Transmission line tree-line distance analysis method based on monocular vision

Abstract

The present invention discloses a method for analyzing the distance between a power transmission line and a tree line based on monocular vision, which belongs to the field of visual analysis and distance measurement technology. The method comprises obtaining the length of a preset transmission line section, marking the length as an inspection length, collecting relevant data of trees on both sides of the inspection length, marking the trees as preset trees, wherein the relevant data includes height data of the trees on both sides and distance data between the ends of the branches of each tree and the preset transmission line section; collecting growth changes of the preset trees in time periods based on the distance data, wherein the time periods are divided into different temperature sections, and a database is constructed based on the collected growth changes; the present invention performs monocular vision collection on the preset trees and analyzes the growth data of different temperature sections, thereby providing more reliable data support for the distance changes between the preset trees and the preset transmission line section in future time periods in addition to the monocular vision data, thereby reducing the potential risks between the tree branches and the preset transmission line section.

Description

Technical Field

[0001] The present invention relates to the technical field of visual analysis and distance measurement, in particular to a method for analyzing the distance between a power transmission line and a tree line based on monocular vision. Background Art

[0002] Power supply is a crucial cornerstone of modern economic development, and transmission lines bear the crucial responsibility of connecting power stations to consumers, ensuring efficient, safe, and reliable power transmission. However, tree obstructions pose a significant challenge to the proper operation of transmission lines, increasing the complexity of power system operations and potentially increasing risks. In recent years, effective tree obstruction inspections have garnered significant attention from numerous research teams, who are dedicated to developing more efficient and accurate inspection methods to mitigate the impact of tree obstructions on transmission lines and ensure safe and stable power transmission. To effectively address the issue of tree obstructions on transmission lines, application number CN202110570500.9 proposes a method for determining the safe distance between trees under transmission lines based on visual analysis and soft sensing. This solution utilizes widely installed online video monitoring equipment for transmission lines, capturing periodic on-site video images. Based on the available information about tower coordinates, tower height, and line length, this method performs visual analysis and calculations. This approach offers the advantage of enabling real-time tree height monitoring and timely warnings without requiring on-site visits or knowledge of tree species.

[0003] Another application document with the technical application number CN202111374751.6 provides a transmission line detection method based on monocular visual positioning. This technical solution obtains the interval distance between adjacent monocular cameras through the positioning module and the calibration module. The image data obtained by the monocular camera is converted by the image processing module and then enters the pan-tilt processor through the image information upload module. The changed feature points in the image are compared with the image at the same position in the previous period of time. After obtaining the data of the feature points, they are judged by the probability detection module. The data of abnormal feature points can be accurately obtained and judged to avoid losses caused by misjudgment.

[0004] However, in real life, power transmission lines are usually located at high altitudes, and the growth of trees that can grow to the same height as the power transmission lines is easily affected by natural environmental factors. Among the many natural environmental factors, the natural wind blowing on the trees will cause the branches at high places to flutter. The above-mentioned technical solutions all focus on inspecting trees in a stationary state through video monitoring equipment, which affects the judgment of the distance between the end of the branch and the power transmission line, making the judgment of this distance inaccurate and posing potential risks. In addition, the above-mentioned technical solutions only perform distance analysis based on the data provided by the video monitoring equipment, which makes it difficult to effectively predict the growth trend of the corresponding tree branches in the future period, resulting in a lag in the distance analysis. Summary of the Invention

[0005] In view of the above-mentioned problems existing in the existing field of visual analysis and ranging technology, the present invention is proposed.

