High-precision analysis method for time-varying reliability of power transmission tower structure

Abstract

The invention relates to the technical field of data processing, in particular to a high-precision analysis method for time-varying reliability of a power transmission tower structure. The method comprises the following steps: acquiring a parameter type and a corresponding parameter value; obtaining the abnormal performance degree of each parameter type in each time of iron tower maintenance according to the stability before maintenance, the stability after maintenance and the change of the parameter values of the same number of days before and after maintenance of the power transmission iron tower in each time of iron tower maintenance; determining an abnormal parameter of each parameter type; obtaining the time-varying correlation degree of the parameter types according to the time difference between the abnormal parameters and the abnormal representation degree; based on the month of the current moment, the influence degree of the environmental factors in the current period on the iron tower is obtained through the maintenance frequency proportion of the same month in the last year and the maintenance frequency difference of the adjacent months; determining key parameter types in combination with the abnormal representation degree and the time-varying correlation degree; and obtaining a reliability degree based on the key parameter type to complete reliability analysis. The accuracy of precision analysis is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a high-precision analysis method for the time-varying reliability of a transmission tower structure. Background Art

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

[0003] At present, when analyzing the time-varying reliability of transmission tower structures, it is usually based on its multi-dimensional data. However, in the analysis process, a large amount of irrelevant data is included, which may mask the factors that have a significant impact on the reliability of the structure. For example, slight 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] In order to solve the technical problem of low monitoring accuracy caused by irrelevant data, this application provides a high-precision analysis method for the time-varying reliability of a transmission tower structure. The technical solution adopted is as follows: This application proposes a high-precision analysis method for the time-varying reliability of a transmission tower structure, which includes 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 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 to obtain the time-varying correlation of the parameter type. 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.

[0005] In the above scheme, the present application proposes a high-precision analysis method for the time-varying reliability of the transmission tower structure. By analyzing the abnormal performance of the data types reflected in the historical maintenance, and analyzing the correlation between each data type and the transmission tower structure based on the time-varying nature of the transmission tower structure, and considering the influence of current environmental factors, the data types with more monitoring value are screened out from the various types of data related to the transmission tower, and this is used as the data source for the high-precision analysis of the time-varying reliability of the transmission tower structure. The time-varying reliability analysis of the transmission tower structure is carried out through these data, avoiding the interference caused by a large amount of irrelevant data and improving the accuracy of the analysis.

[0006] In one embodiment, 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.

[0007] In one embodiment, 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.

[0008] In one embodiment, the method for determining the abnormal parameters of each parameter type based on the abnormal performance is: The abnormal threshold is preset. If the abnormal performance is greater than the preset abnormal threshold, the parameter type is an abnormal parameter during this tower maintenance.

[0009] In one embodiment, the expression of the time-varying correlation degree 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.

[0010] In one embodiment, the method for obtaining the degree of influence of environmental factors on the iron tower in the current period according to the ratio of the number of iron tower maintenance in the previous year to the total number of iron tower maintenance in the previous year and the difference between the number of iron 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.

[0011] In one embodiment, the expression for 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.

[0012] In one embodiment, 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.

[0013] In one embodiment, 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.

[0014] The beneficial effects of this application are: This application proposes a high-precision analysis method for the time-varying reliability of a transmission tower structure. By analyzing the abnormal performance of the data types reflected in the historical maintenance, and analyzing the correlation between each data type and the transmission tower structure based on the time-varying nature of the transmission tower structure, and considering the influence of current environmental factors, the data types with more monitoring value are screened out from the various types of data related to the transmission tower, and this is used as the data source for high-precision analysis of the time-varying reliability of the transmission tower structure. The time-varying reliability analysis of the transmission tower structure is performed through these data, avoiding the interference caused by a large amount of irrelevant data and improving the accuracy of the analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flow chart of a high-precision analysis method for the time-varying reliability of a transmission tower structure provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the high-precision analysis method of the time-varying reliability of a transmission tower structure proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] An embodiment of a high-precision analysis method for time-varying reliability of a transmission tower structure: The specific scheme of a high-precision analysis method for the time-varying reliability of a transmission tower structure provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a high-precision analysis method for time-varying reliability of a transmission tower structure provided by an embodiment of the present application, the method comprising the following steps: Step S001, collecting the maintenance times and corresponding time of the transmission tower.

[0021] For a transmission tower, a plurality of sensors are installed on the tower to detect information related to the health of the transmission tower structure. In this embodiment, the installed sensors include an inclination sensor, a strain sensor and a wind speed sensor.

[0022] The angle collected by the tilt sensor, the strain force collected by the strain sensor, and the wind speed collected by the wind speed sensor are used as parameters. In this embodiment, the parameter values ​​are collected every 1 minute.

[0023] The historical data of the transmission tower is obtained, that is, the parameter values ​​corresponding to different parameters of the tower on a daily basis, and the number of maintenance times and the time of each maintenance of the transmission tower are obtained based on the historical maintenance and inspection records of the transmission tower.

