A High-Precision Analysis Method for the Time-Varying Reliability of Transmission Tower Structures

Abstract

The present application relates to the technical field of data processing, and particularly relates to a high-precision analysis method for the time-varying reliability of a transmission tower structure. The method includes: collecting parameter types and their corresponding parameter values; obtaining the abnormal performance degree of each parameter type during each tower repair according to the stability before repair, the stability after repair, and the change in parameter values for the same number of days before and after repair of the transmission tower; thereby determining the abnormal parameters of each parameter type; obtaining the time-varying correlation degree of the parameter type according to the time difference and abnormal performance degree between the abnormal parameters; based on the month of the current moment, obtaining the influence degree of the current period environmental factors on the tower through the proportion of the number of repairs in the same month of the previous year and the difference in the number of repairs from the adjacent months; and determining the key parameter types in combination with the abnormal performance degree and the time-varying correlation degree; obtaining the reliability degree based on the key parameter types to complete the reliability analysis. The present application improves the accuracy of precision analysis.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and specifically relates to a high-precision analysis method for the time-varying reliability of transmission tower structures. Background Art

[0002] As an important part of the power system, the time-varying reliability analysis of the structure of transmission towers is crucial for ensuring the safe and stable operation of the power grid. With the expansion of the power grid scale and the renewal of old towers, the market's demand for high-precision time-varying reliability analysis is increasing. It is necessary to consider not only the current structural state but also the changes in structural performance over time due to various factors (such as environmental conditions, material aging, fatigue damage, etc.). In the future, with the improvement of computing power and the introduction of new analysis methods, the time-varying reliability analysis of transmission tower structures is expected to achieve higher precision and efficiency, providing more reliable guarantees for the safe and stable operation of the power system.

[0003] When analyzing the time-varying reliability of transmission tower structures currently, it is usually based on the joint analysis of its multi-dimensional data. However, during the analysis process, a large amount of irrelevant data is included, which may obscure those factors that have a significant impact on structural reliability. For example, small changes in certain environmental factors may be more critical than other factors, but due to the interference of irrelevant data, the impact of these subtle changes on the stability of the tower may not be accurately captured during the analysis. Summary of the Invention

[0004] To solve the technical problem of low monitoring accuracy caused by the influence of irrelevant data, this application provides a high-precision analysis method for the time-varying reliability of transmission tower structures. The specific technical solutions adopted are as follows:

[0005] This application proposes a high-precision analysis method for the time-varying reliability of transmission tower structures, which includes the following steps:

[0006] Collect the maintenance times and time of the transmission tower through sensors, as well as the parameter values of each parameter type at different times. The parameter types include angles, strain forces, and wind speeds;

[0007] Each time a transmission tower is repaired, the same preset number of days is selected before and after the repair; the stability of each parameter type before each tower repair is obtained according to the intersection ratio of the ranges of parameter values for two adjacent days before each tower repair; the stability of each parameter type after each tower repair is obtained according to the intersection ratio of the ranges of parameter values for two adjacent days after each tower repair; the abnormal performance degree of each parameter type during each tower repair is obtained by using the intersection ratio of the ranges of parameter values of each parameter type before and after the tower repair and the stability before and after the tower repair; the abnormal parameters of each parameter type are determined based on the abnormal performance degree.

[0008] Obtain the time of each abnormal parameter, sort the abnormal parameters in chronological order, and use the reciprocal of the positive fusion of the difference between the time difference of adjacent abnormal parameters in each parameter type and the mean of the time differences of all adjacent abnormal parameters and the standard deviation of the abnormal performance degree of the parameter type during all tower repairs as the time-varying correlation degree of the parameter type.

[0009] Obtain the number of tower repairs per month, and obtain the influence degree of the current environmental factors on the tower according to the ratio of the number of tower repairs in the current month in the previous year to the total number of tower repairs in the previous year and the difference from the number of tower repairs in the adjacent month; use the abnormal performance degree of each parameter type as the weight, combine the time-varying correlation degree of the parameter type and the influence degree of the current environmental factors on the tower to obtain the key monitoring degree of the parameter type; determine the key parameter type according to the key monitoring degree.

