A method and system for steady-state analysis of line poles and towers based on displacement offset detection

Abstract

The present invention discloses a method and system for steady-state analysis of line towers based on displacement offset detection, which relates to the technical field of on-line monitoring and early warning of power transmission projects. The method includes obtaining the confidence levels of all monitoring point information of a target tower; obtaining a monitoring point confidence sequence based on the confidence levels of each monitoring point, and adjusting the monitoring point confidence sequence to obtain a monitoring point adjustment sequence; obtaining a preliminary steady-state analysis result of the target tower according to the monitoring point adjustment sequence; locating associated towers based on the target tower, and calculating the associated steady-state analysis results of each associated tower; calculating the subsequent steady-state analysis result of the target tower according to the preliminary steady-state analysis result and the associated steady-state analysis results. The method and system can not only judge whether the detection parameters of the target tower itself are reliable, but also combine the parameter detection conditions of the associated towers of the target tower to realize the stability analysis and judgment of the target tower.

Description

Technical Field

[0001] The present invention relates to the technical field of on-line monitoring and early warning for transmission projects. Specifically, it relates to a method and system for steady-state analysis of line towers based on displacement offset detection. Background Art

[0002] The steady-state analysis of line towers is an important node in the monitoring link of the sustainable operation state of transmission projects. Through systematic mechanical property evaluation and health state monitoring, it ensures that the towers can safely and stably bear various expected loads during long-term operation, thus guaranteeing the continuity and reliability of power transmission. Currently, for the steady-state analysis of line towers, generally, the offset displacement detection is realized through a sensor network arranged at key positions of the towers, and then the offset displacement data of the towers is analyzed and judged through a calculation model to obtain the result of whether the tower is in a stable state. The above-mentioned method mainly realizes the steady-state analysis calculation for a single tower, and the accuracy of its calculation result is determined by the detection accuracy of the sensors, the types of sensor detection parameters, and the calculation method. Among them, the detection accuracy of the sensors and the calculation method can be solved through testing and debugging, while the types of sensor detection parameters need to be determined according to experience and methods. Especially for different key positions of the towers, which type of sensor to install in what way directly determines whether the steady-state monitoring of the towers is comprehensive and reliable.

[0003] In the prior art, generally, inclination sensors, laser rangefinder sensors, accelerometers or strain contact sensors are installed at different positions of the towers to obtain monitoring parameters of different objects. Although it can solve the problem of obtaining different types of detection parameters for the towers, its steady-state judgment result highly depends on the parameter detection of the tower itself. Once there is a parameter misdetection or detection omission, the stability of the tower cannot be accurately judged, resulting in problems such as monitoring blanks.

[0004] In view of this, the present application is specifically proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for steady-state analysis of line towers based on displacement offset detection. This analysis method and system can not only judge whether the detection parameters of the target tower itself are reliable, but also combine the detection situation of the associated tower parameters of the target tower to realize the stability analysis and judgment of the target tower, thereby reducing the possibility of inaccurate judgment results caused by parameter misdetection or detection omission.

[0006] The embodiments of the present invention are implemented as follows:

[0007] First aspect, a method for steady-state analysis of line towers based on displacement offset detection, comprising the following steps: obtaining a displacement offset monitoring network architecture of a target tower, identifying all monitoring point information in the displacement offset monitoring network architecture, judging the confidence level of each piece of the monitoring point information, and obtaining the confidence level of each monitoring point in the target tower; sorting the monitoring points according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and adjusting the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; matching an effective monitoring information group of the target tower according to the monitoring point adjustment sequence, and performing stability analysis on the target tower based on the effective monitoring information group to obtain a preliminary steady-state analysis result; locating at least one associated tower of the target tower based on the geographic information of the target tower, and calculating an associated steady-state analysis result of each associated tower; calculating a comprehensive steady-state analysis parameter according to the preliminary steady-state analysis result and the associated steady-state analysis result, and calculating a subsequent steady-state analysis result of the target tower based on the comprehensive steady-state analysis parameter.

[0008] In some optional embodiments, the calculating the associated steady-state analysis result of each associated tower comprises the following steps: obtaining a displacement offset monitoring network architecture of the associated tower, identifying all monitoring point information in the displacement offset monitoring network architecture, judging the confidence level of each piece of the monitoring point information, and obtaining the confidence level of each monitoring point in the associated tower; sorting the monitoring points according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and adjusting the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; matching an effective monitoring information group of the associated tower according to the monitoring point adjustment sequence, and performing stability analysis on the associated tower based on the effective monitoring information group to obtain the associated steady-state analysis result.

[0009] In some optional embodiments, the locating at least one associated tower of the target tower based on the geographic information of the target tower comprises the following steps: determining a tower group range of the target tower, extracting the towers within the tower group range to obtain a plurality of preliminarily screened towers; obtaining the geographic information of the target tower and each preliminarily screened tower, performing a correlation analysis on the geographic information of each preliminarily screened tower and the geographic information of the target tower to obtain a corresponding correlation analysis result, and judging whether the preliminarily screened tower is used as an associated tower based on the correlation analysis result; wherein, the geographic information includes terrain information, topographic information, geological information, and location association information.

[0010] In some alternative embodiments, the correlation analysis of the geographical information of each of the preliminarily screened poles and towers with the geographical information of the target pole and tower to obtain the corresponding correlation analysis result includes the following steps: classifying the geographical information of the preliminarily screened poles and towers to obtain multiple groups of preliminary screening geographical category factors, classifying the geographical information of the target pole and tower to obtain multiple groups of target geographical category factors, comparing the similarity of the preliminary screening geographical category factor groups of the same type with the target geographical category factor groups to obtain a similarity value; combining all similarity values to judge the similarity degree result, and calculating the correlation analysis result based on the similarity degree result; wherein, the similarity degree result includes strong similarity correlation and weak similarity correlation.

[0011] In some alternative embodiments, it further includes the step of determining the type of the similarity degree result corresponding to the correlation analysis result of the correlated pole and tower, and readjusting the monitoring point adjustment sequence of the correlated pole and tower according to different types: If it is a strong similarity correlation type: trace all the similarity value situations calculated by the correlated pole and tower using the preliminary screening geographical category factor groups, sort the preliminary screening geographical category factor groups according to the size of the similarity values to obtain a preliminary screening geographical factor similarity sequence; assign importance parameters to the corresponding monitoring points in the correlated pole and tower according to this preliminary screening geographical factor similarity sequence to recalculate the importance degree of the monitoring points, so as to obtain the monitoring point adjustment sequence after readjustment of the correlated pole and tower; If it is a weak similarity correlation type: trace all the similarity value situations calculated by the correlated pole and tower using the preliminary screening geographical category factor groups, retain the similarity values that meet the similarity preset requirements and sort the preliminary screening geographical category factor groups according to the size of the retained similarity values to obtain a preliminary screening geographical factor similarity sequence; assign importance parameters to the corresponding monitoring points in the correlated pole and tower according to this preliminary screening geographical factor similarity sequence to recalculate the importance degree of the monitoring points, so as to obtain the monitoring point adjustment sequence after readjustment of the correlated pole and tower.