[0024] So far, the historical collection data of the transmission tower, the number of tower maintenance times and the corresponding time have been obtained.

[0025] Step S002, obtaining the abnormal performance of each parameter type at each tower maintenance according to the stability before and after maintenance and the change of parameter values ​​on the same days before and after maintenance each time the transmission tower is maintained; thereby determining the abnormal parameters of each parameter type.

[0026] After long-term operation, transmission towers will inevitably encounter some problems, such as corrosion, rust, mechanical fatigue, structural deformation, etc. This is because the structure and materials of the transmission towers experience varying degrees of fatigue over time and under load.

[0027] When analyzing the time-varying characteristics of the structure of the transmission tower, the reliability of the transmission tower will also decrease over time due to the gradual degradation of the tower's performance over time and due to factors such as fatigue. That is, the reliability of the transmission tower is also time-varying. Therefore, in addition to considering past and current changes, the analysis also requires consideration of future changes in the transmission tower.

[0028] Transmission towers will have corresponding problems in long-term use. In order to ensure that they maintain good structural reliability, they are 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 performance of the tower. Therefore, by comparing the degree of change of various types of tower data before and after each maintenance, it can be reflected in the abnormal parameter types caused by the existing structural problems in the maintenance.

[0029] Each time the tower is repaired, the parameter values ​​of different parameter types are taken several days before and after the tower. In this embodiment, 7 days is taken as an example. For the structural problem corresponding to the tower repair, the tower structure has problems before the repair, so the corresponding data will remain stable under abnormal conditions, and the tower structure has been repaired after the repair, so the corresponding data will be stable under normal conditions; that is, before the repair, the parameter values ​​are similar, and after the repair, the parameter values ​​are also similar.

[0030] Therefore, we can first compare the data before and after the tower maintenance on a day-by-day basis to compare the difference in data before and after the maintenance; and consider the stability of the corresponding data before and after the maintenance, and make corrections to the above to determine what type of parameters are responsible for the structural problems that occur during each tower maintenance.

[0031] In this application, let the number of parameter types be M, the total number of tower maintenance in historical data be N, and the number of days before and after each maintenance be B days respectively.

[0032] Before the tower maintenance, the stability of each parameter type before each tower maintenance is determined by the range of parameter values ​​of each parameter type on each day before each tower maintenance and the range of parameter values ​​on the next day. The range of parameter values ​​is the interval formed by the minimum and maximum values ​​of the parameter type on each day.

[0033] The expression of stability 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.

[0034] Similarly, the stability of each parameter type after each tower maintenance is determined by the range of parameter values ​​of each parameter type on each day after each tower maintenance and the range of parameter values ​​on the next day, recorded as .

[0035] For each parameter type, the abnormal performance of each parameter type during each tower maintenance is obtained based on the difference in range before and after tower maintenance and the stability before and after tower maintenance.

[0036] The expression of abnormal performance 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.

[0037] Among them, for each parameter type, when the tower is repaired, the closer the range of adjacent days before and after the repair, that is, the larger the intersection-and-combination ratio, the greater the stability before and after the repair, the greater the stability, and the greater the abnormal expression. The smaller the intersection-and-combination ratio of the range on the same day before and after the repair, the greater the difference before and after the repair, and the greater the abnormal expression.

[0038] Based on the above steps, the abnormal performance degree of each parameter type during each tower maintenance can be obtained. The higher the abnormal performance degree, the more likely the parameter type is to show abnormal data changes caused by the structural problems corresponding to the tower maintenance.

[0039] Therefore, an abnormal threshold is set. When the abnormal performance is greater than the abnormal threshold, the parameter type corresponding to the abnormal performance during tower maintenance is recorded as an abnormal parameter, which is the cause of the structural problem corresponding to the tower maintenance. In this embodiment, the value of the abnormal threshold is 0.65.

[0040] So far, the abnormal parameters of each parameter type during all tower maintenance operations have been obtained.

[0041] Step S003: obtaining the time-varying correlation of the parameter types according to the time difference between the abnormal parameters and the abnormal expression degree.

[0042] For the abnormal parameters caused by the structural problems corresponding to each tower maintenance screened out in the above process, since these abnormal data types can indirectly reflect the problems of the current tower structure, and since the performance of the tower will change with the change of time in actual application, we analyze the impact of the same abnormal parameters on the time-varying nature of the transmission tower structure through the occurrence of the same abnormal parameters in the historical maintenance process, so as to reflect the correlation between each parameter type and time-varying nature.