[0006] Therefore, one of the objects of the present invention is to provide a method for analyzing the distance between trees and transmission lines based on monocular vision, which utilizes monocular vision to collect data of preset trees and analyzes growth data of different temperature sections. This parallel method can better predict the growth pattern of preset trees, and can provide more reliable data support for the distance changes between preset trees and preset transmission line sections in future time periods in addition to monocular vision data. In addition, it can lock abnormally growing branches based on the analysis of the data, thereby identifying branches that need priority inspection and monitoring to reduce the potential risks between branches and preset transmission line sections.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] The present invention provides a method for analyzing the distance between a power transmission line and a tree line based on monocular vision, comprising the following steps:

[0009] The method for analyzing the distance between trees and lines of power transmission lines based on monocular vision includes the following steps:

[0010] Obtaining the length of a preset transmission line section, marking the length as an inspection length, collecting relevant data of trees on both sides of the inspection length, marking the trees as preset trees, the relevant data including height data of the trees on both sides and distance data between the ends of the branches of each tree and the preset transmission line section;

[0011] Based on the distance data, the growth changes of the preset trees are collected in different time periods, wherein the time periods are divided into different temperature ranges, and a database is constructed based on the collected growth changes;

[0012] Acquiring image data of the end of a branch of the preset tree according to the growth change, preprocessing the image data, and calculating the distance between the end of the branch and the preset power transmission line segment based on the image data;

[0013] The growth characteristics of the preset trees in the different temperature ranges are obtained, the growth characteristics are differentiated, and a prediction model is constructed based on the growth characteristics.

[0014] As a preferred embodiment of the present invention, the different temperature segments include a summer temperature segment and a winter temperature segment. In the summer temperature segment, a given number of statistical days are given, and growth data of at least 10 branches of the preset tree are collected to generate a data set. At the same time, the correlation between the growth data and the temperature values ​​in the corresponding temperature segments is calculated in the data set, and the result is calculated according to the following formula:

[0015] Among them, j o The jth growth data in the oth statistical day of the summer temperature period;

[0016] Where, δ represents the average growth data of the branches of the preset tree in the oth statistical day, x i Indicates the xth data difference obtained from the ith adjacent data in the growth data, u e represents the u-th temperature value collected for the e-th time based on the data difference;

[0017] Collect 3 to 4 of the largest data differences, and obtain the distance between the end of the preset tree branch and the preset transmission line section based on the data differences. Among the 3 to 4 largest data differences collected, if one of the data differences is greater than the distance, the distance between the preset tree branch and the preset transmission line section is determined to be a dangerous distance, and the preset tree branch is pruned. Otherwise, it is determined to be a safe distance.

[0018] As a preferred solution of the present invention, the image features of the ends of the branches of the preset trees are obtained based on the data difference, the image features include edge features of the image before acquisition and edge features of the image after acquisition, at least 3 edge points are collected in the edge features of the image before acquisition, the edge points are marked as reference points, the straight-line distance between the 3 edge points is calculated, the straight-line distance is marked as the reference distance, and the data change of the reference distance is calculated in the edge features of the image after acquisition, the data change is uploaded to the prediction model, and the prediction model is updated.

[0019] As a preferred embodiment of the present invention, the growth pattern of the fastest growing branch is analyzed based on the data changes. The analysis method includes taking 3 days as an analysis period, collecting temperature values ​​corresponding to the number of days in the analysis period, and obtaining a distance image between the end of the corresponding branch and the preset power transmission line segment at the temperature value, and calculating the change pattern of the distance between the two according to the following formula:

[0020] Among them, y t represents the yth temperature value obtained at the tth moment in the analysis period;

[0021] Wherein, p represents the distance image between the corresponding branch end and the preset transmission line segment obtained under the yth temperature value, b orepresents the bth intermediate distance between the end of the tree branch and the preset transmission line segment calculated for the oth time based on the distance image, r represents a predicted value, which is the number of days required for the corresponding tree branch to grow to the intermediate distance based on the temperature value, l o It represents the lth temperature value predicted based on the required number of days for the oth time.

[0022] As a preferred solution of the present invention, the preprocessing of image data includes denoising, image enhancement and image segmentation, wherein, in the image segmentation, three edge points are obtained and transmitted to a segmented image of the preset transmission line segment, and reference points are given in the segmented image. The reference points are given in a manner that includes giving them in equal proportion according to a law of distance change between the end of the corresponding tree branch and the preset transmission line segment, and the given number is at least three according to the distance from the preset transmission line segment, and the three reference points are divided into short-distance reference points, medium-distance reference points and long-distance reference points. In the distance image obtained in the future time period, if the distance between one of the edge points and the preset transmission line segment exceeds the medium-distance reference point, the branch corresponding to the edge point is marked as a key monitoring branch, otherwise it is not marked.