[0010] Take the parameter value and time of the key parameter type as the input to obtain the reliability degree, and complete the reliability analysis.

[0011] In the above solution, the present application proposes a high-precision analysis method for the time-varying reliability of a transmission tower structure. By analyzing the abnormal performance of data types reflected under historical repairs, and based on the time-variability of the transmission tower structure, analyzing the correlation between each data type and it, and considering the influence of current environmental factors at the same time, further screening out the data types with more monitoring value among various types of data related to the transmission tower, using this as the data source for the high-precision analysis of the time-varying reliability of the transmission tower structure, and performing the time-varying reliability analysis of the transmission tower structure through these data, avoiding the interference caused by a large amount of irrelevant data and improving the analysis accuracy.

[0012] In one embodiment, the method for obtaining the stability of each parameter type before each tower repair according to the intersection ratio of the ranges of parameter values for two adjacent days before each tower repair is as follows:

[0013] , represents the range of the parameter value of the m-th parameter type on the b-th day before the n-th tower repair. Represents the range of parameter values of the m-th parameter type on the (b + 1)-th day before the n-th tower maintenance, Represents the number of days selected before the tower maintenance, Represents the stability of the m-th parameter type before the n-th tower maintenance.

[0014] In one embodiment, the method for obtaining the abnormal performance degree of each parameter type during each tower maintenance by using the intersection ratio of the parameter value ranges of each parameter type before and after the tower maintenance and the stability before and after the tower maintenance is as follows:

[0015] , Represents the range of parameter values of the m-th parameter type on the b-th day before the n-th tower maintenance, Represents the range of parameter values of the m-th parameter type on the b-th day after the n-th tower maintenance, Represents the stability of the m-th parameter type before the n-th tower maintenance, Represents the stability of the m-th parameter type after the n-th tower maintenance, Represents the same number of days selected before and after the tower maintenance, Represents the abnormal performance degree of the m-th parameter type during the n-th tower maintenance.

[0016] In one embodiment, the method for determining the abnormal parameters of each parameter type based on the abnormal performance degree is as follows:

[0017] Preset an abnormal threshold. If the abnormal performance degree is greater than the preset abnormal threshold, the parameter type is an abnormal parameter during this tower maintenance.

[0018] In one embodiment, the expression of the time-varying correlation degree of the parameter type is:

[0019] , Represents the standard deviation of the abnormal performance degrees of the m-th parameter type during all tower maintenances, Represents the number of abnormal parameters in the m-th parameter type, Represents the time difference between the c-th abnormal parameter and the (c + 1)-th abnormal parameter of the m-th parameter type, Represents the mean value of the time differences between the abnormal parameters corresponding to the m-th parameter type and adjacent abnormal parameters, Represents the linear normalization function, Represents the time-varying correlation degree of the m-th parameter type.

[0020] In one embodiment, the method for obtaining the influence degree of environmental factors on the iron tower in the current period based on the ratio of the number of iron tower repairs in the previous year in the month where the current moment is located to the total number of iron tower repairs in the previous year and the difference in the number of iron tower repairs in adjacent months is as follows:

[0021] For each month, the difference between the number of repairs in this month and the number of repairs in the previous month is recorded as the first repair difference, the difference between the number of repairs in this month and the number of repairs in the next month is recorded as the second repair difference, and the average value of the first repair difference and the second repair difference is recorded as the difference in the number of repairs between each month and adjacent months;

[0022] Based on the difference in the number of repairs between the month where the current moment is located and adjacent months and the ratio of the number of repairs in the same month of the previous year to the total number of repairs in the previous year at the month where the current moment is located, obtain the influence degree of environmental factors on the iron tower in the current period.

[0023] In one embodiment, the expression for the influence degree of environmental factors on the iron tower in the current period is:

[0024] , where u represents the number of repairs in the same month of the previous year at the month where the current moment is located, U represents the total number of repairs in the previous year, represents the difference in the number of repairs between the same month of the previous year and adjacent months at the month where the current moment is located, represents the influence degree of environmental factors on the iron tower in the current period.