[0012] In some alternative embodiments, calculating the comprehensive steady-state analysis parameter according to the preliminary steady-state analysis result and the correlated steady-state analysis result includes the following steps: determining the combined weight of the correlated steady-state analysis result, and combining the product of the combined weight and the correlated steady-state analysis result with the preliminary steady-state analysis result to obtain the comprehensive steady-state analysis parameter; wherein, determining the combined weight includes the following steps: determining the respective weights of each effective monitoring information in the correlated steady-state analysis result, and calculating the combined weight according to the respective weights of each effective monitoring information.

[0013] In some optional embodiments, it further includes a step of optimizing the combined weight: determining a difference parameter between the monitoring point adjustment sequence before readjustment of the associated pole tower and the monitoring point adjustment sequence after readjustment, determining a weight adjustment parameter based on this difference parameter, and calculating an optimized combined weight according to the weight adjustment parameter and the combined weight.

[0014] In some optional embodiments, judging the confidence level of the monitoring point information in the target pole tower and / or judging the confidence level of the monitoring point information in the associated pole tower both include the following steps: calculating a matching degree according to the position of the pole tower where the monitoring point is located and the attributes of the configured sensors to obtain a first confidence parameter; calculating a matching degree according to the monitoring target of the sensors configured for the monitoring point to obtain a second confidence parameter; wherein the attributes of the configured sensors include the type and quantity of the sensors; the monitoring target of the configured sensors includes the monitoring angle and the monitoring object; calculating the confidence level of this monitoring point according to the first confidence parameter and the second confidence parameter.

[0015] In some optional embodiments, it further includes a step of adjusting the confidence level of the monitoring point information of the target pole tower and / or the confidence level of the monitoring point information in the associated pole tower: determining the planned deployment information of the monitoring points according to the geographical information of the corresponding pole tower, calculating a difference coefficient between the planned deployment information and the actual deployment information, and adjusting the confidence level of this monitoring point according to this difference coefficient, where the planned deployment information refers to the real-time plan for determining the monitoring and sensing deployment of this pole tower according to the geographical information of the pole tower, and the actual deployment information refers to the actual situation of the current monitoring and sensing deployment of the pole tower.

[0016] Second aspect, a line pole tower steady-state analysis system based on displacement offset detection, comprising:

[0017] A first acquisition unit, which is used to acquire the displacement offset monitoring network architecture of the target pole tower, identify all the monitoring point information in this displacement offset monitoring network architecture, judge the confidence level of each piece of monitoring point information, and obtain the confidence level of each monitoring point in this target pole tower;

[0018] A first processing unit, which is used to sort the monitoring points according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and adjust the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence;

[0019] A second processing unit, which is used to match the effective monitoring information group of the target pole tower according to the monitoring point adjustment sequence, and perform a stability analysis on this target pole tower based on the effective monitoring information group to obtain a preliminary steady-state analysis result;

[0020] A first calculation unit, which is configured to locate at least one associated pole tower of the target pole tower based on the geographical information of the target pole tower, and calculate the associated steady-state analysis results of each of the associated pole towers;

[0021] A second calculation unit, which is configured to calculate comprehensive steady-state analysis parameters according to the preliminary steady-state analysis results and the associated steady-state analysis results, and calculate the subsequent steady-state analysis results of the target pole tower based on the comprehensive steady-state analysis parameters.

[0022] The beneficial effects of the embodiments of the present invention are as follows:

[0023] A method and system for steady-state analysis of a line pole tower based on displacement offset detection provided by the embodiments of the present invention perform a reliability judgment on all monitoring points in the displacement offset monitoring network architecture deployed on the target pole tower itself. By obtaining the confidence level of each monitoring point and the importance degree of its monitoring position, a monitoring point adjustment sequence is obtained to display the reliability and criticality ranking of the monitoring points. Then, by extracting and analyzing the effective monitoring information groups corresponding to the monitoring points that meet the requirements in the monitoring point adjustment sequence, the preliminary steady-state analysis results of the target pole tower itself are obtained, thereby reducing the possibility of parameter misdetection by judging whether the sensor deployment on the target pole tower itself is reliable. On this basis, in order to avoid the situation of abnormal or missing overall parameter monitoring of the target pole tower, the associated pole towers of the target pole tower are located, and the above preliminary steady-state analysis results are further given by analyzing the associated steady-state analysis results of the associated pole towers, so as to obtain comprehensive steady-state analysis parameters to calculate the final subsequent steady-state analysis results of the target pole tower, thereby avoiding the problem that the preliminary steady-state analysis results obtained are not accurate enough due to abnormal or missing overall parameter monitoring of the target pole tower.

[0024] Generally speaking, the method and system for steady-state analysis of a line pole tower based on displacement offset detection provided by the embodiments of the present invention not only make a first-layer judgment from the aspect of whether the monitoring point deployment on the target pole tower itself is reliable, but also assist in judging whether the overall parameter monitoring of the target pole tower is normal and reliable through the parameter detection situation of the associated pole towers of the target pole tower. By combining the two-layer judgment method, the possibility that the final analysis results of the target pole tower are not accurate enough due to insufficient accuracy of sensor deployment, insufficient parameter monitoring accuracy, and abnormal or missing parameter monitoring of the target pole tower itself is reduced. Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 Flow chart of the main steps of the analysis method provided by the embodiment of the present invention;

[0027] Figure 2 is Figure 1 Flow chart of one of the main steps shown, step S400;

[0028] Figure 3 is Figure 2 Sub-step flow chart of step S400 shown;

[0029] Figure 4 is Figure 1 Flow chart of one of the main steps shown, step S500;

[0030] Figure 5 is Figure 1 Flow chart of one of the main steps shown, step S100;

[0031] Figure 6 Modularity schematic diagram of the analysis system provided by the embodiment of the present invention.

[0032] Icons: 600 - analysis system; 610 - first acquisition unit; 620 - first processing unit; 630 - second processing unit; 640 - first calculation unit; 650 - second calculation unit. Detailed implementation manners

[0033] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0035] It should be understood that the "system", "device" and / or "module" used in the present invention is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0036] As shown in the present invention and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0037] In the present invention, flowcharts are used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, they can be executed in reverse order or processed simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0038] Embodiment: The stability analysis of transmission line towers in transmission projects is particularly important. The stability and safety of towers mainly rely on regular manual inspections and maintenance. This method is inefficient and it is difficult to detect potential problems in a timely manner. Therefore, long-term, continuous, and automated steady-state monitoring of towers is achieved through intelligent and real-time monitoring means. Previously, we installed high-precision displacement sensors (such as laser rangefinders, tilt sensors, etc.) at key positions on the towers to capture the minute displacement changes of the towers. The sensor data is sent to the central processing unit through a wireless transmission module, and the collected data is processed using algorithms (such as filtering, feature extraction, pattern recognition) to remove noise interference and extract useful signal features. According to the analysis results of the signal features, combined with the mechanical model and historical data, the current state of the tower is evaluated to determine whether there are any abnormalities or potential risks.