[0043] For the same parameter type, obtain the number of times it is an abnormal parameter during all tower maintenance. Count the time of each tower maintenance, expressed as the day; each tower maintenance will have abnormal parameters; thus obtain the time of abnormal parameters of each parameter type; Therefore, here we can analyze through the time when the abnormal parameters of the parameter type appear and the expression of its abnormal number. Because the transmission tower has time-varying structure, its various performances will gradually change with time. Therefore, we can make a comprehensive analysis through the time intervals and abnormal expression differences of the same parameter type under different maintenance times. When the time intervals are closer and the abnormal expression is more consistent, it means that the parameter type will show similar abnormal data performance with the regular passage of time, which reflects a certain structural problem that the transmission tower may correspond to based on the parameter type, that is, the time-varying correlation of the current parameter type.

[0044] For each parameter type, calculate the standard deviation of the abnormal performance of this parameter type during all tower maintenance. The larger the standard deviation, the more obvious the fluctuation of the abnormal performance, and the larger the difference, the smaller the impact of time change. Sort the abnormal parameters of the parameter type during all tower maintenance in chronological order, calculate the time difference of adjacent abnormal parameters, and determine the time-varying correlation of each parameter type based on the difference between the time difference and the mean of the time difference and the standard deviation of the abnormal performance. The expression 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.

[0045] So far, the time-varying correlation of each parameter type has been obtained.

[0046] Step S004, based on the current month, obtain the degree of influence of environmental factors on the tower in the current period through the proportion of maintenance times in the same month of the previous year and the difference in maintenance times with adjacent months; and determine the key parameter type in combination with the abnormal performance and time-varying correlation.

[0047] The tower environment, with the local seasonal environmental characteristics, at different times due to the changes in different environmental factors such as humidity, temperature, wind, etc., will have different degrees of impact on the service life of different layers of the tower. Therefore, the historical environmental conditions can be used to determine the environmental conditions in the same period as the current environment, so as to determine the impact of the environment on the current period.

[0048] Since the degree of environmental impact on the tower varies in different periods, and our goal is to analyze the degree of environmental impact on the tower in different periods, intuitively, the impact of the environment on the tower can be reflected by the number of repairs on the tower. Therefore, the difference in the number of repairs in different periods can be analyzed to define the degree of environmental factors on the tower in different periods.

[0049] Therefore, the number of tower maintenance times in each month within the current year is obtained, and the monthly maintenance times are compared with the total maintenance times. Taking into account the seasonality of the impact of the environment on the tower, the analysis cannot only be compared with the maintenance times in one month, but also the similarity between the maintenance times of the current month and the adjacent month, so as to comprehensively determine the impact of environmental factors on the tower each month.

[0050] The number of maintenance times in the same month of the previous year for the current month is obtained and recorded as the number of maintenance times in the same month. The degree of influence of environmental factors on the tower is obtained based on the difference between the number of maintenance times in the same month and the total number of maintenance times in the previous year and the difference between the number of maintenance times in the same month and the adjacent months in the previous year.

[0051] For each month, the difference between the number of maintenances in that month and the number of maintenances in the previous month is recorded as the first maintenance difference, the difference between the number of maintenances in that month and the number of maintenances 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 maintenances between each month and the adjacent months.

[0052] The expression of the influence of 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. The difference between the number of maintenance in the current month and the adjacent month in the previous year. Indicates the impact of environmental factors on the tower in the current period.

[0053] Through the above steps, we analyzed the abnormal performance of each parameter type during tower maintenance, and analyzed and considered the impact of structural time-varying for the parameter types with outstanding performance, and also analyzed the impact of environmental factors in the current period. Because the parameter types related to the time-varying reliability of the transmission tower structure are complicated, when analyzing, there is a large amount of irrelevant data that will interfere with the key data, so here we determine the parameter types that should be monitored.

[0054] Therefore, taking the abnormal performance degree corresponding to each parameter type during the most recent maintenance as the benchmark, when it is larger, more attention is paid to the corresponding correlation degree based on the time-varying structure of the transmission tower, and when it is smaller, more attention is paid to the impact of environmental factors on the tower in the current period.

[0055] Therefore, taking the abnormal performance as the weight, the key monitoring degree of each parameter type is obtained by weighting the time-varying correlation of the parameter type and the influence of the current environmental factors on the tower. The expression 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.

[0056] Thus, the key monitoring degree of each parameter type is obtained.

[0057] A monitoring threshold is set here. 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 monitoring threshold is 0.35.

[0058] At this point, the key parameter types have been obtained.

[0059] Step S005, obtaining the reliability degree based on the key parameter type and completing the reliability analysis.

[0060] After obtaining the key parameter type at the current moment, each parameter value of the key parameter type and its corresponding time are obtained, which are used as the input of the regression analysis algorithm, and the output is the time-varying reliability of the transmission tower structure, so as to complete high-precision analysis and avoid interference from a large amount of irrelevant data.

[0061] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions 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.

[0062] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced 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 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 to obtain the time-varying correlation of the parameter type. 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: The abnormal threshold is preset. If the abnormal performance is greater than the preset abnormal threshold, the parameter type is an abnormal parameter during this tower maintenance.

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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