[0023] As a preferred solution of the present invention, based on the number of days required for the corresponding branch predicted by the temperature value to grow to the median distance, each week is used as the acquisition period for the distance image, and images of the key monitored branches are acquired. When the distance between the key monitored branch and the preset transmission line section changes faster than the required number of days, the key monitored branch is determined to be an abnormally growing branch and is pruned. Otherwise, it is not determined.

[0024] As a preferred solution of the present invention, when the distance change between the key monitored tree branch and the preset transmission line section is faster than the required number of days, at least 6 to 10 distance change data in the actual number of days are obtained, the distance change data are marked as risk data, and at least 4 of the distance change data are analyzed for regularity. When the distance change between other branches other than the corresponding branch in the future time period and the preset transmission line section conforms to the regular analysis, the branch is determined to be an abnormally growing branch; otherwise, it is not determined.

[0025] As a preferred solution of the present invention, wherein: all distance change data obtained in the actual number of days are counted, the distance change data are divided into several evaluation indicators, and the data are normalized to calculate the weight of the risk data in each evaluation indicator; and a weight value is preset based on the result of the calculation. When the weight of the risk data of other branches outside the corresponding branch in the future time period in each evaluation indicator exceeds the weight value, it is determined that the branch is in the abnormal growth range and / or has an abnormal growth tendency; otherwise, it is not determined.

[0026] A terminal comprises a processor, an input interface, an output interface, and a memory, wherein the processor, input interface, output interface, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

[0027] A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when executed by a processor, the program instructions enable the processor to perform the method according to any one of claims 1 to 8.

[0028] The present invention utilizes monocular vision to collect data on preset trees and analyzes growth data at different temperature ranges. This parallel approach can better predict the growth patterns of preset trees and provide more reliable data support for the distance changes between the preset trees and the preset transmission line sections in future time periods in addition to the monocular vision data. Furthermore, the present invention can identify abnormally growing branches based on the data analysis, thereby identifying branches that require priority inspection and monitoring to reduce potential risks between the branches and the preset transmission line sections. Furthermore, by collecting the straight-line distances between the edge points of each branch in the acquired image features, the distance changes can be accurately calculated when the branches are floating, thereby improving the reliability of the distance analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0030] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0031] Figure 2 Schematic diagram of the process structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0033] Since existing technical solutions all focus on inspecting trees in a stationary state through video monitoring equipment, the judgment of the distance between the end of the tree branch and the transmission line is affected, making the judgment of this distance inaccurate and thus posing potential risks; and the existing technical solutions only perform distance analysis based on the data provided by the video monitoring equipment, which makes it difficult to effectively predict the growth trend of the corresponding tree branches in the future period, resulting in a lag in the distance analysis.

[0034] Based on this, the present invention proposes a transmission line tree-line distance analysis method based on monocular vision, which utilizes monocular vision collection of preset trees and growth data analysis of different temperature sections. Through this parallel method, the growth pattern of preset trees can be better predicted, and more reliable data support can be provided for the distance changes between the preset trees and the preset transmission line sections in future time periods in addition to the monocular vision data. In addition, the abnormally growing branches can be locked according to the analysis of the data. In this way, the branches that need priority inspection and monitoring can be determined to reduce the potential risks between the branches and the preset transmission line sections.

[0035] The present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0036] Reference Figures 1 to 2 , is an embodiment of the present invention, which provides a method for analyzing the distance between a transmission line and a tree line based on monocular vision, comprising the following steps:

[0037] S10: Obtain the length of a preset transmission line section, mark the length as an inspection length, collect relevant data of trees on both sides of the inspection length, mark the trees as preset trees, and the relevant data includes height data of the trees on both sides and distance data between the ends of the branches of each tree and the preset transmission line section;

[0038] S20: Based on the distance data, the growth changes of the preset trees are collected in different time periods. The time periods may be divided into different temperature ranges, and a database is constructed based on the collected growth changes.