[0039] The purpose of remote automatic steady-state monitoring of towers can be achieved through the foregoing method. However, in some cases, the sensor deployment method of the tower itself determines the accuracy of the detection results. If there are defects or problems in the deployment of the sensors themselves, the reliability of the steady-state monitoring results of the tower will be greatly reduced. Especially when arranging inspection personnel to compare and review the measured feedback results with the system monitoring results, it is found that the accuracy and precision of the monitoring results of some towers are insufficient. The further analysis shows that on the one hand, there is a problem of low matching degree between the types of sensors configured at different monitoring points of the tower and the installation methods of the sensors, and on the other hand, it is the problem caused by sensor failure or abnormal parameter detection, thus reducing the accuracy of the remote steady-state monitoring analysis of the tower. To solve the above problems, this embodiment provides a method for steady-state analysis of transmission line towers based on displacement offset detection. On the one hand, it is to detect the matching degree of the sensor deployment network architecture of the tower itself, and on the other hand, it is to make a reasonable inference by referring to the monitoring results of adjacent or physically related towers to ensure that the monitoring parameters and analysis results of the target tower have stronger accuracy and reliability.

[0040] For details, please refer to Figure 1 A method for steady-state analysis of line towers based on displacement offset detection provided in this embodiment includes the following steps:

[0041] S100: Obtain the displacement offset monitoring network architecture of the target tower, identify all the monitoring point information in the displacement offset monitoring network architecture, judge the confidence level of each piece of monitoring point information, and obtain the confidence level of each monitoring point in the target tower; this step means obtaining the sensor method deployed on the target tower (obtaining in advance, after the sensor is installed or temporarily adjusted, the staff uploads the sensor information), and the uploaded sensor information (all sensors on the tower) constitutes the displacement offset monitoring network architecture, which can monitor the tower in a three-dimensional network manner (deploying sensors in three directions: the X-axis - horizontal, the Y-axis - lateral, and the Z-axis - vertical), and detecting changes such as displacement, tilt, and settlement in different directions of the corresponding part of the tower for each sensor installation point (which is also a monitoring point).

[0042] Determine all the monitoring point information of the target tower for the pre-obtained displacement offset monitoring network architecture (initially, the staff conducts on-site monitoring to upload the information, and if there are corrections later, the corrected information is uploaded), and further judge the confidence level of whether each piece of monitoring point information matches (judge whether the uploaded monitoring point information matches the plan according to the background monitoring plan). The confidence level judgment is mainly based on whether the type of sensor installation, the installation method, and the monitoring object / direction of different monitoring point properties meet the plan requirements. According to the calculation of the matching degree, the confidence level value of each monitoring point in the target tower is obtained, so as to provide a judgment basis for whether the subsequent monitoring of this monitoring point is reliable.

[0043] S200: Sort the monitoring points according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and adjust the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; this step means sorting according to the confidence level calculated for all the monitoring points of the target tower to obtain a preliminary monitoring point confidence sequence, which can reflect the sorting of the matching degree between the sensor deployment of all monitoring points and the plan. The higher the ranking, the more reliable the sensor monitoring. On this basis, it is also necessary to further judge whether the monitoring nature of the monitoring point is important according to the monitoring point confidence sequence, and then re-sort the reliability and criticality of all monitoring points, that is, adjust the monitoring point confidence sequence according to the importance degree of each monitoring point to obtain a monitoring point adjustment sequence, which is used to reflect the sorting of the monitoring reliability and importance degree of different monitoring points, so as to facilitate subsequent selection of reliable and important monitoring point information for further processing.

[0044] It should be noted that the importance of the monitoring points is mainly planned and decided in advance according to the location and object of monitoring. For example, the sensor installed at the foundation of the tower is more important than the sensor installed in the middle of the tower. For example, among the several sensors installed on the top of the tower, the sensor used to monitor the vertical inclination with the ground (compared with the sensor monitoring the line tension) is more important. Therefore, by adjusting the sequence according to the monitoring points, the ranking of different monitoring points with both reliability and importance can be obtained.

[0045] S300: Match the effective monitoring information group of the target tower according to the monitoring point adjustment sequence, perform stability analysis on the target tower based on the effective monitoring information group, and obtain preliminary steady-state analysis results; this step means that the information monitored and uploaded by the monitoring points that can be used for the next information analysis is selected from the monitoring point adjustment sequence obtained above, and recorded as the effective monitoring information group. The effective monitoring information group can be the information corresponding to the monitoring and uploaded by all the monitoring points in the monitoring point adjustment sequence, or the information corresponding to the monitoring and uploaded by some of the monitoring points arranged in the front, and can be screened according to different reliability and importance selection criteria. The preliminary steady-state analysis results are calculated by substituting the effective monitoring information group into the mechanical analysis model (such as finite element analysis model, vibration modal analysis model, trained deep learning model, etc.) for stability analysis. The preliminary steady-state analysis results reflect whether the monitoring is reliable based only on the monitoring point of the target tower itself, and further judgment is needed on whether the monitoring data of the monitoring point is reliable through subsequent steps.

[0046] S400: Locate at least one associated pole tower of the target pole tower based on the geographic information of the target pole tower, and calculate the associated steady-state analysis result of each associated pole tower; this step means that the monitoring parameter conditions of other pole towers that are physically connected to the target pole tower are used to further assist the judgment reliability of the monitoring parameters of the target pole tower, and other pole towers are screened by using the geographic information of the target pole tower to determine at least one associated pole tower. The parameter analysis result of each associated pole tower can assist the judgment of the preliminary steady-state analysis result of the target pole tower, that is, by calculating the associated steady-state analysis result of the associated pole tower, it is combined with the preliminary steady-state analysis result of the target pole tower and further judged. It should be noted that the associated steady-state analysis result of the associated pole tower can be calculated in the same principle and method as the preliminary steady-state analysis result of the target pole tower, or it can be calculated in other ways, such as directly substituting the monitoring parameters into the mechanical analysis model for calculation or substituting the monitoring parameters into the mechanical analysis model for calculation after preliminary screening, etc.

[0047] S500: Calculate the comprehensive steady-state analysis parameters based on the preliminary steady-state analysis results and the associated steady-state analysis results, and calculate the subsequent steady-state analysis results of the target tower using the comprehensive steady-state analysis parameters. This step means combining the preliminary steady-state analysis results of the target tower and the associated steady-state analysis results of the associated tower to calculate a comprehensive steady-state analysis parameter. The comprehensive analysis parameter is a data group composed of merged detection parameters of different sensors, which includes aspects such as the height offset displacement, inclination data, strain data, settlement data, contact stress data, etc. calculated by combining the preliminary steady-state analysis results and the associated steady-state analysis results. It can calculate the final steady-state analysis results by substituting into the same or different mechanical analysis models, that is, calculate the subsequent steady-state analysis results as the result data of the steady-state analysis of the target tower. It should be noted that when combining multiple associated steady-state analysis results with the preliminary steady-state analysis results for calculation, each associated steady-state analysis result is combined with the preliminary steady-state analysis result to calculate the comprehensive steady-state analysis parameter respectively, and the subsequent steady-state analysis results of the target tower are calculated through the final multiple comprehensive steady-state analysis parameters.