[0025] In one embodiment, the method for obtaining the key monitoring degree of parameter types by combining the time-varying correlation degree of parameter types and the influence degree of environmental factors on the iron tower in the current period with the abnormal manifestation degree of each parameter type as the weight is:

[0026] , represents the abnormal manifestation degree of the mth parameter type at the most recent repair, represents the time-varying correlation degree of the mth parameter type, represents the influence degree of environmental factors on the iron tower in the current period, represents the key monitoring degree of the mth parameter type.

[0027] In one embodiment, the method for determining the end parameter type according to the key monitoring degree is:

[0028] When the key monitoring degree is greater than or equal to 0.35, the parameter type corresponding to this key monitoring degree is the key parameter type.

[0029] The beneficial effects of this application are:

[0030] The present application proposes a high-precision analysis method for the time-varying reliability of transmission tower structures. By analyzing the abnormal manifestations of data types reflected under historical maintenance, and based on the time-varying nature of transmission tower structures, the correlation between each data type and it is analyzed. At the same time, the influence of current environmental factors is considered, and then among various types of data related to transmission towers, data types with more monitoring value are screened out, which are used as the data source for the high-precision analysis of the time-varying reliability of transmission tower structures, and the time-varying reliability analysis of transmission tower structures is carried out through these data, avoiding the interference brought by a large amount of irrelevant data and improving the analysis accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 It is a flowchart of a high-precision analysis method for the time-varying reliability of a transmission tower structure provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manner, structure, features and effects of a high-precision analysis method for the time-varying reliability of a transmission tower structure proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0035] An embodiment of a high-precision analysis method for the time-varying reliability of a transmission tower structure:

[0036] The following specifically describes the specific solution of a high-precision analysis method for the time-varying reliability of a transmission tower structure provided by the present application in combination with the drawings.

[0037] Please refer to Figure 1 , which shows a flowchart of a high-precision analysis method for the time-varying reliability of a transmission tower structure provided by an embodiment of the present application. The method includes the following steps:

[0038] Step S001, collect the maintenance times of the transmission tower and the corresponding time.

[0039] For a transmission tower, multiple sensors are installed on the tower to detect relevant information on the structural health of the transmission tower. In this embodiment, the installed sensors include an inclination sensor, a strain sensor, and a wind speed sensor.

[0040] Take the angle collected by the inclination sensor, the strain force collected by the strain sensor, and the wind speed collected by the wind speed sensor as parameters. In this embodiment, the parameter values are collected once every 1 minute.

[0041] Obtain the historical acquisition data of the transmission tower, that is, the parameter values corresponding to different parameters of the tower every day in history, and based on the historical maintenance and inspection records of the transmission tower, obtain the maintenance times of the transmission tower and the time at each maintenance.

[0042] So far, the historical acquisition data of the transmission tower, the maintenance times of the tower, and the corresponding time have been obtained.

[0043] Step S002, according to the stability before maintenance, the stability after maintenance, and the change in parameter values of the same number of days before and after maintenance each time the transmission tower is maintained, obtain the abnormal manifestation degree of each parameter type during each tower maintenance; thereby determine the abnormal parameters of each parameter type.

[0044] During the long-term operation and use of the transmission tower, some problems will inevitably occur, such as corrosion, rust, mechanical fatigue, structural deformation, etc. This is because with the changes in time and load, the structure and materials of the transmission tower have experienced different degrees of fatigue.

[0045] When analyzing the time-varying characteristics of the structure of the transmission tower, due to the influence of the passage of time, fatigue, etc. on the tower, the performance gradually degrades. Therefore, the reliability of the transmission tower will also decrease with the change of time, that is, the reliability of the transmission tower also has time-variation. Then when analyzing, in addition to considering the past and current changes, it is also necessary to consider the future changes of the transmission tower.

[0046] Problems will occur during the long-term use of the transmission tower. To ensure its good structural reliability, it is generally solved through maintenance. Each maintenance corresponds to certain structural problems of the corresponding transmission tower, and these problems will be indirectly reflected through certain data manifestations of the tower. Therefore, by comparing the change degree of various types of data of the tower before and after each maintenance, the parameter types that are abnormal due to the existing structural problems in this maintenance can be reflected.