[0039] S30: acquiring image data of the end of a branch of a preset tree according to the growth change, preprocessing the image data, and calculating the distance between the end of the branch and the preset transmission line segment based on the image data;

[0040] S40: Obtaining growth characteristics of preset trees in different temperature ranges, distinguishing the growth characteristics, and building a prediction model based on the growth characteristics;

[0041] It should be noted that in this embodiment, distinguishing the growth characteristics includes presetting the branch growth and growth angle of the tree;

[0042] Meanwhile, it should be noted that the different temperature segments in this embodiment include summer and winter temperature segments. In the summer temperature segment, given statistical days are collected, and growth data of at least 10 branches of a preset tree are collected to generate a data set. At the same time, the correlation between the growth data and the temperature values ​​in the corresponding temperature segments is calculated in the data set, and the result is calculated according to the following formula:

[0043] Among them, j o Represents the jth growth data in the oth statistical day in the summer temperature period;

[0044] In the formula, δ represents the average growth data of the branches of the preset tree in the oth statistical day, x i Indicates the xth data difference obtained from the ith adjacent data in the growth data, u e Indicates the u-th temperature value collected for the e-th time based on the data difference;

[0045] Collecting 3-4 largest data differences, and based on the data differences, obtaining the distance between the end of the branch of the preset tree and the preset transmission line section. If one of the 3-4 largest data differences is greater than the distance, the distance between the branch of the preset tree and the preset transmission line section is determined to be a dangerous distance, and the branch of the preset tree is pruned. Otherwise, the distance is determined to be a safe distance.

[0046] Based on the above, this embodiment obtains image features of the ends of tree branches based on data differences. The image features include edge features of the image before acquisition and edge features of the image after acquisition. At least three edge points are collected in the edge features of the image before acquisition, and the edge points are marked as reference points. The straight-line distance between the three edge points is calculated, and the straight-line distance is marked as the reference distance. The data change of the reference distance is calculated in the edge features of the image after acquisition, and the data change is uploaded to the prediction model, and the prediction model is updated.

[0047] This embodiment further analyzes the growth pattern of the fastest-growing branch based on data changes. The analysis method includes collecting temperature values ​​corresponding to the number of days in the analysis period, taking a 3-day analysis period, and obtaining a distance image between the end of the corresponding branch and a preset transmission line segment under the temperature value. The change pattern of the distance between the two is calculated according to the following formula:

[0048] Among them, y t represents the yth temperature value obtained at the tth moment in the analysis period;

[0049] Where p represents the distance image between the corresponding tree branch end and the preset transmission line section obtained under the yth temperature value, b o represents the bth intermediate distance between the end of the tree branch and the preset transmission line segment calculated based on the distance image for the oth time, r represents the predicted value, which is the number of days required for the corresponding tree branch to grow to the intermediate distance based on the temperature value, l o represents the lth temperature value predicted based on the required number of days for the oth time;

[0050] Specifically, in this embodiment, the preprocessing of image data includes denoising, image enhancement, and image segmentation. In the image segmentation, three edge points are obtained and transmitted to a segmented image of a preset transmission line segment. Reference points are given in the segmented image. The reference points are given in equal proportion according to a change pattern of the distance between the end of the corresponding tree branch and the preset transmission line segment, and the number of given reference points is at least three based on the distance from the preset transmission line segment. The three reference points are divided into short-distance reference points, medium-distance reference points, and long-distance reference points. In a distance image obtained in a future time period, if the distance between one of the edge points and the preset transmission line segment exceeds the medium-distance reference point, the branch corresponding to the edge point is marked as a key monitoring branch; otherwise, it is not marked.

