjective weight and objective weight are generated; Finally, the relative combination weights of the first-level and second-level evaluation indexes are obtained based on the combination weighting theory of game theory; According to the evaluation index interpretation, the application of each second-level index is evaluated to obtain the fuzzy matrix, fuzzy comprehensive vector, and single-level comprehensive evaluation result; Based on the single-level comprehensive evaluation result, the application level of the first-level index of the current optical fiber early warning system in the work station is generated; Based on the single-level comprehensive evaluation result, the fuzzy relationship matrix and fuzzy comprehensive evaluation vector of the management level, technical level, and comprehensive application of the optical fiber early warning system are obtained, and the application level of the optical fiber early warning criterion layer index and the overall application level are generated; And combined with the application level of the first-level index, the application level of the optical fiber early warning criterion layer index, and the overall application level, the comprehensive application of the current pipeline optical fiber early warning system is analyzed, and the analysis result is obtained.

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