[0048] Through the above technical solution, first obtain and evaluate the confidence level of each monitoring point on the target tower and calculate the preliminary steady-state analysis results. That is, by judging and adjusting the confidence level of the monitoring point information, the effectiveness of the sensor deployment is ensured; using the dual sorting of importance and reliability, the data of key monitoring points are preferentially processed; then, combined with the analysis results of the associated towers with proximity or physical connection for reasonable inference. That is, by introducing the associated steady-state analysis results of the associated towers, the monitoring data of the target tower are further verified and corrected, thereby providing more accurate comprehensive steady-state analysis parameters and ensuring the accuracy and reliability of the steady-state monitoring analysis of the target tower finally adopted.

[0049] In this embodiment, the associated steady-state analysis results of each associated tower adopt the same method as the preliminary steady-state analysis results of the target tower, which can ensure the reliability of the associated steady-state analysis results (the monitoring deployment of its own monitoring points is more reliable). That is, the calculation of the associated steady-state analysis results of each associated tower includes the following steps:

[0050] Obtain the displacement offset monitoring network architecture of the associated tower, identify all the monitoring point information in the displacement offset monitoring network architecture, judge the confidence level of each monitoring point information, and obtain the confidence level of each monitoring point in the associated tower; similarly, for the displacement offset monitoring network architecture obtained in advance, determine all the monitoring point information of the associated tower, make a further confidence level judgment on whether each monitoring point information matches, and obtain the confidence level value of each monitoring point in the associated tower according to the calculation of the matching degree.

[0051] Then, the monitoring points are sorted according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and the monitoring point confidence sequence is adjusted based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; similarly, the confidence levels calculated for all monitoring points of the associated tower are sorted to obtain a preliminary monitoring point confidence sequence, and then the monitoring point confidence sequence is adjusted according to the importance degree of each monitoring point to obtain a monitoring point adjustment sequence.

[0052] Finally, the effective monitoring information group of the associated tower is matched according to the monitoring point adjustment sequence, and the stability analysis of the associated tower is carried out based on the effective monitoring information group to obtain the associated steady-state analysis result; that is, similarly, the information monitored and uploaded by the monitoring points that can be used for the next-step information analysis is selected from the monitoring point adjustment sequence obtained above, denoted as the effective monitoring information group, and the associated steady-state analysis result is calculated by substituting the effective monitoring information group into the mechanical analysis model for stability analysis. Thus, the associated steady-state analysis results of each associated tower are calculated through the above-mentioned steps to ensure the data reliability and accuracy in the subsequent calculation process of the comprehensive steady-state analysis parameters.

[0053] Since the final comprehensive steady-state analysis parameters depend on the combined calculation of the preliminary steady-state analysis results of the target tower and the associated steady-state analysis results of the associated towers, therefore, whether different towers are used as associated towers and what proportion of the associated towers participate in the comprehensive steady-state analysis parameters directly affect the accuracy and reliability of the comprehensive steady-state analysis parameters. Therefore, the screening method of the associated towers and the calculation method after screening are relatively important. In some embodiments, please refer to Figure 2 , the method for locating at least one associated tower of the target tower based on the geographical information of the target tower includes the following steps:

[0054] S410: Determine the range of the tower group of the target tower, and extract the towers within the range of the tower group to obtain a plurality of preliminarily screened towers; this step means that the range of the tower group is locked according to the geographical area where the target tower is located. The locking criteria can be determined on the one hand according to the geographical coordinates of the tower to determine the remaining towers within a certain radius, and on the other hand, according to the remaining towers within a limited length directly connected to the tower by cables. By determining the geographical range involved in the tower group, and then extracting all the towers within this geographical range (excluding the target tower), the extracted towers are used as the preliminarily screened towers for the next step of processing.

[0055] S420: Obtain the geographical information of the target tower and each of the preliminary screened towers, perform a correlation analysis on the geographical information of each preliminary screened tower and the geographical information of the target tower to obtain the corresponding correlation analysis result, and determine whether the preliminary screened tower is used as a correlated tower based on the correlation analysis result; wherein, the geographical information includes terrain information, topographic information, geological information, and location correlation information. This step means performing a correlation analysis between each obtained preliminary screened tower and the target tower, calculating the correlation degree between the two through their geographical information, and the geographical information includes terrain information (terrain altitude, terrain undulation, etc., affecting the middle force stability of the tower), topographic information (topographic and geomorphic conditions, topographic environmental conditions, affecting the natural external force interference of the tower), geological information (softness of the geology, depth of the stratum, affecting the stability of the tower foundation), and location correlation information (line connection conditions, tower spacing, affecting the pulling force of the tower).

[0056] Perform a correlation analysis (such as the similarity degree in terms of each geographical information) using the geographical information of the preliminary screened tower and the geographical information of the target tower to calculate the correlation analysis result, and use the correlation analysis result as the basis for judging whether the preliminary screened tower can be used as a correlated tower, that is, determine whether the preliminary screened tower is used as a correlated tower to participate in the calculation of subsequent comprehensive steady-state analysis parameters based on the correlation analysis result.

[0057] Through the above technical solution, accurately locate and screen suitable correlated towers based on the geographical information of the target tower, that is, lock the range of the tower group where the target tower is located, extract the preliminary screened towers, and then perform a detailed comparative analysis on the terrain, topography, geology, and location correlation information between the preliminary screened towers and the target tower, calculate the correlation degree between the two, so as to determine which preliminary screened towers can be used as correlated towers to participate in subsequent calculations. This not only effectively avoids the interference of irrelevant or low-correlation tower data on the analysis result, ensures that representative and relevant data are used for calculation, and guarantees the accuracy and reliability of the comprehensive steady-state analysis parameters, but also through this way of screening correlated towers, it can have the basis for adjusting the participation proportion of each correlated tower in the calculation of comprehensive steady-state analysis parameters according to the influence weights of different geographical information, making the analysis result more in line with the actual situation.

[0058] In order to facilitate the identification of the correlation situation of the screened correlated towers on the basis of their ability to participate in subsequent comprehensive steady-state analysis parameters, when calculating the correlation between the geographical information of the preliminary screened towers and the geographical information of the target tower, it can be calculated separately by the similarity degree in different aspects and then comprehensively calculated. For details, please refer to Figure 3 The step of performing a correlation analysis on the geographical information of each preliminary screened tower and the geographical information of the target tower to obtain the corresponding correlation analysis result includes the following steps:

[0059] S421: Classify the geographical information of the preliminary screening poles and towers to obtain multiple groups of preliminary screening geographical category factors, and classify the geographical information of the target poles and towers to obtain multiple groups of target geographical category factors; this step indicates that by grouping and classifying the geographical information of the preliminary screening poles and towers and the target poles and towers, for example, classifying the terrain information, topographic information, geological information, and location association information into large groups, or classifying the sub-items in each large group (such as terrain altitude, terrain undulation, topographic and geomorphic conditions, topographic environment conditions, geological softness, formation depth, line connection conditions, pole and tower spacing, etc.) into small groups, but the same classification and grouping method needs to be adopted, and then the preliminary screening geographical category factor groups of the preliminary screening poles and towers and the target geographical category factor groups of the target poles and towers are obtained.