[0047] During each maintenance of the iron tower, the parameter values of different parameter types for several days before and after the iron tower are taken as an example of 7 days in this embodiment. For the structural problems corresponding to the iron tower maintenance, problems have occurred in the iron tower structure before the maintenance, so the corresponding data will remain stable under abnormal conditions, while the iron tower structure has been repaired after the maintenance, so the corresponding data will be stable under normal conditions of normal values; that is, before the maintenance, the parameter values are similar, and after the maintenance, the parameter values are also similar.

[0048] Therefore, here, the data before and after the iron tower maintenance can be compared day by day to compare the differences in the data before and after the maintenance; and the stability of the corresponding data before and after the maintenance is considered separately to correct the above, so as to determine which parameter type induces the structural problems that occur during each iron tower maintenance.

[0049] In this application, let the number of parameter types be M, the total number of iron tower maintenance in the historical data be N, and the number of days before and after each maintenance be B days.

[0050] Before the iron tower maintenance, the stability of each parameter type before each iron tower maintenance is determined by the range of the parameter value of each parameter type on each day before the iron tower maintenance and the range of the parameter value on the next day. Where the range of the parameter value is the interval composed of the minimum value and the maximum value of the parameter type on each day.

[0051] The expression of the stability is:

[0052] , represents the range of the parameter value of the m-th parameter type on the b-th day before the n-th iron tower maintenance, represents the range of the parameter value of the m-th parameter type on the (b + 1)-th day before the n-th iron tower maintenance, represents the number of days selected before the iron tower maintenance, represents the stability of the m-th parameter type before the n-th iron tower maintenance.

[0053] Similarly, the stability of each parameter type after each iron tower maintenance is determined by the range of the parameter value of each parameter type on each day after the iron tower maintenance and the range of the parameter value on the next day, denoted as .

[0054] For each parameter type, the abnormal manifestation degree of each parameter type during each iron tower maintenance is obtained according to the difference in the range before and after the iron tower maintenance and the stability before and after the iron tower maintenance.

[0055] The expression of the abnormal manifestation degree is:

[0056] , represents the range of the parameter value of the m-th parameter type on the b-th day before the n-th iron tower maintenance, Represents the range of the parameter value of the m-th parameter type on the b-th day after the n-th tower maintenance. Represents the stability of the m-th parameter type before the n-th tower maintenance. Represents the stability of the m-th parameter type after the n-th tower maintenance. Represents the same number of days selected before and after the tower maintenance. Represents the abnormal performance degree of the m-th parameter type during the n-th tower maintenance.

[0057] Among them, for each parameter type during each tower maintenance, the closer the ranges of adjacent days before and after the maintenance are, that is, the larger the intersection-over-union ratio, the greater the stability before and after the maintenance, and the greater the stability, the greater the abnormal performance degree. And the smaller the intersection-over-union ratio of the ranges on the same day before and after the maintenance, the greater the difference before and after the maintenance, and the greater the abnormal performance degree.

[0058] Based on the above steps, the abnormal performance degree of each parameter type during each tower maintenance can be obtained. The higher it is, the more likely it is that the parameter type shows abnormal data changes caused by the corresponding structural problems of this tower maintenance.

[0059] Therefore, an abnormal threshold is set. When the abnormal performance degree is greater than the abnormal threshold, the parameter type corresponding to this abnormal performance degree during the tower maintenance is recorded as an abnormal parameter, which is the cause of the corresponding structural problem of the tower maintenance. In this embodiment, the value of the abnormal threshold is 0.65.

[0060] So far, the abnormal parameters of each parameter type during all tower maintenances have been obtained.

[0061] Step S003, obtain the time-varying correlation degree of the parameter type according to the time difference and abnormal performance degree between the abnormal parameters.

[0062] For the abnormal parameters caused by the corresponding structural problems of each tower maintenance screened out in the above process, since these abnormal data types can indirectly reflect the problems existing in the current tower structure, and because in the actual application of the tower, its performance will also change with time, so we analyze the influence of the appearance of the same abnormal parameter in the historical maintenance process on the time-variation of the transmission tower structure, so as to analyze and reflect the correlation degree between each parameter type and time-variation.