[0051] Based on the above, the number of days required for the corresponding branch to grow to the median distance predicted by the temperature value is used as the basis, and the distance image acquisition period is set every week. Images of the key monitored branches are acquired. If the distance between the key monitored branch and the preset transmission line section changes faster than the required number of days, the key monitored branch is determined to be an abnormally growing branch and is pruned. Otherwise, it is not determined.

[0052] It should be noted that in this embodiment, real-life studies have shown that the growth rate of trees may be abnormally fast under certain conditions. For example, the urban heat island effect can cause trees to grow 25% faster than in rural areas because heat promotes tree growth. In addition, climate change can also affect the growth rate of trees. Rising global temperatures extend the growing season of trees in cold regions, causing tree rings to thicken.

[0053] Furthermore, in this embodiment, when the distance between a key monitored branch and a preset transmission line segment changes faster than a required number of days, at least 6 to 10 distance change data sets in the actual number of days are obtained, the distance change data sets are marked as risk data, and at least 4 of the distance change data sets are analyzed for patterns. If the distance changes between branches other than the corresponding branch and the preset transmission line segment in the future time period conform to the pattern analysis, the branch is determined to be an abnormally growing branch; otherwise, no determination is made.

[0054] Among them, all the distance change data obtained in the actual days are counted, the distance change data are divided into several evaluation indicators, and the data are normalized to calculate the weight of the risk data in each evaluation indicator; and the weight value is preset according to the calculation result. When the risk data of other branches outside the corresponding branch in the future time period accounts for more than the weight value of each evaluation indicator, it is determined that the branch is in the abnormal growth range and / or has an abnormal growth tendency; otherwise, it is not determined.

[0055] A terminal comprises a processor, an input interface, an output interface and a memory, wherein the processor, the input interface, the output interface and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

[0056] A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 8.

[0057] In summary, the present invention utilizes monocular vision acquisition of preset trees and growth data analysis of different temperature sections. This parallel method can better predict the growth patterns of preset trees, and can provide more reliable data support for the distance changes between the preset trees and the preset transmission line sections in future time periods in addition to the monocular vision data. In addition, the present invention can identify abnormally growing branches based on the data analysis, thereby identifying branches that require priority inspection and monitoring to reduce the potential risks between the branches and the preset transmission line sections. At the same time, by collecting the straight-line distance between the edge points of each branch in the acquired image features, the distance change can be accurately calculated when the branch is floating, thereby improving the reliability of the distance analysis.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing the distance between trees and lines in power transmission lines based on monocular vision, characterized in that: The following steps are involved: Obtaining the length of a preset transmission line section, marking the length as an inspection length, collecting relevant data of trees on both sides of the inspection length, marking the trees as preset trees, the relevant data including height data of the trees on both sides and distance data between the ends of the branches of each tree and the preset transmission line section; Based on the distance data, the growth changes of the preset trees are collected in different time periods, wherein the time periods are divided into different temperature ranges, and a database is constructed based on the collected growth changes; Acquiring image data of the end of a branch of the preset tree according to the growth change, preprocessing the image data, and calculating the distance between the end of the branch and the preset power transmission line segment based on the image data; Acquiring growth characteristics of the preset trees in the different temperature ranges, distinguishing the growth characteristics, and constructing a prediction model based on the growth characteristics; The different temperature segments include a summer temperature segment and a winter temperature segment. In the summer temperature segment, given statistical days, at least 10 growth data of the branches of the preset tree are collected and a data set is generated. At the same time, a correlation between the growth data and the temperature values ​​in the corresponding temperature segments is calculated in the data set, and the correlation is calculated according to the following formula: ;in, Indicates the first The first statistical day Secondary growth data; Where, Indicates that the preset tree branches are in the The average growth data in the statistical days, Indicated in the growth data for the The first adjacent data obtained The data difference, Indicates that according to the data difference The first collection Temperature values; Collect 3 to 4 of the largest data differences, and obtain the distance between the end of the preset tree branch and the preset transmission line section based on the data differences. Among the 3 to 4 largest data differences collected, if one of the data differences is greater than the distance, the distance between the preset tree branch and the preset transmission line section is determined to be a dangerous distance, and the preset tree branch is pruned. Otherwise, it is determined to be a safe distance.