[0060] S422: Compare the similarity between the preliminary screening geographical category factor groups and the target geographical category factor groups of the same category to obtain similarity values; this step indicates directly calculating the similarity (such as calculating using the cosine similarity method) between the obtained preliminary screening geographical category factor groups and the data of the same category within the target geographical category factor groups (field manual inspection or survey data by machine), and obtaining the similarity values of each category of data (multiple categories mean multiple similarity values).

[0061] S423: Combine all similarity values to judge the similarity degree result, and calculate the correlation analysis result based on the similarity degree result; among them, the similarity degree result includes strong similarity correlation and weak similarity correlation; this step indicates calculating the total similarity value by combining all similarity values as the similarity degree result. The larger the similarity degree result value, the stronger the correlation, which is recorded as strong similarity correlation, and vice versa, which is recorded as weak similarity correlation. Finally, calculate the correlation analysis result through this similarity degree result, and the calculation method can be through weight multiplication or unit conversion, etc.

[0062] Through the above technical solution, classify and group the geographical information of the preliminary screening poles and towers and the target poles and towers and calculate the similarity values for each category one by one (by finely classifying and comparing the similarity of geographical category factors such as terrain, topography, geology, and location association information), and the similarity degree of each factor can be quantified using methods such as cosine similarity. Finally, comprehensively evaluate the correlation degree, which can not only judge whether the preliminary screening poles and towers can be used as associated poles and towers, ensure that the selected associated poles and towers have high relevance and accuracy when participating in subsequent comprehensive steady-state analysis parameters, but also lay a foundation for identifying the association situation of each associated pole and tower according to the situation of the similarity values.

[0063] After marking and identifying the association status of associated poles and towers, different associated poles and towers with different degrees of association can adopt different combined calculation methods when participating in the calculation of the final comprehensive steady-state analysis parameters, so that the analysis results are more in line with the actual situation. The calculation of the associated steady-state analysis results of the associated poles and towers can be affected by different association situations of the associated poles and towers (such as at least one of strong terrain association, strong topography association, strong geology association, or strong location association). For example, adjusting the importance of the sensor monitoring directions corresponding to the strong association categories means that the data obtained from the sensor monitoring directions for both aspects of the strong association are more reliable and can be used as a better basis for calculating the associated steady-state analysis results. In the case of weak association categories, the parameters monitored by the sensors can weaken their basis for participating in the calculation.

[0064] Specifically, it also includes the step of determining the type of the similarity degree result corresponding to the associated analysis result of the associated pole and tower, and readjusting the monitoring point adjustment sequence of the associated pole and tower according to different types. That is, the step of re-adjusting the importance of the monitoring point adjustment sequence of the associated pole and tower by determining whether the associated analysis result of each associated pole and tower belongs to a strong similarity association or a weak similarity association:

[0065] If it is a strong similarity association type: Trace all the similarity values calculated by the associated pole and tower using the preliminary screening geographical category factor group, that is, extract all the similarity values obtained during the calculation of the preliminary screening geographical category factor group by the associated pole and tower, sort the preliminary screening geographical category factor group according to the magnitudes of these similarity values to obtain a preliminary screening geographical factor similarity sequence, which means arranging the categories according to the magnitudes (levels of similarity) of the similarity values to obtain a preliminary screening geographical factor similarity sequence; then assign importance parameters to the corresponding monitoring points in the associated pole and tower according to this preliminary screening geographical factor similarity sequence, which means according to each geographical factor category in the preliminary screening geographical factor similarity sequence (such as the terrain factor affects the mid-section force stability of the pole and tower, that is, affects the monitoring importance of strain sensors or accelerometers; the geology factor affects the foundation stability of the pole and tower, that is, affects the monitoring importance of bottom tilt sensors and settlement gauges; the location association factor affects the pulling force situation of the pole and tower, that is, affects the monitoring importance of tension sensors and contact sensors; the topography factor affects the natural external force interference situation of the pole and tower, that is, affects the monitoring importance of inclination sensors and laser rangefinders).

[0066] Adjust the importance degree of the corresponding sensor according to the decreasing relevance in turn, that is, reassign the importance parameter. This importance parameter can be assigned (such as in an additive manner) after the confidence level at the corresponding position in the monitoring point adjustment sequence is adjusted (importance is assigned). The assignment criterion can be determined according to the ratio of floating up and down based on the reference relevance (similarity value). After the importance parameter is assigned to the corresponding monitoring point, the importance degree of this monitoring point can be recalculated, and the monitoring point adjustment sequence is adjusted again to obtain the adjusted monitoring point adjustment sequence of the associated tower.

[0067] If it is a weakly similar association type, the weakly associated similarity value can be removed first and then the above method of assigning importance parameters can be used for adjustment and calculation: Similarly, trace all the similarity values calculated by the associated tower using the preliminary screening geographical category factor group, retain the similarity values that meet the similarity preset requirements (empirical values, determined in advance as needed), remove those that do not meet the similarity preset requirements, and sort the preliminary screening geographical category factor group according to the size of the retained similarity values to obtain the preliminary screening geographical factor similarity sequence; Similarly, assign importance parameters to the corresponding monitoring points in the associated tower according to this preliminary screening geographical factor similarity sequence to recalculate the importance degree of this monitoring point, so as to obtain the adjusted monitoring point adjustment sequence of the associated tower.

[0068] Through the above technical solution, the importance of the monitoring points of the associated tower is dynamically adjusted according to the similarity degree between the associated tower and the target tower, that is, by sorting the preliminary screening geographical category factor groups of strong similarity associations and weak similarity associations, and reassigning the importance of each monitoring point according to the size of the similarity value, ensuring that when calculating the associated steady-state analysis result, it relies more on the sensor data that has strong relevance in key geographical factors, and dealing with the weakly similar association type by removing it to reduce the interference of low-correlation data. This not only optimizes the data quality of the parameters participating in the comprehensive steady-state analysis calculation, but also improves the accuracy and reliability of the analysis result.

[0069] Through the above calculation logic and acquisition method of the preliminary steady-state analysis result and the associated steady-state analysis result, the rationality basis for calculating the comprehensive steady-state analysis parameters can be guaranteed. On this basis, when the associated steady-state analysis results obtained from associated towers with different association degrees or association natures are combined with the preliminary steady-state analysis result, the preliminary steady-state analysis result is used as the main steady-state judgment data, and the associated steady-state analysis result is used as the auxiliary judgment data. Then, the associated steady-state analysis needs to be more reliable in the aspect of auxiliary judgment. On the one hand, the acquisition method of this associated steady-state analysis result is reliable, and on the other hand, the combination method with the preliminary steady-state analysis result is reliable. The combination method can be to calculate the comprehensive steady-state analysis parameters by using weighted combination. For details, please refer to Figure 4, the calculation of the comprehensive steady-state analysis parameters based on the preliminary steady-state analysis results and the associated steady-state analysis results includes the following steps:

[0070] S510: Determine the combined weight of the associated steady-state analysis results. This step means first determining the weight of an associated steady-state analysis result participating in the calculation of the comprehensive steady-state analysis parameters, denoted as the combined weight. The determination of the combined weight is as follows in step S511: Determine the respective weights of each valid monitoring information in the associated steady-state analysis results, and calculate the combined weight according to the respective weights of each valid monitoring information. That is, by assigning a (different) weight to each valid monitoring information (the monitoring information of each monitoring point sensor) in the associated steady-state analysis results. For example, the valid monitoring information with higher confidence and importance is assigned a slightly larger weight, and the rest are decreased in turn; then calculate the combined weight by combining (such as summing, averaging, etc.) the weights of all valid monitoring information.