[0063] For the same parameter type, obtain the number of times it is an abnormal parameter during all tower maintenances. Count the time of each tower maintenance, expressed as the number of days; and there will be abnormal parameters in each tower maintenance; thus, the time of the abnormal parameters of each parameter type is obtained.

[0064] Therefore, here we can analyze through the time when the abnormal parameters of the parameter type appear and their abnormal number manifestation degree. Because the transmission tower has structural time-variability and its various performances will gradually change with time, we can comprehensively analyze through the time interval and the difference in abnormal manifestation degree of the same parameter type under different current maintenance times. When the time intervals of multiple times are closer and the abnormal manifestation degrees are more consistent, it indicates that this parameter type will present similar abnormal data manifestations as the regular time passes, thereby reflecting a certain structural problem that the transmission tower may correspond to based on this parameter type, that is, the time-varying correlation degree of the current parameter type.

[0065] For each parameter type, calculate the standard deviation of the abnormal manifestation degree of this parameter type during all tower maintenance times. The larger the standard deviation, the more obvious the fluctuation of the abnormal manifestation degree, and the greater the difference, the smaller the influence with time change. Sort the abnormal parameters of the parameter type during all tower maintenance times in chronological order, calculate the time difference between adjacent abnormal parameters, and determine the time-varying correlation degree of each parameter type according to the difference between the time difference and the mean value of the time difference and the standard deviation of the abnormal manifestation degree. The expression is:

[0066] , represents the standard deviation of the abnormal manifestation degree of the m-th parameter type during all tower maintenance times, represents the number of abnormal parameters in the m-th parameter type, represents the time difference between the c-th abnormal parameter and the (c + 1)-th abnormal parameter of the m-th parameter type, represents the mean value of the time difference between the abnormal parameters corresponding to the m-th parameter type and the adjacent abnormal parameters, represents the linear normalization function, represents the time-varying correlation degree of the m-th parameter type.

[0067] So far, the time-varying correlation degree of each parameter type has been obtained.

[0068] Step S004, based on the month of the current moment, obtain the influence degree of the current period environmental factors on the tower through the proportion of the maintenance times in the same month of the previous year and the difference in the maintenance times from the adjacent months; and determine the key parameter types by combining the abnormal manifestation degree and the time-varying correlation degree.

[0069] Under the environment where the tower is located, with the local seasonal environmental characteristics, due to the changes in different environmental factor conditions such as humidity, temperature, wind force, etc. in different periods, there will be different degrees of influence on the service life of different levels of the tower. Therefore, the environmental conditions in the same period as the current environment can be determined through the historical environmental conditions, so as to determine the influence degree of the environment on the current period.

[0070] Since the degree of environmental impact on the iron tower varies at different times, and our purpose is to analyze the degree of environmental impact on the iron tower at different times. Intuitively, the environmental impact on the iron tower can be reflected by the number of repairs to the iron tower. Therefore, the degree of environmental impact of environmental factors on the iron tower at different times can be defined by analyzing the differences in the number of repairs at different times.

[0071] Therefore, obtain the number of repairs to the iron tower each month within one year at the current moment. By comparing the number of repairs each month with the total number of repairs, and considering the seasonal nature of the environmental impact on the iron tower, when analyzing, we cannot simply compare the number of repairs in one month. We also need to consider the similarity of the number of repairs between the current month and the adjacent months to comprehensively determine the degree of environmental impact of environmental factors on the iron tower each month.

[0072] Obtain the number of repairs in the same month of the previous year for the month where the current moment is located, denoted as the number of repairs in the same month. Obtain the degree of environmental impact of environmental factors on the iron tower during the period based on the difference between the number of repairs in the same month and the total number of repairs in the previous year, as well as the difference in the number of repairs between the same month and the adjacent months in the previous year.

[0073] For each month, record the difference between the number of repairs in this month and the number of repairs in the previous month as the first repair difference, record the difference between the number of repairs in this month and the number of repairs in the next month as the second repair difference, and record the average of the first repair difference and the second repair difference as the difference in the number of repairs between each month and the adjacent months.