2. The method for analyzing the distance between a power transmission line and a tree line based on monocular vision according to claim 1, wherein: Based on the data difference, image features of the ends of the branches of the preset trees are obtained, and the image features include edge features of the image before acquisition and edge features of the image after acquisition. At least three edge points are collected in the edge features of the image before acquisition, and the edge points are marked as reference points. The straight-line distance between the three edge points is calculated, and the straight-line distance is marked as the reference distance. The data change of the reference distance is calculated in the edge features of the image after acquisition, and the data change is uploaded to the prediction model, and the prediction model is updated.

3. The method for analyzing the distance between a power transmission line and a tree line based on monocular vision according to claim 2, wherein: The growth pattern of the fastest-growing branch is analyzed based on the data changes. The analysis method includes collecting temperature values ​​corresponding to the number of days in the analysis period, taking 3 days as an analysis period, and obtaining the distance between the end of the corresponding branch and the preset power transmission line section at the temperature value, and calculating the change pattern of the distance between the two according to the following formula: ;in, The first The first Temperature values; Where, Indicated in the The distance between the corresponding tree branch end and the preset transmission line section obtained under the condition of the temperature value, Indicates the The distance between the end of the tree branch and the preset transmission line section is calculated based on the distance. The median distance, represents a predicted value, which is the number of days required for the corresponding branch to grow to the median distance based on the temperature value. Indicates the The first forecast based on the number of days required A temperature value.

4. The method for analyzing the distance between a power transmission line and a tree line based on monocular vision according to claim 3, wherein: The preprocessing of the image data includes denoising, image enhancement and image segmentation, wherein, in the image segmentation, the three edge points are obtained and transmitted to the segmented image of the preset transmission line segment, and reference points are given in the segmented image. The reference points are given in a manner that includes giving them in equal proportion according to the distance change law between the corresponding tree branch end and the preset transmission line segment, and the given number is at least three according to the distance from the preset transmission line segment. The three reference points are divided into short-distance reference points, medium-distance reference points and long-distance reference points. In the distance image obtained in the future time period, if the distance between one of the edge points and the preset transmission line segment exceeds the medium-distance reference point, the branch corresponding to the edge point is marked as a key monitoring branch, otherwise it is not marked.

5. The method for analyzing the distance between a power transmission line and a tree line based on monocular vision according to claim 4, characterized in that: Based on the number of days required for the corresponding branch predicted by the temperature value to grow to the median distance, each week is used as the acquisition period for the distance image, and images of the key monitored branches are acquired. When the distance between the key monitored branch and the preset transmission line section changes faster than the required number of days, the key monitored branch is determined to be an abnormally growing branch and is pruned. Otherwise, it is not determined.

6. The method for analyzing the distance between a power transmission line and a tree line based on monocular vision according to claim 5, characterized in that: When the distance between the key monitored branch and the preset transmission line section changes faster than the required number of days, at least 6 to 10 distance change data in the actual number of days are obtained, the distance change data are marked as risk data, and at least 4 of the distance change data are analyzed for regularity. If the distance changes between other branches other than the corresponding branch and the preset transmission line section in the future time period meet the regularity analysis, the branch is determined to be an abnormally growing branch; Otherwise, no judgment is made.

7. The method for analyzing the distance between a power transmission line and a tree line based on monocular vision according to claim 6, characterized in that: Counting all distance change data obtained in the actual number of days, dividing the distance change data into several evaluation indicators, normalizing the data, and calculating the weight of the risk data in each evaluation indicator; A weight value is preset based on the result of the calculation, and when the weight of the risk data of branches other than the corresponding branch in the future period accounts for each evaluation indicator exceeding the weight value, it is determined that the branch is in an abnormal growth range and / or has an abnormal growth tendency; Otherwise, no judgment is made.

8. A terminal, characterized in that: The method comprises a processor, an input interface, an output interface and a memory, wherein the processor, the input interface, the output interface and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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