[0071] S520: Combine the product of the combined weight and the associated steady-state analysis results with the preliminary steady-state analysis results to obtain the comprehensive steady-state analysis parameters; this step means directly assigning (such as adding) the product of the associated steady-state analysis results and the combined weight to the preliminary steady-state analysis results to calculate the comprehensive steady-state analysis parameters. Since the comprehensive steady-state analysis parameters are a data group, the same type of data is calculated according to the above combination method, and each calculation result is constructed into the comprehensive steady-state analysis parameters (data group).

[0072] Based on the above technical solution, considering that the combined weight is calculated according to the weights of each valid monitoring information, and the weight of each valid monitoring information is determined according to its confidence and importance. During the calculation of the associated steady-state analysis results, each monitoring point itself has a preliminary judgment of confidence and importance, and after calculating the degree of association with the geographical information of the target tower, its importance is readjusted. Therefore, the weights of the valid monitoring information can be further optimized. Please refer to Figure 4 , and also includes a step of optimizing the combined weight:

[0073] S512: Determine the difference parameter between the monitoring point adjustment sequence before readjustment and the monitoring point adjustment sequence after readjustment of the associated tower, that is, it means comparing the difference between the value (the combined value of confidence and importance) of each valid monitoring information in the monitoring point adjustment sequence before readjustment and the value after readjustment, statistically counting the valid monitoring information with differences, calculating the difference parameter according to all difference statistical situations, and finally converting (converting into a percentage according to the difference comparison table) the difference parameter into a weight adjustment parameter. Calculate the optimized combined weight according to the weight adjustment parameter and the combined weight, and then use the optimized combined weight to perform step S520.

[0074] Through the above technical solution, the preliminary steady-state analysis result and the optimized associated steady-state analysis result are fused and calculated to obtain the comprehensive steady-state analysis parameters by combining the merging weights. That is, the merging weights are determined according to the confidence levels and importance of each effective monitoring information, and the differential parameters after the adjustment sequence of the associated tower monitoring points are readjusted are fully considered in the calculation process. By statistically analyzing and transforming these differential parameters, the merging weights are further optimized, making the associated steady-state analysis result more reliable in assisting judgment. Finally, the optimized merging weights are multiplied by the associated steady-state analysis result and then combined with the preliminary steady-state analysis result to construct more accurate and reliable comprehensive steady-state analysis parameters.

[0075] Based on the above technical solution, the rationality of the importance calculation of the effective monitoring information in the adjustment sequence of the associated tower monitoring points is ensured. The calculation method of the confidence level of the associated towers (including the target tower) also needs to be dynamically judged by combining on-site measurement and remote calculation, so as to ensure the reliability of the confidence level calculation of the tower monitoring points. For details, please refer to Figure 5 , judging the confidence level of the monitoring point information in the target tower and / or judging the confidence level of the monitoring point information in the associated towers both include the following steps:

[0076] S110: Calculate the matching degree according to the position of the monitoring point on the tower (target tower and / or associated tower) and the attributes of the configured sensors to obtain the first confidence parameter, where the attributes of the configured sensors include the type and quantity of the sensors; this step means that different parameter monitoring needs to be carried out at different parts or positions on the tower. For example, settlement parameters and tilt displacement amounts need to be monitored at the tower base, expansion and contraction stresses and vibration conditions need to be detected in the middle of the tower, offset distances and tilt angles need to be monitored at the top of the tower, and tensile forces and contact stresses need to be monitored at the tower connectors (insulators, cross arms, etc.). Therefore, corresponding types of sensors need to be installed at each monitoring point position. If the installed types of sensors do not match, it means that the matching degree is low. If one sensor among the sensors (one or more sensors are configured) at this monitoring point does not match, the overall matching degree will be correspondingly reduced, and the first confidence parameter is calculated and converted according to the final matching degree.

[0077] S120: Calculate the matching degree according to the monitoring targets of the sensors configured at the monitoring points to obtain the second confidence parameter, where the monitoring targets of the configured sensors include the monitoring angle and the monitoring object; this step means further considering whether the monitoring angle and the monitoring object match on the basis of the sensor matching type. If there is a difference between the monitoring direction of a monitoring point (one or more sensors are configured) and the required direction, the matching degree is correspondingly reduced. Similarly, if the detected objects of this monitoring point (one or more sensors are configured) do not completely include or do not completely correspond, the matching degree is also correspondingly reduced. Thus, the finally obtained matching degree is calculated and converted into the second confidence parameter. Then, perform step S130: Calculate the confidence degree of this monitoring point according to the first confidence parameter and the second confidence parameter, that is, it means that the confidence degree of this monitoring point is obtained by combining the first confidence parameter and the second confidence parameter calculated at the corresponding monitoring point.

[0078] Through the above technical solution, the sensor attributes of the monitoring point positions are accurately matched, and the matching degree calculation of the refined sensor monitoring targets is carried out to respectively obtain the first and second confidence parameters, and on this basis, the confidence degree of the monitoring point is calculated, ensuring the reliability and effectiveness of the tower (including the target tower and the associated tower) monitoring system and improving the accuracy of the tower state assessment. On this basis, considering that the confidence degree of each monitoring point is calculated based on the difference between the actual installation situation of the actual sensor and the required installation situation, and the actual installation situation of the actual sensor and the required installation situation may change. For example, there may be a temporary adjustment in the actual installation of the sensor, or the planned installation of the sensor will also be adjusted according to different monitoring tasks. If the two adjustments are not synchronized, the confidence degree calculated above has an inaccurate problem. Then, the confidence degree of the corresponding monitoring point needs to be optimized and adjusted, that is, on the basis of the above technical solution, it also includes the step of adjusting the confidence degree of the monitoring point information of the target tower and / or the confidence degree of the monitoring point information in the associated tower:

[0079] S140: Determine the planned deployment information of the monitoring points of the corresponding tower according to the geographical information of the tower, calculate the difference coefficient between the planned deployment information and the actual deployment information, and adjust the confidence degree of this monitoring point according to the difference coefficient, where the planned deployment information refers to the real-time plan for the monitoring and sensing deployment of this tower determined according to the geographical information of the tower, and the actual deployment information refers to the actual situation of the current monitoring and sensing deployment of the tower.