[0074] The expression for the degree of environmental impact of environmental factors during the current period on the iron tower is:

[0075] , where u represents the number of repairs in the same month of the previous year for the month where the current moment is located, U represents the total number of repairs in the previous year, the difference in the number of repairs between the same month and the adjacent months of the month where the current moment is located in the previous year, represents the degree of environmental impact of environmental factors during the current period.

[0076] Through the above steps, we analyzed the abnormal manifestation degree of each parameter type during the repair of the iron tower. For the parameter types with prominent manifestations, we analyzed and considered the impact brought by structural time-variation, and also analyzed the impact brought by environmental factors during the current period. Since the parameter types related to the time-varying reliability of transmission iron tower structures are complex, when analyzing, there will be a large amount of irrelevant data interfering with the key data. Therefore, here we determine the parameter types that should be key monitored.

[0077] Therefore, based on the abnormal manifestation degree corresponding to each parameter type during the most recent maintenance, the greater it is, the more attention is paid to the correlation degree corresponding to the time-varying structure of the transmission tower, and the smaller it is, the more attention is paid to the influence degree of environmental factors on the tower during the current period.

[0078] Therefore, using the abnormal manifestation degree as the weight, the key monitoring degree of each parameter type is obtained by weighting the time-varying correlation degree of the parameter type and the influence degree of environmental factors on the tower during the current period. The expression is:

[0079] , represents the abnormal manifestation degree of the m-th parameter type during the most recent maintenance. represents the time-varying correlation degree of the m-th parameter type. represents the influence degree of environmental factors on the tower during the current period. represents the key monitoring degree of the m-th parameter type.

[0080] Thus, the key monitoring degree of each parameter type is obtained.

[0081] Here, a monitoring threshold is set. If the key monitoring degree is greater than or equal to the monitoring threshold, the parameter type corresponding to the key monitoring degree is the key parameter type. In this embodiment, the value of the monitoring threshold is 0.35.

[0082] So far, the key parameter types are obtained.

[0083] Step S005, obtaining the reliability degree based on the key parameter types to complete the reliability analysis.

[0084] After obtaining the key parameter types at the current moment, obtain each parameter value of the key parameter types and its corresponding time, and use them as the input of the regression analysis algorithm. The output is the reliability degree of the time-varying structure of the transmission tower, so as to complete the high-precision analysis and avoid the interference of a large amount of irrelevant data.

[0085] It should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0086] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A high-precision analysis method for the time-varying reliability of a transmission tower structure, characterized in that: The method comprises the following steps: The sensors collect the number and time of maintenance of the transmission towers, as well as the parameter values ​​of each parameter type at different times, including angle, strain force and wind speed; Each time a transmission tower is repaired, the same preset number of days is selected before and after the repair; the stability of each parameter type before each tower repair is obtained according to the intersection-and-joint ratio of the range of parameter values ​​of each parameter type in two adjacent days before each tower repair; the stability of each parameter type after each tower repair is obtained according to the intersection-and-joint ratio of the range of parameter values ​​of each parameter type in two adjacent days after each tower repair; the abnormal performance degree of each parameter type during each tower repair is obtained by using the intersection-and-joint ratio of the parameter value range of each parameter type before and after the tower repair and the stability before and after the tower repair; and the abnormal parameter of each parameter type is determined based on the abnormal performance degree; The time of each abnormal parameter is obtained, and the abnormal parameters are sorted in chronological order. The time difference between the time difference of adjacent abnormal parameters in each parameter type and the mean of the time difference of all adjacent abnormal parameters and the standard deviation of the abnormal performance of the parameter type during all tower maintenance are forward fused as the time-varying correlation of the parameter type; the time of the abnormal parameter is the time when the abnormal parameter appears during tower maintenance; Obtain the number of tower maintenances each month, and obtain the degree of influence of environmental factors on the towers in the current period based on the ratio of the number of tower maintenances in the previous year to the total number of tower maintenances in the previous year and the difference between the number of tower maintenances in adjacent months; obtain the key monitoring degree of the parameter type by taking the abnormal performance degree of each parameter type as the weight, combining the time-varying correlation degree of the parameter type and the degree of influence of environmental factors on the towers in the current period; determine the key parameter type based on the key monitoring degree; The parameter values ​​and time of the key parameter types are used as input to obtain the reliability degree and complete the reliability analysis.

2. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The method for obtaining the stability of each parameter type before each tower maintenance according to the intersection and union ratio of the range of parameter values ​​of each parameter type in two consecutive days before each tower maintenance is: , Indicates the range of parameter values ​​of the mth parameter type on the bth day before the nth tower maintenance. Indicates the range of parameter values ​​of the mth parameter type on the b+1th day before the nth tower maintenance. Indicates the number of days selected before tower maintenance. Indicates the stability of the mth parameter type before the nth tower maintenance.

3. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The method of using the intersection-and-joint ratio of the parameter value range of each parameter type before and after the tower maintenance and the stability before and after the tower maintenance to obtain the abnormal performance of each parameter type during each tower maintenance is: , Indicates the range of parameter values ​​of the mth parameter type on the bth day before the nth tower maintenance. Indicates the range of parameter values ​​of the mth parameter type on the bth day after the nth tower maintenance. Indicates the stability of the mth parameter type before the nth tower maintenance. Indicates the stability of the mth parameter type after the nth tower maintenance. Indicates the same number of days selected before and after the tower maintenance. Indicates the abnormal performance of the mth parameter type during the nth tower maintenance.

4. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The method for determining the abnormal parameters of each parameter type based on the abnormal performance is: A preset abnormal threshold is set. If the abnormal performance degree is greater than the preset abnormal threshold, the parameter type during the tower maintenance is an abnormal parameter.

5. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The expression of the time-varying correlation of the parameter type is: , It represents the standard deviation of the abnormal performance of the mth parameter type during all tower maintenance. Indicates the number of abnormal parameters in the mth parameter type, Indicates the time difference between the cth abnormal parameter and the c+1th abnormal parameter of the mth parameter type, It represents the mean of the time difference between the abnormal parameter corresponding to the mth parameter type and the adjacent abnormal parameters. represents the linear normalization function, Represents the time-varying relevance of the mth parameter type.

6. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The method for obtaining the influence of environmental factors on the tower in the current period according to the ratio of the number of tower maintenance in the previous year to the total number of tower maintenance in the previous year and the difference between the number of tower maintenance in adjacent months is: For each month, the difference between the number of maintenance in that month and the number of maintenance in the previous month is recorded as the first maintenance difference, the difference between the number of maintenance in that month and the number of maintenance in the next month is recorded as the second maintenance difference, and the average of the first maintenance difference and the second maintenance difference is recorded as the difference in the number of maintenance between each month and the adjacent months; The influence of environmental factors on the tower in the current period is obtained based on the difference in maintenance times between the current month and the adjacent months and the ratio of the maintenance times of the current month in the same month of the previous year to the total maintenance times of the previous year.

7. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The expression of the influence degree of the environmental factors on the tower in the current period is: , u represents the number of maintenances in the same month of the previous year in the current month, and U represents the total number of maintenances in the previous year. Indicates the difference in the number of maintenance times between the same month of the previous year and the adjacent month of the current month. Indicates the impact of environmental factors on the tower in the current period.

8. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The method of obtaining the key monitoring degree of the parameter type by taking the abnormal performance degree of each parameter type as the weight and combining the time-varying correlation degree of the parameter type and the influence degree of the environmental factors in the current period on the tower is: , Indicates the abnormal performance of the mth parameter type during the most recent maintenance. represents the time-varying relevance of the mth parameter type, Indicates the degree of impact of environmental factors on the tower in the current period, Indicates the emphasis monitoring degree of the mth parameter type.

9. A high-precision analysis method for time-varying reliability of a transmission tower structure as claimed in claim 1, characterized in that: The method for determining the endpoint parameter type according to the key monitoring degree is: When the key monitoring degree is greater than or equal to 0.35, the parameter type corresponding to this key monitoring degree is the key parameter type.

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