[0080] The above steps represent calculating the difference between the sensor planned deployment information of a preset pole tower (target pole tower or associated pole tower) monitoring point and the actual sensor deployment information of the monitoring point, so as to obtain a difference coefficient. This difference coefficient indicates that there is an out-of-sync update between the planned deployment information and the actual deployment information, or the plan has been adjusted but the on-site sensors have not been adjusted, or the on-site sensors have been temporarily adjusted but not uploaded to the system for verification. Finally, the confidence level of the monitoring point is adjusted by a corresponding margin according to the situation of this difference coefficient. For example, trace which of the planned deployment information and the actual deployment information has been updated, and then adjust the corresponding value to the updated (planned or on-site detection) value in the above calculation process.

[0081] Through the foregoing technical solution, the difference coefficient between the planned deployment information and the actual deployment information is introduced to optimize the calculation of the confidence level of the monitoring point, ensuring that the reliability and accuracy of the pole tower (including the target pole tower and the associated pole tower) monitoring system can be maintained even when there are temporary adjustments in the sensor installation situation or changes in the monitoring task. It not only considers the matching of the sensor type, quantity, and its monitoring target with the monitoring point location, but also dynamically evaluates the synchronization between the planned deployment information and the actual deployment information, thereby making a more accurate adjustment to the confidence level of the monitoring point.

[0082] In this embodiment, a line pole tower steady-state analysis system 600 based on displacement offset detection is also provided. Please refer to Figure 6 the modular schematic diagram of the line pole tower steady-state analysis system 600 based on displacement offset detection in it, which is mainly used to divide the functional modules of the line pole tower steady-state analysis system 600 based on the embodiments of the above method. For example, each functional module can be divided, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the present invention is illustrative, only a logical function division, and there can be other division methods in actual implementation. For example, in the case of dividing each functional module corresponding to each function, Figure 6 only a system / device schematic diagram is shown. Among them, the line pole tower steady-state analysis system 600 based on displacement offset detection may include a first acquisition unit 610, a first processing unit 620, a second processing unit 630, a first calculation unit 640, and a second calculation unit 650. The functions of each unit module will be described below.

[0083] The first acquisition unit 610 is configured to acquire the displacement offset monitoring network architecture of the target tower, identify all the monitoring point information in the displacement offset monitoring network architecture, judge the confidence level of each piece of the monitoring point information, and obtain the confidence level of each monitoring point in the target tower; In some optional implementation manners, the first acquisition unit 610 is further configured to calculate the matching degree according to the position of the monitoring point on the tower and the attributes of the configured sensor, and obtain the first confidence parameter; calculate the matching degree according to the monitoring target of the sensor configured for the monitoring point, and obtain the second confidence parameter; where the attributes of the configured sensor include the type and quantity of the sensor; the monitoring target of the configured sensor includes the monitoring angle and the monitoring object; calculate the confidence level of the monitoring point according to the first confidence parameter and the second confidence parameter. And it is configured to determine the planned deployment information of the monitoring points according to the geographical information of the corresponding tower, calculate the difference coefficient between the planned deployment information and the actual deployment information, and adjust the confidence level of the monitoring point according to the difference coefficient.

[0084] The first processing unit 620 is configured to sort the monitoring points according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and adjust the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence;

[0085] The second processing unit 630 is configured to match the effective monitoring information group of the target tower according to the monitoring point adjustment sequence, and perform a stability analysis on the target tower based on the effective monitoring information group to obtain a preliminary steady-state analysis result;

[0086] The first calculation unit 640 is configured to locate at least one associated tower of the target tower based on the geographical information of the target tower, and calculate the associated steady-state analysis result of each associated tower; In some optional implementation manners, the first calculation unit 640 is further configured to determine the range of the tower group of the target tower, extract the towers within the range of the tower group to obtain a plurality of pre-screened towers; acquire the geographical information of the target tower and each pre-screened tower, perform a correlation analysis on the geographical information of each pre-screened tower and the geographical information of the target tower to obtain a corresponding correlation analysis result, and judge whether the pre-screened tower is used as an associated tower based on the correlation analysis result. And it is configured to classify the geographical information of the pre-screened towers to obtain a plurality of pre-screened geographical category factor groups, classify the geographical information of the target tower to obtain a plurality of target geographical category factor groups, compare the similarity between the pre-screened geographical category factor groups of the same type and the target geographical category factor groups to obtain a similarity value; combine all the similarity values to judge the similarity degree result, and calculate the correlation analysis result based on the similarity degree result.

[0087] A second calculation unit 650, which is configured to calculate a comprehensive steady-state analysis parameter according to the preliminary steady-state analysis result and the associated steady-state analysis result, and calculate a subsequent steady-state analysis result of the target tower pole based on the comprehensive steady-state analysis parameter; in some alternative embodiments, the second calculation unit 650 is further configured to determine a merging weight of the associated steady-state analysis result, and combine the product of the merging weight and the associated steady-state analysis result with the preliminary steady-state analysis result to obtain the comprehensive steady-state analysis parameter; wherein, determining the merging weight includes the following steps: determining the weight of each valid monitoring information in the associated steady-state analysis result, and calculating the merging weight according to the weight of each valid monitoring information. And it is configured to determine a difference parameter between the monitoring point adjustment sequence before readjustment of the associated tower pole and the monitoring point adjustment sequence after readjustment, determine a weight adjustment parameter based on the difference parameter, and calculate an optimized merging weight according to the weight adjustment parameter and the merging weight.

[0088] In the above embodiments, for the more specific working processes of each functional unit, reference may be made to the corresponding content disclosed in the foregoing method embodiments. In addition, each functional unit may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more available media integrated therein. The available media may be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media (such as solid state disks (SSDs)), etc.

[0089] Embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0090] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0092] Obviously, those skilled in the art can make various modifications and variations to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A steady-state analysis method for line towers based on displacement offset detection, characterized in that It includes the following steps: Obtain the displacement offset monitoring network architecture of the target pole tower, identify all the monitoring point information in the displacement offset monitoring network architecture, judge the confidence of each piece of the monitoring point information, and obtain the confidence of each monitoring point in the target pole tower; Sort the monitoring points according to the confidence of each monitoring point to obtain a monitoring point confidence sequence, and adjust the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; Match the effective monitoring information group of the target pole tower according to the monitoring point adjustment sequence, and perform a stability analysis on the target pole tower based on the effective monitoring information group to obtain a preliminary steady-state analysis result; Locate at least one associated pole tower of the target pole tower based on the geographic information of the target pole tower, and calculate the associated steady-state analysis result of each associated pole tower; Calculate a comprehensive steady-state analysis parameter according to the preliminary steady-state analysis result and the associated steady-state analysis result, and calculate the subsequent steady-state analysis result of the target pole tower with the comprehensive steady-state analysis parameter; Judging the confidence of the monitoring point information in the target pole tower and / or judging the confidence of the monitoring point information in the associated pole tower both include the following steps: Calculate the matching degree according to the position of the monitoring point in the pole tower and the attributes of the configured sensor to obtain a first confidence parameter; calculate the matching degree according to the monitoring target of the sensor configured at the monitoring point to obtain a second confidence parameter; wherein the attributes of the configured sensor include the type and quantity of the sensor; the monitoring target of the configured sensor includes the monitoring angle and the monitoring object; calculate the confidence of the monitoring point according to the first confidence parameter and the second confidence parameter.

2. The method for steady-state analysis of line poles and towers based on displacement offset detection according to claim 1, wherein The calculating the associated steady-state analysis result of each associated pole tower includes the following steps: Obtain the displacement offset monitoring network architecture of the associated pole tower, identify all the monitoring point information in the displacement offset monitoring network architecture, judge the confidence of each piece of the monitoring point information, and obtain the confidence of each monitoring point in the associated pole tower; sort the monitoring points according to the confidence of each monitoring point to obtain a monitoring point confidence sequence, and adjust the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; match the effective monitoring information group of the associated pole tower according to the monitoring point adjustment sequence, and perform a stability analysis on the associated pole tower based on the effective monitoring information group to obtain the associated steady-state analysis result.

3. The method for steady-state analysis of line towers based on displacement offset detection according to claim 2, wherein The locating at least one associated pole tower of the target pole tower based on the geographic information of the target pole tower includes the following steps: Determine the pole tower group range of the target pole tower, extract the pole towers within the pole tower group range to obtain a plurality of preliminarily screened pole towers; obtain the geographic information of the target pole tower and each preliminarily screened pole tower, perform a correlation analysis on the geographic information of each preliminarily screened pole tower and the geographic information of the target pole tower to obtain a corresponding correlation analysis result, and judge whether the preliminarily screened pole tower is used as an associated pole tower based on the correlation analysis result; wherein, the geographic information includes terrain information, topographic information, geological information, and location association information.

4. The method for steady-state analysis of line towers based on displacement offset detection according to claim 3, characterized in that, Performing correlation analysis on the geographical information of each of the preliminary screening poles and towers with the geographical information of the target pole and tower to obtain corresponding correlation analysis results includes the following steps: Classify the geographical information of the preliminary screening poles and towers to obtain multiple groups of preliminary screening geographical category factors, classify the geographical information of the target pole and tower to obtain multiple groups of target geographical category factors, and compare the similarity between the preliminary screening geographical category factor groups of the same type and the target geographical category factor groups to obtain similarity values; Combine all similarity values to judge the similarity degree result, and calculate the correlation analysis result based on the similarity degree result; wherein, the similarity degree result includes strong similarity correlation and weak similarity correlation.

5. The steady-state analysis method of line towers based on displacement offset detection according to claim 4, wherein, It also includes the step of determining the type of the similarity degree result corresponding to the correlation analysis result of the associated pole and tower, and readjusting the monitoring point adjustment sequence of the associated pole and tower according to different types: If it is a strong similarity correlation type: Trace all the similarity values calculated by the associated pole and tower using the preliminary screening geographical category factor groups, sort the preliminary screening geographical category factor groups according to the size of the similarity values to obtain a preliminary screening geographical factor similarity sequence; Assign importance parameters to the corresponding monitoring points in the associated pole and tower according to this preliminary screening geographical factor similarity sequence to recalculate the importance degree of the monitoring points, so as to obtain the monitoring point adjustment sequence after readjustment of the associated pole and tower; If it is a weak similarity correlation type: Trace all the similarity values calculated by the associated pole and tower using the preliminary screening geographical category factor groups, retain the similarity values that meet the similarity preset requirements and sort the preliminary screening geographical category factor groups according to the size of the retained similarity values to obtain a preliminary screening geographical factor similarity sequence; Assign importance parameters to the corresponding monitoring points in the associated pole and tower according to this preliminary screening geographical factor similarity sequence to recalculate the importance degree of the monitoring points, so as to obtain the monitoring point adjustment sequence after readjustment of the associated pole and tower.

6. The method for steady-state analysis of line towers based on displacement offset detection according to claim 5, characterized in that, Calculating the comprehensive steady-state analysis parameter according to the preliminary steady-state analysis result and the associated steady-state analysis result includes the following steps: Determine the combined weight of the associated steady-state analysis result, and combine the product of the combined weight and the associated steady-state analysis result with the preliminary steady-state analysis result to obtain the comprehensive steady-state analysis parameter; wherein, determining the combined weight includes the following steps: Determine the respective weights of each effective monitoring information in the associated steady-state analysis result, and calculate the combined weight according to the respective weights of each effective monitoring information.

7. The line tower steady-state analysis method based on displacement offset detection according to claim 6, characterized in that It also includes the step of optimizing the combined weight: Determine the difference parameter between the monitoring point adjustment sequence before readjustment and the monitoring point adjustment sequence after readjustment of the associated pole and tower, determine the weight adjustment parameter based on this difference parameter, and calculate the optimized combined weight according to the weight adjustment parameter and the combined weight.

8. The method for steady-state analysis of line towers based on displacement offset detection according to claim 1, characterized in that, It further includes the step of adjusting the confidence level of the information of the monitoring points of the target pole tower and / or the confidence level of the information of the monitoring points in the associated pole towers: determining the planned deployment information of the monitoring points of a corresponding pole tower according to the geographical information of the pole tower, calculating the difference coefficient between the planned deployment information and the actual deployment information, and adjusting the confidence level of the monitoring point according to the difference coefficient, where the planned deployment information refers to the real-time plan for the monitoring and sensing deployment of the pole tower determined according to the geographical information of the pole tower, and the actual deployment information refers to the actual situation of the current monitoring and sensing deployment of the pole tower.

9. A line pole and tower steady-state analysis system based on displacement offset detection, characterized in that It includes: A first acquisition unit, which is used to acquire the displacement offset monitoring network architecture of the target pole tower, identify all the monitoring point information in the displacement offset monitoring network architecture, judge the confidence level of each piece of the monitoring point information, and obtain the confidence level of each monitoring point in the target pole tower; it is also used to calculate the matching degree according to the position of the pole tower where the monitoring point is located and the attributes of the configured sensor, and obtain the first confidence parameter; calculate the matching degree according to the monitoring target of the sensor configured at the monitoring point, and obtain the second confidence parameter; where the attributes of the configured sensor include the type and quantity of the sensor; the monitoring target of the configured sensor includes the monitoring angle and the monitoring object; calculate the confidence level of the monitoring point according to the first confidence parameter and the second confidence parameter; A first processing unit, which is used to sort the monitoring points according to the confidence level of each monitoring point to obtain a monitoring point confidence sequence, and adjust the monitoring point confidence sequence based on the importance degree of each monitoring point to obtain a monitoring point adjustment sequence; A second processing unit, which is used to match the effective monitoring information group of the target pole tower according to the monitoring point adjustment sequence, and perform a stability analysis on the target pole tower based on the effective monitoring information group to obtain a preliminary steady-state analysis result; A first calculation unit, which is used to locate at least one associated pole tower of the target pole tower based on the geographical information of the target pole tower, and calculate the associated steady-state analysis result of each associated pole tower; A second calculation unit, which is used to calculate a comprehensive steady-state analysis parameter according to the preliminary steady-state analysis result and the associated steady-state analysis result, and calculate the subsequent steady-state analysis result of the target pole tower with the comprehensive steady-state analysis parameter.

Images